Combined DTI–fMRI Analysis for a Quantitative Assessment of Connections Between WM Bundles and Their Peripheral Cortical Fields in Verbal Fluency

Abstract

Diffusion tensor imaging (DTI) tractography and functional magnetic resonance imaging (fMRI) are powerful techniques to elucidate the anatomical and functional aspects of brain connectivity. However, integrating these approaches to describe the precise link between structure and function within specific brain circuits remains challenging. In this study, a novel DTI-fMRI integration method is proposed, to provide the topographical characterization and the volumetric assessment of the functional and anatomical connections within the language circuit. In a group of 21 healthy elderly subjects (mean age 68.5 ± 5.8 years), the volume of connection between the cortical activity elicited by a verbal fluency task and the cortico-cortical fiber tracts associated with this function are mapped and quantified. An application of the method to a case study in neuro-rehabilitation context is also presented. Integrating structural and functional data within the same framework, this approach provides an overall view of white and gray matter when studying specific brain circuits.

Cognitive decline in metabolic syndrome is linked to microstructural white matter abnormalities

“…Specifically, although WM structural alterations lead to impaired structural connectivity [9, 52], we have not accounted for this aspect of white matter network pathology. Accounting for functional connectivity could add another layer towards more elaborate characterization of WM network performance [53, 54]. …”

White matter information flow mapping from diffusion MRI and EEG

“…An intuitive approach to quantify information flow is therefore to combine diffusion MRI with functional MRI, EEG, or MEG. Indeed, the joint use of diffusion and functional MRI has received considerable attention (Hutchisson et al, 2013;Zhu et al, 2014;Scaccianoce et al, 2016). For example, Jbabdi et al (2007) propose a global tractography algorithm which can include functional connectivity information.…”

A methodological scoping review of the integration of fMRI to guide dMRI tractography. What has been done and what can be improved: A 20-year perspective

“…In a different manner, 5 articles (6%) performed fiber tracking in a standard space (Figure 7C). Scaccianoce et al, 2016 warped their participants' DTI data in MNI space before performing fiber tracking, while Bonner, Peelle, Cook, & Grossman, 2013 and Brauer, Anwander, & Friederici, 2011 produced a single averaged "DT image" across the participants, then warped this "DT image" in a standardized space for fiber tracking.…”

“…Note that the user must remain vigilant about the risk of resampling errors when carrying ROIs between different MRI spatial resolutions (nearest neighbor interpolation should be used when reslicing images). The sphere method (or extremely similar methods) was performed in the following articles: Blank et al, 2011;Caeyenberghs et al, 2013;Feng et al, 2016;Gomez et al, 2015;Gschwind et al, 2012;Gurtubay-Antolin et al, 2021;Guye et al, 2003;Hamzei et al, 2016;Hartwigsen et al, 2013;Javad et al, 2014;Jeon et al, 2014;Jouen et al, 2015;Klein et al, 2013;Kleiser et al, 2010;Musso et al, 2015;Niu et al, 2016;Oechslin et al, 2017;Péron et al, 2015;Riley et al, 2015;Saur et al, 2010Saur et al, , 2008Scaccianoce et al, 2016;Schonberg et al, 2006;Schott et al, 2011;Shimono et al, 2012;Staempfli et al, 2008;Vry et al, 2015Vry et al, , 2012Wu et al, 2014;Xing et al, 2018. The manual tracing method implies to manually draws the cluster of activation to transform it into a ROI (or volume of interest [VOI]) to seed/filter fiber tracking. It was used in the following four J o u r n a l P r e -p r o o f articles: Lanyon et al, 2009;Lee et al, 2012;andPowell et al, 2007, 2006. Note that this technique is highly similar to the cluster method previously mentioned.…”

“…Four studies performed their fMRI analysis and then integrated their fiber tracking results in a standard space. Preti et al, 2014 andScaccianoce et al, 2016 normalized their participants' whole-brain tractogram in MNI space and then filtered out the track of interest using the group-wise activated brain J o u r n a l P r e -p r o o f regions. Streamlines obtained in the participant's dMRI native space can be warped to a standard template (i.e., MNI template) using software such as Megatrack (Dell'Acqua et al, 2015) or DIPY direct streamlines Normalization (https://dipy.org/) (Avants et al, 2011;Greene et al, 2018).…”