CAMPUS INNENSTADT

KLINIK FÜR PSCHIATRY UND PSYCHOTHERAPIE

Prof. Dr. Peter Falkai

M081 - Comparison of two brain parcellations in functionalconnectivity-based classification of psychosis

Johanna Weiske1*, Shalaila S. Haas1,2*, Anne Ruef1, Linda T. Betz3, Giulio Pergola4,5, Nikolaos Koutsouleris1,2, Lana Kambeitz-Ilankovic1*, Linda A. Antonucci1,4*

• Multivariate pattern analysis has been increasingly used to investigate the potential of resting-state functional connectivity (FC) as a biomarker to identify brain patternanomalies in patients at the early stages of psychosis with promising results1.

• Yet, a number of studies still use structural parcellation of the brain, instead of using parcellations based on brain functioning to investigate multivariate problems.• The aim of this study was to compare the effects of a structural and functional parcellation-based atlas on the classification performance at the multivariate level to

discriminate patients with recent-onset psychosis (ROP) from healthy controls (HC) using a Support Vector Machine (SVM).• We applied Automated Anatomical Labeling (AAL) and literature-based functional Dosenbach2 brain parcellations to a state-of-the-art preprocessing pipeline for

resting-state (RS) fMRI images to generate region of interest (ROI)-by-ROI FC matrices.

1. Section for Neurodiagnostic Applications, Clinic for Psychiatry and Psychotherapy, LMU, Munich, Germany2. International Max Planck Research School for Translation Psychiatry, Munich, Germany 3. Department of Psychiatry and Psychotherapy, University of Cologne, Faculty of Medicine and University Hospital of Cologne, Cologne, Germany4. Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy5. Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD* Authors contributed equally to this work

Figure 2: Schematic representation of a support vector machine (SVM)classification embedded in a cross-validation (CV). A: CV structure withtraining and test data set. B: Representation of a SVM. C: An example of a FCmatrix used as features in the SVM classification.

• 53 HC (age: 26.6 (6.7) years, 36% male) and 39 ROP (age: 25.9 (5.8) years, 69% male) underwent MRI scanning for the PRONIA study at Ludwig-Maximilians-University Munich.

• RS images were preprocessed (Fig. 1), following a new, cutting edge, state-of-the-art preprocessing pipeline3.

• Brain parcellations based on AAL (116 regions) and Dosenbach (160 regions) atlases were applied and the generated FC matrices served as features in 2 separate SVM classifications discriminating ROP from HC.

• The machine learning algorithms were applied using NeuroMiner 1.0 (https://www.pronia.eu/neurominer/) using a 10x10 repeated-nested double cross-validation scheme with pruning of non-informative variables, Principal Component Analysis, and scaling (0-1) as preprocessing (Fig. 2).

Predicted

Label

Actual

Label

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1

5

24

30

Training

Data

Test

Data

14

2

30

9

8

11

1

27

30

30

9

9

1

1vs.

...

Cross-validation Support Vector ClassificationA B

C

• Following the implementation of a more rigorousmethod of correcting for motion artifacts whichcombines new data quality indices5 and toolsable to remove spatial and temporalheterogeneity associated with head motion6, wewere able to deliver brain FC features able toclassify HC from ROP individuals with goodaccuracy.

• The classifiers based on either structural orfunctional parcellation delivered comparableresults in terms of classification accuracy.Despite this lack of difference, the most reliablefeatures within the two classifiers werecompletely non-overlapping.

• Future research to fully understand differencesbetween discriminative FC patterns in AAL,Dosenbach and other parcellations is warranted.Automated parcellation methods, such asInstantaneous Connectivity Parcellation (ICP)which shows overlaps with underlyingcytoarchitectonics7, may prove superior toarbitrary selection of atlases.

References1) Kambeitz J., et al., (2015), ‘Detecting Neuroimaging Biomarkers for Schizophrenia: A

Meta-Analysis of Multivariate Pattern Recognition Studies’, Neuropsychopharmacology,

Vol 40, 1742-1751. 2) Dosenbach N., et al., (2010), ’ Prediction of Individual Brain

Maturity Using fMRI’, Science, Vol 10, 1358-1361. 3) Power et al., (2012). ‘Spurious but

systematic correlations in functional connectivity MRI networks arise from subject

motion. Neuroimage, Vol 59(3), 2142-2154.’ 4) Xia M., Wang J., He Y. (2013), ‘BrainNet

Viewer: A Network Visualization Tool for Human Brain Connectomics’, PLoS ONE, Vol 8,

e68910.) 5) Power, J.D., et al., (2012). ‘Spurious but systematic correlations in

functional connectivity MRI networks arise from subject motion.’ Neuroimage, Vol. 59

(3), 2142-2154. 6) Patel A., et al., (2014). ‘A wavelet method for modeling and despiking

motion artifacts from resting-state fMRI time series. Neuroimage, Vol. 95, 287-304. 7)

van Oort, E. S., et al., (2018). ‘Functional parcellation using time courses of

instantaneous connectivity.’ NeuroImage, Vol. 170, 31-40.

Figure 1: Flowchart of the RS fMRI preprocessingpipeline.

AAL Dosenbach

Table 1: Performance measures of the classificationROP vs. HC using AAL and Dosenbach parcellated FCmatrices as features.

Contact: johanna.weiske@med.uni-muenchen.deshalaila.haas@med.uni-muenchen.de

Dosenbach Region 1 Dosenbach Region 2

Medial cerebellum R Precuneus R

Post cingulate R Basal ganglia L

Inferior temporal R Mid insula L

Precuneus R Anterior insula L

Occipital R Ventral prefrontal cortex L

Inferior temporal L Ventral frontal cortex R

Angular gyrus R Inferior temporal L

Inferior temporal L Post cingulate R

Parietal L Ventral prefrontal cortex L

Inferior temporal L Precentral gyrus L

Table 2: The 10 most predictive FC measures for the classification ofROP vs. HC based on AAL parcellation.

Background

Methods

Results Conclusions

AAL Region 1 AAL Region 2

Frontal Sup Medial L Cingulum Mid R

SupraMarginal R Frontal Sup Medial R

SupraMarginal R Frontal Sup Medial L

Frontal Sup Medial R Cingulum Mid R

Vermis 6 Cingulum Mid R

Precentral R Frontal Sup L

Parietal Sup R Frontal Sup Medial L

Temporal Mid L Cerebelum Crus2 R

Frontal Sup Medial L Cingulum Mid L

Temporal Sup R Pallidum R

Table 3: The 10 most predictive FC measures for the classification ofROP vs. HC based on Dosenbach parcellation.

Accuracy Sensitivity Specificity McNemar (P)

AAL 68.2 % 77.4 % 59.0 % 0.9

Dosenbach 67.8 % 86.8 % 48.7 %

Figure 3: Visualization of the 99.7th percentile of most relevant FC in the classification ROP vs. HC based on AAL parcellation (left) and Dosenbachparcellation (right). The color bar indicates CV ratio of feature weights. Visualization was performed using BrainNet Viewer4.