Learning About the Brain: Neuroimaging and Beyond

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Quantifying mental states and identifying "statistical biomarkers" of mental disorders from neuroimaging data is an exciting and rapidly growing research area at the intersection of neuroscience and machine learning. Given the focus on gaining better insights about the brain functioning, rather than just learning accurate "black-box" predictors, interpretability and reproducibility of learned models become particularly important in this field. We will discuss promises and limitations of machine learning in neuroimaging, and lessons learned from applying various approaches, from sparse models to deep neural nets, to a wide range of neuroimaging studies involving pain perception, schizophrenia, cocaine addiction and other mental disorders. Moreover, we will also go "beyond the scanner" and discuss some recent work on inferring mental states from relatively cheap and easily collected data, such as speech and wearable sensors, with applications ranging from clinical settings ("computational psychiatry") to everyday life ("augmented human").





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The Discussion

  • User profile image
    mark eccles

    Regarding detecting schizophrenia, no one mentions if the subjects are drug naive or not.
    If someone has had psychiatric medicine affecting their brain for some time, it changes their brain.
    You can claim it is the schizophrenia changing the brain, but science does not know which it is.
    I would say at 50:07 at the end "Eye movement detection", science is measuring the drugs effects on the brain, not detecting schizophrenia.

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