Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts

Abstract

Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD.

Effect of anti-P ribosomal and anti-NR2 antibodies on depression and cognitive processes in SLE: an integrated clinical and functional MRI study

“…In our cohort, ROI alterations were mainly located in the temporal and frontal medial cortex, parahippocampal gyrus and cerebellar structures, with a predilection for the vermis. Our results establish a link between previous fMRI studies in patients with primary depression, showing neural circuitry alterations in the medial frontal cortex, temporal gyrus, hippocampus, thalamus [37][38][39] and cerebellum, 40 and immunohistological studies proving that anti-P bound to neurons in specific areas of mice brains, including the cingulate cortex, the dentate gyrus of the hippocampus and the piriform cortex. 14 Preziosa et al 41 showed that more severe structural global and nodal abnormalities were found in patients with SLE with anti-dsDNA positivity, irrespective of the serum levels.…”

Generalisable functional imaging classifiers of schizophrenia have multifunctionality as trait, state, and staging biomarkers

“…These biomarkers have been developed separately in some studies 16,17 , and other studies assumed a shared background [18][19][20] . In SSD research, few studies have succeeded in developing state markers using neuroimaging, and for staging markers, there is no consensus on biological staging models 21 .…”

Learning Multitask Gaussian Bayesian Networks

“…(2) Due to the difficulty and expensive data acquisition, the amount of rs-fMRI data is relatively small. In the Strategic Research Program for Brain Sciences (SRPBS) database, only 255 MDD patients' rs-fMRI data were collected (Yamashita et al, 2021). Since the heterogeneity of brain functional connections in patients with depression varies from person to person Young et al (2016).…”