A systematic review of fMRI neurofeedback interventions in depression shows that this type of training can modulate neural activity related to depression in a single session. A learning effect is also evident over multiple sessions. The paper was published in Clinical Neurophysiology.
fMRI neurofeedback is a technique that uses real-time functional magnetic resonance imaging (fMRI) to train individuals to consciously regulate their brain activity. Participants observe visual representations of their brain activity, typically displayed as a graph or thermometer, while engaging in specific mental strategies to modulate that activity. This method targets specific brain regions or networks associated with functions such as emotion regulation, pain perception, or cognitive control.
Neurofeedback aims to improve self-regulation of brain activity, potentially offering therapeutic benefits for conditions like anxiety, depression, PTSD, and chronic pain. The process involves repeated training sessions, during which individuals practice and refine their ability to influence brain activity patterns. fMRI neurofeedback is non-invasive, but it requires specialized equipment and expertise, making it less accessible than other neurofeedback methods, such as EEG.
Study author Ana Rita Barreiros and her colleagues sought to integrate the scientific findings on the effects of fMRI neurofeedback interventions in depression. They searched five databases of scientific research publications (PubMed, ScienceDirect, Web of Science, Scopus, and PsycINFO) using keywords such as “fMRI,” “functional magnetic resonance imaging,” “functional MRI,” combined with “depression,” “major depression,” or “major depressive disorder,” and “neurofeedback.”
The researchers looked for peer-reviewed publications written in English that explored the effects of fMRI neurofeedback interventions and included at least one group of participants with major depressive disorder. Ultimately, their search yielded 16 studies that met these criteria. Ten of these studies focused on individuals with major depressive disorder (310 participants), three on recurrent major depressive disorder (67 participants), and three included individuals with mild depression (23 participants). The number of participants per study ranged from 6 to 43, and 77% of the participants were female.
The authors focused on the effects of fMRI neurofeedback training on functional connectivity and BOLD activation patterns reported by these studies. The BOLD signal measures changes in blood oxygenation and flow in specific brain areas. It serves as an indirect indicator of neural activity, as active regions consume more oxygen, prompting an increase in oxygen-rich blood flow to that area. This alters the ratio of oxygenated to deoxygenated hemoglobin in that region. fMRI can detect these changes, allowing researchers to infer neural activity.
Overall, most studies indicated that fMRI neurofeedback training can modulate BOLD activity even within a single session. However, a significant learning effect was observed over multiple sessions. Neurofeedback training targeting specific brain regions led to changes in functional connectivity across broad neural networks, including the default-mode network (active when at rest) and the executive control network (primarily involving the prefrontal cortex and parietal regions, responsible for high-level cognitive functions like decision-making, problem-solving, and regulating attention and behavior).
“fMRI neurofeedback shows promise as a modulatory technique for depression, with the potential to induce significant changes in neural activity and connectivity of networks implicated in depression.”, study authors concluded.
The study integrates the findings of existing studies on the effects of fMRI neurofeedback training in individuals with depression. However, the studies included in this analysis were methodologically diverse and did not follow standardized protocols, limiting how generalizable the results are.
The paper, “fMRI neurofeedback for the modulation of the neural networks associated with depression,” was authored by Ana Rita Barreiros, Isabella B. Breukelaar, Anthony W.F. Harris, and Mayuresh S. Korgaonkar.