Functional Neuroimaging: Can We Visualise Depression?

If you peered into the brain of someone living with depression, what would you see? 

In a depressive state, someone may feel hopeless or worthless. Daily tasks become burdensome, and thoughts of death may cloud their headspace. Depression is a serious mood disorder, and impacts 280 million people globally.

To unearth the mental cogs that keep depression turning, scientists have studied how the condition drives physical changes in the brain that disturb its normal functioning. Atypical brain activity and changes to chemical messengers, for instance, may be potential culprits.

Functional Neuroimaging tools can help tackle this conundrum. This class of computer-imaging devices can take detailed snapshots of how brain areas are functioning, all without inserting a single instrument into your head. 

Electricity Inspectors: Electroencephalography (EEG) 

Your brain contains a web of interconnected neurons, string-like cells that deliver information between regions. This information flows as waves of electricity between these neurons.  

EEG scans work by capturing these electrical signals. First, an electricity-conducting paste is used to attach electrodes across your scalp. More active brain regions exchange more information, boosting the signals coming from this area. The electrodes detect this flurry of signals almost instantaneously, capturing real-time brain activity. In other words, it has a high temporal resolution.

 Scientists can use these snapshots to discover electrical blips associated with depression. For instance, in the brains of people with depression, some EEG studies have found unusual activity in the left brain. Compared to a non-depressed brain, electrical waves that arise when the left brain is relaxed move more energetically. This suggests this area is too relaxed, and isn’t working as hard as usual on motivation, planning, and processing positive emotions.

EEG, however, has a low spatial resolution. On their journey to the electrodes, signals must dig through layers of tissue, skull, and hair, so when they reach the scalp, they’re too weak to pinpoint their exact origin.

Magnetic Monitors: Magnetoencephalography (MEG) 

As electrical waves flow through neurons, they leave a faint, invisible magnetic field that wraps around the waves’ path. MEG scans locate these delicate patterns using Super Quantum Interference Devices (SQUIDs), which observe very faint magnetic fields and generate an image of their source in the brain.

 Like EEG, MEG can detect signals as fast as a millisecond, and can be used to locate electrical activity connected to depression. According to one MEG study, fewer electrical waves flow through areas vital in emotional control, motivation, memory and decision-making compared to a typical brain.

MEG has a superior spatial resolution to EEG, as tissue and bone don’t impair magnetic fields. This allows SQUIDs to reveal their origin more clearly.

Radiation Radars: Positron Emission Tomography (PET) 

Whenever neurons communicate, they send each other neurotransmitters, chemicals that transfer electrical signals between neurons. These molecules must fit onto a molecular ‘lock’, or receptor, on the next neuron’s surface.  

PET scans can compare the number of receptors for specific neurotransmitters across brain areas. At the start of a PET scan, a radioactive molecule, shaped like the neurotransmitter under investigation, is injected into the blood and attaches to receptors in the brain. At the receptor, the molecule breaks apart and expels gamma rays. The more rays detected by the PET sensor, the more receptors there are in that region.

When the real neurotransmitter arrives at the receptor, it banishes the radioactive molecule, reducing the number of rays. The sensor detects this change: the faster the drop, the more neurotransmitter at the receptor.

 PET scans can uncover how neurotransmitters might behave differently in the brains of people with depression. Several studies have focused on serotonin, a neurotransmitter involved in mood control. According to their findings, depressed brains have lower levels of serotonin and serotonin receptors in areas involved in emotional control, memory and decision-making.  

Gamma rays, however, are radioactive and can cause cancer, and PET has low spatial resolution: gamma rays come from an area near, but not at, the receptor, so PET can't identify the exact source of brain activity. Moreover, PET has a low temporal resolution, as gamma rays generate faster than the sensor can process.

Detective Dracula: Functional Magnetic Resonance Imaging (fMRI) 

When one brain region is more active than another, oxygen-carrying blood floods this region to deposit glucose and oxygen, substances that neurons need for energy. The more oxygen-carrying blood there is compared to blood without, the less magnetic the blood in this area is overall. 

fMRI scanners sniff out differences in blood magnetism between brain regions and convert this into a signal. The less magnetic blood is in one area, the higher the signal, and the more active the area is.

Some fMRI studies show that, in a depressed brain, emotional control centres react more strongly to negative information than a typical brain, which might explain why people with depression overfocus on negative thoughts.

FMRI, however, can’t capture real-time brain activity, as blood flow to a brain area is slower than the area’s actual activity. fMRI scanners can also make subjects uncomfortable: they’re loud, narrow, and its use of magnetic fields can interfere with medical implants made from magnetic material. However, it generates more detailed images than other methods, so has the best spatial resolution.

Who is the Best? 

Each method best suits studying physical, depression-related changes in the brain in different contexts. EEG and MEG’s superior temporal resolution makes them better for recording the timing of atypical brain activity. PET and fMRI, however, are better for uncovering chemical abnormalities. PET can track how neurotransmitters misbehave in a depressed brain, and fMRI can interpret blood magnetism to see which brain areas are under- or overactive. 

References 

Carlson, N., & Birkett, M. (2020). Foundations of Behavioral Neuroscience, Global Edition. Pearson Education, Limited. http://ebookcentral.proquest.com/lib/kcl/detail.action?docID=6265329 

de Aguiar Neto, F. S., & Rosa, J. L. G. (2019). Depression biomarkers using non-invasive EEG: A review. Neuroscience and Biobehavioral Reviews, 105, 83–93. https://doi.org/10.1016/j.neubiorev.2019.07.021 

Depressive disorder (depression). (n.d.). Retrieved 26 October 2024, from https://www.who.int/news-room/fact-sheets/detail/depression 

Domschke, K., Zwanzger, P., Rehbein, M. A., Steinberg, C., Knoke, K., Dobel, C., Klinkenberg, I., Kugel, H., Kersting, A., Arolt, V., Pantev, C., & Junghofer, M. (2015).

Magnetoencephalographic Correlates of Emotional Processing in Major Depression Before and After Pharmacological Treatment. International Journal of Neuropsychopharmacology, 19(2), pyv093. https://doi.org/10.1093/ijnp/pyv093 

Hamilton, J. P., Etkin, A., Furman, D. J., Lemus, M. G., Johnson, R. F., & Gotlib, I. H. (2012). Functional neuroimaging of major depressive disorder: A meta-analysis and new integration of base line activation and neural response data. The American Journal of Psychiatry, 169(7), 693–703. https://doi.org/10.1176/appi.ajp.2012.11071105 

Kang, S.-G., & Cho, S.-E. (2020). Neuroimaging Biomarkers for Predicting Treatment Response and Recurrence of Major Depressive Disorder. International Journal of Molecular Sciences, 21(6), 2148. https://doi.org/10.3390/ijms21062148 

Singh, S. B., Tiwari, A., Katta, M. R., Kafle, R., Ayubcha, C., Patel, K. H., Bhattarai, Y.,

Werner, T. J., Alavi, A., & Revheim, M.-E. (2024). The utility of PET imaging in depression. Frontiers in Psychiatry, 15, 1322118. https://doi.org/10.3389/fpsyt.2024.1322118 

Zhang, X., Xie, J., Fan, C., & Wang, J. (2022). Research on the MEG of Depression Patients Based on Multivariate Transfer Entropy. Computational Intelligence and Neuroscience, 2022, 7516627. https://doi.org/10.1155/2022/7516627 

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