STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data Article Swipe
Wei Zhang
,
Weiming Zeng
,
Hongyu Chen
,
Jie Liu
,
Hongjie Yan
,
Kaile Zhang
,
Ran Tao
,
Wai Ting Siok
,
Nizhuan Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/tomography10120138
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/tomography10120138
Background: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and clinical assessments, lacking objective biomarkers. Combining functional magnetic resonance imaging (fMRI) with artificial intelligence can enhance depression diagnosis using neuroimaging indicators, but depression-specific fMRI datasets are often small and imbalanced, posing challenges for classification models. New Method: We propose the Spatio-Temporal Aggregation Network (STANet) for diagnosing depression by integrating convolutional neural networks (CNN) and recurrent neural networks (RNN) to capture both temporal and spatial features of brain activity. STANet comprises the following steps: (1) Aggregate spatio-temporal information via independent component analysis (ICA). (2) Utilize multi-scale deep convolution to capture detailed features. (3) Balance data using the synthetic minority over-sampling technique (SMOTE) to generate new samples for minority classes. (4) Employ the attention-Fourier gate recurrent unit (AFGRU) classifier to capture long-term dependencies, with an adaptive weight assignment mechanism to enhance model generalization. Results: STANet achieves superior depression diagnostic performance, with 82.38% accuracy and a 90.72% AUC. The Spatio-Temporal Feature Aggregation module enhances classification by capturing deeper features at multiple scales. The AFGRU classifier, with adaptive weights and a stacked Gated Recurrent Unit (GRU), attains higher accuracy and AUC. SMOTE outperforms other oversampling methods. Additionally, spatio-temporal aggregated features achieve better performance compared to using only temporal or spatial features. Comparison with existing methods: STANet significantly outperforms traditional classifiers, deep learning classifiers, and functional connectivity-based classifiers. Conclusions: The successful performance of STANet contributes to enhancing the diagnosis and treatment assessment of depression in clinical settings on imbalanced and small fMRI.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/tomography10120138
- OA Status
- gold
- Cited By
- 2
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404819843
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4404819843Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/tomography10120138Digital Object Identifier
- Title
-
STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-28Full publication date if available
- Authors
-
Wei Zhang, Weiming Zeng, Hongyu Chen, Jie Liu, Hongjie Yan, Kaile Zhang, Ran Tao, Wai Ting Siok, Nizhuan WangList of authors in order
- Landing page
-
https://doi.org/10.3390/tomography10120138Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/tomography10120138Direct OA link when available
- Concepts
-
Oversampling, Computer science, Artificial intelligence, Classifier (UML), Pattern recognition (psychology), Convolutional neural network, Functional magnetic resonance imaging, Neuroimaging, Machine learning, Psychology, Biology, Computer network, Neuroscience, Psychiatry, Bandwidth (computing)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
60Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4404819843 |
|---|---|
| doi | https://doi.org/10.3390/tomography10120138 |
| ids.doi | https://doi.org/10.3390/tomography10120138 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39728900 |
| ids.openalex | https://openalex.org/W4404819843 |
| fwci | 1.40560113 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D006801 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Humans |
| mesh[1].qualifier_ui | Q000379 |
| mesh[1].descriptor_ui | D008279 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | methods |
| mesh[1].descriptor_name | Magnetic Resonance Imaging |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D016571 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Neural Networks, Computer |
| mesh[3].qualifier_ui | Q000000981 |
| mesh[3].descriptor_ui | D003863 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | diagnostic imaging |
| mesh[3].descriptor_name | Depression |
| mesh[4].qualifier_ui | Q000503 |
| mesh[4].descriptor_ui | D003863 |
| mesh[4].is_major_topic | True |
| mesh[4].qualifier_name | physiopathology |
| mesh[4].descriptor_name | Depression |
| mesh[5].qualifier_ui | Q000000981 |
| mesh[5].descriptor_ui | D001921 |
| mesh[5].is_major_topic | True |
| mesh[5].qualifier_name | diagnostic imaging |
| mesh[5].descriptor_name | Brain |
| mesh[6].qualifier_ui | Q000503 |
| mesh[6].descriptor_ui | D001921 |
| mesh[6].is_major_topic | True |
| mesh[6].qualifier_name | physiopathology |
| mesh[6].descriptor_name | Brain |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D000328 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Adult |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D008297 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Male |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D005260 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Female |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D000077321 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Deep Learning |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D006801 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Humans |
| mesh[12].qualifier_ui | Q000379 |
| mesh[12].descriptor_ui | D008279 |
| mesh[12].is_major_topic | True |
| mesh[12].qualifier_name | methods |
| mesh[12].descriptor_name | Magnetic Resonance Imaging |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D016571 |
| mesh[13].is_major_topic | True |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Neural Networks, Computer |
| mesh[14].qualifier_ui | Q000000981 |
| mesh[14].descriptor_ui | D003863 |
| mesh[14].is_major_topic | True |
| mesh[14].qualifier_name | diagnostic imaging |
| mesh[14].descriptor_name | Depression |
| mesh[15].qualifier_ui | Q000503 |
| mesh[15].descriptor_ui | D003863 |
| mesh[15].is_major_topic | True |
| mesh[15].qualifier_name | physiopathology |
| mesh[15].descriptor_name | Depression |
| mesh[16].qualifier_ui | Q000000981 |
| mesh[16].descriptor_ui | D001921 |
| mesh[16].is_major_topic | True |
| mesh[16].qualifier_name | diagnostic imaging |
| mesh[16].descriptor_name | Brain |
| mesh[17].qualifier_ui | Q000503 |
| mesh[17].descriptor_ui | D001921 |
| mesh[17].is_major_topic | True |
| mesh[17].qualifier_name | physiopathology |
| mesh[17].descriptor_name | Brain |
| mesh[18].qualifier_ui | |
| mesh[18].descriptor_ui | D000328 |
| mesh[18].is_major_topic | False |
| mesh[18].qualifier_name | |
| mesh[18].descriptor_name | Adult |
| mesh[19].qualifier_ui | |
| mesh[19].descriptor_ui | D008297 |
| mesh[19].is_major_topic | False |
| mesh[19].qualifier_name | |
| mesh[19].descriptor_name | Male |
| mesh[20].qualifier_ui | |
| mesh[20].descriptor_ui | D005260 |
| mesh[20].is_major_topic | False |
| mesh[20].qualifier_name | |
| mesh[20].descriptor_name | Female |
| mesh[21].qualifier_ui | |
| mesh[21].descriptor_ui | D000077321 |
| mesh[21].is_major_topic | False |
| mesh[21].qualifier_name | |
| mesh[21].descriptor_name | Deep Learning |
| mesh[22].qualifier_ui | |
| mesh[22].descriptor_ui | D006801 |
| mesh[22].is_major_topic | False |
| mesh[22].qualifier_name | |
| mesh[22].descriptor_name | Humans |
| mesh[23].qualifier_ui | Q000379 |
| mesh[23].descriptor_ui | D008279 |
| mesh[23].is_major_topic | True |
| mesh[23].qualifier_name | methods |
| mesh[23].descriptor_name | Magnetic Resonance Imaging |
| mesh[24].qualifier_ui | |
| mesh[24].descriptor_ui | D016571 |
| mesh[24].is_major_topic | True |
| mesh[24].qualifier_name | |
| mesh[24].descriptor_name | Neural Networks, Computer |
| mesh[25].qualifier_ui | Q000000981 |
| mesh[25].descriptor_ui | D003863 |
| mesh[25].is_major_topic | True |
| mesh[25].qualifier_name | diagnostic imaging |
| mesh[25].descriptor_name | Depression |
| mesh[26].qualifier_ui | Q000503 |
| mesh[26].descriptor_ui | D003863 |
| mesh[26].is_major_topic | True |
| mesh[26].qualifier_name | physiopathology |
| mesh[26].descriptor_name | Depression |
| mesh[27].qualifier_ui | Q000000981 |
| mesh[27].descriptor_ui | D001921 |
| mesh[27].is_major_topic | True |
| mesh[27].qualifier_name | diagnostic imaging |
| mesh[27].descriptor_name | Brain |
| mesh[28].qualifier_ui | Q000503 |
| mesh[28].descriptor_ui | D001921 |
| mesh[28].is_major_topic | True |
| mesh[28].qualifier_name | physiopathology |
| mesh[28].descriptor_name | Brain |
| mesh[29].qualifier_ui | |
| mesh[29].descriptor_ui | D000328 |
| mesh[29].is_major_topic | False |
| mesh[29].qualifier_name | |
| mesh[29].descriptor_name | Adult |
| mesh[30].qualifier_ui | |
| mesh[30].descriptor_ui | D008297 |
| mesh[30].is_major_topic | False |
| mesh[30].qualifier_name | |
| mesh[30].descriptor_name | Male |
| mesh[31].qualifier_ui | |
| mesh[31].descriptor_ui | D005260 |
| mesh[31].is_major_topic | False |
| mesh[31].qualifier_name | |
| mesh[31].descriptor_name | Female |
| mesh[32].qualifier_ui | |
| mesh[32].descriptor_ui | D000077321 |
| mesh[32].is_major_topic | False |
| mesh[32].qualifier_name | |
| mesh[32].descriptor_name | Deep Learning |
| mesh[33].qualifier_ui | |
| mesh[33].descriptor_ui | D006801 |
| mesh[33].is_major_topic | False |
| mesh[33].qualifier_name | |
| mesh[33].descriptor_name | Humans |
| mesh[34].qualifier_ui | Q000379 |
| mesh[34].descriptor_ui | D008279 |
| mesh[34].is_major_topic | True |
| mesh[34].qualifier_name | methods |
| mesh[34].descriptor_name | Magnetic Resonance Imaging |
| mesh[35].qualifier_ui | |
| mesh[35].descriptor_ui | D016571 |
| mesh[35].is_major_topic | True |
| mesh[35].qualifier_name | |
| mesh[35].descriptor_name | Neural Networks, Computer |
| mesh[36].qualifier_ui | Q000000981 |
| mesh[36].descriptor_ui | D003863 |
| mesh[36].is_major_topic | True |
| mesh[36].qualifier_name | diagnostic imaging |
| mesh[36].descriptor_name | Depression |
| mesh[37].qualifier_ui | Q000503 |
| mesh[37].descriptor_ui | D003863 |
| mesh[37].is_major_topic | True |
| mesh[37].qualifier_name | physiopathology |
| mesh[37].descriptor_name | Depression |
| mesh[38].qualifier_ui | Q000000981 |
| mesh[38].descriptor_ui | D001921 |
| mesh[38].is_major_topic | True |
| mesh[38].qualifier_name | diagnostic imaging |
| mesh[38].descriptor_name | Brain |
| mesh[39].qualifier_ui | Q000503 |
| mesh[39].descriptor_ui | D001921 |
| mesh[39].is_major_topic | True |
| mesh[39].qualifier_name | physiopathology |
| mesh[39].descriptor_name | Brain |
| mesh[40].qualifier_ui | |
| mesh[40].descriptor_ui | D000328 |
| mesh[40].is_major_topic | False |
| mesh[40].qualifier_name | |
| mesh[40].descriptor_name | Adult |
| mesh[41].qualifier_ui | |
| mesh[41].descriptor_ui | D008297 |
| mesh[41].is_major_topic | False |
| mesh[41].qualifier_name | |
| mesh[41].descriptor_name | Male |
| mesh[42].qualifier_ui | |
| mesh[42].descriptor_ui | D005260 |
| mesh[42].is_major_topic | False |
| mesh[42].qualifier_name | |
| mesh[42].descriptor_name | Female |
| mesh[43].qualifier_ui | |
| mesh[43].descriptor_ui | D000077321 |
| mesh[43].is_major_topic | False |
| mesh[43].qualifier_name | |
| mesh[43].descriptor_name | Deep Learning |
| mesh[44].qualifier_ui | |
| mesh[44].descriptor_ui | D006801 |
| mesh[44].is_major_topic | False |
| mesh[44].qualifier_name | |
| mesh[44].descriptor_name | Humans |
| mesh[45].qualifier_ui | Q000379 |
| mesh[45].descriptor_ui | D008279 |
| mesh[45].is_major_topic | True |
| mesh[45].qualifier_name | methods |
| mesh[45].descriptor_name | Magnetic Resonance Imaging |
| mesh[46].qualifier_ui | |
| mesh[46].descriptor_ui | D016571 |
| mesh[46].is_major_topic | True |
| mesh[46].qualifier_name | |
| mesh[46].descriptor_name | Neural Networks, Computer |
| mesh[47].qualifier_ui | Q000000981 |
| mesh[47].descriptor_ui | D003863 |
| mesh[47].is_major_topic | True |
| mesh[47].qualifier_name | diagnostic imaging |
| mesh[47].descriptor_name | Depression |
| mesh[48].qualifier_ui | Q000503 |
| mesh[48].descriptor_ui | D003863 |
| mesh[48].is_major_topic | True |
| mesh[48].qualifier_name | physiopathology |
| mesh[48].descriptor_name | Depression |
| mesh[49].qualifier_ui | Q000000981 |
| mesh[49].descriptor_ui | D001921 |
| mesh[49].is_major_topic | True |
| mesh[49].qualifier_name | diagnostic imaging |
| mesh[49].descriptor_name | Brain |
| type | article |
| title | STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data |
| biblio.issue | 12 |
| biblio.volume | 10 |
| biblio.last_page | 1914 |
| biblio.first_page | 1895 |
| topics[0].id | https://openalex.org/T10241 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2805 |
| topics[0].subfield.display_name | Cognitive Neuroscience |
| topics[0].display_name | Functional Brain Connectivity Studies |
| topics[1].id | https://openalex.org/T11304 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.998199999332428 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2741 |
| topics[1].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[1].display_name | Advanced Neuroimaging Techniques and Applications |
| topics[2].id | https://openalex.org/T10429 |
| topics[2].field.id | https://openalex.org/fields/28 |
| topics[2].field.display_name | Neuroscience |
| topics[2].score | 0.9973999857902527 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2805 |
| topics[2].subfield.display_name | Cognitive Neuroscience |
| topics[2].display_name | EEG and Brain-Computer Interfaces |
| is_xpac | False |
| apc_list.value | 1800 |
| apc_list.currency | CHF |
| apc_list.value_usd | 1949 |
| apc_paid.value | 1800 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 1949 |
| concepts[0].id | https://openalex.org/C197323446 |
| concepts[0].level | 3 |
| concepts[0].score | 0.7697460651397705 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q331222 |
| concepts[0].display_name | Oversampling |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.7403061985969543 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.7144259214401245 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C95623464 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5610360503196716 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1096149 |
| concepts[3].display_name | Classifier (UML) |
| concepts[4].id | https://openalex.org/C153180895 |
| concepts[4].level | 2 |
| concepts[4].score | 0.527239978313446 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[4].display_name | Pattern recognition (psychology) |
| concepts[5].id | https://openalex.org/C81363708 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4751332104206085 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[5].display_name | Convolutional neural network |
| concepts[6].id | https://openalex.org/C2779226451 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4680114984512329 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q903809 |
| concepts[6].display_name | Functional magnetic resonance imaging |
| concepts[7].id | https://openalex.org/C58693492 |
| concepts[7].level | 2 |
| concepts[7].score | 0.46687883138656616 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q551875 |
| concepts[7].display_name | Neuroimaging |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4617258906364441 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C15744967 |
| concepts[9].level | 0 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[9].display_name | Psychology |
| concepts[10].id | https://openalex.org/C86803240 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[10].display_name | Biology |
| concepts[11].id | https://openalex.org/C31258907 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[11].display_name | Computer network |
| concepts[12].id | https://openalex.org/C169760540 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[12].display_name | Neuroscience |
| concepts[13].id | https://openalex.org/C118552586 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q7867 |
| concepts[13].display_name | Psychiatry |
| concepts[14].id | https://openalex.org/C2776257435 |
| concepts[14].level | 2 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1576430 |
| concepts[14].display_name | Bandwidth (computing) |
| keywords[0].id | https://openalex.org/keywords/oversampling |
| keywords[0].score | 0.7697460651397705 |
| keywords[0].display_name | Oversampling |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.7403061985969543 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.7144259214401245 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/classifier |
| keywords[3].score | 0.5610360503196716 |
| keywords[3].display_name | Classifier (UML) |
| keywords[4].id | https://openalex.org/keywords/pattern-recognition |
| keywords[4].score | 0.527239978313446 |
| keywords[4].display_name | Pattern recognition (psychology) |
| keywords[5].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[5].score | 0.4751332104206085 |
| keywords[5].display_name | Convolutional neural network |
| keywords[6].id | https://openalex.org/keywords/functional-magnetic-resonance-imaging |
| keywords[6].score | 0.4680114984512329 |
| keywords[6].display_name | Functional magnetic resonance imaging |
| keywords[7].id | https://openalex.org/keywords/neuroimaging |
| keywords[7].score | 0.46687883138656616 |
| keywords[7].display_name | Neuroimaging |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.4617258906364441 |
| keywords[8].display_name | Machine learning |
| language | en |
| locations[0].id | doi:10.3390/tomography10120138 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210172093 |
| locations[0].source.issn | 2379-1381, 2379-139X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2379-1381 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Tomography |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Tomography |
| locations[0].landing_page_url | https://doi.org/10.3390/tomography10120138 |
| locations[1].id | pmid:39728900 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Tomography (Ann Arbor, Mich.) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39728900 |
| locations[2].id | pmh:oai:doaj.org/article:e5083feb3b514c12b1756e6a4e578659 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Tomography, Vol 10, Iss 12, Pp 1895-1914 (2024) |
| locations[2].landing_page_url | https://doaj.org/article/e5083feb3b514c12b1756e6a4e578659 |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:11679224 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Tomography |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11679224 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5101064439 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Wei Zhang |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I96733725 |
| authorships[0].affiliations[0].raw_affiliation_string | Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China |
| authorships[0].institutions[0].id | https://openalex.org/I96733725 |
| authorships[0].institutions[0].ror | https://ror.org/04z7qrj66 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I96733725 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Shanghai Maritime University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Wei Zhang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China |
| authorships[1].author.id | https://openalex.org/A5102819746 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-3810-8258 |
| authorships[1].author.display_name | Weiming Zeng |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I96733725 |
| authorships[1].affiliations[0].raw_affiliation_string | Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China |
| authorships[1].institutions[0].id | https://openalex.org/I96733725 |
| authorships[1].institutions[0].ror | https://ror.org/04z7qrj66 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I96733725 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Shanghai Maritime University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Weiming Zeng |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China |
| authorships[2].author.id | https://openalex.org/A5100332207 |
| authorships[2].author.orcid | https://orcid.org/0009-0004-2934-3135 |
| authorships[2].author.display_name | Hongyu Chen |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I96733725 |
| authorships[2].affiliations[0].raw_affiliation_string | Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China |
| authorships[2].institutions[0].id | https://openalex.org/I96733725 |
| authorships[2].institutions[0].ror | https://ror.org/04z7qrj66 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I96733725 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Shanghai Maritime University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Hongyu Chen |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China |
| authorships[3].author.id | https://openalex.org/A5052226742 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-9297-7729 |
| authorships[3].author.display_name | Jie Liu |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I96733725 |
| authorships[3].affiliations[0].raw_affiliation_string | Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China |
| authorships[3].institutions[0].id | https://openalex.org/I96733725 |
| authorships[3].institutions[0].ror | https://ror.org/04z7qrj66 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I96733725 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Shanghai Maritime University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Jie Liu |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Lab of Digital Image and Intelligent Computation, College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China |
| authorships[4].author.id | https://openalex.org/A5101973346 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Hongjie Yan |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I177388780, https://openalex.org/I4210094843 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang 222002, China |
| authorships[4].institutions[0].id | https://openalex.org/I4210094843 |
| authorships[4].institutions[0].ror | https://ror.org/00rkprb29 |
| authorships[4].institutions[0].type | healthcare |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210094843 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Lianyungang Oriental Hospital |
| authorships[4].institutions[1].id | https://openalex.org/I177388780 |
| authorships[4].institutions[1].ror | https://ror.org/04fe7hy80 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I177388780 |
| authorships[4].institutions[1].country_code | CN |
| authorships[4].institutions[1].display_name | Xuzhou Medical College |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Hongjie Yan |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Neurology, Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang 222002, China |
| authorships[5].author.id | https://openalex.org/A5063466392 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-6910-8050 |
| authorships[5].author.display_name | Kaile Zhang |
| authorships[5].countries | HK |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I14243506 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China |
| authorships[5].institutions[0].id | https://openalex.org/I14243506 |
| authorships[5].institutions[0].ror | https://ror.org/0030zas98 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I14243506 |
| authorships[5].institutions[0].country_code | HK |
| authorships[5].institutions[0].display_name | Hong Kong Polytechnic University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Kaile Zhang |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China |
| authorships[6].author.id | https://openalex.org/A5067803447 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-5243-7189 |
| authorships[6].author.display_name | Ran Tao |
| authorships[6].countries | HK |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I14243506 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China |
| authorships[6].institutions[0].id | https://openalex.org/I14243506 |
| authorships[6].institutions[0].ror | https://ror.org/0030zas98 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I14243506 |
| authorships[6].institutions[0].country_code | HK |
| authorships[6].institutions[0].display_name | Hong Kong Polytechnic University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Ran Tao |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China |
| authorships[7].author.id | https://openalex.org/A5091302561 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-2154-5996 |
| authorships[7].author.display_name | Wai Ting Siok |
| authorships[7].countries | HK |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I14243506 |
| authorships[7].affiliations[0].raw_affiliation_string | Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China |
| authorships[7].institutions[0].id | https://openalex.org/I14243506 |
| authorships[7].institutions[0].ror | https://ror.org/0030zas98 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I14243506 |
| authorships[7].institutions[0].country_code | HK |
| authorships[7].institutions[0].display_name | Hong Kong Polytechnic University |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Wai Ting Siok |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China |
| authorships[8].author.id | https://openalex.org/A5047632784 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-9701-2918 |
| authorships[8].author.display_name | Nizhuan Wang |
| authorships[8].countries | HK |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I14243506 |
| authorships[8].affiliations[0].raw_affiliation_string | Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China |
| authorships[8].institutions[0].id | https://openalex.org/I14243506 |
| authorships[8].institutions[0].ror | https://ror.org/0030zas98 |
| authorships[8].institutions[0].type | education |
| authorships[8].institutions[0].lineage | https://openalex.org/I14243506 |
| authorships[8].institutions[0].country_code | HK |
| authorships[8].institutions[0].display_name | Hong Kong Polytechnic University |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Nizhuan Wang |
| authorships[8].is_corresponding | True |
| authorships[8].raw_affiliation_strings | Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hong Kong, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.3390/tomography10120138 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10241 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2805 |
| primary_topic.subfield.display_name | Cognitive Neuroscience |
| primary_topic.display_name | Functional Brain Connectivity Studies |
| related_works | https://openalex.org/W2766503024, https://openalex.org/W2781247653, https://openalex.org/W4206637278, https://openalex.org/W3196098778, https://openalex.org/W3211250490, https://openalex.org/W4360584310, https://openalex.org/W2981515171, https://openalex.org/W80466363, https://openalex.org/W2924282518, https://openalex.org/W3080872054 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 4 |
| best_oa_location.id | doi:10.3390/tomography10120138 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210172093 |
| best_oa_location.source.issn | 2379-1381, 2379-139X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2379-1381 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Tomography |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Tomography |
| best_oa_location.landing_page_url | https://doi.org/10.3390/tomography10120138 |
| primary_location.id | doi:10.3390/tomography10120138 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210172093 |
| primary_location.source.issn | 2379-1381, 2379-139X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2379-1381 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Tomography |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Tomography |
| primary_location.landing_page_url | https://doi.org/10.3390/tomography10120138 |
| publication_date | 2024-11-28 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4205208574, https://openalex.org/W4399563700, https://openalex.org/W4391892457, https://openalex.org/W3112952980, https://openalex.org/W4366452046, https://openalex.org/W3196444363, https://openalex.org/W3186881383, https://openalex.org/W4392909479, https://openalex.org/W4311709467, https://openalex.org/W3012919764, https://openalex.org/W2728360444, https://openalex.org/W2161502085, https://openalex.org/W2100634972, https://openalex.org/W3196564794, https://openalex.org/W2967967941, https://openalex.org/W3096960547, https://openalex.org/W2946763314, https://openalex.org/W4321194469, https://openalex.org/W4391020095, https://openalex.org/W6767121180, https://openalex.org/W2148143831, https://openalex.org/W2964877070, https://openalex.org/W2765820969, https://openalex.org/W2132791018, https://openalex.org/W2511002675, https://openalex.org/W6795543198, https://openalex.org/W2104933073, https://openalex.org/W3138595103, https://openalex.org/W3011893870, https://openalex.org/W3125774480, https://openalex.org/W2116649573, https://openalex.org/W2113704703, https://openalex.org/W2091177453, https://openalex.org/W2541843830, https://openalex.org/W2082207932, https://openalex.org/W2079450984, https://openalex.org/W2600547107, https://openalex.org/W2007450474, https://openalex.org/W2027116896, https://openalex.org/W1998650100, https://openalex.org/W2752611890, https://openalex.org/W3122732550, https://openalex.org/W6850174231, https://openalex.org/W2964010366, https://openalex.org/W4320035362, https://openalex.org/W4396983056, https://openalex.org/W3019119961, https://openalex.org/W4400727858, https://openalex.org/W4292858485, https://openalex.org/W2110168541, https://openalex.org/W2981446641, https://openalex.org/W4391260605, https://openalex.org/W3007419742, https://openalex.org/W2033490394, https://openalex.org/W4391496274, https://openalex.org/W2058046532, https://openalex.org/W4205900283, https://openalex.org/W4323796700, https://openalex.org/W2968692091, https://openalex.org/W3162718111 |
| referenced_works_count | 60 |
| abstract_inverted_index.a | 162, 186 |
| abstract_inverted_index.We | 57 |
| abstract_inverted_index.an | 142 |
| abstract_inverted_index.at | 176 |
| abstract_inverted_index.by | 67, 172 |
| abstract_inverted_index.in | 249 |
| abstract_inverted_index.is | 5 |
| abstract_inverted_index.of | 3, 85, 237, 247 |
| abstract_inverted_index.on | 16, 252 |
| abstract_inverted_index.or | 214 |
| abstract_inverted_index.to | 78, 107, 121, 137, 147, 210, 240 |
| abstract_inverted_index.(1) | 93 |
| abstract_inverted_index.(2) | 102 |
| abstract_inverted_index.(3) | 111 |
| abstract_inverted_index.(4) | 128 |
| abstract_inverted_index.New | 55 |
| abstract_inverted_index.The | 165, 179, 234 |
| abstract_inverted_index.and | 10, 19, 48, 73, 82, 161, 185, 195, 229, 244, 254 |
| abstract_inverted_index.are | 45 |
| abstract_inverted_index.but | 41 |
| abstract_inverted_index.can | 34 |
| abstract_inverted_index.for | 7, 52, 64, 125 |
| abstract_inverted_index.new | 123 |
| abstract_inverted_index.the | 59, 90, 115, 130, 242 |
| abstract_inverted_index.via | 97 |
| abstract_inverted_index.AUC. | 164, 196 |
| abstract_inverted_index.Unit | 190 |
| abstract_inverted_index.both | 80 |
| abstract_inverted_index.data | 113 |
| abstract_inverted_index.deep | 105, 226 |
| abstract_inverted_index.fMRI | 43 |
| abstract_inverted_index.gate | 132 |
| abstract_inverted_index.only | 212 |
| abstract_inverted_index.rely | 15 |
| abstract_inverted_index.unit | 134 |
| abstract_inverted_index.with | 31, 141, 158, 182, 218 |
| abstract_inverted_index.(CNN) | 72 |
| abstract_inverted_index.(RNN) | 77 |
| abstract_inverted_index.AFGRU | 180 |
| abstract_inverted_index.Early | 1 |
| abstract_inverted_index.Gated | 188 |
| abstract_inverted_index.SMOTE | 197 |
| abstract_inverted_index.brain | 86 |
| abstract_inverted_index.fMRI. | 256 |
| abstract_inverted_index.model | 149 |
| abstract_inverted_index.often | 46 |
| abstract_inverted_index.other | 199 |
| abstract_inverted_index.small | 47, 255 |
| abstract_inverted_index.using | 38, 114, 211 |
| abstract_inverted_index.(GRU), | 191 |
| abstract_inverted_index.(ICA). | 101 |
| abstract_inverted_index.(fMRI) | 30 |
| abstract_inverted_index.82.38% | 159 |
| abstract_inverted_index.90.72% | 163 |
| abstract_inverted_index.Employ | 129 |
| abstract_inverted_index.STANet | 88, 152, 221, 238 |
| abstract_inverted_index.better | 207 |
| abstract_inverted_index.deeper | 174 |
| abstract_inverted_index.higher | 193 |
| abstract_inverted_index.module | 169 |
| abstract_inverted_index.neural | 70, 75 |
| abstract_inverted_index.posing | 50 |
| abstract_inverted_index.steps: | 92 |
| abstract_inverted_index.weight | 144 |
| abstract_inverted_index.(AFGRU) | 135 |
| abstract_inverted_index.(SMOTE) | 120 |
| abstract_inverted_index.Balance | 112 |
| abstract_inverted_index.Feature | 167 |
| abstract_inverted_index.Method: | 56 |
| abstract_inverted_index.Network | 62 |
| abstract_inverted_index.Utilize | 103 |
| abstract_inverted_index.achieve | 206 |
| abstract_inverted_index.attains | 192 |
| abstract_inverted_index.capture | 79, 108, 138 |
| abstract_inverted_index.crucial | 6 |
| abstract_inverted_index.enhance | 35, 148 |
| abstract_inverted_index.imaging | 29 |
| abstract_inverted_index.lacking | 22 |
| abstract_inverted_index.methods | 14 |
| abstract_inverted_index.models. | 54 |
| abstract_inverted_index.propose | 58 |
| abstract_inverted_index.samples | 124 |
| abstract_inverted_index.scales. | 178 |
| abstract_inverted_index.spatial | 83, 215 |
| abstract_inverted_index.stacked | 187 |
| abstract_inverted_index.suicide | 11 |
| abstract_inverted_index.weights | 184 |
| abstract_inverted_index.(STANet) | 63 |
| abstract_inverted_index.Results: | 151 |
| abstract_inverted_index.accuracy | 160, 194 |
| abstract_inverted_index.achieves | 153 |
| abstract_inverted_index.adaptive | 143, 183 |
| abstract_inverted_index.analysis | 100 |
| abstract_inverted_index.classes. | 127 |
| abstract_inverted_index.clinical | 20, 250 |
| abstract_inverted_index.compared | 209 |
| abstract_inverted_index.datasets | 44 |
| abstract_inverted_index.detailed | 109 |
| abstract_inverted_index.enhances | 170 |
| abstract_inverted_index.existing | 219 |
| abstract_inverted_index.features | 84, 175, 205 |
| abstract_inverted_index.generate | 122 |
| abstract_inverted_index.learning | 227 |
| abstract_inverted_index.magnetic | 27 |
| abstract_inverted_index.methods. | 201 |
| abstract_inverted_index.methods: | 220 |
| abstract_inverted_index.minority | 117, 126 |
| abstract_inverted_index.multiple | 177 |
| abstract_inverted_index.networks | 71, 76 |
| abstract_inverted_index.settings | 251 |
| abstract_inverted_index.superior | 154 |
| abstract_inverted_index.temporal | 81, 213 |
| abstract_inverted_index.Aggregate | 94 |
| abstract_inverted_index.Combining | 25 |
| abstract_inverted_index.Recurrent | 189 |
| abstract_inverted_index.activity. | 87 |
| abstract_inverted_index.capturing | 173 |
| abstract_inverted_index.component | 99 |
| abstract_inverted_index.comprises | 89 |
| abstract_inverted_index.diagnosis | 2, 37, 243 |
| abstract_inverted_index.effective | 8 |
| abstract_inverted_index.enhancing | 241 |
| abstract_inverted_index.features. | 110, 216 |
| abstract_inverted_index.following | 91 |
| abstract_inverted_index.long-term | 139 |
| abstract_inverted_index.mechanism | 146 |
| abstract_inverted_index.objective | 23 |
| abstract_inverted_index.recurrent | 74, 133 |
| abstract_inverted_index.resonance | 28 |
| abstract_inverted_index.synthetic | 116 |
| abstract_inverted_index.technique | 119 |
| abstract_inverted_index.treatment | 9, 245 |
| abstract_inverted_index.Comparison | 217 |
| abstract_inverted_index.aggregated | 204 |
| abstract_inverted_index.artificial | 32 |
| abstract_inverted_index.assessment | 246 |
| abstract_inverted_index.assignment | 145 |
| abstract_inverted_index.challenges | 51 |
| abstract_inverted_index.classifier | 136 |
| abstract_inverted_index.depression | 4, 36, 66, 155, 248 |
| abstract_inverted_index.diagnosing | 65 |
| abstract_inverted_index.diagnostic | 156 |
| abstract_inverted_index.functional | 26, 230 |
| abstract_inverted_index.imbalanced | 253 |
| abstract_inverted_index.successful | 235 |
| abstract_inverted_index.Aggregation | 61, 168 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Traditional | 13 |
| abstract_inverted_index.biomarkers. | 24 |
| abstract_inverted_index.classifier, | 181 |
| abstract_inverted_index.contributes | 239 |
| abstract_inverted_index.convolution | 106 |
| abstract_inverted_index.imbalanced, | 49 |
| abstract_inverted_index.independent | 98 |
| abstract_inverted_index.indicators, | 40 |
| abstract_inverted_index.information | 96 |
| abstract_inverted_index.integrating | 68 |
| abstract_inverted_index.multi-scale | 104 |
| abstract_inverted_index.outperforms | 198, 223 |
| abstract_inverted_index.performance | 208, 236 |
| abstract_inverted_index.prevention. | 12 |
| abstract_inverted_index.self-report | 17 |
| abstract_inverted_index.traditional | 224 |
| abstract_inverted_index.Conclusions: | 233 |
| abstract_inverted_index.assessments, | 21 |
| abstract_inverted_index.classifiers, | 225, 228 |
| abstract_inverted_index.classifiers. | 232 |
| abstract_inverted_index.intelligence | 33 |
| abstract_inverted_index.neuroimaging | 39 |
| abstract_inverted_index.oversampling | 200 |
| abstract_inverted_index.performance, | 157 |
| abstract_inverted_index.Additionally, | 202 |
| abstract_inverted_index.convolutional | 69 |
| abstract_inverted_index.dependencies, | 140 |
| abstract_inverted_index.over-sampling | 118 |
| abstract_inverted_index.significantly | 222 |
| abstract_inverted_index.classification | 53, 171 |
| abstract_inverted_index.questionnaires | 18 |
| abstract_inverted_index.Spatio-Temporal | 60, 166 |
| abstract_inverted_index.generalization. | 150 |
| abstract_inverted_index.spatio-temporal | 95, 203 |
| abstract_inverted_index.attention-Fourier | 131 |
| abstract_inverted_index.connectivity-based | 231 |
| abstract_inverted_index.depression-specific | 42 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5047632784, https://openalex.org/A5102819746 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I14243506, https://openalex.org/I96733725 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.800000011920929 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.76100763 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |