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Analysis of feature importance in deep neural networks in psychiatric disorders using magnetic resonance imaging

Authors:

Abstract

Current methods to diagnose psychiatric disorders are based on possible manifestations of the disease and behavioral criteria. Symptoms and manifestations overlap between disorders rendering the diagnostic process extremely difficult. Neuroimaging provides information about the structure and function of the brain. This information, combined with DL techniques, has a huge potential shortening and improving the diagnostic process. In this work, we train a neural network to differentiate between healthy subjects and patients of six different mental illnesses with accuracy of 64%. Finally, we analysed the network weights of the model to identify the most important regions of the brain for classification.
Analysis of feature importance in deep
neural networks in psychiatric disorders
using magnetic resonance imaging
Irina Sánchez 1, Carles Soriano-Mas 2,3, Antonio Verdejo-García 4, Narcís Cardoner 3,5,
Fernando Fernández-Aranda 2,6, José Manuel Menchón 2,3, Paulo Rodrigues 1,
Vesna Prčkovska 1 and Matt Rowe 1
QMENTA Inc1IDIBELL2CIBERSAM3MICCN4Parc Taulí5CIBEROBN6
2019 - Irina Sanchez - QMENTA® - irina@qmenta.com 2
Speaker Name: Santi Puch
I have the following financial interest or relationship to disclose with
regard to the subject matter of this presentation:
Company Name: QMENTA
Type of Relationship: Employee and stock options holder
Declaration of
Financial Interests or Relationships
2019 - Irina Sanchez - QMENTA® - irina@qmenta.com 3
Motivation
No priors
Avoid bias from
prior knowledge
Subjective evaluation of the
patient
Objective biomarker based on
anatomy or function.
Representation
Learning
Extraction of
high-level features
Deep Neural Networks
+
2019 - Irina Sanchez - QMENTA® - irina@qmenta.com 4
Data
4
Volumetric
information
of brain
regions
Connectivity
matrix
between
regions
Several mental disorders
- Obesity
- Anorexia
- Binge eating disorder
- Major depressive disorder
- Mild cognitive impairment
- Obsessive compulsive disorder
143 healthy subjects
137 patients with psychiatric disorders
ANTs
DSK atlas rs-fMRI connectome1
1. Hagmann, P., et al.. (2008).
Mapping the structural core of human cerebral cortex.
PLoS biology, 6(7):e159.
2019 - Irina Sanchez - QMENTA® - irina@qmenta.com 5
Model and Training Details
Common training details
L1 and L2 regularization.
Optimizer Adagrad with 1E-3 as learning
rate.
Early stopping based on accuracy
Batch size: 15
Dense (64)
LReLU
Dense(2)
SOFTMAX
Volumetry
Dense (32)
LReLU
Dense (16)
LReLU
50 % dropout
Dense (32)
LReLU
Dense(2)
SOFTMAX
rs-fMRI
Connectome
Dense (16)
LReLU
Dense (8)
LReLU
60% dropout
Cross validation strategy
10 different partitions of the dataset
2019 - Irina Sanchez - QMENTA® - irina@qmenta.com 6
Analysis of Feature importance
Feature vector that describes which elements of the input are most
relevant to a neuron.
Fisher’s scores to select most discriminative
neurons
k
Layer J+1Layer J
n
i
1
...
... W
Kim, J., et al. (2016). Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification
performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage, 124:127–146.
2019 - Irina Sanchez - QMENTA® - irina@qmenta.com 7
Analysis of Feature importance
Layer 1Layer N ...
TOP
2
TOP
k
...
TOP
2
TOP
k
...
Relevant regions
Relevant regions
Relevant regions
Relevant regions
Top ranked regions per
importance
2019 - Irina Sanchez - QMENTA® - irina@qmenta.com 8
Results: Volumetry
8
CSF
Atrophy of
structures that
surround them
Human behaviour
Interpersonal
interaction
Empathy
Inferior frontal
gyrus
Reward system
Accumbens area
Conditioning
Learning
Addiction
Brain stem
Images obtained using BrainNet Viewer
Xia, M., Wang, J., and He, Y. (2013). Brainnet viewer: a network visualization tool for human brain connectomics. PloS one, 8(7):e68910.
Lateral Ventricles
64.04% Accuracy
62.38% F1-score
2019 - Irina Sanchez - QMENTA® - irina@qmenta.com 9
Results: rs-fMRI Connectome
Reward
Memory Processing
of different
senses
Emotional
control
Attention
63.33% Accuracy
63.24% F1-score
Images obtained using BrainNet Viewer
Xia, M., Wang, J., and He, Y. (2013). Brainnet viewer: a network visualization tool for human
brain connectomics. PloS one, 8(7):e68910.
2019 - Irina Sanchez - QMENTA® - irina@qmenta.com 10
Conclusions
Different findings than prior knowledge
Mix of diseases
Errors in measurements
Small database
No prior information
Highlighted areas are directly related with
psychiatric disorders present in the
database
Biomarker discovery framework
Applicable to other domains and tasks
Thank you for your
attention
Santi Puch
Contact
santi@qmenta.com
Booth 1010
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