Functional MRI (fMRI) has revealed that cognitive behavioral therapy (CBT) positively changes the brain, altering brain structure and boosting grey-matter volume, according to a study published on 27 August in Translational Psychiatry.
The findings confirm the efficacy of CBT, wrote a team of researchers from Martin Luther University Halle-Wittenberg (MLU) and the University of Münster, both in Germany.
"Cognitive behavioral therapy was already known to work," corresponding author Prof. Ronny Redlich, PhD, said in a statement released by Martin Luther University. "Now, for the first time, we have a reliable biomarker for the effect of psychotherapy on brain structure. Put simply, psychotherapy changes the brain."
More than 280 million people around the world suffer from severe depression, a condition that can lead to changes in the brain mass of the anterior hippocampus and amygdala -- both of which are part of the limbic system and are responsible for processing and controlling emotions, the group explained. CBT is an established method for treating depression, as it addresses thought patterns, emotions, and behavior, and is assumed to be linked to functional and structural changes in the brain, the team noted.
Using functional MRI, doctoral students Esther Zwiky and Tiana Borgers and their colleagues analyzed the brains of 30 patients with acute depression before and after 20 sessions of CBT; these patients also underwent clinical interviews.
The group reported that more than half of the patients diagnosed with acute depression -- 19 of 30 -- had hardly any acute depressive symptoms after therapy, and that their brains had altered in a positive manner: Those with a greater increase in grey matter in the amygdala also showed a stronger reduction in their emotional dysregulation.
"Our results support the assumption that CBT affects not just the patient's experiences and behavior but also brain structure and add to previous findings in depressive disorders and other mental disorders (e.g., social anxiety disorder, obsessive-compulsive disorder, spider phobia) that demonstrated the structural plastic effects of CBT," the researchers wrote.
Gray-matter volume changes in the amygdala within patients and the association with improvements in alexithymia (Difficulty Identifying Feelings). (Left) Scatter plots depicting GMV changes (delta = t2-t1) within the cluster of the right amygdala (x = 32, y = −3, z = −27) on the y-axis correlated with changes (delta = t1-t2) in the Toronto Alexithymia Scale (TAS20) subscale Difficulty Identifying Feelings (DIF; rs = 0.321, p = 0.042) on the x-axis within the patient group. Line: regression slope. (Right) Coronal view (Montreal-Neurological-Institute coordinate y = 0) depicts the results of the paired t-tests (t1 vs. t2) in the patient group within the bilateral amygdala. Graphics, images, and caption courtesy of Translational Psychiatry via a Creative Commons License.
The study findings are positive, but do not necessarily mean that CBT is the best depression treatment for everyone, Redlich said, noting that some people respond better to medication, while others respond well to electrostimulation or CBT.
"It is … encouraging that we were able to show in our study that psychotherapy is an equally effective alternative from a medical and scientific standpoint," he said.
The complete study can be found here.




![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)







![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)








