Connectomics & Major Depressive Disorder

Major depressive disorder (MDD) as a mental illness is commonly communicated as a chemical imbalance in the brain, causing changes in a person’s mood, energy, and concentration. However, our understanding of the disorder has improved such that we now understand that these chemical imbalances cause shifts in the way regions and networks of the brain communicate, generating the variety of symptoms which define it^1-3.

Growing evidence from brain imaging studies supports that changes in functional connectivity—the coordinated activity between different brain regions—plays a critical role in the progression of the disease⁴. Connectomics is a tool that allows for streamlined investigation of connectivity and could lead to more effective, individualized treatments for MDD.

MDD is one of the most common psychiatric disorders, affecting at least five percent of the world’s population^4,5. Individuals suffering from MDD experience persistently low moods, feelings of hopelessness, and anhedonia, also known as the inability to feel pleasure^4,5. Nearly two-thirds of people with MDD have suicidal ideation, and roughly 15% of those affected die by suicide⁴. 

The symptoms of MDD disrupt one’s ability to carry out their day-to-day responsibilities and place undue strain on relationships and career performance. Worryingly, MDD carries high comorbidities with developing substance use disorders, obsessive-compulsive disorder, bipolar disorder, and anxiety⁴.

Antidepressant medication is the frontline treatment for MDD and is normally administered in conjunction with psychotherapy⁴. However, these frontline medications are only effective in roughly 50% of patients who present with MDD, leaving a large proportion of sufferers as “treatment resistant”, unlikely to reach remission with basic pharmaceutical intervention⁶. Neurostimulation via transcranial magnetic stimulation (TMS) or electroconvulsive therapy (ECT) are employed when pharmaceutical approaches are ineffective or cause intolerable side effects like sexual dysfunction and weight gain^4,7,8.

There is no “silver bullet” treatment for depression, and each of the above carry their own unique drawbacks. For instance, Cognitive Behavioural Therapy is known to be highly variable and produce a weak response when used in isolation to treat depression⁹. While ECT is more effective, it is highly invasive and can cause memory loss and changes in blood pressure - hence why it is usually a last resort treatment¹⁰. No silver bullet exists to treat depression because depression is not just one disease. It is ultimately defined by its heterogeneity, and it is unique to each sufferer. For this reason, a more personalized approach to depression, such as the one offered by connectomics, becomes a necessity to determine optimal treatment pathways.

Figure 1.  The most common treatment option for MDD is antidepressants

Research has confirmed a long-suspected idea about depression—it isn’t a single disorder, but many⁴.

Drysdale et al. investigated nearly 1,200 depression patients, and successfully identified four distinct subtypes based upon connectivity alterations in the limbic system and frontostriatal pathways (subcortical pathways which feed into the default mode, salience, and central executive networks)^11,12. By clustering subjects based upon network abnormalities, researchers developed sensitive, highly-specific neuroimaging biomarkers to differentiate depression subtypes¹¹.

In another paper, resting-state fMRI revealed that patients with treatment-resistant depression had more localized network alterations than those patients who responded to treatment⁸. While both groups had disruptions to prefrontal-limbic-thalamic connectivity, treatment-resistant depression was characterized by stark alterations to prefrontal-limbic connectivity¹³.

Future work could provide psychiatrists and psychologists with the information they need to treat the patient’s depression subtype rather than the current generalized approach. In addition, identifying abnormal network connectivity could provide targets for neurostimulation techniques like TMS.

A number of academic publications have highlighted underlying neural changes during the presence of depressive symptoms. The limbic system appears to play a prominent role in the progression of these symptoms - likely due to its role in emotional regulation. Functional connectivity analyses have consistently demonstrated abnormal communication patterns between limbic system circuits and areas of the prefrontal cortex, which encode cognition ^14,15.

Moreover, Fang et al. recently utilized structural connectivity to delineate depressive patients from healthy controls, identifying that greater connectivity between frontal areas and the limbic system as the key biomarker for categorizing individuals ¹⁶.

Successful antidepressant treatment regimes also appear to target these same systems, as it was recently observed that functional connectivity changes between the dorsolateral prefrontal cortex (dlPFC) and insula cortex were associated with early treatment responses ¹⁷. 

Research has associated depression with significant disruptions to connectivity in many key brain networks—tightly coupled brain regions that fluctuate in activity together during a task or at rest.  

These networks and their contributions to depressive symptoms are:  

• The salience network undergoes structural changes during depression, reducing in volume and increasing in coupling with the Default Mode Network, likely underpinning the characteristic changes in mood during MDD¹⁸.
• The sensorimotor network which demonstrates altered functional connectivity which scales with Depression intensity, and manifests feelings of fatigue, heaviness of limbs, and body and chest discomfort¹⁹.
• The visual system receives and processes visual stimuli. In MDD, retinal input to the visual system is compromised, causing deficiencies in contrast interpretation, and perceptual loss of complex visual information²⁰.
• The limbic system, the core network for emotion, reactions to new stimuli, and memory - during depression induces feelings of detachment from self and others, as well as emotional reactivity²¹.
• The default mode network (DMN) which has reliably demonstrated heightened functional connectivity in MDD, relating directly to the severity and length of depressive episodes. The DMN additionally encodes self-referential thought, future thinking, and autobiographical memory, functions which become compromised in MDD²².
  

Identifying and characterizing alterations in these important brain networks is the first step to advancing our understanding of depression.

Figure 2.  The salience network is one of many networks associated with symptoms of depression

Recently, connectomics has been used to explore the heterogeneity of depression. Siddiqi et al identified that independent depressive symptoms and their severity are driven by different connections between the major brain networks which underpin the disorder. Namely, anxiogenic symptoms were driven by connections from the Default Mode Network, whereas dysphoric symptoms - such as sadness and anhedonia - were driven by connections arising from the salience network²³.

Figure 3. Color map of brain areas responsible for Anxiosomatic vs. Dysphoric symptoms of depression - adapted from Siddiqi et al., 2020

Moreover, evaluating an individual’s unique depression via their own brain connectivity enabled these researchers to adopt a more personalized neurostimulatory approach to effectively treat their patients symptoms. Another study, the Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT), adopted a similar approach, targeting unique brain regions with TMS based on their patients' underlying functional connectivity. This study found that 90.5% of the MDD patients investigated met remission criteria after five days of treatment, demonstrating the time-saving and effective response of leveraging connectomics to treat MDD²⁴. 

Connectomics is an exciting tool for studying depression, and it is clear that the human connectome plays an important role in the symptoms which define it. As the field of connectomics continues to progress, future individualized treatments may soon be uncovered.