Short-Term Classification Learning Promotes Rapid Global Improvements of Information Processing in Human Brain Functional Connectome

Precuneus connectivity and symptom severity in chronic depression✰

Although abnormal resting state connectivity within several brain networks has been repeatedly reported in depression, little is known about connectivity in patients with early onset chronic depression. We compared resting state connectivity in a homogenous sample of 32 unmedicated patients with early onset chronic depression and 40 healthy control participants in a seed-to-voxel-analysis. According to previous meta-analyses on resting state connectivity in depression, 12 regions implicated in default mode, limbic, frontoparietal and ventral attention networks were chosen as seeds. We also investigated associations between connectivity values and severity of depression. Patients with chronic depression exhibited stronger connectivity between precuneus and right pre-supplementary motor area than healthy control participants, possibly reflecting aberrant information processing and emotion regulation deficits in depression. Higher depression severity scores (Hamilton Rating Scale for Depression) were strongly and selectively associated with weaker connectivity between the precuneus and the subcallosal anterior cingulate. Our findings correspond to results obtained in studies including both episodic and chronic depression. This suggests that there may be no strong differences between subtypes of depression regarding the seeds analyzed here. To further clarify this issue, future studies should directly compare patients with different courses of depression.

Interpretable brain age prediction using linear latent variable models of functional connectivity

Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we seek to build models of brain age based on functional connectivity while prioritizing model interpretability and understanding. This way, the models serve to both provide accurate estimates of brain age as well as allow us to investigate changes in functional connectivity which occur during the ageing process. The methods proposed in this work consist of a two-step procedure: first, linear latent variable models, such as PCA and its extensions, are employed to learn reproducible functional connectivity networks present across a cohort of subjects. The activity within each network is subsequently employed as a feature in a linear regression model to predict brain age. The proposed framework is employed on the data from the CamCAN repository and the inferred brain age models are further demonstrated to generalize using data from two open-access repositories: the Human Connectome Project and the ATR Wide-Age-Range.