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Functional MRI (fMRI) using the blood-oxygen level dependent (BOLD) signal provides valuable insight into grey matter activity. However, uncertainty surrounds the white matter BOLD signal. Apparent diffusion coefficient (ADC) offers an alternative fMRI contrast sensitive to transient cellular deformations during neural activity, facilitating detection of both grey and white matter activity. Further, through minimising vascular contamination, ADC-fMRI has the potential to overcome the limited temporal specificity of the BOLD signal. However, the use of linear diffusion encoding introduces sensitivity to fibre directionality, while averaging over multiple directions comes at great cost to temporal resolution. In this study, we used spherical b-tensor encoding to impart diffusion sensitisation in all directions per shot, providing an ADC-fMRI contrast capable of detecting activity independently of fibre directionality. We provide evidence from two task-based experiments on a clinical scanner that isotropic ADC-fMRI is more temporally specific than BOLD-fMRI, and offers more balanced mapping of grey and white matter activity. We further demonstrate that isotropic ADC-fMRI detects white matter activity independently of fibre direction, while linear ADC-fMRI preferentially detects activity in voxels containing fibres perpendicular to the diffusion encoding direction. Thus, isotropic ADC-fMRI opens avenues for investigation into whole-brain grey and white matter functional connectivity. Detecting neural signals in white matter remains a challenge. Here, the authors introduce an isotropic apparent diffusion coefficient fMRI contrast based on neuromorphological coupling, which provides sensitivity to neural signals in both grey and white matter in the human brain.

Resting-state functional magnetic resonance imaging (fMRI) is used to derive functional connectivity (FC) between brain regions. Typically, blood oxygen level-dependent (BOLD) contrast is used. However, BOLD’s reliance on neurovascular coupling poses challenges in reflecting brain activity accurately, leading to reduced sensitivity in white matter (WM). WM BOLD signals have long been considered physiological noise, although recent evidence shows that both stimulus-evoked and resting-state WM BOLD signals resemble those in gray matter (GM), albeit smaller in amplitude. We introduce apparent diffusion coefficient fMRI (ADC-fMRI) as a promising functional contrast for GM and WM FC, capturing activity-driven neuromorphological fluctuations. Our study compares BOLD-fMRI and ADC-fMRI FC in GM and WM, showing that ADC-fMRI mirrors BOLD-fMRI connectivity in GM, while capturing more robust FC in WM. ADC-fMRI displays higher average clustering and average node strength in WM, and higher inter-subject similarity, compared to BOLD. Taken together, this suggests that ADC-fMRI is a reliable tool for exploring FC that incorporates gray and white matter nodes in a novel way. de Riedmatten et al. propose apparent diffusion coefficient functional MRI as a novel contrast for assessing functional connectivity in both grey matter and white matter, addressing the limitations of blood oxygen level-dependent fMRI in capturing white matter activity. Their study demonstrates that this novel methods provides a more reliable white matter connectivity measure, with higher inter-subject similarity and reduced sensitivity to vascular noise, making it a promising alternative for investigating whole-brain functional connectivity.

In contrast to blood‐oxygenation level‐dependent (BOLD) functional MRI (fMRI), which relies on changes in blood flow and oxygenation levels to infer brain activity, diffusion fMRI (DfMRI) investigates brain dynamics by monitoring alterations in the apparent diffusion coefficient (ADC) of water. These ADC changes may arise from fluctuations in neuronal morphology, providing a distinctive perspective on neural activity. The potential of ADC as an fMRI contrast (ADC‐fMRI) lies in its capacity to reveal neural activity independently of neurovascular coupling, thus yielding complementary insights into brain function. To demonstrate the specificity and value of ADC‐fMRI, both ADC‐ and BOLD‐fMRI data were collected at 3 T in human subjects during visual stimulation and motor tasks. The first aim of this study was to identify an acquisition design for ADC that minimises BOLD contributions. By examining the timings in responses, we report that ADC 0/1 timeseries (acquired with b values of 0 and 1 ms/μm2 $$ {\\upmu \\mathrm{m}}^2 $$ ) exhibit residual vascular contamination, while ADC 0.2/1 timeseries (with b values of 0.2 and 1 ms/μm2 $$ {\\upmu \\mathrm{m}}^2 $$ ) show minimal BOLD influence and higher sensitivity to neuromorphological coupling. Second, a general linear model was employed to identify activation clusters for ADC 0.2/1 and BOLD, from which the average ADC and BOLD responses were calculated. The negative ADC response exhibited a significantly reduced delay relative to the task onset and offset as compared to BOLD. This early onset further supports the notion that ADC is sensitive to neuromorphological rather than neurovascular coupling. Remarkably, in the group‐level analysis, positive BOLD activation clusters were detected in the visual and motor cortices, while the negative ADC clusters mainly highlighted pathways in white matter connected to the motor cortex. In the averaged individual level analysis, negative ADC activation clusters were also present in the visual cortex. This finding confirmed the reliability of negative ADC as an indicator of brain function, even in regions with lower vascularisation such as white matter. Finally, we established that ADC‐fMRI time courses yield the expected functional organisation of the visual system, including both grey and white matter regions of interest. Functional connectivity matrices were used to perform hierarchical clustering of brain regions, where ADC‐fMRI successfully reproduced the expected structure of the dorsal and ventral visual pathways. This organisation was not replicated with the b = 0.2 ms/μm2 $$ {\\upmu \\mathrm{m}}^2 $$diffusion‐weighted time courses, which can be seen as a proxy for BOLD (via T2‐weighting). These findings underscore the robustness of ADC time courses in functional MRI studies, offering complementary insights into BOLD‐fMRI regarding brain function and connectivity patterns. This article validates ADC‐fMRI as a tool complementing BOLD‐fMRI, detecting neural activity via neuromorphological coupling. In experiments with motor and visual stimuli at 3 T, ADC‐fMRI was shown to be less prone to vascular contamination and to capture white matter activity.

Functional MRI (fMRI) using the blood-oxygen level dependent (BOLD) signal provides valuable insight into grey matter activity. However, uncertainty surrounds the white matter BOLD signal. Apparent diffusion coefficient (ADC) offers an alternative fMRI contrast sensitive to transient cellular deformations during neural activity, facilitating detection of both grey and white matter activity. Further, through minimising vascular contamination, ADC-fMRI has the potential to overcome the limited temporal specificity of the BOLD signal. However, the use of linear diffusion encoding introduces sensitivity to fibre directionality, while averaging over multiple directions comes at great cost to temporal resolution. In this study, we used spherical b-tensor encoding to impart diffusion sensitisation in all directions per shot, providing an ADC-fMRI contrast capable of detecting activity independently of fibre directionality. We provide evidence from two task-based experiments on a clinical scanner that isotropic ADC-fMRI is more temporally specific than BOLD-fMRI, and offers more balanced mapping of grey and white matter activity. We further demonstrate that isotropic ADC-fMRI detects white matter activity independently of fibre direction, while linear ADC-fMRI preferentially detects activity in voxels containing fibres perpendicular to the diffusion encoding direction. Thus, isotropic ADC-fMRI opens avenues for investigation into whole-brain grey and white matter functional connectivity.

Apparent diffusion coefficient (ADC)-fMRI offers a promising functional contrast, capable of map-ping neuronal activity directly in both grey and white matter. However, previous studies have shown that diffusion-weighted fMRI (dfMRI), from which ADC-fMRI derives, is influenced by BOLD effects, leading to a concern that the dfMRI contrast is still rooted in neurovascular rather than neuromor-phological coupling. Mitigation strategies have been proposed to remove vascular contributions while retaining neuromorphological coupling, by: i) analysing ADC timecourses calculated from two inter-leaved diffusion-weightings, known as ADC-fMRI; ii) using b-values of at least 200 s mm-2; and iii) using a sequence compensated for cross-terms with fluctuating background field gradients associated with blood oxygenation. Respiration-induced haemodynamic fluctuations, which are dissociated from neural activity, are an excellent test-bed for the robustness of ADC-fMRI to vascular contributions. In this study, we investigate the association between end-tidal CO2 and ADC-fMRI, in comparison with dfMRI and BOLD, in both breath-hold and resting-state paradigms in the human brain. We confirm a strong dependence of the BOLD signal on respiration, and find a pattern of delayed haemodynamic response to respiration in regions comprising the default mode network. While dfMRI mitigates much of the vascular contribution, it retains some association with respiration, as expected. Conversely, ADC-fMRI is mostly unaffected by vascular contribution, exhibiting minimal correlation between ex-pired CO2 and ADC timeseries, as well as low inter-and intra-subject reproducibility in correlation maps. These findings validate ADC-fMRI as a predominantly non-vascular contrast sensitive to mi-crostructural dynamics, enabling whole-brain functional imaging unconstrained by vascular confounds. Apparent diffusion coefficient (ADC)-fMRI offers a unique neuromorpho-logical contrast, enabling functional imaging of both grey and white matter with greater spatial and tem-poral specificity than BOLD, as demonstrated in task and resting-state studies. Recent methodological advances have isolated ADC-fMRI from confounding haemodynamic influences, increasing specificity to microstructural neuronal dynamics. Here, we provide the first evaluation of these strategies in breath-hold and resting-state conditions, monitoring respiration as a proxy for haemodynamic fluctuations dissoci-ated from neural activity. Our results show minimal association between ADC-fMRI and respiration, confirming that ADC-fMRI is largely immune to haemodynamic fluctuations. It provides crucial vali-dation that ADC-fMRI reflects a non-vascular functional contrast distinct from and complementary to BOLD, supporting its utility in mapping activity across the human brain.

Resting-state functional magnetic resonance imaging (fMRI) is used to derive functional connectivity (FC) between brain regions. Typically, blood oxygen level-dependent (BOLD) contrast is used. However, BOLD's reliance on neurovascular coupling poses challenges in reflecting brain activity accurately, leading to reduced sensitivity in white matter (WM). WM BOLD signals have long been considered physiological noise, although recent evidence shows that both stimulus-evoked and resting-state WM BOLD signals resemble those in gray matter (GM), albeit smaller in amplitude. We introduce apparent diffusion coefficient fMRI (ADC-fMRI) as a promising functional contrast for GM and WM FC, capturing activity-driven neuromorphological fluctuations. Our study compares BOLD-fMRI and ADC-fMRI FC in GM and WM, showing that ADC-fMRI mirrors BOLD-fMRI connectivity in GM, while capturing more robust FC in WM. ADC-fMRI displays higher average clustering and average node strength in WM, and higher inter-subject similarity, compared to BOLD. Taken together, this suggests that ADC-fMRI is a reliable tool for exploring FC that incorporates gray and white matter nodes in a novel way.Competing Interest StatementThe authors have declared no competing interest.Footnotes* We have added a figure to clarify the methods (Figure 1), and a figure to visualize the functional connectivity of ADC-fMRI and BOLD-fMRI (Figure 2). We have updated the discussion with the effect of the SNR (Supplementary Table 1) and the effect of the higher sampling rate of BOLD-fMRI (Supplementary Figures 6-10) on the results.

In contrast to blood-oxygenation-level-dependent (BOLD) functional MRI (fMRI), which relies on changes in blood flow and oxygenation levels to infer brain activity, diffusion fMRI (DfMRI) investigates brain dynamics by monitoring alterations in the Apparent Diffusion Coefficient (ADC) of water. These ADC changes may arise from fluctuations in neuronal morphology, providing a distinctive perspective on neural activity. The potential of ADC as an fMRI contrast (ADC-fMRI) lies in its capacity to reveal neural activity independently of neurovascular coupling, thus yielding complementary insights into brain function. To demonstrate the specificity and value of ADC-fMRI, both ADC-and BOLD-fMRI data were collected at 3T in human subjects during visual stimulation and motor tasks. The first aim of this study was to identify an acquisition design for ADC that minimises BOLD contributions. By examining the timings in responses, we report that ADC 0/1 timeseries (acquired with b-values of 0 and 1 ms/µm2) exhibit residual vascular contamination while ADC 0.2/1 timeseries (with b-values of 0.2 and 1 ms/µm2) show minimal BOLD influence and higher sensitivity to neuromorphological coupling. Second, a General Linear Model was employed to identify activation clusters for ADC 0.2/1 and BOLD, from which average ADC and BOLD responses were calculated. The negative ADC response exhibited a significantly reduced delay relative to the task onset and offset as compared to BOLD. This early onset further supports the notion that ADC is sensitive to neuromorphological rather than neurovascular coupling. Remarkably, in the group-level analysis, positive BOLD activation clusters were detected in the visual and motor cortices, while the negative ADC clusters mainly highlighted pathways in white matter connected to the motor cortex. In the averaged individual level analysis, negative ADC activation clusters were also present in the visual cortex. This finding confirmed the reliability of negative ADC as an indicator of brain function, even in regions with lower vascularisation such as white matter. Finally, we established that ADC-fMRI timecourses yield the expected functional organisation of the visual system, including both gray and white matter regions of interest. Functional connectivity matrices were used to perform hierarchical clustering of brain regions, where ADC-fMRI successfully reproduced the expected structure of the dorsal and ventral visual pathways. This organisation was not replicated with the b=0.2 ms/µm2 diffusion-weighted time courses, which can be seen as a proxy for BOLD (via T2-weighting). These findings underscore the robustness of ADC time courses in functional MRI studies, offering complementary insights to BOLD-fMRI regarding brain function and connectivity patterns. The functional time course of the Apparent Diffusion Coefficient (ADC), specifically measured with alternating b-values of 0.2 and 1 ms/µm2 at 3T, appears to be minimally affected by BOLD contamination. In the activity maps, the location of negative ADC clusters suggests neural activity in WM tracts that are connected to the motor cortex, which is not detected with positive BOLD. Functional Connectivity analysis utilising ADC is better able to detect the organisation of the dorsal and ventral visual streams than diffusion- and T2-weighted time courses.