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**Disclosure Statement of the Editor**

Olivier Colliot is a civil servant of the French government, employed by the Centre National de la Recherche Scientifique. He reports that his work has been funded by the French government under management of the Centre National de la Recherche Scientifique, by the French government under management of Agence Nationale de la Recherche, by the European Union, by the Paris Brain Institute, by Inria, by the Abeona Foundation, and by the Fondation Vaincre Alzheimer. He reports having received consulting fees from AskBio and TheraPanacea. Members from his laboratory have co-supervised a PhD thesis with Qynapse. He reports that his spouse is an employee of myBrainTechnologies. He reports that he holds the following patent registered at the International Bureau of the World Intellectual Property Organization: PCT/IB2016/0526993, Schiratti J-B, Allassonniere S, Colliot O, Durrleman S, A method for determining the temporal progression of a biological phenomenon and associated methods and devices.
INDEX

A

ABCD, *see* Adolescent brain cognitive development (ABCD)

Accelerometer.................................................. 361-366,
371-373, 760, 768, 793, 795, 864, 867

Adagrad..................................................................96-97

Adam........................................................................97-98, 700

ADC, *see* Apparent diffusion coefficient (ADC)

ADHD, *see* Attention deficit hyperactivity disorder (ADHD)

ADNI, *see* Alzheimer's disease neuroimaging initiative (ADNI)

Adolescent brain cognitive development (ABCD) ....680,
681, 758, 771, 778, 980

AE, *see* Autoencoder (AE)

Affine registration ........................................264, 406, 438-440

AIBL, *see* Australian imaging biomarkers and lifestyle study of aging (AIBL)

Akinesia..................................................................848

AlexNet................................................................108, 553, 661, 662, 722

Alignment................................................................. 135, 318, 323, 436,
437, 440, 444, 447, 449, 454, 512, 784

ALS, *see* Amyotrophic lateral sclerosis (ALS)

Alzheimer .................... 249, 372, 535, 758, 763, 772, 822

Alzheimer's disease (AD)................................. ix, 60, 234, 237,
246, 247, 249, 254, 256, 265, 267, 273-276,
302, 303, 326, 327, 370-372, 391, 462, 463,
466, 468, 469, 472, 473, 476, 478, 491, 495,
500, 503, 511, 514, 520-525, 534-536, 542,
553, 554, 557, 559, 574, 577, 605, 619, 623,
631-633, 657, 679-683, 685-688, 690-692,
697, 700, 724, 726, 754, 758, 770, 773, 775,
777, 791, 794, 807-833, 847, 848, 940, 1015

Alzheimer's disease neuroimaging initiative
(ADNI) .................................................... 273, 376, 478,
521, 523, 633, 679-681, 690, 692, 754, 758,
770, 771, 773, 822, 826-828

Amyloid ............ 273, 303, 463, 468, 472, 681, 687, 700,
772, 773, 778, 808, 809, 812, 815-817, 822, 945

Amyotrophic lateral sclerosis (ALS).................. 247, 303,
534, 760, 762, 763, 791, 811

Aneurysm..................................................269, 701, 922, 937-940

Apparent diffusion coefficient (ADC) ............... 267, 926,
929-931, 968

Area under the curve (AUC) ................................. 468-472,
474, 604, 607, 608, 610, 611, 625, 731-734,
741, 794, 827, 904, 905, 929, 932, 933, 935,
936, 938-940, 971, 993, 998, 1016, 1021, 1024,
1025, 1027

Arterial spin labeling (ASL)..................................758, 760,
761, 922, 926, 932, 934, 935, 956

Arteriovenous malformation (AVM) ............ 922, 923, 936

Artificial intelligence (AI) .............................. vii, 3-6, 8-11,
21, 77, 225, 345, 347-349, 376, 476, 479, 480,
534, 535, 537, 558-561, 717, 720, 739, 832,
874, 937-940, 948, 963-973

ASL, *see* Arterial spin labeling (ASL)

Astrocytoma ..................................................247, 555, 729, 964, 965

Atlas ................................................................ 260, 264, 274, 320,
326, 377, 404, 406, 412, 435, 436, 441, 453,
499, 687, 829, 861, 881, 928, 930, 944, 948

Attention deficit hyperactivity disorder (ADHD)......371,
500, 576, 758, 760, 763, 771, 781, 977-982,
986, 987, 993-997, 1030

Attention U-Net ...... 392-394, 554, 558, 560, 561, 932

AUC, *see* Area under the curve (AUC)

Australian imaging biomarkers and lifestyle study of aging
(AIBL)..................................680, 681, 758, 770, 771, 773

Autism spectrum disorder (ASD) .......................... 12, 136,
499, 500, 576, 645, 977-979, 981-984,
986-989, 993-997, 1000, 1013, 1021, 1028

Autoencoder (AE).................................................. 13, 78,
110-112, 134, 152-160, 176, 184, 185,
211-212, 421-424, 445-447, 452, 471, 561,
588-590, 659, 683, 684, 691, 825, 826, 929

AVM, *see* Arteriovenous malformation (AVM)

Axial ................................................................256-258, 266, 267, 427,
428, 467, 848, 850, 861, 903, 931, 967

B

Backpropagation ............................................ 78, 85, 91-92,
152, 168, 185, 588, 679-681

Batch normalization..................95-96, 164, 170, 181, 427

BATS, *see* Brisbane adolescent twin study (BATS)

BCI, *see* Brain computer interface (BCI)

BD, *see* Bipolar disorder (BD)

Bias field correction ........................................263-265, 404

Bioinformatics ............................................ 319, 323-325, 719, 782
Biomarker ..................................................256, 302, 303, 327, 368, 427, 454, 460, 462, 463, 466, 468, 472, 477, 495, 501, 502, 504, 513–517, 519, 520, 523–525, 536, 537, 540–542, 557, 561, 562, 575, 641, 681, 700, 758, 770, 773, 778, 786, 787, 808, 812, 815, 821–826, 828, 829, 849, 863, 869, 880, 889, 946, 968–969, 1014, 1015

Bipolar disorder (BD) ....................................ix, 268, 371, 372, 534, 535, 758, 771, 994, 1009–1011, 1013–1016, 1018–1023, 1025–1031

Blood-oxygen-level-dependent (BOLD)..................267, 268, 769, 872, 904

BOLD, see Blood-oxygen-level-dependent (BOLD)

Brain computer interface (BCI) ..........................303–307, 759, 760, 775

Brain tumour segmentation challenge (BraTS) .........176, 417, 426, 427, 470, 474, 475, 555, 969, 970, 972

BraTS, see Brain tumour segmentation challenge (BraTS)

Brisbane adolescent twin study (BATS) ............761, 768, 793, 794

C

CAD, see Computer assisted diagnosis (CAD)

CADASIL, see Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL)

Calibration ..................................................305, 306, 609, 610

Canadian institute for advanced research (CIFAR) ..................................................86, 203, 207

CBD, see Cortico-basal degeneration (CBD)

CBF, see Cerebral blood flow (CBF)

CBV, see Cerebral blood volume (CBV)

Cerebellar variant of multiple system atrophy (MSA-C) ........................848, 853, 857, 861, 862

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) ..................................................810

Cerebral blood flow (CBF) ..................................267, 270, 275, 923, 925, 926, 932, 934–935

Cerebral blood volume (CBV) ..............................270, 923, 925, 926, 968

Cerebral microbleed (CMB) ..............................940, 941, 945–946, 948

Cerebral small vessel disease (cSVD) ............922, 940–949

Cerebrospinal fluid (CSF) ....................264, 327, 405, 460, 463, 468, 472, 682, 700, 758, 815, 824, 829, 853, 857, 860, 863, 869, 909, 936, 947, 1030

Cerebrovascular ....................................ix, 270, 374, 921–949

CIFAR, see Canadian institute for advanced research (CIFAR)

Classification ................................................viii, 10, 25, 79, 118, 140, 203, 249, 268, 286, 338, 356, 399, 446, 460, 498, 534, 602, 657, 707, 774, 823, 849, 927, 965, 983, 1010

Clinically isolated syndrome (CIS) ............902, 912, 913

Clustering ..................................................12, 13, 15, 26, 60–66, 301, 303, 492–505, 517, 524, 525, 527, 551, 555, 557, 764, 828, 868, 881, 888, 889, 906, 931, 987–989, 992–994, 996, 1015, 1019–1020, 1023, 1027

CMB, see Cerebral microbleed (CMB)

CNN, see Convolutional neural network (CNN)

Coding schemes ........................................339, 340, 344

Cognitive ..................................................4, 234, 256, 288, 358, 460, 511, 612, 681, 758, 808, 848, 879, 899, 940, 965, 980, 1010

Computational pathology ..............................viii, 533–563

Computed tomography (CT) ..............................vii, 144, 145, 149, 173, 176, 177, 183, 253, 254, 256, 259, 269–271, 274, 277, 392, 396, 425, 447, 449, 465, 471, 553, 660, 679–681, 686, 689, 701, 707, 721, 725, 726, 732, 766, 768, 815, 831, 922–925, 927, 929–932, 934–938, 940

Computer assisted diagnosis (CAD) ......................937

Confidence interval ........................................viii, 602, 620–622, 624, 625, 633, 641, 740, 744, 746

Connected objects ........................................vii, 355–383

Convolution ..................................................10, 78, 133, 142, 203, 392, 446, 552, 662, 928, 997

Convolutional neural network (CNN) ............vii, 77–112, 133–136, 140, 142, 153, 161, 174, 175, 182, 185, 203, 205–207, 210, 212, 213, 216, 222, 224, 392, 394, 408, 410, 411, 414, 437, 446–450, 468, 474, 551–553, 555–557, 561, 563, 632, 660, 661, 666, 676, 681, 682, 684–686, 688, 689, 698, 722, 853–855, 857, 886, 904, 913, 927, 930–932, 934, 935, 938, 939, 944–946, 948, 969, 971

Coronal ..................................................256, 257, 428, 688, 883, 903, 931

Cortico-basal degeneration (CBD) ............557, 849, 850

Cost function ........................................17, 19, 21, 31, 37, 39, 41, 71, 72, 87–90, 110, 164, 377, 437, 438, 441–445

Cross-attention ........................................196–197, 199, 201, 202, 209, 212, 214, 216, 219, 220, 225, 411

Cross-entropy loss ........................................50, 88, 164, 172, 206, 215, 398, 399, 497

Cross-sectional .................................495, 512–518, 523, 526, 758–766, 768, 786, 787, 828, 831, 868, 906–908

Cross-validation (CV) ....................427, 469, 501, 615–619, 622, 624, 626, 713, 714, 846, 858–862, 866, 867, 869, 870, 872, 873, 886, 891, 909, 928, 933, 985, 995, 998, 1022, 1023, 1025–1027
cSVD, see Cerebral small vessel disease (cSVD)

CT, see Computed tomography (CT)

CV, see Cross-validation (CV)

CycleGAN ....................................176-178, 404, 448

D

Data augmentation ....................................95, 376, 408-409, 417, 427, 428, 535, 747

Database of genotypes and phenotypes (dbGAP) ........................765, 783, 784, 787, 788

Data-driven disease progression modelling (D3PM) ........................468, 511-527, 828, 869

Data harmonization ....................................404, 478, 823, 828-830

Data leakage ............................................602, 617-619, 633, 640, 641, 649, 829

Data split............................................426, 615, 619, 633, 638, 640-642

Data warehouse ........................................332, 766, 789-790

dbGAP, see Database of genotypes and phenotypes (dbGAP)

Decoder ............................................110, 111, 127-129, 134, 135, 153, 154, 156, 183, 185, 186, 194, 199-202, 209, 211, 212, 215, 392, 409-412, 421, 423, 424, 427, 448, 676, 930, 932, 938, 939

Deep learning (DL) ....................................vii, 6, 10, 11, 13, 18, 73, 77-112, 166, 198, 202, 214, 224, 258, 261, 264, 277, 348, 360, 391-428, 435-454, 460, 469, 470, 474, 497, 501, 534, 537, 544, 549, 552-554, 557, 559, 561, 563, 618, 619, 638, 647, 657, 659, 665, 669, 675, 681, 707, 719, 722, 727, 729, 789, 824-827, 859, 865, 882, 883, 885, 886, 890, 905, 909, 927, 931, 937, 939, 942-946, 963, 969-972, 983, 984, 986, 991-993, 997-998, 1014

Deformable registration....................406, 438, 440, 441, 445

Dementia ............................................ix, 234, 254, 303, 459, 499, 511, 535, 681, 768, 807, 848, 922

Dementia with Lewy bodies (DLB)............ix, 276, 463, 469, 811-812, 821, 848, 850

Diagnosis ............................................12, 48, 136, 234, 253, 303, 342, 371, 459, 500, 534, 576, 642, 690, 705, 768, 808, 849, 902, 964, 979, 1010

DIAN, see Dominantly inherited Alzheimer network (DIAN)

Dice loss............................................398-400, 427

Dice similarity coefficient (DSC) ............401, 402, 425, 553, 728, 926, 930-932, 936, 939, 943, 944, 947

Differential diagnosis ....................................48, 234, 240, 461-464, 467-469, 471, 476, 480, 812, 817, 823, 825, 827, 833, 849, 851, 853, 861-862, 873, 903-905, 915

Diffusion models......................vii, 148, 150, 186-187, 215

Diffusion tensor imaging (DTI) ....................267, 468, 573, 829, 861, 862, 887, 903, 904, 909

Diffusion-weighted Imaging (DWI)........................469, 471, 574, 755, 758-762, 769, 820, 861, 902, 903, 922, 926, 929-933, 941, 985, 998, 1017-1020, 1030

Digital pathology ....................................534, 536, 537, 544-545, 552

Dimensionality reduction ........................12, 13, 26, 63, 66-70, 494, 555, 618, 619, 682, 683, 717, 871

Direct problem ........................................298

DI, see Deep learning (DL)

DLB, see Dementia with Lewy bodies (DLB)

Domain adaptation ..........................178, 188, 425, 447-449

Dominantly inherited Alzheimer network (DIAN) ....................................822, 828

Dopamine ........................................238, 247, 275, 689, 701, 811, 848, 850, 857, 860, 863, 869, 871-872

D3PM, see Data-driven disease progression modelling (D3PM)

DSC, see Dice similarity coefficient (DSC)

DTI, see Diffusion tensor imaging (DTI)

DWI, see Diffusion-weighted Imaging (DWI)

E

EBM, see Event-based model (EBM)

EEG, see Electroencephalography (EEG)

EfficientNet ........................................109-110, 556

EHR, see Electronic health record (EHR)

Electrode ............................................99, 247, 289, 291, 292, 296, 300, 681, 682, 774, 775, 872, 884

Electroencephalography (EEG) ......................vii, 248, 285-307, 356, 380, 460, 481, 500, 576, 679, 681, 682, 755, 757-760, 762, 764, 766, 768, 774-775, 780, 830, 855-857, 872, 884, 986, 1013, 1016, 1017, 1023, 1024

Electronic health record (EHR)....................vii, 331-351, 594, 714, 755, 757, 761, 789-792, 995

Encoder ............................................110, 111, 127-129, 134, 135, 153-156, 188, 194, 199-203, 209, 211, 212, 214-216, 220-221, 223, 392, 394, 395, 409-412, 421, 423, 424, 448, 675, 691, 693, 930, 932, 938, 939

Enhancing neuroimaging genetics through meta-analysis (ENIGMA) ....................................758, 771, 772, 780-781, 1020, 1027, 1030

ENIGMA, see Enhancing neuroimaging genetics through meta-analysis (ENIGMA)

Epilepsy............................................ix, 246-248, 273, 302-303, 327, 370, 373, 480, 536, 576, 726, 760, 773-775, 792, 879-891, 978, 996

Event-based model (EBM)............................495, 512, 514-517, 523, 524, 828, 855, 869, 912

Evoked activity ....................................287-288, 299
## F

FCD, see Focal cortical dysplasia (FCD)

Feature extraction .................................... 172, 299–302,
366, 377, 403, 446–447, 639, 647, 682, 698,
707, 722, 754, 860, 928, 991

Features.................................................. 5, 26, 77, 118, 164,
199, 236, 254, 285, 319, 332, 358, 392, 435,
464, 493, 512, 534, 575, 614, 639, 655, 707,
754, 808, 848, 880, 899, 927, 963, 978, 1011

Feature selection .................................... 32, 34, 299–302,
366, 618, 619, 657, 721, 722, 858, 860, 866,
936, 988, 1018, 1021, 1025

FLAIR, see Fluid attenuated inversion recovery (FLAIR)

Fluid attenuated inversion recovery (FLAIR) ........ 263,
265, 412, 426, 427, 469, 471, 555, 755, 758,
760, 761, 769, 818, 820, 825, 882–886, 890,
902, 903, 906, 907, 909, 911, 922, 926, 929,
930, 941, 943, 944, 946, 969, 970, 972

fMRI, see Functional magnetic resonance imaging (fMRI)

Focal cortical dysplasia (FCD) ....................879, 884–888

Focal loss ....................................................................399

Fronto-temporal lobar degeneration
(FTLD) .................................................... 463, 468, 469

Functional magnetic resonance imaging (fMRI) ......267,
268, 358, 502, 573, 574, 576, 581, 680, 681,
755, 759, 769, 821, 853, 856, 857, 993, 994,
1013, 1017–1025, 1028, 1030, 1031

## G

GAN, see Generative adversarial network (GAN)

Gated recurrent unit (GRU) ....................................118,
124–125, 932, 944

Gaussian mixture model (GMM).............. 26, 60, 63–66,
147–149, 424, 497, 824, 825, 857, 859, 942,
944, 946, 948, 988

Gaussian process........................................512, 520,
521, 523, 689, 826, 990, 1016, 1028

GBM, see Glioblastoma (GBM)

GDL, see Generalized Dice loss (GDL)

Generalization ............................................ 78, 92–96, 400,
495, 496, 550, 574, 602, 615, 617, 619, 620,
622–625, 825, 984, 1000, 1021

Generalized Dice loss (GDL) .................................398–400

Generative adversarial network (GAN)..............vii, 112,
139–188, 209, 212, 404, 424–425, 448, 496,
497, 534, 550, 659, 909, 928

Generative models........................................vii, 112, 134,
139–188, 212, 376, 496, 524, 577, 579, 591, 659

Genetic FTD initiative (GENFI) ......................822, 828

GENFO, see Genetic FTD initiative (GENFI)

Genome-wide association study (GWAS) ...............326,
755, 764, 783, 789, 813, 989, 994

Genomics.................................................. 315–320, 322,
324–327, 474, 504, 593, 756–758, 760,
762–764, 766, 768, 770, 780–783, 785–789,
887, 889, 989, 997, 998, 1030

Glioblastoma (GBM)....................................247, 467,
471, 476, 504, 964–969, 971, 972

Glioma ..................................................176, 275, 463, 464, 467,
469–471, 474, 552, 553, 555–557, 963–972

Global positioning system (GPS)....................367, 371,
372, 378, 793

GMM, see Gaussian mixture model (GMM)

GPS, see Global positioning system (GPS)

GPU, see Graphical processing unit (GPU)

Grad-CAM ............................................ 551, 561, 660, 666,
667, 670, 679–681, 685, 686, 694–696, 698

Gradient descent .................................... 19, 20, 35, 78,
79, 89–91, 93, 96, 132, 153, 588, 700

Graph-based ........................................259, 260, 265, 267, 274, 276

Graphical processing unit (GPU) ....................10, 78, 91,
180, 183, 408, 638, 640, 790

Gray matter (GM).................................264, 265, 405, 468, 502,
554, 684, 688, 690, 769, 817–821, 824, 860,
884, 889, 912–914, 979, 1016, 1018, 1027–1029

GRU, see Gated recurrent unit (GRU)

GWAS, see Genome-wide association study (GWAS)

Gyroscope ............361–366, 795, 858, 864, 865, 867, 872

## H

HAR, see Human activity recognition (HAR)

HGG, see High-grade glioma (HGG)

Hierarchical clustering ..... 492–494, 500, 502, 503, 993

High-grade glioma (HGG) ....................467, 469, 553, 972

High-throughput .................... 315, 317–319, 321, 491, 785

Hippocampal sclerosis (HS) ......................809, 880–883

Hippocampus .................... 264, 425, 473, 631, 686–688,
808, 811, 824–826, 866, 880, 881, 996, 1018

Histology ............................................ 474, 504, 536, 540,
553, 555, 562, 563, 681, 888, 964

Histopathology .................................109, 175, 176, 474, 540,
541, 543, 549, 551, 558, 560–563, 884, 889

HS, see Hippocampal sclerosis (HS)

Human activity recognition (HAR)......................356,
360–367, 375

## I

IA, see Intracranial aneurysm (IA)

ID, see Intelligence disabilities (ID)

Idiopathic rapid eye movement sleep behavior disorder
(iRBD) .................................................... 852, 857, 859

Inertial systems........................................357, 360–362

Intelligence disabilities (ID) .......................341, 786,
788, 977, 979, 981
Intensity rescaling .................................................. 263-264

Interpretability .................................................... viii, 38, 333, 477, 479, 480, 559, 574, 575, 592, 655-701, 744, 988, 992, 999-1000

Intracranial aneurysm (IA) .................................... 922, 923, 935, 937-940

Inverse problem ........................................................ 298-299

iRBD, see Idiopathic rapid eye movement sleep behavior disorder (iRBD)

The ischemic stroke lesion segmentation (ISLES) ................................. 417, 930, 931, 933

## K

KDE, see Kernel density estimate (KDE)

Kernel .................................................. 9, 26, 39, 41, 42, 44-47, 70-73, 100-106, 108, 393, 406, 409, 472, 512, 582-583, 591, 661, 662, 864, 1020, 1026

Kernel density estimate (KDE) ................................ 405-406, 512, 515, 527

k-means .................................................. 13, 26, 60-63, 65, 66, 69, 492-494, 500-502, 524, 551, 555, 855, 906, 931, 988, 1020, 1023, 1027

k-nearest neighbors (kNN)................................. 28, 336, 721, 851, 852, 854, 855, 857, 859, 860, 864, 866, 942

kNN, see k-nearest neighbors (kNN)

Kullback-Leibler divergence (KL/KLD)................. 111, 154, 156-160, 162, 163, 166, 416, 423, 589

## L

Lacunes .......................................................... 809, 810, 819, 825, 940, 941, 946-948

Large vessel occlusion (LVO) .................... 464, 737, 923-929

LASSO, see Least absolute shrinkage and selection operator (LASSO)

LATE, see Limbic-predominant age-related TDP-43 encephalopathy (LATE)

Latent variable .................................................. 86, 89, 134, 146, 154, 186, 208, 575, 577-591, 691, 868

Layer-wise relevance (LRP) .................................... 660, 668, 669, 679-681, 685, 686, 693-695, 697, 698

LDA, see Linear discriminant analysis (LDA)

Least absolute shrinkage and selection operator (LASSO) ................................. 657, 904, 1021, 1024

Lesion ............................................ viii, 11, 235, 265, 374, 391, 462, 543, 576, 686, 711, 769, 809, 880, 899, 922, 969

LGG, see Low-grade glioma (LGG)

Limbic-predominant age-related TDP-43 encephalopathy (LATE).................................................. 809

Linear discriminant analysis (LDA)............ 26, 55, 69-71, 852-854, 856, 857, 883, 1023

Linear regression ............................................ 9, 14, 19, 20, 25, 26, 30-32, 35, 38, 40, 42, 523, 578, 579, 583, 585, 657, 829, 865, 944, 1021

Logistic regression (LR) .................... 4, 13, 25, 26, 32-35, 37, 42, 48-51, 86, 258, 471, 472, 555, 607, 621, 852-858, 860, 862, 863, 866, 873, 904, 929, 986, 993, 997, 998, 1021-1025

Logopenic progressive aphasia (LPA)........................ 809

Longitudinal................ vii, 136, 262, 336, 373, 392, 414, 440, 473, 503, 512-514, 519-525, 619, 692, 757-768, 770, 771, 777, 786, 791, 828, 829, 831, 868-870, 881, 906, 908, 941, 946, 1016

Long short-term memory (LSTM) ............ 118, 122-125, 133-136, 187, 194, 202, 676, 852, 857, 859, 873, 933

Loss ............... 16, 27, 79, 119, 139, 205, 265, 318, 333, 367, 397, 445, 465, 497, 511, 581, 639, 664, 718, 808, 848, 880, 899, 923, 965, 997, 1010

Low-grade glioma (LGG) ................ 467, 469, 474, 553, 972

LPA, see Logopenic progressive aphasia (LPA)

LR, see Logistic regression (LR)

LRP, see Layer-wise relevance (LRP)

LSTM, see Long short-term memory (LSTM)

LVO, see Large vessel occlusion (LVO)

## M

Machine learning (ML) .................................... 3, 25, 77, 348, 355, 437, 460, 491, 511, 536, 575, 601, 631, 655, 705, 755, 789, 822, 847, 880, 905, 949, 963, 979, 1009

Magnetic resonance imaging (MRI)...................... vii, 19, 144-149, 176, 254, 256-270, 274, 277, 298, 396, 404, 405, 412, 413, 425, 426, 447, 464, 468, 472, 474, 477, 479, 555-558, 561, 590, 621, 632, 645, 660, 681, 686, 700, 707, 726, 754, 756-769, 809, 880, 922, 924, 1028

Magnetoencephalography (MEG) .................... viii, 285-307, 460, 755, 757-762, 766, 768, 774, 775

Major depressive disorder (MDD)...................... 500, 502, 758, 760, 763, 771, 853, 857, 1010, 1013, 1014, 1016, 1018, 1020-1028

Major depressive episode (MDE) ................................. 1010

Malignant .................................................. 248, 464, 723, 745, 767, 787, 791, 964

Markov chain monte carlo (MCMC) .................... 150, 515

MDD, see Major depressive disorder (MDD)

MDE, see Major depressive episode (MDE)

Mean squared error (MSE) .................................... 48, 58, 88, 107, 154, 416, 422, 441, 449, 700, 718, 719

MEDA, see Minimal evidence of disease activity (MEDA)

Medical segmentation decathlon (MSD) .................... 425

MEG, see Magnetoencephalography (MEG)

Meningioma .................................................. 247, 471, 729, 965

Messenger RNA (mRNA) ......................... 315, 321, 324, 325

Metabolomics........................................ vii, 315, 317, 318, 320, 323, 324, 327, 758, 761-763, 998, 1013
MI, see Mutual information (MI)

Microbleed ix, 818, 921,
935, 940, 945, 947

Minimal evidence of disease activity (MEDA) 907

ML, see Machine learning (ML)

MLP, see Multi-layer perceptron (MLP)

MND, see Motor neuron disease (MND)

Mobile devices vii, 355–383

Morphometry 453, 454, 468, 552,
860, 889, 999, 1019, 1025, 1028

Motor 235, 237, 238, 249, 250, 264,
276, 288, 304, 305, 371, 373, 382, 701, 759,
774–776, 793, 810–812, 848–851, 862–865,
867–869, 871, 872, 889, 891, 899, 965, 977

Motor neuron disease (MND) 371, 768, 776, 811

MRI, see Magnetic resonance imaging (MRI)

mRNA, see Messenger RNA (mRNA)

MS, see Multiple sclerosis (MS)

MSA, see Multiple system atrophy (MSA)

MSD, see Medical segmentation decathlon (MSD)

MSE, see Mean squared error (MSE)

Multi-layer perceptron (MLP) 81–83, 102, 104,
126, 199–204, 217–219, 222, 398, 852, 854, 857

Multimodal viii, 196, 197,
212–216, 225, 256, 372, 392, 413, 425, 444,
468, 475, 550, 562, 573–594, 769, 770, 823,
830, 882, 889, 890, 969, 986, 995, 1030, 1031

Multiple sclerosis (MS) ix, 247, 249,
263, 265, 274, 324, 342, 343, 370, 392, 410,
411, 414, 417, 425, 427, 428, 462, 465, 466,
473, 474, 476, 536, 680, 686, 701, 726, 768,
769, 773, 792, 899–915

Multiple system atrophy (MSA) ix, 848, 850, 857

Mutual information (MI) 443–444

N

National Institutes of Health (NIH) 332, 771,
777, 783, 787, 927

Natural language processing (NLP) vii, 15, 136,
193, 199, 202, 203, 225, 236, 337, 338, 346, 360

NAWM, see Normal appearing white matter (NAWM)

NCC, see Normalized cross correlation (NCC)

NDDs, see Neurodevelopmental disorders (NDDs)

Neural network (NN) 8–11, 78,
82–95, 99, 102, 106, 109, 110, 118, 130, 132,
142, 154, 155, 169, 177, 186, 270, 377, 412,
421, 423, 446, 468, 551, 558, 588, 590, 591,
593, 594, 620, 659, 668, 699, 722, 824, 873,
883, 890, 928, 931, 938, 971, 986, 991

Neurodevelopmental disorders
(NDDs) 499, 701, 977–1000

Neuroimaging vii, 240, 253–277,
371, 376, 377, 380, 391, 392, 403, 412, 460,
462, 463, 478, 491–505, 521, 575–577, 581,
592, 593, 648, 655–701, 754, 769–774, 780,
822, 879–891, 940, 946, 981, 988, 989,
991–993, 998, 1013, 1016, 1018, 1019, 1030

Neurology vii, 241, 246, 249,
250, 275, 307, 512, 525, 691, 761, 763, 1017

Neuromyelitis optica spectrum disorder
(NMOSD) 904, 905

Neuropsychology 240, 241

NIH, see National Institutes of Health (NIH)

NLP, see Natural language processing (NLP)

NMOSD, see Neuromyelitis optica spectrum disorder
(NMOSD)

NN, see Neural network (NN)

Normal appearing white matter
(NAWM) 405, 914

Normalized cross correlation (NCC) 442, 447

O

Obsessive-compulsive disorder (OCD) 500,
758, 771, 978, 994, 1009, 1010

OCD, see Obsessive-compulsive disorder (OCD)

Oligodendroglioma 247, 555, 965

Optimization 4, 9, 17, 31,
35, 38–42, 78, 88–98, 126, 132, 146, 159, 160,
163, 172, 323, 358, 419, 438, 441, 442, 444,
445, 452, 467, 480, 498, 499, 548, 553, 578,
580, 581, 583–585, 588–593, 620, 621, 663,
672, 711, 719, 722, 906, 939

Oscillatory activity 287–289, 299, 302, 303

Overfitting 17, 25, 32,
34–37, 40, 58, 78, 92–96, 445, 556, 642, 714,
723, 944, 984–986, 988, 990, 993, 998

P

Parkinson ix, 144, 235, 238,
246, 247, 249, 276, 370, 372, 374, 391, 478,
514, 516, 534–536, 577, 645, 681, 689, 701,
772, 781, 791, 794, 795, 812, 847–874

Parkinsonian variant of multiple system atrophy
(MSA-P) 848, 853, 857, 861, 862

Parkinson's disease (PD) ix, 144, 238,
246, 247, 249, 276, 370–374, 391, 478, 514,
516, 534–536, 577, 645, 681, 689, 701, 772,
781, 791, 794, 795, 812, 847–874

Parkinson's progression markers initiative
(PPMI) 276, 478, 680,
681, 772, 852, 856, 860, 869
Partial least squares (PLS) 472, 575, 579-585, 588

Partial volume 936, 944

Patch 185, 188, 203, 204, 206-208, 210, 215, 217, 220-222, 408-410, 446, 447, 449, 549-553, 555, 556, 558-561, 577, 670, 672, 688, 690, 691, 698, 824, 826, 882, 885, 886, 928, 930, 938, 944

PCA, see Posterior cortical atrophy (PCA); Principal component analysis (PCA)

PD, see Parkinson's disease (PD)

Performance assessment viii, 171, 705-747

Performance metrics viii, 171, 478, 479, 602-613, 625, 712, 718, 730, 732, 733, 736, 738, 741, 745, 746

Perivascular spaces (PVS) 819, 825, 940, 941, 946-948

PET, see Positron emission tomography (PET)

p-hacking 633, 649

Pick's disease (PiD) 557

PiD, see Pick's disease (PiD)

PLS, see Partial least squares (PLS)

PML, see Progressive multifocal leukoencephalopathy (PML)

Positioning systems 357, 360-362, 367

Positron emission tomography (PET) vii, 165, 253, 254, 256, 257, 259, 271-277, 392, 463, 468, 472, 473, 558, 573, 574, 581, 619, 700, 707, 726, 755, 757, 758, 772-774, 778, 811, 815-817, 819, 822, 826-828, 860, 909, 910, 934, 935

Posterior cortical atrophy (PCA) 809, 828

PPA, see Primary progressive aphasia (PPA)

PPMI, see Parkinson's progression markers initiative (PPMI)

PPMS, see Primary progressive multiple sclerosis (PPMS)

Prediction 9, 27, 79, 118, 151, 206, 265, 401, 446, 459, 521, 551, 577, 601, 657, 717, 775, 823, 849, 880, 905, 923, 963, 980, 1014

Preregistration 633, 642, 1000

Primary progressive aphasia (PPA) 246, 811

Primary progressive multiple sclerosis

(PPMS) 343, 900, 912

Principal component analysis (PCA) 26, 66-67, 300, 366, 551, 555, 582

Progressive multifocal leukoencephalopathy

(PML) 901-903

Progressive supranuclear palsy (PSP) ix, 286, 287, 557, 811, 848, 850

Proteomics vii, 315-318, 320, 323-327, 757, 758

PSP, see Progressive supranuclear palsy (PSP)

Psychiatry vii, 376, 758, 777, 781, 985, 989, 1012-1017, 1031

Q

QTAB, see Queensland twin adolescent brain (QTAB)

QTIM, see Queensland twin imaging (QTIM)

Queensland twin adolescent brain (QTAB) 760, 772, 776, 793, 794

Queensland twin imaging (QTIM) 761, 772, 776, 777

R

Random forest 13, 26, 58-59, 460, 471-473, 722, 826, 858, 860, 862, 865, 866, 869, 873, 929, 930, 932, 940, 945, 947, 1019, 1022, 1025

Rapid eye movement

(REM) 811, 859, 870

Reader study 707, 726, 740-744, 747

Rectified linear unit (ReLU) 85-87, 91, 100, 101, 107, 108, 392, 393, 395, 665-668, 695, 698

Recurrent neural networks (RNNs) vii, 78, 117-137, 149, 194, 202, 460, 551, 691, 692, 826, 856, 857, 859, 870

Registration viii, 263-265, 403, 406, 427, 435-454, 558, 642, 679, 708, 777, 794, 824, 907, 928, 1000

Regression 9, 25, 88, 151, 258, 336, 446, 460, 513, 578, 602, 657, 851, 904, 929, 983, 1019

Regulatory viii, 250, 376, 378, 381, 517, 549, 614, 637, 649, 705-747, 808, 997

Reinforcement learning 12-14, 551, 872

Relapsing remitting multiple sclerosis

(RRMS) 343, 900, 901, 905, 912, 913

Reliability 243, 246, 301, 305, 479, 635, 659, 661, 677, 687, 696, 777, 865

ReLU, see Rectified linear unit (ReLU)

REM, see Rapid eye movement (REM)

Repeatability 534, 632, 635

Replicability 632, 635

Representativeness 350, 715-717

Reproducibility viii, 319, 382, 475, 479, 501, 549, 631-649, 690, 719, 720, 728, 747, 753, 786, 827, 830, 968, 969, 989, 993, 999, 1030

Residual neural network (ResNet) 109, 140, 215, 448, 553, 557, 561, 682, 935, 938

Ribonucleic acid (RNA) 313-316, 318, 319, 323-326, 563, 766, 787, 788, 821, 968

Ridge regression 26, 39, 41, 72

Rigidity 237, 238, 848

Rigid registration 264, 439, 440

RMSE, see Root mean square error (RMSE)

RNA, see Ribonucleic acid (RNA)

RNNs, see Recurrent neural networks (RNNs)

Root mean square error

(RMSE) 612, 613
S

Sagittal .................................................. 256, 257, 428, 886, 903, 931

Saliency .................................................. 659, 665, 696

Schizophrenia .................................... ix, 246, 268, 371, 491, 495, 501, 534, 535, 726, 763, 771, 979, 994, 996, 1009, 1011–1017, 1019, 1028, 1030, 1031

Secondary progressive multiple sclerosis (SPMS) ........................................ 343, 900

Segmentation ........................................ 13, 88, 140, 207, 263, 363, 391, 435, 470, 534, 602, 645, 682, 707, 824, 881, 906, 923, 966

Self-attention ........................................ 128, 196–199, 201–203, 208, 209, 218, 219

Semi-supervised learning (SSL) ................ 415–417, 419

Sensors ...................... vii, 289–292, 296–298, 300, 355–383, 460, 757–760, 762, 764, 768, 774, 775, 793–795, 851, 858, 861, 864, 865, 867, 872, 874

Similarity function .................................... 437, 447

Single-cell ............................................ 316, 323–324, 326, 757, 787, 821, 968

Single-nucleotide polymorphism (SNP) ........ 324, 326, 574, 592–594, 761, 780, 784, 979, 997, 1014

Single-photon emission computed tomography (SPECT) .............................. vii, 253, 254, 256, 259, 275–277, 680, 681, 689, 701, 707, 811

Skull stripping ........................................ 264, 403, 404, 427

Smartphone .................................... 349, 356, 360, 361, 366, 367, 369–371, 373–375, 460, 756, 758, 760, 762, 764, 766, 768, 793–795, 832, 851, 858, 1031

SNP, see Single-nucleotide polymorphism (SNP)

Source reconstruction ................................ 295, 297

Sparsity ............................................ 39, 111, 498, 593

Spatial normalization ................................ 274, 276, 406, 453, 829

SPECT, see Single-photon emission computed tomography (SPECT)

SPMS, see Secondary progressive multiple sclerosis (SPMS)

SSD, see Sum of squared difference (SSD)

SSL, see Semi-supervised learning (SSL)

Standardized uptake value ratio (SUVR) ........................................ 274, 817, 862

Statistical testing .................................... 619–622, 624

STR, see Swedish rwin registry (STR)

Stratification ........................................ 492, 500, 514, 552, 557, 559, 576, 613–617, 684, 685, 717, 886–888, 913–915, 983, 987, 988, 1025

Stroke ............................................ 143, 237, 270, 303, 333, 392, 459, 536, 576, 737, 792, 810, 879, 921

StyleGAN ................................................ 178–180

Substance use disorder (SUD) .......................... 535, 758, 763, 1009

Subtyping ............................................ 267, 277, 464, 491–495, 497, 499–501, 503–505, 512, 513, 517, 524, 526, 527, 993, 1016

SUD, see Substance use disorder (SUD)

Sum of squared difference (SSD) .................... 441, 442

Supervised learning ................................ 12–16, 20, 25, 26, 60, 110, 376, 406, 415, 420, 985

Support vector machine (SVM) .................... 9, 13, 25, 26, 42–48, 460, 468, 471, 472, 474, 496, 498, 553, 556, 621, 721, 852–861, 863–866, 882, 883, 905, 913, 919, 929, 933, 946, 969, 970, 986, 1013, 1028

SUVR, see Standardized uptake value ratio (SUVR)

SVM, see Support vector machine (SVM)

Swedish twin registry (STR) .......................... 761, 776

Synucleinopathy ........................................ 848

T

Tau ............................................ 273, 463, 472, 473, 542, 553, 554, 557, 558, 700, 808, 810, 812, 815–817, 848, 850

Temporal lobe epilepsy (TLE) ............ 880–883, 887–891

Testing set/test set ................................ 92, 93, 400, 612, 614–622, 624, 625, 633, 641, 692, 696, 714, 715, 718, 859, 864, 867, 935, 936, 939, 949, 985

TLE, see Temporal lobe epilepsy (TLE)

Training set .................... 12–14, 16, 17, 36, 79, 87–89, 91, 92, 111, 399, 400, 426, 591, 614, 615, 618, 619, 633, 687, 714, 715, 718, 832, 883, 943, 987

Transcriptomics ................................ vii, 315–319, 322, 324, 325, 327, 504, 549, 558, 563, 758, 762, 763, 998

Transformation model .................... 437–441, 446, 449, 452

Transformer ................................ vii, 112, 128–129, 140, 184, 185, 187, 188, 193–225, 396–398, 411, 449–451

Tremor ........................................ 238, 239, 246, 247, 276, 373, 701, 847, 848, 854, 858, 865, 868, 872, 873

Tumor ................................................ 11, 104, 176, 248, 254, 326, 394, 459, 463, 500, 533, 603, 644, 701, 707, 773, 815, 879, 963

Tversky loss ............................................ 400

U

UKB, see UK biobank (UKB)

UK biobank (UKB) .................................... 754, 757, 761, 769, 771, 778–781, 784, 793, 794, 822, 1030

Underfitting ........................................ 35, 36, 39, 94

U-Net ................................................ 111, 151, 170, 171, 173–176, 392–398, 410–412, 427, 447, 449, 451, 551, 553, 554, 556, 558, 560, 561, 824, 825, 905, 927, 928, 930–935, 938, 943–945

United States food and drug administration (FDA) .................................... 534, 705–708

Unsupervised learning ............................ 12–14, 26, 60, 110, 415, 445, 512, 534, 551, 987–989
V

VaD, *see* Vascular dementia (VaD)

VAE, *see* Variational autoencoder (VAE)

Validation..................................................92, 171, 250, 305,
333, 377, 400, 468, 501, 552, 601, 633, 713,
824, 859, 882, 914, 927, 984, 1030

Validation set ..................................................92, 93, 400,
469, 617–619, 622, 623, 641, 714

Variational autoencoder (VAE) ............ 13, 134, 152–160,
184–186, 412, 423–424, 452, 588, 589, 659

Vascular cognitive impairment (VCI) ....................463, 810

Vascular dementia (VaD) .............................. ix, 809–810,
812, 821, 940

VCI, *see* Vascular cognitive impairment (VCI)

Vertex-based ..................................................258, 259,
265, 268, 274, 276, 1019

VETSA, *see* Vietnam era twin study of aging (VETSA)

Vietnam era twin study of aging
(VETSA) ........................................761, 772, 776, 777

V-Net .......................................................... 392, 393, 936

Voxel-based ....................258, 259, 264, 265, 267, 268,
270, 274, 276, 453, 468, 860, 884, 948, 1019

W

Wasserstein ..................................................166–170, 172

Wearables .................................................. 291, 356, 357,
367, 370, 373, 374, 378–380, 383, 463, 757,
770, 793, 821, 832, 851, 865

WGS, *see* Whole genome sequence (WGS)

White matter hyperintensity (WMH) .................... 941–943

White matter (WM) ........................................ viii, 264, 265,
267, 270, 273, 343, 405, 541, 576, 682, 684,
700, 701, 769, 777, 809, 817–820, 824, 825,
860, 869, 884, 909, 913, 914, 929, 940–943,
996, 1015, 1017, 1019, 1025, 1028–1030

WHO, *see* World Health Organization (WHO)

Whole genome sequence (WGS) ......................... 758, 760,
779, 780, 783–785, 787, 788

Whole slide image (WSI) .............................. 533–535, 537,
541, 542, 544–559, 561, 563

WM, *see* White matter (WM)

WMH, *see* White matter hyperintensity (WMH)

World Health Organization (WHO) .................... 326, 341,
342, 464, 967

WSI, *see* Whole slide image (WSI)