Improving Functional MRI Registration Using Whole-Brain Functional Correlation Tensors

Zhou, Yujia; Yap, Pew Thian; Zhang, Han; Zhang, Lichi; Feng, Qianjin; Shen, Dinggang

doi:10.1007/978-3-319-66182-7_48

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Code availability Patch-based Functional Correlation Tensor code can be retrieved from: https:// github. com/ zyjsh mily/ ts-PFCTs (Zhou et al 2017).…”

Section: Supplementary Informationmentioning

confidence: 99%

Imaging functional neuroplasticity in human white matter tracts

et al. 2021

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Magnetic resonance imaging (MRI) studies are sensitive to biological mechanisms of neuroplasticity in white matter (WM). In particular, diffusion tensor imaging (DTI) has been used to investigate structural changes. Historically, functional MRI (fMRI) neuroplasticity studies have been restricted to gray matter, as fMRI studies have only recently expanded to WM. The current study evaluated WM neuroplasticity pre–post motor training in healthy adults, focusing on motor learning in the non-dominant hand. Neuroplasticity changes were evaluated in two established WM regions-of-interest: the internal capsule and the corpus callosum. Behavioral improvements following training were greater for the non-dominant hand, which corresponded with MRI-based neuroplasticity changes in the internal capsule for DTI fractional anisotropy, fMRI hemodynamic response functions, and low-frequency oscillations (LFOs). In the corpus callosum, MRI-based neuroplasticity changes were detected in LFOs, DTI, and functional correlation tensors (FCT). Taken together, the LFO results converged as significant amplitude reductions, implicating a common underlying mechanism of optimized transmission through altered myelination. The structural and functional neuroplasticity findings open new avenues for direct WM investigations into mapping connectomes and advancing MRI clinical applications.

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“…Code availability Patch-based Functional Correlation Tensor code can be retrieved from: https:// github. com/ zyjsh mily/ ts-PFCTs (Zhou et al 2017).…”

Section: Supplementary Informationmentioning

confidence: 99%

Imaging functional neuroplasticity in human white matter tracts

et al. 2021

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“…Some authors have claimed that task-based methods rely on the implausible assumption that different people's brains are performing the same functions at precisely the same times (Jiang et al [2013]). They argue that we ought instead to use resting-state fMRI to build a functional connectivity profile for each subject and then find transformations that bring the individual connectivity patterns into alignment (Jiang et al [2013]; Zhou et al [2017]; Nenning et al [2017]; Chen et al [2017]). I am somewhat skeptical of these methods, partly because I share others' doubts about what resting-state fMRI really tells us (Buckner et al [2013]; McCaffrey and Danks [forthcoming]), and partly because functional connectivity-based methods are meant to be generalpurpose.…”

Section: Functional Registrationmentioning

confidence: 99%

Registration Pluralism and the Cartographic Approach to Data Aggregation across Brains

Ward

2022

The British Journal for the Philosophy of Science

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Abstract Neuroscience has become increasingly reliant on multi-subject research in addition to studies of unusual single patients. This research has brought with it a challenge: how are data from different human brains to be combined? The dominant strategy for aggregating data across brains is what I call the ‘cartographic approach’, which involves mapping data from individuals to a spatial template. Here I characterize the cartographic approach and argue that one of its key steps, registration, should be carried out in a way that is sensitive to the target of investigation. Because registration aims to align homologous brain locations, but not all homologous locations can be simultaneously aligned, a multiplicity of registration methods is required to meet the needs of researchers investigating different phenomena. I call this position ‘registration pluralism’. Registration pluralism has potential implications for neuroscientific practice, three of which I discuss here. This work shows the importance of reflecting more carefully on data aggregation methods, especially in light of the substantial individual differences that exist between brains. 1Introduction: Data Aggregation in Neuroscience2The Contemporary Cartographic Approach to Aggregation across Brains3Registration Pluralism4Homology and the Goal of Registration5Organizational Variation and Failures of Simultaneous Alignment6The Scope of Registration Pluralism7Potential Methodological Implications of Registration Pluralism7.1Purpose-sensitive selection of registration methods7.2Functional registration7.3Standardized preprocessing pipelines8Registration Pluralism and the Study of the Brain9Conclusion

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“…Currently, no gold standard for evaluating the fMRI registration performance exists. Most previous fMRI registration methods (Khullar et al 2011, Jiang et al 2013, Zhou et al 2017, 2018 use the group-level statistical maps of resting-state brain functional networks for validation. The evaluation criterion is to maximize the correlation in resting-state brain functional networks across the testing dataset.…”

Section: Evaluation Criteriamentioning

confidence: 99%

Identity-mapping cascaded network for fMRI registration

Zhu¹,

Bai²,

Wu³

et al. 2021

Phys. Med. Biol.

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Neuroscience researches based on functional magnetic resonance imaging (fMRI) rely on accurate inter-subject image registration of functional regions. The intersubject alignment of fMRI can improve the statistical power of group analyses. Recent studies have shown the deep learning-based registration methods can be used for registration. In our work, we proposed a 30-Identity-Mapping Cascaded network (30-IMCNet) for rs-fMRI registration. It is a cascaded network that can warp the moving image progressively and finally align to the fixed image. A Combination unit with an identity-mapping path is added to the inputs of each IMCNet to guide the network training. We implemented 30-IMCNet on an rs-fMRI dataset (1000 Functional Connectomes Project dataset) and a task-related fMRI dataset (Eyes Open Eyes Closed fMRI dataset). To evaluate our method, a group-level analysis was implemented in the testing dataset. For rs-fMRI, the criterions such as peak t-value of group-level t-maps, cluster-level evaluation, and intersubject functional network correlation were used to evaluate the quality of the registrations. For task-related fMRI, peak t-value in ALFF paired-t map and peak t-value in ReHo paired-t maps were used. Compared with traditional algorithm FSL, SPM, and deep learning algorithm Kim et al, Zhao et al our method has improvements of 48.90%, 30.73%, 36.38%, and 16.73% in the peak t value of t-maps. Our proposed method can achieve superior functional registration performance and thus gain a significant improvement in functional consistency.

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Improving Functional MRI Registration Using Whole-Brain Functional Correlation Tensors

Cited by 3 publications

References 12 publications

Imaging functional neuroplasticity in human white matter tracts

Imaging functional neuroplasticity in human white matter tracts

Registration Pluralism and the Cartographic Approach to Data Aggregation across Brains

Identity-mapping cascaded network for fMRI registration

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