Major depressive disorder (MDD) is closely associated with suicide, which often begins with suicidal ideation (SI). However, the underlying neural mechanisms remain unclear.

We included 73 MDD patients with SI (MDD-SI), 44 MDD patients without SI (MDD-NSI) and 78 healthy controls (HCs), then compared the amplitude of low-frequency fluctuations (ALFF), functional connectivity (FC), and effective connectivity (EC) differences across groups and analyzed their relationship with SI severity. FC and EC analyses used brain regions with ALFF differences between MDD-SI and MDD-NSI as seed points. ALFF findings were validated using the REST-meta-MDD consortium dataset (N = 1 596, 24 sites). Additionally, we explored the trend of changes in abnormal activity and connectivity of SI and suicidal behavior (SB) in MDD-SI.

Compared to MDD-NSI, MDD-SI showed increased ALFF in the right anterior cingulate cortex (ACC), validated by the REST-meta-MDD consortium dataset. MDD-SI also exhibited reduced FC between the right ACC and the left inferior frontal gyrus and decreased EC from the right ACC to the right fusiform gyrus, which were negatively correlated with the Hamilton Depression Rating Scale (HAMD)-suicidality item scores. Increased EC was observed in MDD-SI from the right ACC to the right cerebellar tonsil and from the left inferior parietal lobule (IPL) to the right ACC, following a progressive increase pattern (HC < MDD-NSI < MDD-SI without SB < MDD-SI with SB).

Increased activity and aberrant connectivity of the ACC may be associated with SI in MDD patients and potentially serve as biomarkers for suicide risk.

In contemporary neuroimaging studies, it has been observed that patients with major depressive disorder (MDD) exhibit aberrant spontaneous neural activity, commonly quantified through the amplitude of low-frequency fluctuations (ALFF). However, the substantial individual heterogeneity among patients poses a challenge to reaching a unified conclusion.

To address this variability, our study adopts a novel framework to parse individualized ALFF abnormalities. We hypothesize that individualized ALFF abnormalities can be portrayed as a unique linear combination of shared differential factors. Our study involved two large multi-center datasets, comprising 2424 patients with MDD and 2183 healthy controls. In patients, individualized ALFF abnormalities were derived through normative modeling and further deconstructed into differential factors using non-negative matrix factorization.

Two positive and two negative factors were identified. These factors were closely linked to clinical characteristics and explained group-level ALFF abnormalities in the two datasets. Moreover, these factors exhibited distinct associations with the distribution of neurotransmitter receptors/transporters, transcriptional profiles of inflammation-related genes, and connectome-informed epicenters, underscoring their neurobiological relevance. Additionally, factor compositions facilitated the identification of four distinct depressive subtypes, each characterized by unique abnormal ALFF patterns and clinical features. Importantly, these findings were successfully replicated in another dataset with different acquisition equipment, protocols, preprocessing strategies, and medication statuses, validating their robustness and generalizability.

This research identifies shared differential factors underlying individual spontaneous neural activity abnormalities in MDD and contributes novel insights into the heterogeneity of spontaneous neural activity abnormalities in MDD.

Major depressive disorder (MDD) is associated with high risk of suicide. Conventional neuroimaging works showed abnormalities of static brain activity and connectivity in MDD with suicidal ideation (SI). However, little is known regarding alterations of brain dynamics. More broadly, it remains unclear whether temporal dynamics of the brain activity could predict the prognosis of SI.

We included MDD patients (n = 48) with and without SI and age-, gender-, and education-matched healthy controls (n = 30) who underwent resting-state functional magnetic resonance imaging. We first assessed dynamic amplitude of low-frequency fluctuation (dALFF) – a proxy for intrinsic brain activity (iBA) – using sliding-window analysis. Furthermore, the temporal variability (dynamics) of iBA was quantified as the variance of dALFF over time. In addition, the prediction of the severity of SI from temporal variability was conducted using a general linear model.

Compared with MDD without SI, the SI group showed decreased brain dynamics (less temporal variability) in the dorsal anterior cingulate cortex, the left orbital frontal cortex, the left inferior temporal gyrus, and the left hippocampus. Importantly, these temporal variabilities could be used to predict the severity of SI (r = 0.43, p = 0.03), whereas static ALFF could not in the current data set.

These findings suggest that alterations of temporal variability in regions involved in executive and emotional processing are associated with SI in MDD patients. This novel predictive model using the dynamics of iBA could be useful in developing neuromarkers for clinical applications.