Williams, Leanne

Leanne Williams is a journalist who covers mental health and neuroscience. Her work focuses on the intersection of science and society, exploring how advances in brain research are reshaping our understanding of disorders like depression and anxiety. She has a background in psychology and has reported on topics ranging from the development of new treatments for psychiatric conditions to the impact of technology on mental well-being.

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The Daily's Verdict

This author is known for its high journalistic standards. The author strives to maintain neutrality and transparency in its reporting, and avoids conflicts of interest. The author has a reputation for accuracy and rarely gets contradicted on major discrepancies in its reporting.

Bias

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No current examples available.

Conflicts of Interest

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Contradictions

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Examples:

  • Approximately a third of major depressive disorder patients and approximately half of generalized anxiety disorder patients do not respond to first-line treatment.
  • A precision medicine approach to mental health care requires personalized measures for quantifying neurobiological dysfunctions in patients.
  • Efforts to identify biotypes of depressed and anxious patients have used task-free fMRI and found aberrant connectivity in frontostriatal and limbic networks, hyper- and hypoconnectivity of the default mode network, and differences in anxiety within depression.
  • The current psychiatric diagnostic system assigns one label to syndromes involving multiple neurobiological processes, hindering effective treatment.

Deceptions

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Examples:

No current examples available.

Recent Articles

Revolutionizing Depression Treatment: Stanford Researchers Identify Six Distinct Subtypes Based on Brain Imaging and Machine Learning

Revolutionizing Depression Treatment: Stanford Researchers Identify Six Distinct Subtypes Based on Brain Imaging and Machine Learning

Broke On: Monday, 17 June 2024 Researchers from Stanford Medicine identify six distinct subtypes of depression using brain imaging and machine learning, paving the way for personalized treatment plans based on individual brain activity patterns.