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Godlewska, B.

Publications and source records attributed to Godlewska, B..

2 recordsLinked to original sources

A hippocampal neuroimaging signature of neurovascular insulin signalling links metabolism to mood

Depression is a leading cause of global disability and is increasingly linked to systemic metabolic dysfunctions, including insulin resistance. However, the biological pathways connecting metabolic state to affective symptoms are unresolved. A growing body of evidence indicates that insulin, beyond its role in systemic glucose homeostasis, supports brain metabolism, synaptic function and cognition, yet its contribution to mood regulation remains unclear. Here, we identify a human hippocampal metabolic signature associated with glycaemic variation in individuals with depression. Using multimodal neuroimaging, we show that hippocampal concentrations of GABA and lactate correlate with HbA1c and with mood severity, while functional connectivity between the hippocampus and the default mode network tracks affective symptoms independently of adiposity. To causally probe these relationships, we generated a mouse model of hippocampus-specific insulin receptor depletion. Unexpectedly, reducing insulin signalling at the blood brain barrier enhanced neuronal metabolism and attenuated anxiety-like behaviour. Notably, hippocampal lactate and GABA levels similarly tracked anxiety-related behaviour in mice, mirroring their association with symptom severity in humans. Together, these findings identify a conserved neurometabolic signature linking hippocampal insulin signalling to affective state, and reveal that brain insulin resistance exerts context- and cell-type-specific effects on behaviour. This work establishes a mechanistic basis for brain insulin resistance in depression and highlights the hippocampus as a critical hub for metabolic modulation of mood.

neuroscience↗

Brain Aging in Major Depressive Disorder: Results from the ENIGMA Major Depressive Disorder working group

BackgroundMajor depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset. MethodsWe performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain predicted age difference (brain-PAD). FindingsOn average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohens d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with higher self-reported depressive symptomatology (b=0.05, p=0.004). InterpretationThis highly powered collaborative effort showed subtle patterns of abnormal structural brain aging in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive value of these brain-PAD estimates. FundingThis work was supported, in part, by NIH grants U54 EB020403 and R01 MH116147.

neuroscience↗