Measuring macroscopic brain connections in vivo
Open accessAbstract
Decades of detailed anatomical tracer studies in non-human animals point to a rich and complex organization of long-range white matter connections in the brain. State-of-the art in vivo imaging techniques are striving to achieve a similar level of detail in humans, but multiple technical factors can limit their sensitivity and fidelity. In this review, we mostly focus on magnetic resonance imaging of the brain. We highlight some of the key challenges in analyzing and interpreting in vivo connectomics data, particularly in relation to what is known from classical neuroanatomy in laboratory animals. We further illustrate that, despite the challenges, in vivo imaging methods can be very powerful and provide information on connections that is not available by any other means.
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Long-distance connections are more difficult to track, as reconstruction error accumulates with each tracking step.
“Consequently, long-distance connections are more difficult to track, as the reconstruction error accumulates with each tracking step (Zalesky and Fornito 2009; Jbabdi et al. 2015; Reveley et al. 2015; Donahue et al. 2016).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Jbabdi et al. 2015
Section 3.1 says tractography uses local orientation information to infer long-range connections, can take wrong turns, and is affected by the distance a pathway travels. It does not state that reconstruction error accumulates with each tracking step, so the claimed mechanism is not established by this source. Page numbers are unavailable in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThere was a relatively high rate of false negative reconstructions for connected cortical areas located at large distance from each other, especially for global tractography.
“In agreement with previous studies (Zalesky and Fornito 2009; Jbabdi et al. 2015), we found a relatively high rate of false negative reconstructions for connected cortical areas located at large distance from each other, especially for global tractography, which may consequently lead to omissions in identification of important inter-modular hub-node connections.”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Jbabdi et al. 2015
Sections 2 and 3.3 say distant cortical connections tend to be weaker and that sparse long-distance pathways are likely to be difficult to identify with tractography. Section 3.1 also acknowledges false-negative connections, but it does not report a relatively high false-negative rate for distant cortical areas or say the problem is especially pronounced with global tractography. No page numbers are provided in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredNeural tracer data is the gold standard for mesoscale connectivity studies in a number of model organisms.
“Neural tracer data is the gold standard for mesoscale connectivity studies in a number of model organisms (Bota et al., 2015; Girard et al., 2020; Jbabdi et al., 2015; Markov et al., 2014; 2013; Oh et al., 2014).”
From The role of spatial embedding in mouse brain networks constructed from diffusion tractography and tracer injections (2021) · cites this paper as Jbabdi et al., 2015
Some underlying ambiguities in tractography have been discussed using schematic representations and theoretical arguments.
“some of the underlying ambiguities in tractography have been discussed using schematic representations and theoretical arguments1, 7, 8, 37.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [1]
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References 118 · Cited by 334
- Authors
- Jbabdi, S., Sotiropoulos, S. N., Haber, S. N., Van Essen, D. C., & Behrens, T. E.
- Affiliations
- University of Oxford (GB)
- University of Rochester Medicine (US)
- Wellcome Centre for Human Neuroimaging (GB)
- Venue
- Nature Neuroscience
- Published
- 2015-10-27
- Volume
- 18
- Issue
- 11
- Pages
- 1546-1555
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2196267284
- Cited by
- 334
- References
- 118
- Citations by year
- 2026: 8 · 2025: 29 · 2024: 18 · 2023: 16 · 2022: 24 · 2021: 32 · 2020: 36 · 2019: 45 · 2018: 54 · 2017: 40 · 2016: 31 · 2015: 1
- FWCI
- 6.68
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Repository@Nottingham (University of Nottingham) (acceptedVersion, other-oa)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Nottingham ePrints (University of Nottingham) (submittedVersion)
- Funders
- Wellcome Trust (091593, 104765/Z/14/Z, 098369, 104765, WT104765MA)
- National Institutes of Health (5u54mh091657-05)
- Medical Research Council (MR/L009013/1)
- Engineering and Physical Sciences Research Council (EP/L023067/1)
- National Institute of Mental Health (R01 MH60974, R01MH060974, 1U54-MH091657-01, P50MH106435)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- connectomics, white matter connections, human-animal comparison, neuroanatomy, in vivo MRI
- MeSH terms
- Animals, Brain, Brain Mapping, Humans, Nerve Net, Neural Pathways, Neuroanatomy, Connectome
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