1
|
Liu Z, Rolls ET, Liu Z, Zhang K, Yang M, Du J, Gong W, Cheng W, Dai F, Wang H, Ugurbil K, Zhang J, Feng J. Brain annotation toolbox: exploring the functional and genetic associations of neuroimaging results. Bioinformatics 2020; 35:3771-3778. [PMID: 30854545 DOI: 10.1093/bioinformatics/btz128] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 01/25/2019] [Accepted: 02/20/2019] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and identify the roots of psychiatric diseases. However, the results from most neuroimaging studies, i.e. activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear. RESULTS We describe a brain annotation toolbox that generates functional and genetic annotations for neuroimaging results. The voxel-level functional description from the Neurosynth database and gene expression profile from the Allen Human Brain Atlas are used to generate functional/genetic information for region-level neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and genetic similarity network are highly correlated for major brain atlases. One application of brain annotation toolbox is to help provide functional/genetic annotations for newly discovered regions with unknown functions, e.g. the 97 new regions identified in the Human Connectome Project. Importantly, this toolbox can help understand differences between psychiatric patients and controls, and this is demonstrated using schizophrenia and autism data, for which the functional and genetic annotations for the neuroimaging changes in patients are consistent with each other and help interpret the results. AVAILABILITY AND IMPLEMENTATION BAT is implemented as a free and open-source MATLAB toolbox and is publicly available at http://123.56.224.61:1313/post/bat. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Zhaowen Liu
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Edmund T Rolls
- Department of Computer Science, University of Warwick, Coventry, UK.,Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Zhi Liu
- The School of Information Science and Engineering, Shandong University, Jinan, China
| | - Kai Zhang
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Ming Yang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Jingnan Du
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Weikang Gong
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Fei Dai
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Jie Zhang
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.,Ministry of Education, Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK.,Ministry of Education, Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Shanghai, China.,Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China.,Shanghai Center for Mathematical Sciences, Shanghai, China
| |
Collapse
|
2
|
Lindegaard MR, Bernasco W. Lessons Learned from Crime Caught on Camera. THE JOURNAL OF RESEARCH IN CRIME AND DELINQUENCY 2018; 55:155-186. [PMID: 29472728 PMCID: PMC5808820 DOI: 10.1177/0022427817727830] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
OBJECTIVES The widespread use of camera surveillance in public places offers criminologists the opportunity to systematically and unobtrusively observe crime, their main subject matter. The purpose of this essay is to inform the reader of current developments in research on crimes caught on camera. METHODS We address the importance of direct observation of behavior and review criminological studies that used observational methods, with and without cameras, including the ones published in this issue. We also discuss the uses of camera recordings in other social sciences and in biology. RESULTS We formulate six key insights that emerge from the literature and make recommendations for future research. CONCLUSIONS Camera recordings of real-life crime are likely to become part of the criminological tool kit that will help us better understand the situational and interactional elements of crime. Like any source, it has limitations that are best addressed by triangulation with other sources.
Collapse
Affiliation(s)
- Marie Rosenkrantz Lindegaard
- Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Amsterdam, the Netherlands
- Department of Sociology, University of Copenhagen, Copenhagen, Denmark
| | - Wim Bernasco
- Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Amsterdam, the Netherlands
- Department of Spatial Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| |
Collapse
|
3
|
Abstract
OBJECTIVE Outline effects of functional neuroimaging on neuropsychology over the past 25 years. METHOD Functional neuroimaging methods and studies will be described that provide a historical context, offer examples of the utility of neuroimaging in specific domains, and discuss the limitations and future directions of neuroimaging in neuropsychology. RESULTS Tracking the history of publications on functional neuroimaging related to neuropsychology indicates early involvement of neuropsychologists in the development of these methodologies. Initial progress in neuropsychological application of functional neuroimaging has been hampered by costs and the exposure to ionizing radiation. With rapid evolution of functional methods-in particular functional MRI (fMRI)-neuroimaging has profoundly transformed our knowledge of the brain. Its current applications span the spectrum of normative development to clinical applications. The field is moving toward applying sophisticated statistical approaches that will help elucidate distinct neural activation networks associated with specific behavioral domains. The impact of functional neuroimaging on clinical neuropsychology is more circumscribed, but the prospects remain enticing. CONCLUSIONS The theoretical insights and empirical findings of functional neuroimaging have been led by many neuropsychologists and have transformed the field of behavioral neuroscience. Thus far they have had limited effects on the clinical practices of neuropsychologists. Perhaps it is time to add training in functional neuroimaging to the clinical neuropsychologist's toolkit and from there to the clinic or bedside. (PsycINFO Database Record
Collapse
Affiliation(s)
- David R. Roalf
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine Philadelphia, Philadelphia, PA, 19104
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine Philadelphia, Philadelphia, PA, 19104
- Lifespan Brain Institute (LiBI) at the University of Pennsylvania and Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| |
Collapse
|