Class information for: |
Basic class information |
Class id | #P | Avg. number of references |
Database coverage of references |
---|---|---|---|
20122 | 475 | 45.2 | 74% |
Hierarchy of classes |
The table includes all classes above and classes immediately below the current class. |
Cluster id | Level | Cluster label | #P |
---|---|---|---|
11 | 4 | NEUROSCIENCES//CLINICAL NEUROLOGY//NEUROL | 1112395 |
49 | 3 | ALZHEIMERS DISEASE//AMYOTROPHIC LATERAL SCLEROSIS//DEMENTIA | 92146 |
518 | 2 | NEUROIMAGE//FMRI//BOLD | 15113 |
20122 | 1 | MULTIVOXEL PATTERN ANALYSIS//MVPA//MULTIVARIATE PATTERN ANALYSIS | 475 |
Terms with highest relevance score |
rank | Term | termType | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|---|
1 | MULTIVOXEL PATTERN ANALYSIS | authKW | 614034 | 7% | 29% | 33 |
2 | MVPA | authKW | 612652 | 10% | 21% | 46 |
3 | MULTIVARIATE PATTERN ANALYSIS | authKW | 349279 | 5% | 23% | 24 |
4 | FUNCTIONAL MAGNETIC RESONANCE IMAGING DATA ANALYSIS | authKW | 205707 | 1% | 80% | 4 |
5 | HYPERALIGNMENT | authKW | 192852 | 1% | 100% | 3 |
6 | MULTIVARIATE PATTERN ANALYSIS MVPA | authKW | 178011 | 1% | 46% | 6 |
7 | VOXEL SELECTION | authKW | 171421 | 1% | 67% | 4 |
8 | BRAIN DECODING | authKW | 144631 | 1% | 38% | 6 |
9 | REPRESENTATIONAL SIMILARITY ANALYSIS | authKW | 137127 | 2% | 27% | 8 |
10 | HILBERT SCHMIDT PRODUCT | authKW | 128568 | 0% | 100% | 2 |
Web of Science journal categories |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | Neuroimaging | 29208 | 31% | 0% | 146 |
2 | Neurosciences | 6865 | 69% | 0% | 326 |
3 | Radiology, Nuclear Medicine & Medical Imaging | 3436 | 33% | 0% | 158 |
4 | Computer Science, Artificial Intelligence | 483 | 9% | 0% | 42 |
5 | Psychology, Experimental | 407 | 7% | 0% | 32 |
6 | Mathematical & Computational Biology | 266 | 5% | 0% | 22 |
7 | Imaging Science & Photographic Technology | 211 | 3% | 0% | 15 |
8 | Psychology | 208 | 5% | 0% | 24 |
9 | Engineering, Biomedical | 185 | 5% | 0% | 26 |
10 | Computer Science, Interdisciplinary Applications | 105 | 5% | 0% | 22 |
Address terms |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | STUDY LANGUAGE INFORMAT | 88987 | 3% | 12% | 12 |
2 | CIMA MATH ITS PLICAT | 85711 | 0% | 67% | 2 |
3 | SECT FUNCT IMAGING METHODS | 65072 | 2% | 11% | 9 |
4 | AKAMA | 64284 | 0% | 100% | 1 |
5 | ATHINOULA A MARTIOS BIOMED IMAGING | 64284 | 0% | 100% | 1 |
6 | BRAIN IMAGC ANAL UNIT | 64284 | 0% | 100% | 1 |
7 | BRAIN RIO GRANDE DO SUL INSCER RS | 64284 | 0% | 100% | 1 |
8 | COGNITVE NEUROSCI LEARNING | 64284 | 0% | 100% | 1 |
9 | COMPLEXUS GRP | 64284 | 0% | 100% | 1 |
10 | DYNAM COGNIT | 64284 | 0% | 100% | 1 |
Journals |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | NEUROIMAGE | 70438 | 25% | 1% | 117 |
2 | FRONTIERS IN NEUROINFORMATICS | 20728 | 2% | 4% | 9 |
3 | NEUROINFORMATICS | 7044 | 1% | 2% | 6 |
4 | FRONTIERS IN HUMAN NEUROSCIENCE | 4291 | 3% | 0% | 16 |
5 | HUMAN BRAIN MAPPING | 3764 | 3% | 0% | 14 |
6 | IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT | 3656 | 1% | 2% | 3 |
7 | NOUVELLE REVUE FRANCAISE | 1946 | 0% | 3% | 1 |
8 | IEEE TRANSACTIONS ON MEDICAL IMAGING | 1678 | 2% | 0% | 10 |
9 | CEREBRAL CORTEX | 1424 | 2% | 0% | 10 |
10 | JOURNAL OF SPECULATIVE PHILOSOPHY | 1310 | 0% | 2% | 1 |
Author Key Words |
Core articles |
The table includes core articles in the class. The following variables is taken into account for the relevance score of an article in a cluster c: (1) Number of references referring to publications in the class. (2) Share of total number of active references referring to publications in the class. (3) Age of the article. New articles get higher score than old articles. (4) Citation rate, normalized to year. |
Rank | Reference | # ref. in cl. |
Shr. of ref. in cl. |
Citations |
---|---|---|---|---|
1 | HAYNES, JD , (2015) A PRIMER ON PATTERN-BASED APPROACHES TO FMRI: PRINCIPLES, PITFALLS, AND PERSPECTIVES.NEURON. VOL. 87. ISSUE 2. P. 257 -270 | 42 | 45% | 22 |
2 | HAXBY, JV , CONNOLLY, AC , GUNTUPALLI, JS , (2014) DECODING NEURAL REPRESENTATIONAL SPACES USING MULTIVARIATE PATTERN ANALYSIS.ANNUAL REVIEW OF NEUROSCIENCE, VOL 37. VOL. 37. ISSUE . P. 435 -456 | 31 | 50% | 48 |
3 | VAROQUAUX, G , RAAMANA, PR , ENGEMANN, DA , HOYOS-IDROBO, A , SCHWARTZ, Y , THIRION, B , (2017) ASSESSING AND TUNING BRAIN DECODERS: CROSS-VALIDATION, CAVEATS, AND GUIDELINES.NEUROIMAGE. VOL. 145. ISSUE . P. 166 -179 | 23 | 50% | 1 |
4 | ETZEL, JA , ZACKS, JM , BRAVER, TS , (2013) SEARCHLIGHT ANALYSIS: PROMISE, PITFALLS, AND POTENTIAL.NEUROIMAGE. VOL. 78. ISSUE . P. 261 -269 | 25 | 68% | 32 |
5 | ALLEFELD, C , GORGEN, K , HAYNES, JD , (2016) VALID POPULATION INFERENCE FOR INFORMATION-BASED IMAGING: FROM THE SECOND-LEVEL T-TEST TO PREVALENCE INFERENCE.NEUROIMAGE. VOL. 141. ISSUE . P. 378 -392 | 28 | 58% | 1 |
6 | PEREIRA, F , MITCHELL, T , BOTVINICK, M , (2009) MACHINE LEARNING CLASSIFIERS AND FMRI: A TUTORIAL OVERVIEW.NEUROIMAGE. VOL. 45. ISSUE 1. P. S199-S209 | 14 | 70% | 476 |
7 | MISAKI, M , KIM, Y , BANDETTINI, PA , KRIEGESKORTE, N , (2010) COMPARISON OF MULTIVARIATE CLASSIFIERS AND RESPONSE NORMALIZATIONS FOR PATTERN-INFORMATION FMRI.NEUROIMAGE. VOL. 53. ISSUE 1. P. 103-118 | 19 | 68% | 126 |
8 | HAUSFELD, L , VALENTE, G , FORMISANO, E , (2014) MULTICLASS FMRI DATA DECODING AND VISUALIZATION USING SUPERVISED SELF-ORGANIZING MAPS.NEUROIMAGE. VOL. 96. ISSUE . P. 54 -66 | 25 | 58% | 5 |
9 | NASELARIS, T , KAY, KN , NISHIMOTO, S , GALLANT, JL , (2011) ENCODING AND DECODING IN FMRI.NEUROIMAGE. VOL. 56. ISSUE 2. P. 400-410 | 31 | 36% | 117 |
10 | TONG, F , PRATTE, MS , (2012) DECODING PATTERNS OF HUMAN BRAIN ACTIVITY.ANNUAL REVIEW OF PSYCHOLOGY, VOL 63. VOL. 63. ISSUE . P. 483-509 | 38 | 29% | 88 |
Classes with closest relation at Level 1 |