Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Personal Representations from fMRIby Predicting Neurofeedback
  Performance   We present a deep neural network method for learning a personal
representation for individuals that are performing a self neuromodulation task,
guided by functional MRI (fMRI). This neurofeedback task (watch vs. regulate)
provides the subjects with a continuous feedback contingent on down regulation
of their Amygdala signal and the learning algorithm focuses on this region's
time-course of activity. The representation is learned by a self-supervised
recurrent neural network, that predicts the Amygdala activity in the next fMRI
frame given recent fMRI frames and is conditioned on the learned individual
representation. It is shown that the individuals' representation improves the
next-frame prediction considerably. Moreover, this personal representation,
learned solely from fMRI images, yields good performance in linear prediction
of psychiatric traits, which is better than performing such a prediction based
on clinical data and personality tests. Our code is attached as supplementary
and the data would be shared subject to ethical approvals.

13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph