Table 4. The 30-STM results with the discriminating terms, topical proportions in the whole dataset, suggested topic labels, and topical developmental trends.
The rows marked in dark grey are topics whose proportions are above 4%, those in light grey are topics whose proportions are between 3% and 4%, and those in white are topics whose proportions are below 3%.
| Discriminating terms | % | Suggested topic | trend |
|---|---|---|---|
| vector, machine, SVM, support, kernel, feature, selection, classification, dimensionality, ELM, feature-selection, discriminative, classifier | 7.28 | Classification Algorithms | ↑↑↑ |
| EMD, IMF, multifractal, apnea, non-focal, ApEn, k-complex, sleep, entropy, wavelet, epileptic, REM, transform | 6.35 | EEG Signals Analysis | ↑↑ |
| multi-atlas, FCM, segmentation, superpixel, c-means, PVS, deformable, MR -image, contour, label, registration, inhomogeneity, IBSR | 6.17 | Brain Image Processing | ↓ |
| speller, CSP, SSVEP, MI—BCI, RSVP, ERRP, BCI, single-trial, brain-computer, imagery, p300, interface, MI | 5.39 | Brain-Computer-Interface | ↓ |
| AD, MCI, amnestic, AMCI, BVFTD, mild, MCI-C, alzheimer, dementia, PD, impairment, ADNI, atrophy | 4.71 | Brain Disease | ↑ |
| small-world, RSN, CNN, convolutional, network, graph-theoretical, granger, FC, node, deep, topological, topology, centrality | 4.16 | Network | ↑↑↑ |
| ADHD, MDD, first-episode, OCD, BD, REHO, SZ, ALFF, ASD, schizophrenia, autism, psychotic, depression | 4.13 | Mental Disorder | ↑↑↑ |
| bayesian, gaussian, mixture, markov, estimation, modeling, model, regression, inference, monte, sampling, GMM, carlo | 4.01 | Statistical Modeling | ↓ |
| CAD, GLCM, biogeography-based, computer-aided, CMB, texture, medical, co-occurrence, GEPSVM, curvelet, eigenbrain, landmark, image | 3.96 | Computer-Aided Diagnosis | ↑↑ |
| multivoxel, MVPA, scene, visual-cortex, ategory, categorization, representation, natural, decoding, pattern-analysis, identity, naturalistic, face | 3.7 | Vision | ↓↓ |
| brainmap, parcellation, insula, STS, subregion, insular, cingulate, empathy, social, amygdala, gyrus, connectivity-based, anterior | 3.52 | Functional Connectivity | ↓ |
| brainage, thickness, IQ, aging, morphometry, age, gray, gyrification, neuroanatomical, voxel-based, surface-based, GM, young | 3.46 | Brain Development | ↑ |
| music, band, emotion, theta, PLV, unpleasant, arousal, valence, affective, power, schizotypy, oscillation, synchronization | 3.46 | Emotion | ↑↑ |
| synapsis, memristor, neuromorphic, memristive, reservoir, STDP, SNN, self-organization, latching, synaptic, spiking, associative, neuron, HTM | 3.44 | Nervous System | ↓ |
| dictionary, swarm, particle, sparse, ICA, removal, sparsity, inverse, denoising, optimization, PSO, separation, beamformer | 3.41 | Optimization Algorithms | ↑ |
| reward, FRN, aversive, reinforcement, dopamine, striatum, ganglion, valuation, tegmental, decision-making, BG, reversal, punishment | 3.15 | Decision-Making | ↓↓ |
| exoskeleton, upper-limb, extremity, brain-machine, BMI, brain-robot, flexion, movement, finger, rehabilitation, hand, arm, TDCS | 2.89 | Motor & Robot | ↓ |
| driver, drowsiness, wearable, drowsy, consumer, SOC, driving, fatigue, aesthetic, workload, neuro-fuzzy, vigilance, ANFIS | 2.83 | Fatigue Driving | ↑↑↑ |
| metastasis, radiomic, PTSD, RCBV, glioma, glioblastoma, neuro-oncology, non-enhancing, multiforme, grade, GBM, survival, spectroscopic | 2.78 | Brain Tumor | ↑ |
| TBI, preterm, cost-effectiveness, TCD, infant, hypoxic-ischaemic, aneurysm, neonatal, traumatic, injury, gestation, HIE, prehospital | 2.7 | Infant, Fatal & Child | ↓ |
| tensor, DTI, tractography, anisotropy, diffusivity, microstructural, peduncle, capsule, HARDI, DMRI, diffusion, cartilage, microstructure | 2.54 | Brain Structure | ↓ |
| neglect, visual-search, attentional, attention, microstate, orienting, saliency, selective, visuospatial, search, RTMS, gaze, top-down | 2.46 | Attention & Vision | ↓↓↓ |
| PET/MRI, MR-AC, GTV, penumbra, attenuation, infarct, vessel, PET/MR, F-18-FET, positron, SUV, PET/CT, emission | 2.41 | Brain Imaging | ↓ |
| lexical, verb, p600, MMN, semantic, word, sentence, syntax, syntactic, RHD, ERP, reading, classifier-noun | 2.21 | Semantic Cognition | ↓↓↓ |
| TLE, STN, IED, IEEG, neurostimulation, focal, epilepsy, mesial, DBS, epileptiform, SEEG, epileptogenic, pre-surgical | 2.09 | Epilepsy | ↑ |
| methylation, microarray, genome-wide, epigenetic, mirna, BDNF, GWAS, single-nucleotide, microrna, galectin, mitotic, histone, methyltransferase | 1.68 | Gene | ↓↓ |
| HIV, meningitis, virus, TDP-43, neurofibrillary, hypomyelination, CJD, TLR, parasite, aseptic, retinopathy, antiretroviral, NFT | 1.42 | Virus & Pathology | ↓↓↓ |
| speech, tinnitus, vowel, cochlear, pitch, prosody, sensorineural, dysarthria, stuttering, monolingual, sound, hearing, auditory | 1.28 | Phonological Cognition | ↓ |
| near-infrared, FNIRS, anesthesia, infrared, vegetative, propofol, sevoflurane, BI, HBO, DOA, consciousness, optical, depth | 1.27 | Near-Infrared Spectroscopy | ↑ |
| metabolomic, blood-brain, BBB, NMF, PNES, influx, microscopy, spectrometry, DCE-MRI, mass, factorization, permeability, barrier | 1.12 | Molecule | ↓↓ |
Topics are ranked by proportion in a descending order. %: topic proportions in the dataset (with the θ matrix estimated by STM, where θij (i = 1,2,…6317, j = 1,2,…30) denotes the proportion of document i allocated to topic j. Proportion of each topic obtained by summing up θij by topic). Abbreviations are shown in S3 Table. ↑(↓): increasing (decreasing) trend but not statistically significant (p > 0.05); ↑↑(↓↓), ↑↑↑(↓↓↓), ↑↑↑↑(↓↓↓↓): statistically significant increasing (decreasing) trend (p < 0.05, p < 0.01, and p < 0.001, respectively)