Abstract:
Major depressive disorder (MDD) is linked to hippocampal volume loss, suggesting that structural brain changes contribute to mood dysregulation. Numerous MRI studies report smaller hippocampi in depression, correlating with illness duration, cortisol imbalance, and poorer outcomes. Conventional statistical approaches quantify associations but overlook the mechanistic feedback between hippocampal integrity and depressive load. This study proposes a mathematical and circuit-based model where hippocampal volume behaves like an electrical potential, and neural resilience is modeled as adaptive resistance. The framework integrates this analogy with logistic regression to produce a unified predictive expression for depression probability.
A synthetic dataset of 500 samples (250 depressed, 250 controls) was generated using meta- analytic means and standard deviations of hippocampal volumes. A univariate logistic regression was performed to estimate the probability (p) of depression as a function of hippocampal volume
(V). The model yielded coefficients and , demonstrating a strong inverse relationship between hippocampal size and depression risk. A clinically meaningful threshold of 1.1348 cm³ corresponded to , indicating high susceptibility to MDD.
A synthetic dataset of 500 samples (250 depressed, 250 controls) was generated using meta- analytic means and standard deviations of hippocampal volumes. A univariate logistic regression was performed to estimate the probability (p) of depression as a function of hippocampal volume.
(V). The model yielded coefficients and , demonstrating a strong inverse relationship between hippocampal size and depression risk. A clinically meaningful threshold of 1.1348 cm³ corresponded to , indicating high susceptibility to MDD.
To enhance interpretability, a novel circuit-theoretic analogy was introduced, representing hippocampal volume as “voltage” (V) and depressive load as a resistance-modulated current, defined by . This circuit output (I) was used within the logistic framework, demonstrating that reduced hippocampal integrity amplifies functional deficits in a nonlinear manner. The integrated model achieved an AUC of 0.720 in ROC analysis, confirming good predictive performance.
Overall, the study presents a transparent and mechanistically grounded approach linking hippocampal structure to depression risk. By establishing a high-risk anatomical cutoff and embedding structural parameters into a functional model, this work offers a novel framework for translating neuroimaging findings into clinically interpretable predictors, paving the way for more precise stratification and mechanistic understanding of MDD.
Biography:
T. Naveen Keerthi is a third-year MBBS student at MIMER Medical College and a gold medalist in pathology. Deeply passionate about neuroscience and translational medicine, he is driven by a vision to bridge molecular research with clinical practice. Naveen has been selected for research programs at premier institutes such as ACTREC and IISc Bangalore’s Centre for Neuroscience. His work spans diverse areas of biomedical science, integrating neurobiology, microbiome research, and mental health to explore how biological systems shape human disease and behavior. He also serves as a research lead at IEEE for a neuromathematics project and is a review board member of Acta Scientifica. Having presented papers at multiple conferences, including the NJ State AAPI Biomedical Symposium, Naveen aims to pursue a career in neurosurgery and neuroscience research, combining clinical precision with scientific inquiry to advance human understanding of the brain.
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