Depression disrupts emotional regulation, influences attention and affects the way we engage with our thoughts. These changes can be observed beyond behaviour, at a physiological level in our brains. Brain mapping offers a new perspective on depressive symptoms and raises questions about their diagnosis, treatment and relapse. What can brain mapping for depression actually tell us? We take a closer look at the latest research.
Brain mapping for depression: what can we learn?
Brain mapping for depression reveals the changes in brain activity and networks. Discover what it can actually tell us about symptoms, relapse and treatment.
Overview.
Key takeaways.
Brain mapping uses different technologies to study neural activity, structure and connectivity.
Research in depression has revealed changes in networks supporting self-referential thinking, attention and emotional regulation.
The Default Mode Network (DMN) plays an important role in rumination and internally focused thought, making it a key area of interest in depression research.
Brain mapping can reveal patterns associated with depression, but it cannot currently diagnose the condition on its own.
Studying brain networks provide greater insight into vulnerability to relapse and inform new approaches to prevention.
Neuromind builds on these findings with an EEG-based neurofeedback approach designed to support depression relapse prevention.
01
What is brain mapping?
From brain activity to a map.
Brain mapping refers to a range of techniques used to measure and visualise patterns of brain activity, structure or connectivity. Rather than producing a single picture of the brain, these methods generate data that researchers can analyse to understand how different regions and networks function and interact.
Therefore, a brain map is not necessarily a literal image of the brain. Depending on the technology used, it can show patterns of electrical activity, changes in blood oxygenation, brain structure, metabolism or functional connectivity between regions. Through these recordings, researchers can identify whether certain regions are underactive, overactive or miscommunicating with others.
Technologies used for brain mapping.
Distinct technologies provide different pieces of the puzzle and several neuroimaging techniques can be considered part of brain mapping.
Quantitative electroencephalography (qEEG) records electrical activity from the scalp using electrodes. It has excellent temporal resolution and can detect changes in neural activity within milliseconds.
fMRI (functional magnetic resonance imaging) doesn’t directly measure neurons firing. Instead, it detects changes in blood oxygenation associated with local changes in brain activity. Structural MRI focuses on anatomy rather than moment-to-moment activity.
PET (positron emission tomography) can measure aspects of brain metabolism and molecular activity using radioactive tracers.
For depression research, these methods complement each other. fMRI is particularly useful for studying networks and connectivity, while EEG can track rapid changes in electrical activity.
02
What can brain mapping reveal about depression?
The Default Mode Network and self-referential thinking.
The Default Mode Network (DMN) is a group of interconnected brain regions that is particularly active during internally focused mental processes, including self-reflection, autobiographical memory and spontaneous thought. Research in people with major depressive disorder (MDD) has identified altered activity and regulation within several regions of this network [1].
One particularly important process in depression is rumination: repetitive, often difficult-to-disengage thinking about negative experiences or feelings. A meta-analysis of 14 fMRI studies involving 286 healthy participants found a consistent association between rumination and activity in core regions of the DMN, as well as the dorsomedial prefrontal cortex subsystem [2]. In a meta-analysis including 618 people with MDD and 683 healthy controls, researchers also found reduced connectivity within the DMN core in the MDD group [3].
Taken together, these findings suggest that depression is associated with altered organisation and communication within and between brain networks, with the DMN being one important part of that picture.
Brain activity, attention and emotional regulation.
The Default Mode Network isn’t the only network involved in depressive symptoms. A meta-analysis of 27 resting-state functional connectivity datasets, including 556 people with MDD and 518 healthy controls, identified several differences in large-scale network communication [4], including:
changes within the frontoparietal network, which contributes to cognitive control;
altered communication involving the dorsal attention network and systems engaged in emotion and salience.
These findings help explain why depression can affect more than mood. When the systems responsible for internal thought, attention and emotional regulation don’t work together effectively, it may become harder to shift attention away from negative thoughts or regulate emotional responses.
03
Can brain mapping diagnose depression?
Patterns, not proof.
Depression remains a clinical diagnosis based on:
symptoms;
duration;
severity;
impact on daily life.
Brain imaging can recognise patterns, but there is no single brain map that can currently confirm the diagnosis in an individual.
A review of the clinical application of brain imaging in mood disorders concluded that, despite promising findings, no neuroimaging biomarker had reached the level of clinical usefulness required to establish a diagnosis or predict treatment response [5].
EEG-based biomarkers face similar challenges. A meta-analysis of 76 studies examining 81 qEEG biomarkers found promising predictive performance overall, but substantial publication bias, limited external validation and insufficient direct replication [6].
04
What can brain mapping tell us about depression relapse?
Vulnerability between episodes.
In a 10-year prospective study of 318 people with unipolar major depressive disorder, nearly two-thirds experienced at least one relapse. The risk of recurrence increased with each successive episode, while longer periods of recovery were associated with a lower risk [7]. Could some brain patterns associated with depression remain during remission?
One proposed model focuses on the balance between the DMN and networks involved in attention and goal-directed activity. According to this model, an imbalance between these systems could contribute to rumination and cognitive vulnerability, even when depressive symptoms have improved [8].
Even though brain mapping can’t currently predict who will relapse, it gives a way to investigate what may keep someone vulnerable to another episode and which brain processes could potentially be targeted by prevention strategies.
05
Neuromind: a neuromodulation approach to target depression relapse
A precision augmentation layer.
Existing approaches to depression relapse prevention remain essential. However, they don’t address how the brain becomes locked into the patterns that make depression return. Neuromind is designed as a precision augmentation layer to shed light on the neurophysiological states underlying relapse vulnerability.
Our neurofeedback solution for depression combines wearable EEG, artificial intelligence and immersive virtual reality to monitor biomarkers related to attention, arousal and emotional state. When shifts in neural activity associated with increased DMN engagement emerge, the system provides real-time feedback. The VR environment adapts to reinforce more regulated attentional states and help users shift away ruminative states.
From observation to training.
This approach builds directly on the potential of brain mapping: instead of simply observing differences in brain activity, we use this information to create a personalised training experience. Our sessions incorporate MBCT-aligned mindfulness, with the aim of helping users recognise and regulate patterns associated with rumination and excessive DMN engagement.
If your institution is working on depression relapse prevention, DMN-targeted interventions, digital therapeutics, or neurofeedback-augmented psychotherapy, we would be delighted to collaborate. Contact us to explore a partnership.
A brain scan can reveal differences in brain activity, structure or connectivity that are associated with depression. However, there is no single scan that can confirm depression in an individual. Brain imaging remains primarily a research tool for investigating the neural mechanisms involved.
References
[1] Sheline YI, Barch DM, Price JL, Rundle MM, Vaishnavi SN, Snyder AZ, Mintun MA, Wang S, Coalson RS, Raichle ME. The default mode network and self-referential processes in depression. Proc Natl Acad Sci U S A. 2009 Feb 10;106(6):1942-7. doi: 10.1073/pnas.0812686106. Epub 2009 Jan 26. PMID: 19171889; PMCID: PMC2631078.
[2] Zhou HX, Chen X, Shen YQ, Li L, Chen NX, Zhu ZC, Castellanos FX, Yan CG. Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depression. Neuroimage. 2020 Feb 1;206:116287. doi: 10.1016/j.neuroimage.2019.116287. Epub 2019 Oct 23. PMID: 31655111.
[3] Tozzi L, Zhang X, Chesnut M, Holt-Gosselin B, Ramirez CA, Williams LM. Reduced functional connectivity of default mode network subsystems in depression: Meta-analytic evidence and relationship with trait rumination. Neuroimage Clin. 2021;30:102570. doi: 10.1016/j.nicl.2021.102570. Epub 2021 Jan 18. PMID: 33540370; PMCID: PMC7856327.
[4] Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. JAMA Psychiatry. 2015 Jun;72(6):603-11. doi: 10.1001/jamapsychiatry.2015.0071. PMID: 25785575; PMCID: PMC4456260.
[5] Savitz JB, Rauch SL, Drevets WC. Clinical application of brain imaging for the diagnosis of mood disorders: the current state of play. Mol Psychiatry. 2013 May;18(5):528-39. doi: 10.1038/mp.2013.25. Epub 2013 Apr 2. PMID: 23546169; PMCID: PMC3633788.
[6] Widge AS, Bilge MT, Montana R, Chang W, Rodriguez CI, Deckersbach T, Carpenter LL, Kalin NH, Nemeroff CB. Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis. Am J Psychiatry. 2019 Jan 1;176(1):44-56. doi: 10.1176/appi.ajp.2018.17121358. Epub 2018 Oct 3. PMID: 30278789; PMCID: PMC6312739.
[7] Solomon DA, Keller MB, Leon AC, Mueller TI, Lavori PW, Shea MT, Coryell W, Warshaw M, Turvey C, Maser JD, Endicott J. Multiple recurrences of major depressive disorder. Am J Psychiatry. 2000 Feb;157(2):229-33. doi: 10.1176/appi.ajp.157.2.229. PMID: 10671391.
[8] Marchetti I, Koster EH, Sonuga-Barke EJ, De Raedt R. The default mode network and recurrent depression: a neurobiological model of cognitive risk factors. Neuropsychol Rev. 2012 Sep;22(3):229-51. doi: 10.1007/s11065-012-9199-9. Epub 2012 May 9. PMID: 22569771.
[9] Xia Z, Yang PY, Chen SL, Zhou HY, Yan C. Uncovering the power of neurofeedback: a meta-analysis of its effectiveness in treating major depressive disorders. Cereb Cortex. 2024 Jun 4;34(6):bhae252. doi: 10.1093/cercor/bhae252. PMID: 38889442.
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