2022
DOI: 10.1016/j.neuroimage.2022.119063
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Higher-order multi-shell diffusion measures complement tensor metrics and volume in gray matter when predicting age and cognition

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Cited by 17 publications
(16 citation statements)
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“…This suggests that including both structural and diffusion MRI measures increased our sensitivity to complementary underlying structural alterations. While the complexities of DWI underpinnings in grey matter remain as discussed above, our findings support a growing body of literature which demonstrates the utility of cortical DWI measurements to capture age- and cognitive-related variance ( Callow et al, 2021 ; Grydeland et al, 2013 ; Philippi et al, 2016 ; Radhakrishnan et al, 2022 ; Raz & Rodrigue, 2006 ; Reas et al, 2018 ; Rodriguez-Vieitez et al, 2021 ; Schneider et al, 2019 ). We also note that across all components identified as contributing to brain-cognition relationships, DWI metrics displayed a complementary nature.…”
Section: Discussionsupporting
confidence: 82%
“…This suggests that including both structural and diffusion MRI measures increased our sensitivity to complementary underlying structural alterations. While the complexities of DWI underpinnings in grey matter remain as discussed above, our findings support a growing body of literature which demonstrates the utility of cortical DWI measurements to capture age- and cognitive-related variance ( Callow et al, 2021 ; Grydeland et al, 2013 ; Philippi et al, 2016 ; Radhakrishnan et al, 2022 ; Raz & Rodrigue, 2006 ; Reas et al, 2018 ; Rodriguez-Vieitez et al, 2021 ; Schneider et al, 2019 ). We also note that across all components identified as contributing to brain-cognition relationships, DWI metrics displayed a complementary nature.…”
Section: Discussionsupporting
confidence: 82%
“…The copyright holder for this preprint (which this version posted July 13, 2022. ; https://doi.org/10.1101/2022.07.13.499977 doi: bioRxiv preprint hippocampal diffusion and volume to episodic memory performance (den Heijer et al, 2012), but not with other studies whose approaches have focused on the additive (Radhakrishnan et al, 2022) or shared (Köhncke et al, 2021) variance across these structural MRI measures. Similar results have also been observed in multimodal MRI studies that examined the contribution of volume in hippocampal gray matter and diffusion in white matter emanating from the hippocampus (e.g., fornix) to memory performance in aging (Foster et al, 2019;Gorbach et al, 2017;Hayek et al, 2020), with one study in adults across the lifespan finding that the effect of age on associative memory was mediated by fornix diffusion, but not volume of medial temporal structures that included the hippocampus (Foster et al, 2019).…”
Section: Discussionmentioning
confidence: 97%
“…At least one previous study that measured both volume and diffusion in hippocampal gray matter found that they make independent contributions, with separate regression models for each modality revealing that hippocampal diffusion, but not hippocampal volume, predicted episodic memory performance within older adults (den Heijer et al, 2012). However, other multimodal MRI studies provide evidence of joint contributions, with episodic memory performance across younger and older adults being better predicted when hippocampal volume and diffusion were both in the same model (Radhakrishnan et al, 2022) and episodic memory in older adults relating to a latent construct that captured shared variance among structural imaging modalities, including T1- and diffusion-weighted metrics, in the hippocampus (Köhncke et al, 2021). Thus, it remains unclear whether hippocampal volume and diffusion jointly contribute to age-related episodic memory deficits, particularly when their unique contributions are not also reported.…”
Section: Introductionmentioning
confidence: 99%
“…Importantly, our pipeline only uses diffusion metrics as derived from tensor analysis and the NODDI model. We picked these methods because the tensor is still one the simplest and most popular models used for diffusion analysis; and the NODDI model leverages more complex multi-shell sequences and has previously been demonstrated to detect microstructural variance that complements the tensor metrics (Radhakrishnan et al, 2022). There are a plethora of other analysis techniques and models made possible by diffusion imaging that might be equally, if not better, suited to study gray matter cytoarchitecture non-invasively.…”
Section: Discussionmentioning
confidence: 99%