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Table 2 Principal components of PCA with eigenvalue higher than 1

From: An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders

Principal components analyses

     

PC

EigenValue

Proportion

Cumulative

F1

F2

F3

F4

F5

1

29.385

0.249

0.249

Frontal Mid Orb L

Frontal Inf Orb L

CingulumAnt L

Frontal Inf Tri L

Frontal Sup Orb L

2

19.402

0.164

0.413

Occipital Mid L

Frontal Inf Orb R

TemporalMid L

Fusiform L

Occipital Inf L

3

13.378

0.113

0.527

Precentral R

Frontal Mid R

ParietalInf R

Poscentral L

Poscentral R

4

7.585

0.064

0.591

TemporalPole Sup R

Parahippocamp R

Hippocampus R

TemporalPole Mid R

Precentral L

5

6.411

0.054

0.645

Thalamus R

Heschl L

Calcarine L

Heschl R

Cerebellum Crus1 L

6

4.807

0.041

0.686

TemporalInf R

TemporalMid R

EdadPET

Rectus R

Frontal Sup Orb R

7

3.946

0.033

0.720

Rolandic Oper L

Thalamus R

CingulumPost R

Poscentral L

TemporalPole Mid L

8

3.318

0.028

0.748

Pallidum L

Thalamus L

Vermis 10

Pallidum R

Putamen L

9

2.845

0.024

0.772

Sexo

Rolandic Oper L

Rolandic Oper R

Heschl R

Paracentrallobule R

10

2.314

0.020

0.791

CingulumPost R

CingulumPost L

Vermis 10

Caudate R

Cerebellum 9 R

11

2.279

0.019

0.811

Vermis 10

SMA L

Cerebellum Crus1 R

Rectus R

Pallidum L

12

1.779

0.015

0.826

Amygdala R

TemporalPole Mid R

Amygdala L

Sexo=2

Vermis 9

13

1.674

0.014

0.840

Vermis 1 2

ParietalSup L

Calcarine L

ParietalInf R

Cerebellum 3 L

14

1.460

0.012

0.852

Cerebellum 10 R

Cerebellum 10 L

Amygdala L

Cerebellum 3 R

Amygdala R

15

1.156

0.010

0.862

Olfactory R

Olfactory L

TemporalPole Sup R

TemporalPole Sup L

Sexo

16

1.122

0.010

0.872

Putamen R

Putamen L

Cerebellum Crus2 R

Parahippocamp R

Frontal Inf Oper R

17

1.066

0.009

0.881

Amygdala R

Cerebellum 10 R

Amygdala L

Cerebellum 10 L

TemporalInf R