Chatterjee5656. Chatterjee M, Stratou G, Scherer S, Morency LP. Context-based signal descriptors of heart-rate variability for anxiety assessment. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing – Proceedings; 2014 Jul 14. Florence, Italy. p. 3631-5. |
GAD |
Visually inferred heart-rate measurements |
1. Logistic regression 2. Naïve Bayes 3. Bayesian network |
The Bayesian network was the most significant method (73% accuracy) |
48 (33 GAD and 15 HC) |
McGinnis5757. McGinnis RS, McGinnis EW, Hruschak J, Lopez-Duran NL, Fitzgerald K, Rosenblum KL, et al. Wearable sensors and machine learning diagnose anxiety and depression in young children. In: 2018 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI 2018); 2018; Las Vegas, NV, USA. p. 410-3. |
Various subtypes |
Motion during a fear induction task |
K-nearest neighbor binary classification models |
75% accuracy |
63 |
Sribala5858. Sribala M. An approach of artificial neural networks for prediction of generalized anxiety disorder [Internet]. 2015 [cited 2020 Feb 4]. http://www.ijrcar.com/Volume_3_Issue_3/v3i314.pdf http://www.ijrcar.com/Volume_3_Issue_3/v...
|
GAD |
1. DSM-5 questionnaire 2. Sociodemographic attributes |
Artificial neural networks |
1. Without sensitivity analysis 90% 2. With sensitivity analysis 96% |
66 |
Chi5959. Chi M, Guo S, Ning Y, Li J, Qi H, Gao M, et al. Using support vector machine to identify imaging biomarkers of major depressive disorder and anxious depression. Int Conf Bioinspired Comput Theor Appl. 2014;472:63-7. |
GAD |
fMRI |
SVM |
82% |
38 (18 depressive - anxiety, 20 depression + AD) |
Liu6060. Liu F, Guo W, Fouche JP, Wang Y, Wang W, Ding J, et al. Multivariate classification of social anxiety disorder using whole brain functional connectivity. Brain Struct Funct. 2013;220:101-15. |
SAD |
fMRI |
SVM |
83% accuracy |
40 (20 SAD, 20 HC) |
Frick6161. Frick A, Gingnell M, Marquand AF, Howner K, Fischer H, Kristiansson M, et al. Classifying social anxiety disorder using multivoxel pattern analyses of brain function and structure. Behav Brain Res. 2014;259:330-5. |
SAD |
fMRI and sMRI |
SVM |
1. Whole-brain fMRI (68%) 2. Fear-networks alone fMRI (72%) 3. Whole-brain grey matter volume (85%) |
26 (14 AD, 12 HC) |
Pantazatos6262. Pantazatos SP, Talati A, Schneier FR, Hirsch J. Reduced anterior temporal and hippocampal functional connectivity during face processing discriminates individuals with social anxiety disorder from healthy controls and panic disorder, and increases following treatment. Neuropsychopharmacology. 2014;39:425-34. |
SAD and PD |
fMRI |
SVM |
1. SAD vs HC 88% 2. SAD vs PD 82% |
51 (16 SAD, 19 HC, 16 PD) |
Lueken6363. Lueken U, Straube B, Yang Y, Hahn T, Beesdo-Baum K, Wittchen HU, et al. Separating depressive comorbidity from panic disorder: a combined functional magnetic resonance imaging and machine learning approach. J Affect Disord. 2015;184:182-92. |
PD |
fMRI |
Random undersampling tree ensemble |
73% accuracy |
59 (26 PD and 33 HC) |
Sundermann6464. Sundermann B, Bode J, Lueken U, Westphal D, Gerlach AL, Straube B, et al. Support vector machine analysis of functional magnetic resonance imaging of interoception does not reliably predict individual outcomes of cognitive behavioral therapy in panic disorder with agoraphobia. Front Psychiatry. 2017;8:99. |
PD/AG |
1. fMRI 2. Cognitive behavioral therapy |
SVM |
Not able to reliably predict individual response to cognitive behavioral therapy |
59 (30 responders,29 non-responders) |
Boeke6565. Boeke EA, Holmes AJ, Phelps EA. Toward robust anxiety biomarkers: a machine learning approach in a large-scale sample. Biol Psychiatry Cogn Neurosci Neuroimaging. 2020;5:799-807. |
Trait anxiety |
fMRI |
Various algorithms |
Not able to reliably predict individual anxiety |
Discovery: 531,307Test: 348, 209 |
Hilbert6666. Hilbert K, Lueken U, Muehlhan M, Beesdo-Baum K. Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: a multimodal machine learning study. Brain Behav. 2017;7:e00633. |
GAD |
1. Clinical questionnaires 2. Cortisol release 3. Grey and white matter volumes |
Binary SVM |
1. Combined measures improved classifications 2. Case-classification accuracy of 90% 3. Disorder-classification accuracy of 67%. |
57 (19 GAD, 14 MD-GAD, 24 HC) |
Whitfield-Gabrieli6767. Whitfield-Gabrieli S, Ghosh SS, Nieto-Castanon A, Saygin Z, Doehrmann O, Chai XJ, et al. Brain connectomics predict response to treatment in social anxiety disorder. Mol Psychiatry. 2016;21:680-5. |
SAD |
1. fMRI 2. DTI |
Logistic regression |
Combined measures achieved 84% accuracy |
38 |
So6868. So HC, Chau CKL, Lau A, Wong SY, Zhao K. Translating GWAS findings into therapies for depression and anxiety disorders: gene-set analyses reveal enrichment of psychiatric drug classes and implications for drug repositioning. Psychol Med. 2019;49:2692-708. |
Anxiety and depression |
1. GWAS data of anxiety and depression 2. Gene sets from drugs in DSigDB |
1. Gene set analysis 2. Repositioning analyses |
Antipsychotic medications in clinical trials and heart medications may be useful for treating AD |
N/A |
Zhao & So6969. Zhao K, So HC. Drug repositioning for schizophrenia and depression/anxiety disorders: a machine learning approach leveraging expression data. IEEE J Biomed Health Inform. 2019;23:1304-15. |
Various combined subtypes |
Transcriptome |
1. Deep neural network 2. SVM |
Support for psychiatric medications considered in clinical trials |
N/A |