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Long-term Depression01:05

Long-term Depression

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Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
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Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...
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Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
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Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning

Bing Cao1, Erkun Yang2, Lihong Wang3

  • 1College of Intelligence and Computing, Tianjin University, Tianjin, China.

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|August 4, 2023
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Summary
This summary is machine-generated.

Deep learning models identified specific brain regions, including the anterior cingulate and orbital frontal cortex, linked to late-life depression (LLD) symptom phenotypes. This research aids in developing targeted treatments for LLD.

Keywords:
Alzheimer's diseasecognitive impairmentcross-sectional late-life depressiondeep learningfactor score predictionstructural MRI Frontiers

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Artificial Intelligence

Background:

  • Late-life depression (LLD) presents complex symptom profiles.
  • Identifying specific neurobiological underpinnings for LLD symptoms is crucial for effective treatment.

Purpose of the Study:

  • To employ deep learning models to pinpoint brain regions associated with distinct LLD symptom phenotypes.
  • To investigate the utility of structural magnetic resonance imaging (sMRI) in predicting these symptom phenotypes.

Main Methods:

  • Utilized deep learning on sMRI data from 116 LLD patients.
  • Predicted five depression symptom factors (Anhedonia, Suicidality, Appetite, Sleep Disturbance, Anxiety) using 3D sMRI patches.
  • Identified discriminative brain regions using ROI-level prediction accuracy.

Main Results:

  • Deep learning models successfully predicted Anxiety and Suicidality factors.
  • The anterior cingulate and orbital frontal cortex were identified as key discriminative regions for all five symptom phenotypes.
  • Localized morphological differences in the brain were associated with LLD symptom phenotypes.

Conclusions:

  • Deep learning on sMRI is effective for predicting LLD symptom phenotypes.
  • Findings highlight the potential for symptom-targeted treatments in LLD.
  • Future research should integrate multimodal data for enhanced prediction.