Imaging Depression
Digital Poster
Neuro B
Wednesday, 13 May 2026
This session showcases cutting‑edge MRI research examining the biological underpinnings of depression across structural, metabolic, functional, and network domains.
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564-04-001.
MRE-Driven Lesions Identification in Temporal Lobe Epilepsy with Comorbid Depression via Multimodal MRI: A Case Report
Impact: This study demonstrates that MRE combined with multimodal MRI can precisely localize epileptogenic and depression-associated lesions in temporal lobe epilepsy, revealing microstructural changes undetectable by conventional imaging and supporting personalized, mechanism-informed clinical management.
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564-04-002.
Using 7T fMRI of bipolar depression patients to predict response with ketamine treatment
Impact: For patients with treatment-resistant bipolar depression undergoing hallucinogen therapy with ketamine, an important gap is prediction of treatment response. This study uniquely uses 7T MRI and resting-state connectivity to bridge this gap and improve scientific and medical knowledge.
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564-04-003.
Disrupted Oxygen Metabolism in Major Depression Revealed by Non-invasive MR-Based OEF Mapping
Impact: This study reveals disrupted oxygen metabolism
in specific brain regions in depression, suggesting altered neurovascular
coupling. These findings highlight MR-based OEF mapping as a promising
biomarker for assessing depression severity and advancing understanding of cerebral
metabolic dysfunction in MDD.
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564-04-004.
NODDI and DTI-ALPS Reveal White Matter Deficits and Glymphatic impairment in First-Episode Adolescent MDD
Impact: Findings provide neurobiological clues for FEA-MDD, aid early clinical assessment via NODDI/DTI-ALPS, and guide research on adolescent depression mechanisms.
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564-04-005.
Reduced Brain Serotonergic Metabolites in Depression Detected by CEST MRI
Impact: This
study is the first to demonstrate in vivo
detection of reduced brain serotonergic metabolites under depression using an
integral-based CEST method. Its non-invasive, contrast-free approach offers
potential clinical biomarkers for quantitative assessment of depression.
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564-04-006.
Brain Mechanical Properties of the Salience and Limbic Networks Reflect Mild Depressive Symptoms
Impact: Lower MRE-derived tissue stiffness within salience and limbic networks, but not sensory networks, are associated with mild depressive symptoms. Brain mechanical properties could serve as biomarkers to predict and personalize treatment response to neuromodulation therapies.
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564-04-007.
Microstructural Changes in Cortex and Deep Gray Nuclei Following ECT Therapy
Impact: Multi-shell diffusion imaging reveals microstructural changes in the brain after ECT for treatment of depression. This may explain how ECT impacts the brain and elucidate the underlying pathophysiology furthering our understanding of depression, and offer a potential imaging biomarker.
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564-04-008.
Reorganization of Hypothalamic and Reward Network Connectivity in Depression: Insights from Open fMRI Data
Impact: Resting-state fMRI analysis revealed altered connectivity within the reward network, particularly among the hypothalamus, amygdala, and ventral tegmental area, highlighting the role of the understudied hypothalamus and contributing to potential biomarker identification in depression.
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564-04-009.
Integrative NMR-Based Metabolomic and Predictive Modeling Approach for Differential Diagnosis of Anxiety and Depression
Impact: The present study
enhances understanding of GAD and MDD pathophysiology, aiding their differentiation.
Combinatorial biomarkers (dimethylamine, glucose, Phosphocreatine, leucine, isoleucine)
identified via 1H NMR metabolomics and logistic regression may serve
as supplementary diagnostic tools and guide targeted therapeutic strategies.
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564-04-010.
Subcortical volume alterations in first-episode, drug-naïve adolescents with major depressive disorder
Impact: Adolescent MDD patients showed significantly reduced volumes in
multiple subregions of thalamus, hypothalamus and brainstem compared to HCs. These abnormalities in limbic structures, potentially reflecting
neurodevelopmental alterations relevant to pathophysiology. These findings may help in early diagnosis and targeted interventions.
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564-04-011.
Research on Cerebral Metabolites and Brain Network Characteristics in Non-Small Cell Lung Cancer Patients with Depression
Impact: Reduced GABA+ levels in the mPFC, coupled with DMN/LN network disruption, explain the neurochemical basis of depressive symptoms in DNSCLC patients. This finding can potentially aid in diagnosing cancer-related depression and inform research on new drug targets.
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564-04-012.
Structural rather than functional changes that characterize the differences between major depressive disorder and insomnia
Impact: By distinguishing shared functional hyperactivity and disorder-specific structural deficits between MDD and PI, this study advances understanding of emotion–sleep interactions and enables development of imaging-based biomarkers for precise diagnosis and individualized therapeutic strategies.
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564-04-013.
Global and regional glymphatic system dysfunction in patients with major depressive disorder: A 5-Tesla MRI study
Impact: This study
investigated the impairment of glymphatic function in MDD patients. Revealing
the pattern of glymphatic system dysfunction in brain networks and paving the
way for the development of targeted treatment in clinic.
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564-04-014.
White-Matter and Glymphatic Alterations in Adolescent Major Depression: Evidence from DTI and Perivascular Imaging
Impact: This study demonstrates coupled alterations of white-matter integrity and glymphatic function in major depression during the developmental stage, suggesting that impaired brain–fluid interaction may underlie neurodevelopmental vulnerability and shape trajectories of mood and cognitive regulation.
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564-04-015.
Partial Least Square Analysis of Brain–Behavior Relationships Across Structural and Functional MRI in Psychiatric Disorders
Impact: Multimodal MRI combined with PLS revealed that cortical-thickness plasticity explains behavioral improvement after TMS. This multivariate framework advances understanding of neuromodulatory mechanisms and supports cortical metrics as biomarkers for predicting and optimizing therapeutic response.
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