Major depressive disorder (MDD) remains one of the world’s most disabling mental illnesses, and while transcranial magnetic stimulation (TMS) is an FDA-approved treatment, its effectiveness varies widely between individuals. Now, a team from the Tsinghua University and the Institute of Psychology, Chinese Academy of Sciences has published a breakthrough study in Science Bulletin that may help change that.
The team used resting-state functional MRI data from 1660 patients with depression and 1341 healthy controls collected through the Depression Imaging REsearch ConsorTium (DIRECT) Phase II. They systematically mapped abnormalities in the brain’s subgenual anterior cingulate cortex (sgACC) connectivity—a region repeatedly implicated in depression—and its interaction with the left dorsolateral prefrontal cortex (DLPFC), the standard target of TMS treatment.
Crucially, the researchers found that abnormal sgACC–DLPFC connectivity patterns influenced both the anatomical location of optimal TMS targets and patient outcomes. Building on this, they proposed a novel algorithm that integrates large-scale group-level statistical maps with individual brain data using dual regression. This “MDD big data-guided individualized TMS targeting algorithm” was validated in three independent clinical datasets, including patients with treatment-resistant depression and suicidal ideation.
Compared to conventional “group average” or anatomical targeting methods, this individualized approach produced targets more closely associated with symptom improvement, suggesting a major step forward for precision psychiatry.
Science Bulletin
Observational study