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Published in final edited form as: Pharmacol Biochem Behav. 2020 Jul 1;196:172981. doi: 10.1016/j.pbb.2020.172981

Neuroinflammation in psychiatric disorders: An introductory primer

Geoffrey A Dunn a, Jennifer M Loftis b,c, Elinor L Sullivan a,c,d,e,*
PMCID: PMC7430067  NIHMSID: NIHMS1614530  PMID: 32621927

The term neuroinflammation is generally used to describe inflammation within neural tissue. A diverse range of cell types and molecular processes characterize neuroinflammation, these include alterations in microglia, astrocytes, cytokines, and chemokines (Table 1) within the central nervous system (CNS). Neuroinflammation can be induced by a number of different modalities ranging from environmental challenges (e.g., diet, toxin exposure), mental and physical health illnesses (e.g., substance use disorders, traumatic brain injury), to genetic predisposition. There is strong evidence linking neuroinflammation to neurodegenerative conditions such as Alzheimer’s disease and multiple sclerosis however the focus of this special issue is on the role of neuroinflammation in psychiatric disorders which has been less extensively examined. The primary methods for characterizing neuroinflammation across conditions include the quantification of alterations in the number, morphology and gene expression of glial cells within the CNS and more recent techniques, such as resting state functional connectivity magnetic resonance imaging (rs-fcMRI) to assess inflammation-associated changes in connectivity of large scale networks that lead to cognitive and behavioral changes (Passamonti et al., 2019). Additionally, the use of novel radioligand tracers specific for activated microglia in conjunction with positron emission tomography (PET), is becoming more common to image and assess levels of neuroinflammation in neurodegenerative disorders such as Alzheimer’s disease (Edison et al., 2018), and these tools are starting to be used to characterize neuroinflammation in neuropsychiatric disorders such as schizophrenia and substance use disorders (Notter et al., 2018; Hillmer et al., 2017).

Table 1.

List of commonly studied cytokines and their function related to the central nervous system and neuroinflammation.

Cytokines Functions (relevant to neuroinflammation)
IFN-γ In the CNS, IFN-γ appears to be multifunctional with roles in induction of inflammatory responses as well as regulating neuroprotective mechanisms. IFN-γ signaling has been implicated in autoimmune neuroinflammation disorders such as multiple sclerosis (Ottum et al., 2015).
IL-1β Part of multifaceted IL-1 family of cytokines. In the CNS, IL-1β functions as a neuromodulator to facilitate long term potentiation in the acquisition and retention of memories, where high concentrations of IL-1β in the hippocampus lead to memory deficits (Goshen et al., 2007).
IL-2 Promotes neuronal survival and growth, regulates neurotransmitter release, and stimulates oligodendrocyte proliferation and maturation (Jiang and Lu, 1998; Hanisch and Quirion, 1995).
IL-4 An essential anti-inflammatory signaling molecule in the central nervous system that regulates autoimmune activation (Ponomarev et al., 2007). However, not exclusively anti-inflammatory and plays roles in priming macrophages that then result in an exaggerated inflammatory response after stimulus (Gadani et al., 2012). Both microglia and astrocytes respond to IL-4 to produce mostly neuroprotective, and growth responses, however the interplay is complex and requires further study to understand IL-4’s role more comprehensively (Gadani et al., 2012).
IL-6 A pleiotropic cytokine that induces inflammatory signaling through its receptor (IL-6R) in many different cell types (e.g. T-cell, B-cells, hepatocytes, and microglia). Plays roles in both inflammation as well as neuroprotection, through activating microglia and astrocytes as well as promoting differentiation of oligodendrocytes and acting as a neurotrophic factor (Rothaug et al., 2016).
IL-8 A pleiotropic chemokine that is primarily released from microglia and endothelial cells. Can have both neuroprotective and neurotropic effects as well as neurotoxic effects under various conditions (Semple et al., 2010; Liu et al., 2010). Facilitates leukocyte migration across the BBB in both normal and pathological states (i.e. Alzheimer’s disease) (Hickey, 1999; Franciosi et al., 2005).
IL-10 Considered to be a main immunosuppressant cytokine. Acts to promote cell survival and growth, in neurons as well as glial cells, and suppress proapoptotic pathways that may lead to cell death (Strle et al., 2001). Additionally it suppresses inflammatory responses in glial cells (Burmeister and Marriott, 2018).
MCP-1
 (CCL2 (chemokine (C–;C motif) ligand 2)		 A chemokine that has strong chemotactic activity. It primarily recruits other immune cell after insult. In the CNS, MCP-1 is expressed by most cell types including astrocytes, microglia, neurons, and endothelial cells. MCP-1 has been demonstrated to be a central player in BBB permeability and breakdown, which has been correlated with CNS diseases such as Alzheimer’s and amyotrophic lateral sclerosis (ALS) (Yao and Tsirka, 2014).
TNF-α The prototypic pro-inflammatory cytokine that is mainly produced by microglia in the CNS. It is essential in host defense and inflammatory responses. More recently, it becoming more clear that TNF-α is pleiotropic in its role and promotes the maintenance of myelin as well as regulating neuronal activity (Probert, 2015). Additionally, TNF-α has regulatory function on synaptic plasticity (Kaneko et al., 2008), learning and memory, sleep (Krueger, 2008), and food intake (Plata-Salamán, 2001).
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Abbreviations: IL = interleukin; IFN = interferon; TNF = tumor necrosis factor; MCP = monocyte chemoattractant protein; BBB = blood brain barrier; CNS = central nervous system.

Microglia are the main immunocompetent cells and resident macrophages of the CNS. These glial cells are the principal players involved in mounting an immune and inflammatory response within the CNS and are therefore the most heavily studied cell type in the discussion of neuroinflammation. In response to injury or inflammatory signals, microglia undergo changes in morphology, motility, as well as gene expression. While originally thought to primarily function during injury or infection, a vast literature now indicates that microglia play an important role in developing and maintaining the CNS outside of a compromised state. In adults, microglia are highly dynamic playing an important role in the maintenance of brain homeostasis through constant sampling of the surrounding microenvironment, phagocytosing of dead cells or inactive synapses, as well as providing neurotropic factors (Nimmerjahn et al., 2005; Trang et al., 2011). During development, microglia functions include synaptic pruning, neuronal phagocytosis, and refinement of network connectivity (Eyo and Dailey, 2013; Salter and Beggs, 2014; Cunningham et al., 2013). Additionally, another glial cell, the astrocyte, is critical in the maintenance and normal functioning of neurons. Research indicates that astrocytes are also highly involved in the inflammatory response of neural tissue and can influence and modulate microglial activity as well as neuronal function (Colombo and Farina, 2016). Other important cell types that are often overlooked in the discussion of neuroinflammation are the endothelial cells and pericytes that are intimately involved in forming the blood-brain barrier (BBB). They are critical in the communication between the peripheral and central immune networks (Rustenhoven et al., 2017).

Peripheral immune factors are able to modulate central immune functions through cytokines and chemokines that can either pass through the BBB or signal through the endothelial cells and pericytes that make up the BBB (Rustenhoven et al., 2017; Duan et al., 2018), as well as signals via the vagus nerve (Breit et al., 2018). Additionally, there is evidence that the BBB can be compromised during injury, infection, or even psychological stress and allow peripheral monocytes to infiltrate into neural tissue and impact the inflammatory environment and cells of the CNS. In humans, measures of peripheral immune factors, such as cytokine levels and immune cell frequencies, are often used as indirect measures of neuroinflammation. Measures of peripheral inflammation are important in assessing the role of inflammation in psychiatric disorders (Table 2) because although direct evidence links neuroinflammation to neurodegenerative disorders, there is limited direct evidence of neuroinflammation in psychiatric disorders. However, the impact and role of peripheral immune factors on modulation of the central nervous system inflammatory environment, related to psychiatric disorders, is a rapidly growing field with promising results (Hayashi-Takagi et al., 2014). Differences in the measurement methods, effect sizes, the subset of cytokines and chemokines examined, and sample sizes do not allow large general claims to be made and require a critical look at each individual disorder for more precise understanding. Further work with consistent study design is needed in order to accurately compare findings for different disorders.

Table 2.

Evidence from systematic reviews and meta-analyses of circulating cytokine level alterations for different psychiatric disorders1.

Circulating cytokine levels compared to controls
Disorder Cytokine Increased Decreased No change Inconsistent
Major depressive disorder IFN-γ (Mitchell and Goldstein, 2014) (Köhler et al, 2017) (Dowlati et al., 2010)  
  IL-1β (Mitchell and Goldstein, 2014; Ng et al., 2018; Howren et al., 2009; Black and Miller, 2015)   (Köhler et al., 2017; Dowlati et al., 2010; Haapakoski et al., 2015)  
  IL-2     (Köhler et al., 2017; Dowlati et al., 2010)  
  IL-4     (Köhler et al., 2017; Dowlati et al., 2010)  
  IL-6 (Mitchell and Goldstein, 2014; Köhler et al., 2017; Dowlati et al., 2010; Ng et al., 2018; Howren et al., 2009; Black and Miller, 2015; Haapakoski et al., 2015; Martinez-Cengotitabengoa et al., 2016; Goldsmith et al., 2016; Valkanova et al., 2013; Hiles et al., 2012; Liu et al., 2012)      
  IL-8 (Martinez-Cengotitabengoa et al., 2016)   (Köhler et al., 2017; Dowlati et al., 2010; Eyre et al., 2016)  
  IL-10 (Köhler et al., 2017)   (Dowlati et al., 2010; Hiles et al., 2012)  
  MCP-1 (Köhler et al., 2017; Eyre et al., 2016)      
  TNF-α (Köhler et al., 2017; Dowlati et al., 2010; Haapakoski et al., 2015; Martinez-Cengotitabengoa et al., 2016; Goldsmith et al., 2016; Liu et al., 2012)   (Mitchell and Cm Idstein, 2014; Ng et al., 2018)  
PTSD IFN-γ (Passos et al., 2015)      
  IL-1β (Passos et al., 2015; Renna et al., 2018)     (Waheed et al., 2018)
  IL-6 (Passos et al., 2015; Renna et al., 2018)      
  TNF-α (Passos et al., 2015; Renna et al., 2018; Hussein et al., 2017)      
Bipolar disorder IFN-γ     (Munkholm et al., 2013a; Modabbernia et al., 2013; Munkholm et al., 2013b) (Sayana et al., 2017)
  IL-1β (Goldsmith et al., 2016)   (Munkholm et al., 2013a; Munkholm et al., 2013b) (Sayana et al., 2017)
  IL-2     (Munkholm et al., 2013a; Modabbernia et al., 2013; Munkholm et al., 2013b)  
  IL-4 (Munkholm et al., 2013a; Modabbernia et al., 2013)   (Munkholm et al., 2013b)  
  IL-6 (Goldsmith et al., 2016; Modabbernia et al., 2013)   (Munkholm et al., 2013a; Munkholm et al., 2013b) (Sayana et al., 2017)
  IL-8     (Munkholm et al., 2013a; Modabbernia et al., 2013; Munkholm et al., 2013b)  
  IL-10 (Modabbernia et al., 2013)   (Munkholm et al., 2013b) (Sayana et al., 2017)
  MCP-1     (Modabbernia et al., 2013) (Sayana et al., 2017)
  TNF-α (Goldsmith et al., 2016; Munkholm et al., 2013a; Modabbernia et al., 2013; Munkholm et al., 2013b)      
ADHD IFN-γ     (Anand et al., 2017)  
  IL-1β     (Anand et al., 2017)  
  IL-2     (Anand et al., 2017)  
  IL-4     (Anand et al., 2017)  
  IL-6       (Anand et al., 2017; Leffa et al., 2018)
  IL-10       (Anand et al., 2017; Leffa et al., 2018)
  TNF-α     (Anand et al., 2017; Leffa et al., 2018)  
Autism spectrum disorder IFN-γ (Mitchell and Goldstein, 2014; Masi et al., 2015)      
  IL-1β (Masi et al., 2015)   (Mitchell and Goldstein, 2014)  
  IL-2     (Mitchell and Goldstein, 2014; Masi et al., 2015)  
  IL-4     (Masi et al., 2015)  
  IL-6 (Masi et al., 2015)     (Mitchell and Goldstein, 2014)
  IL-8 (Masi et al., 2015)      
  IL-10   (Mitchell and Goldstein, 2014) (Masi et al., 2015)  
  MCP-1 (Masi et al., 2015)      
  TNF-α     (Masi et al., 2015) (Mitchell and Goldstein, 2014)
Schizophrenia IFN-γ (Miller et al., 2011; Rodrigues-Amorim et al., 2018)   (Upthegrove et al., 2014)  
  IL-1β (Goldsmith et al., 2016; Miller et al., 2011; Rodrigues-Amorim et al., 2018; Upthegrove et al., 2014)   (Potvin et al., 2008)  
  IL-2 (Rodrigues-Amorim et al., 2018)   (Upthegrove et al., 2014; Potvin et al., 2008)  
  IL-4     (Upthegrove et al., 2014)  
  IL-6 (Goldsmith et al., 2016; Miller et al., 2011; Rodrigues-Amorim et al., 2018; Upthegrove et al., 2014; Potvin et al., 2008)      
  IL-8 (Rodrigues-Amorim et al., 2018; Frydecka et al., 2018)     (Stuart and Baune, 2014)
  MCP-1 (Frydecka et al., 2018; Stuart and Baune, 2014)      
  TNF-α (Goldsmith et al., 2016; Miller et al., 2011; Rodrigues-Amorim et al., 2018; Upthegrove et al., 2014)   (Potvin et al., 2008)  
Substance use disorder No meta-analyses or systematic reviews found      
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Abbreviations: ADHD = attention deficit hyperactivity disorder; PTSD = posttraumatic stress disorder; IL = interleukin; IFN = interferon; TNF = tumor necrosis factor; MCP = monocyte chemoattractant protein.

1

Search criteria: In the formation of this table we implemented strict criteria to provide the most representative sample of the current known research regarding circulating cytokine levels and their association with psychiatric disorders. We only included meta-analyses and systematic reviews that reported human plasma or serum concentrations of cytokines. For the literature searches, we used all combinations of search terms (e.g., “depression and cytokines,” “depression and inflammation,” “major depressive disorder and cytokines”). When a systematic review reported contradicting results for a certain cytokine and psychiatric disorder, the result is recorded under the “inconsistent” column of the table. No treatment/supplementation studies are included in this table.

Over the last few decades increasing evidence has linked certain psychiatric disorders to developmental origins. Various risk factors during gestation have been associated with alterations in offspring predisposition to psychiatric disorders (Schmitt et al., 2014; Rivera et al., 2015; DeCapo et al., 2019). Many of these stressors on maternal health such as hypoxia due to obstructive sleep apneas (McNicholas, 2009), exposure to environmental toxins (Monn and Becker, 1999), psychological stress (Cohen et al., 2012), depression (Gustafsson et al., 2018), diet, and infection during pregnancy are associated with increased systemic inflammation, thus inflammation may be a common mechanistic pathway by which these environmental factors influence offspring brain development and behavior (Gustafsson et al., 2018; Hagberg et al., 2012; Thompson et al., 2018). Importantly, many psychiatric disorders such as autism spectrum disorders (Vargas et al., 2005), schizophrenia (Monji et al., 2013; Lee et al., 2019), depression (Kim et al., 2016), anxiety, and bipolar disorder (Rao et al., 2010) have been associated with exposure to increased maternal peripheral and/or central inflammatory response during perinatal development.

A substantial limitation in the understanding of neuroinflammation and psychiatric disorders is the intrinsic invasive methodology needed to study neuronal, glial and inflammatory alterations. Most psychiatric disorders do not result in early-life mortality which means post- mortem studies are limited to adult tissue which is highly variable. Novel technologies have been developed which allow noninvasive in vivo characterization of neuroinflammation in humans, such as PET scans with radiotracers specific for microglia (Doorduin et al., 2008; Cosenza-Nashat et al., 2009). However, as the development of this technology is fairly recent the evidence remains limited. Therefore, much of the research directly examining neuroinflammation and neuropsychiatric disorders has been performed in animal models of the various psychiatric disorders. Animal models have been developed that display behavioral signs that resemble symptoms present in different human disorders. For example, the spontaneous hypertensive rat model is a common model used in the study of ADHD as these animals display increased rates of inattention, impulsivity, and hyperactivity (Banerjee and Nandagopal, 2015; Adriani et al., 2003). Animal models have been used to examine various environmental risk factors that directly or indirectly expose adult or developing offspring to elevated inflammation. For example, in adults, diet-induced obesity has been linked with altered immune cell polarization and low-grade systemic inflammation (Lumeng et al., 2007). In developing offspring, maternal administration of lipopolysaccharide (LPS) has been extensively studied, and these studies provide evidence that systemic maternal inflammation results in exposure of the developing offspring to a proinflammatory in utero environment (Fricke et al., 2018). It has been postulated that this proinflammatory environment leads to neuroinflammation in the offspring. This neuroinflammation is suggested to influence the developing brain in ways that may lead to the onset of many different neurodevelopmental disorders. A number of mechanisms of action have been proposed by which neuroinflammation influences brain development including reduced neurotropic support (Sen et al., 2008), aberrant neuronal development (Belmadani et al., 2006), altered neurotransmitter function (Kronfol and Remick, 2000), excessive glial activation (Reus et al., 2015), and increased oxidative stress (Hassan et al., 2016).

In addition to impacting the developing brain, studies suggests that inflammation plays a role in other psychiatric disorders such as, substance use disorder (Wilhelm et al., 2017), bipolar disorder (Chakrabarty et al., 2019), major depressive disorder (Strawbridge et al., 2019), and posttraumatic stress disorder (PTSD) (Imai et al., 2018), which are not typically considered neurodevelopmental disorders. However, while developmental influences do not directly result in the manifestation of this subset of psychiatric impairments, there is growing appreciation that gestational and early life inflammatory insults could influence susceptibility in these individuals. A few mechanisms have been proposed to link exposure to a proinflammatory environment and increased risk for psychiatric disorders that manifest later in life. One such mechanism is the gut-brain axis. Recently research has begun to link gut microbiota health with mental health and psychiatric disorders (Valles-Colomer et al., 2019). As the gut contains the largest pool of immune cells in the body (~80%) (Nguyen et al., 2018) and the microbiota is thought to be crucial in the correct functioning of the immune system (Kamada et al., 2013), it is proposed that alterations in the gut microbiota can impact mental health through an immune and inflammatory mechanism.

Overall, the understanding of neuroinflammation in relation to psychiatric disorders is growing rapidly. However, this area of research is still in the infancy phase and many important questions remain unanswered. Pinning down mechanistic changes that occur in the development of psychiatric disorders will allow for more specific conclusions to be made about the role neuroinflammation plays in these conditions. Further insight into the etiology of psychiatric impairments is necessary to develop more targeted and effective treatments and interventions. As with many biological phenomenon, psychiatric disorders are also subject to sex differences in the a) rates of individuals who are affected, b) nature and course of illness, and c) response to treatment. Understanding how differences in the biology of sex leads to the uniquely different experiences of individuals diagnosed with psychiatric disorders will allow for more targeted treatment strategies. The development of new technologies such as, specific radioligand tracers for microglia that can be visualized in PET scans and resting state fMRI that allows for minimally invasive ways to visualize connectivity changes as a result of neuroinflammation that are possibly occurring in psychiatric disorders. Early detection and treatment has been shown to substantially improve long-term outcomes of certain psychiatric disorders such as autism (Bryson et al., 2003). Thus, in addition to the use of peripheral samples, the development and refinement of these new imaging technologies are important in the identification of common biological mechanisms in the periphery and brain. Collectively, these advances could enable the discovery of early biomarkers for different psychiatric disorders that may allow for early detection and therefore earlier treatment opportunities.

Acknowledgments

This work was supported by the National Institutes of Health, National Institute on Drug Abuse [R41DA047865 (JML)]; the Department of Veterans Affairs, Veterans Health Administration Office of Research and Development, Biomedical Laboratory Research and Development Merit Review Award [1I01BX002061 (JML)]; the National Institute of Mental Health [R01 MH107508R01 (ES) and R01 738MH117177-01(ES)]. Author JML (Research Scientist) acknowledges her appointment at the VA Portland Health Care System, Portland, Oregon.

Footnotes

Disclosures

The contents do not represent the views of the Department of Veterans Affairs, National Institutes of Health, or the United States Government.

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