Recently, the field of developmental neuroscience has aimed to uncover the developmental trajectory of the human brain and to understand the changes that occur as a function of ageing. Here, we present a dataset of functional magnetic resonance imaging (fMRI) data covering the adult lifespan that includes structural MRI and resting-state functional MRI. Four hundred ninety-four healthy adults (age range: 19-80 years; Males=187) were recruited and completed two multi-modal MRI scan sessions at the Brain Imaging Center of Southwest University, Chongqing, China. The goals of the dataset are to give researchers the opportunity to map the developmental trajectories of structural and functional changes in the human brain and to replicate previous findings.

Multimodal magnetic resonance imaging (mMRI) has been widely used to map the structure and function of the human brain, as well as its behavioral associations. However, to date, a large sample with a long-term longitudinal design and a narrow age-span has been lacking for the assessment of test-retest reliability and reproducibility of brain-behavior correlations, as well as the development of novel causal insights into these correlational findings. Here we describe the SLIM dataset, which includes brain and behavioral data across a long-term retest-duration within three and a half years, mMRI scans provided a set of structural, diffusion and resting-state functional MRI images, along with rich samples of behavioral assessments addressed—demographic, cognitive and emotional information. Together with the Consortium for Reliability and Reproducibility (CoRR), the SLIM is expected to accelerate the reproducible sciences of the human brain by providing an open resource for brain-behavior discovery sciences with big-data approaches.

Despite a growing neuroimaging literature on the pathophysiology of major depressive disorder (MDD), reproducible findings are lacking, probably reflecting mostly small sample sizes and heterogeneity in analytic approaches. To address these issues, the Depression Imaging REsearch ConsorTium (DIRECT) was launched. The REST-meta-MDD project, pooling 2428 functional brain images processed with a standardized pipeline across all participating sites, has been the first effort from DIRECT. In this review, we present an overview of the motivations, rationale, and principal findings of the studies so far from the REST-meta-MDD project. Findings from the first round of analyses of the pooled repository have included alterations in functional connectivity within the default mode network, in whole-brain topological properties, in dynamic features, and in functional lateralization. These well-powered exploratory observations have also provided the basis for future longitudinal hypothesis-driven research. Following these fruitful explorations, DIRECT has proceeded to its second stage of data sharing that seeks to examine ethnicity in brain alterations in MDD by extending the exclusive Chinese original sample to other ethnic groups through international collaborations. A state-of-the-art, surface-based preprocessing pipeline has also been introduced to improve sensitivity. Functional images from patients with bipolar disorder and schizophrenia will be included to identify shared and unique abnormalities across diagnosis boundaries. In addition, large-scale longitudinal studies targeting brain network alterations following antidepressant treatment, aggregation of diffusion tensor images, and the development of functional magnetic resonance imaging-guided neuromodulation approaches are underway. Through these endeavours, we hope to accelerate the translation of functional neuroimaging findings to clinical use, such as evaluating longitudinal effects of antidepressant medications and developing individualized neuromodulation targets, while building an open repository for the scientific community.

Major depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach. Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels. The group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14×10-3). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients. Our findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.