| Title |
Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders
|
|---|---|
| Published in |
Brain Informatics, August 2015
|
| DOI | 10.1007/s40708-015-0019-x |
| Pubmed ID | |
| Authors |
Sidong Liu, Weidong Cai, Siqi Liu, Fan Zhang, Michael Fulham, Dagan Feng, Sonia Pujol, Ron Kikinis |
| Abstract |
Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research. |
Mendeley readers
The data shown below were compiled from readership statistics for 204 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | <1% |
| Cuba | 1 | <1% |
| Australia | 1 | <1% |
| United Kingdom | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 199 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 45 | 22% |
| Student > Master | 31 | 15% |
| Researcher | 29 | 14% |
| Student > Bachelor | 17 | 8% |
| Student > Doctoral Student | 9 | 4% |
| Other | 33 | 16% |
| Unknown | 40 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 36 | 18% |
| Engineering | 28 | 14% |
| Psychology | 26 | 13% |
| Computer Science | 21 | 10% |
| Medicine and Dentistry | 11 | 5% |
| Other | 23 | 11% |
| Unknown | 59 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 October 2015.
All research outputs
#5,942,633
of 22,985,065 outputs
Outputs from Brain Informatics
#29
of 103 outputs
Outputs of similar age
#68,549
of 267,202 outputs
Outputs of similar age from Brain Informatics
#1
of 4 outputs
Altmetric has tracked 22,985,065 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 103 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 68% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,202 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them