FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2883373048> ?p ?o ?g. }

• W2883373048 endingPage "747" @default.
• W2883373048 startingPage "734" @default.
• W2883373048 abstract "This work presents a novel approach to finding linkage/association between multimodal brain imaging data, such as structural MRI (sMRI) and functional MRI (fMRI). Motivated by the machine translation domain, we employ a deep learning model, and consider two different imaging views of the same brain like two different languages conveying some common facts. That analogy enables finding linkages between two modalities. The proposed translation-based fusion model contains a computing layer that learns alignments (or links) between dynamic connectivity features from fMRI data and static gray matter patterns from sMRI data. The approach is evaluated on a multi-site dataset consisting of eyes-closed resting state imaging data collected from 298 subjects (age- and gender matched 154 healthy controls and 144 patients with schizophrenia). Results are further confirmed on an independent dataset consisting of eyes-open resting state imaging data from 189 subjects (age- and gender matched 91 healthy controls and 98 patients with schizophrenia). We used dynamic functional connectivity (dFNC) states as the functional features and ICA-based sources from gray matter densities as the structural features. The dFNC states characterized by weakly correlated intrinsic connectivity networks (ICNs) were found to have stronger association with putamen and insular gray matter pattern, while the dFNC states of profuse strongly correlated ICNs exhibited stronger links with the gray matter pattern in precuneus, posterior cingulate cortex (PCC), and temporal cortex. Further investigation with the estimated link strength (or alignment score) showed significant group differences between healthy controls and patients with schizophrenia in several key regions including temporal lobe, and linked these to connectivity states showing less occupancy in healthy controls. Moreover, this novel approach revealed significant correlation between a cognitive score (attention/vigilance) and the function/structure alignment score that was not detected when data modalities were considered separately." @default.
• W2883373048 created "2018-08-03" @default.
• W2883373048 creator A5006880704 @default.
• W2883373048 creator A5011715160 @default.
• W2883373048 creator A5013958031 @default.
• W2883373048 creator A5017435698 @default.
• W2883373048 creator A5030435608 @default.
• W2883373048 creator A5032850756 @default.
• W2883373048 creator A5057107680 @default.
• W2883373048 creator A5070489711 @default.
• W2883373048 creator A5074900697 @default.
• W2883373048 creator A5082230429 @default.
• W2883373048 date "2018-11-01" @default.
• W2883373048 modified "2023-10-15" @default.
• W2883373048 title "Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia" @default.
• W2883373048 cites W1165417979 @default.
• W2883373048 cites W1872166422 @default.
• W2883373048 cites W1943480391 @default.
• W2883373048 cites W1967621336 @default.
• W2883373048 cites W1968248619 @default.
• W2883373048 cites W1972498024 @default.
• W2883373048 cites W197326298 @default.
• W2883373048 cites W1974660211 @default.
• W2883373048 cites W1974673999 @default.
• W2883373048 cites W1975939672 @default.
• W2883373048 cites W1982461150 @default.
• W2883373048 cites W1985327120 @default.
• W2883373048 cites W1987946595 @default.
• W2883373048 cites W1991138506 @default.
• W2883373048 cites W1995362640 @default.
• W2883373048 cites W1996781291 @default.
• W2883373048 cites W1998399571 @default.
• W2883373048 cites W2004695100 @default.
• W2883373048 cites W2007309426 @default.
• W2883373048 cites W2019725905 @default.
• W2883373048 cites W2030747477 @default.
• W2883373048 cites W2035887588 @default.
• W2883373048 cites W2040709265 @default.
• W2883373048 cites W2047225915 @default.
• W2883373048 cites W2058187841 @default.
• W2883373048 cites W2062121811 @default.
• W2883373048 cites W2064598115 @default.
• W2883373048 cites W2065131965 @default.
• W2883373048 cites W2066554258 @default.
• W2883373048 cites W2076503306 @default.
• W2883373048 cites W2080748152 @default.
• W2883373048 cites W2090004063 @default.
• W2883373048 cites W2093551237 @default.
• W2883373048 cites W2108406339 @default.
• W2883373048 cites W2111783629 @default.
• W2883373048 cites W2119720840 @default.
• W2883373048 cites W2126159475 @default.
• W2883373048 cites W2128588517 @default.
• W2883373048 cites W2130970445 @default.
• W2883373048 cites W2132555912 @default.
• W2883373048 cites W2136922672 @default.
• W2883373048 cites W2140343355 @default.
• W2883373048 cites W2141279969 @default.
• W2883373048 cites W2159644148 @default.
• W2883373048 cites W2166793287 @default.
• W2883373048 cites W2336687820 @default.
• W2883373048 cites W2343857670 @default.
• W2883373048 cites W2498492890 @default.
• W2883373048 cites W2508748554 @default.
• W2883373048 cites W2537885134 @default.
• W2883373048 cites W2561231623 @default.
• W2883373048 cites W2569531558 @default.
• W2883373048 cites W2582180708 @default.
• W2883373048 cites W2747936636 @default.
• W2883373048 cites W4230920194 @default.
• W2883373048 cites W4231109964 @default.
• W2883373048 doi "https://doi.org/10.1016/j.neuroimage.2018.07.047" @default.
• W2883373048 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6321628" @default.
• W2883373048 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30055372" @default.
• W2883373048 hasPublicationYear "2018" @default.
• W2883373048 type Work @default.
• W2883373048 sameAs 2883373048 @default.
• W2883373048 citedByCount "46" @default.
• W2883373048 countsByYear W28833730482019 @default.
• W2883373048 countsByYear W28833730482020 @default.
• W2883373048 countsByYear W28833730482021 @default.
• W2883373048 countsByYear W28833730482022 @default.
• W2883373048 countsByYear W28833730482023 @default.
• W2883373048 crossrefType "journal-article" @default.
• W2883373048 hasAuthorship W2883373048A5006880704 @default.
• W2883373048 hasAuthorship W2883373048A5011715160 @default.
• W2883373048 hasAuthorship W2883373048A5013958031 @default.
• W2883373048 hasAuthorship W2883373048A5017435698 @default.
• W2883373048 hasAuthorship W2883373048A5030435608 @default.
• W2883373048 hasAuthorship W2883373048A5032850756 @default.
• W2883373048 hasAuthorship W2883373048A5057107680 @default.
• W2883373048 hasAuthorship W2883373048A5070489711 @default.
• W2883373048 hasAuthorship W2883373048A5074900697 @default.
• W2883373048 hasAuthorship W2883373048A5082230429 @default.
• W2883373048 hasBestOaLocation W28833730482 @default.
• W2883373048 hasConcept C118552586 @default.
• W2883373048 hasConcept C141516989 @default.
• W2883373048 hasConcept C153180895 @default.

• next