Conference PaperPDF Available

Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image

  • September 2016
  • Conference: ISMRM Workshop on Breaking the Barriers of Diffusion MRI
  • At: Lisbon, Portugal
  • Volume: pp. 5
Authors:
Thijs Dhollander at Murdoch Children's Research Institute
Thijs Dhollander
David Raffelt at The Florey Institute of Neuroscience and Mental Health
David Raffelt
Alan Connelly at The Florey Institute of Neuroscience and Mental Health
Alan Connelly
Download full-text PDF
Read full-text
Download citation

Abstract

To overcome the fact that the fibre orientation distribution (FOD) from constrained spherical deconvolution (CSD) assumes a single-fibre white matter (WM) response function—and is thus inappropriate and distorted in voxels containing grey matter (GM) or cerebrospinal fluid (CSF)—multi-shell multi-tissue CSD (MSMT-CSD) was proposed. MSMT-CSD can resolve WM, GM and CSF signal contributions, but requires multi-shell data. Very recently, we proposed a novel method that can achieve the same results using just single-shell data. We refer to this method as "single-shell 3-tissue CSD" (SS3T-CSD). Both MSMT-CSD and SS3T-CSD require WM, GM and CSF response functions. These can be obtained from manually selected exemplary voxels of the tissue classes, or via the procedure described initially in the MSMT-CSD paper, which relies on a highly accurately co-registered T1 image. We propose an unsupervised procedure that does not depend on a T1 image, nor registration, and works for both single-shell and multi-shell data.
Content uploaded by Thijs Dhollander
Author content
All content in this area was uploaded by Thijs Dhollander on Sep 07, 2016
Content may be subject to copyright.
Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image
Thijs Dhollander
1
, David Raffelt
1
, and Alan Connelly
1,2
1
The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia,
2
The Florey Department of Neuroscience, University of Melbourne, Melbourne, Australia
Target audience: people interested in obtaining multi-tissue CSD results from single-shell or multi-shell data without any hassle
Purpose: To overcome the fact that the fibre orientation distribution (FOD) from constrained spherical deconvolution (CSD)
[1]
assumes a single-fibre white matter (WM) response function—and is thus inappropriate and distorted in voxels containing grey
matter (GM) or cerebrospinal fluid (CSF)—multi-shell multi-tissue CSD (MSMT-CSD)
[2]
was proposed. MSMT-CSD can resolve WM,
GM and CSF signal contributions, but requires multi-shell data. Very recently, a novel method that can achieve the same results
using just single-shell data was proposed in [3]. We refer to this method as “single-shell 3-tissue CSD” (SS3T-CSD). Both MSMT-CSD
and SS3T-CSD require WM, GM and CSF response functions. These can be obtained from manually selected exemplary voxels of
the tissue classes, or via the procedure described in [2], which relies on a highly accurately co-registered T1 image. We propose an
unsupervised procedure that does not depend on a T1 image, nor registration, and works for both single-shell and multi-shell data.
Data & preprocessing: Single-subject diffusion MR data were acquired on a Siemens Skyra 3T scanner using a 32-channel head coil,
with voxel size 2.5 × 2.5 × 2.5 mm
3
and a single-shell scheme (60 directions at b = 3000 s/mm
2
and 7 b=0 images) as well as a
separate multi-shell scheme (30, 45, 60 directions respectively at b = 1000, 2000, 3000 s/mm
2
and 16 b=0 images). A T1 dataset
with voxel size 0.9 × 0.9 × 0.9 mm
3
was also acquired. All data were jointly corrected for susceptibility-induced distortions (using an
extra pair of b=0 images acquired with opposite phase encodings), eddy-current distortions, motion, and bias fields.
Methods: Fig.1 shows the 4 main steps of our method, running on the single-shell data. In the 1
st
step, a brain mask is computed as
follows: for each separate b-value (including b=0), all images are averaged and an optimal threshold
[4]
is computed; all b-value
masks are combined; a median filter is applied to the combined mask; the largest connected component is selected and holes are
filled. The following steps use 2 metrics: a fractional anisotropy (FA) map and a simple signal decay metric (SDM). For single-shell
data, the SDM is defined as the (natural) logarithm of the ratio of the average b=0 to the average diffusion-weighted image. For
multi-shell data, this is done for each shell and an average across shells, weighted by the number of images per shell, is obtained. In
the 2
nd
step, crude WM-GM-CSF segmentations are computed: the brain mask is eroded (by 3 voxels); the WM is separated from
the rest by a low FA threshold (0.2); the remaining voxels are split in GM and CSF by an optimal threshold
[4]
computed on the SDM
values. In the 3
rd
step, the WM-GM-CSF segmentations are refined and made more conservative, to remove the bulk of multi-tissue
partial volumed voxels. High WM SDM outliers above Q
3
+(Q
3
-Q
1
) are removed. For both the voxels below and above the GM SDM
median, optimal thresholds
[4]
are computed and both parts closer to the initial GM median are retained. The high SDM outliers that
were removed from the WM are reconsidered for the CSF if they have higher SDM than the current minimal CSF SDM. An optimal
threshold
[4]
is computed for the resulting CSF and only the higher SDM valued voxels are retained. In the 4
th
step, a number of final
best single-fibre (SF) WM, GM and CSF voxels are selected, as a percentage of the current conservative WM, GM and CSF masks;
respectively 0.5%, 2% and 10%. For the WM, a modified version of the algorithm in [5] is used to obtain the best SF WM voxels. For
the GM, the voxels closest to the GM SDM median are selected. For the CSF, the highest SDM valued voxels are selected.
We compare our algorithm to the one in [2], which is based on a probabilistic WM-GM-CSF segmentation of a co-registered T1
image. It retains voxels with more than 95% tissue probability and applies an additional upper FA threshold (0.2) for both GM and
CSF. Both algorithms are applied on the multi-shell and single-shell data. The obtained response functions are then used to perform
MSMT-CSD on the multi-shell data and SS3T-CSD on the single-shell data.
Results & discussion: The algorithm took about 35 sec. on a standard desktop computer (mostly spent on the selection of SF WM
voxels using [5]). The resulting response functions as obtained from the multi-shell and single-shell data are shown respectively in
Fig.2 and Fig.3, and compared to those obtained using the algorithm in [2]. The WM and GM responses from both algorithms—as
well as the angular profiles of the b≠0 WM responses (not shown)—are very similar. Surprisingly, the CSF responses do differ
significantly: compared to our proposed method, the algorithm in [2] appears to underestimate the b=0 CSF intensity by more than
30%. There are several possible explanations here: the method in [2] may easily suffer from even the slightest (T1) registration
inaccuracies (and hence include nearby WM or GM partial volume in the diffusion MR data); it may also miss out on several good
CSF voxels due to the FA threshold, which our method recovers at step 3 (Fig.1). Finally, our algorithm only retains the best 10%
voxels of the CSF in step 4. The MSMT-CSD and SS3T-CSD results are shown respectively in Fig.2 and Fig.3. The results using the
responses from the [2] algorithm (top) are similar to those using the responses from our method (bottom); except for the fact that
the former overestimate the CSF. This causes, e.g., underestimation of the WM FOD in voxels containing free water partial volume.
SS3T-CSD appears to be more resilient to the CSF miscalibration caused by [2]. The most WM is recovered by combining the proposed method with SS3T-CSD (Fig.3., bottom).
Conclusion: We propose a method for unsupervised estimation of WM-GM-CSF responses from single-shell or multi-shell data that doesn’t require a co-registered T1 image.
Combined with SS3T-CSD, this allows for fast and fully automated 3-tissue CSD processing of simple single-shell diffusion MR data without any other external prerequisites.
References: [1] Tournier et al, NeuroImage 2007;35(4):1459–1472. [2] Jeurissen et al, NeuroImage 2014;103:411–426. [3] Dhollander et al, Proc ISMRM 2016;24:3010.
[4] Ridgway et al, NeuroImage 2009;44(1):99–111. [5] Tournier et al, NMR Biomed 2013;26(12):1775–1786.
95530
37476
–
25255
–
34893
–
15802
–
174
–
316
–
220
Fig.1
:
the 4 m
ain steps of the algorithm,
running on the single-shell dataset. Two
slices are shown. The numbers below
each step indicate how many voxels are
still left in the WM – GM – CSF masks.
Fig.3
:
Left
:
single
-
shell data
WM
-
GM
-
CSF
responses.
Dashed line
: the method in
[2]
,
full line: our method. Middle: SS3T-CSD results using responses from the method
in
[2]
(
top
) and
our method
(
bottom
).
Right
: FODs in region at arrow tip
.
Fig.2
:
Left
: m
ulti
-
shell
data
WM
-
GM
-
CSF
responses.
Dashed line
: the method in
[2]
,
full line: our method. Middle: MSMT-CSD results using responses from the method
in
[2]
(
top
) and
our method
(
bottom
).
Right
: FODs in region at arrow
tip
.
0 3000
0
0.2
0.4
0.6
0.8
1
0 1000 2000 3000
0
0.2
0.4
0.6
0.8
1
... Brain masks were obtained for all subjects by performing a recursive application of the Brain Extraction Tool 40 . Response functions from each of the three tissue types were estimated from a randomly selected subset of nearly 500 subjects and averaged to produce a single set of tissue response functions 41 . The response functions were selected via an unsupervised method described by briefly, the WM tissue response function was selected from an FA thresholded mask, the CSF tissue response function was selected in voxels with the highest signal decay metric between the averaged b-0 and b=3000 s/mm 2 shells, and the GM tissue response function was selected from voxels closest to the median voxel-wise signal decay metric, after a conservative GM mask was constructed. ...
Developmental Ramifications of the Screen Age: Associations between Screen Time and Neural Maturation in a Massive Adolescent Cohort
Preprint
Full-text available
  • Sep 2023
A growing body of literature associates increases in electronic screen time with a vast array of psychological consequences amongst adolescents, but little is known about the neurological underpinnings of this relationship. This longitudinal study examines structural and diffusion brain MRI scans from the Adolescent Brain Cognitive Development (ABCD) Study: a large multi-site study with thousands of participants. By assessing both gray matter density (GMD) and grey matter measurements of diffusion microstructure in the adolescent brain, we describe how the developmental trajectory of the brain changes with screen-based media consumption at the sub-cellular level. Grey matter microstructure was measured across 13 bilateral regions functionally implicated with screen time use, and associated with either the control or reward system. After controlling for age, sex, total brain volume, scanning site, sibling relationships, physical activity, and socioeconomic status, this study finds significant positive correlations between increased screen time and axonal signal across 6 of the 13 regions while also finding significantly decreased intracellular signal in 8 regions. Comparing these associations to normal developmental trajectories suggests adolescent age-related brain development may be accelerated by increased screen time in brain areas associated with reward processing while age-related brain development may be decelerated in regions of the control system. Highlighting the sensitivity of microstructural analysis, no significant cross-sectional or longitudinal relationship with increased screen time was found using GMD, or fractional anisotropy. This work suggests that increased screen usage during adolescent development has a complex association with brain tissue that cannot be completely described by traditional quantifications of tissue microstructure.
View
... DWI data was preprocessed using the MRtrix3 package [150] (https://www.mrtrix.org/). More specifically, fiber orientation distributions were generated using the multi-shell multi-tissue constrained spherical deconvolution algorithm from MRtrix [151,152]. White matter edges were then reconstructed using probabilistic streamline tractography based on the generated fiber orientation distributions [153]. The tract weights were then optimized by estimating an appropriate cross-section multiplier for each streamline following the procedure proposed by [154] and a connectivity matrix was built for each participant using the 400-region Schaefer parcellation [48]. ...
Integrating multimodal and multiscale connectivity blueprints of the human cerebral cortex in health and disease
Article
Full-text available
The brain is composed of disparate neural populations that communicate and interact with one another. Although fiber bundles, similarities in molecular architecture, and synchronized neural activity all reflect how brain regions potentially interact with one another, a comprehensive study of how all these interregional relationships jointly reflect brain structure and function remains missing. Here, we systematically integrate 7 multimodal, multiscale types of interregional similarity (“connectivity modes”) derived from gene expression, neurotransmitter receptor density, cellular morphology, glucose metabolism, haemodynamic activity, and electrophysiology in humans. We first show that for all connectivity modes, feature similarity decreases with distance and increases when regions are structurally connected. Next, we show that connectivity modes exhibit unique and diverse connection patterns, hub profiles, spatial gradients, and modular organization. Throughout, we observe a consistent primacy of molecular connectivity modes—namely correlated gene expression and receptor similarity—that map onto multiple phenomena, including the rich club and patterns of abnormal cortical thickness across 13 neurological, psychiatric, and neurodevelopmental disorders. Finally, to construct a single multimodal wiring map of the human cortex, we fuse all 7 connectivity modes and show that the fused network maps onto major organizational features of the cortex including structural connectivity, intrinsic functional networks, and cytoarchitectonic classes. Altogether, this work contributes to the integrative study of interregional relationships in the human cerebral cortex.
View
... Preprocessing steps included: denoising [54], unringing [55], distortion correction with the Synb0-DisCo tool [56], eddy-currents and subject movement correction [57]. To obtain wholebrain probabilistic tractography, we first estimated the individual multi-shell multi-tissue response functions [58]. Based on the population-averaged response functions, we derived the patient-specific fiber orientation distribution through constrained spherical deconvolution [59] and performed intensity normalization [60]. ...
Linking connectivity of deep brain stimulation of nucleus accumbens area with clinical depression improvements: a retrospective longitudinal case series
Article
Full-text available
Treatment-resistant depression is a severe form of major depressive disorder and deep brain stimulation is currently an investigational treatment. The stimulation’s therapeutic effect may be explained through the functional and structural connectivities between the stimulated area and other brain regions, or to depression-associated networks. In this longitudinal, retrospective study, four female patients with treatment-resistant depression were implanted for stimulation in the nucleus accumbens area at our center. We analyzed the structural and functional connectivity of the stimulation area: the structural connectivity was investigated with probabilistic tractography; the functional connectivity was estimated by combining patient-specific stimulation volumes and a normative functional connectome. These structural and functional connectivity profiles were then related to four clinical outcome scores. At 1-year follow-up, the remission rate was 66%. We observed a consistent structural connectivity to Brodmann area 25 in the patient with the longest remission phase. The functional connectivity analysis resulted in patient-specific R-maps describing brain areas significantly correlated with symptom improvement in this patient, notably the prefrontal cortex. But the connectivity analysis was mixed across patients, calling for confirmation in a larger cohort and over longer time periods.
View
... For details regarding quality control and MRI data exclusion, please see Supporting Information B. Following pre-processing, data were upsampled to an isotropic voxel size of 1.30 mm 3 . Group average response functions for gray matter, white matter, and CSF used to generate individual FOD maps using single-shell three-tissue constrained spherical deconvolution (SS3T-CSD; Dhollander et al., 2016). Next, FOD images underwent intensity normalization to ensure that FOD magnitudes were consistent across participants (Raffelt et al., 2017). ...
Adolescents with ADHD and co-occurring motor difficulties show a distinct pattern of maturation within the corticospinal tract from those without: A longitudinal fixel-based study
Article
It is well documented that attention-deficit hyperactivity disorder (ADHD) often presents with co-occurring motor difficulties. However, little is known about the biological mechanisms that explain compromised motor skills in approximately half of those with ADHD. To provide insight into the neurobiological basis of poor motor outcomes in ADHD, this study profiled the development of white matter organization within the cortico-spinal tract (CST) in adolescents with ADHD with and without co-occurring motor problems, as well as non-ADHD control children with and without motor problems. Participants were 60 children aged 9-14 years, 27 with a history of ADHD and 33 controls. All underwent high-angular resolution diffusion MRI data at up to three time points (115 in scans total). We screened for motor impairment in all participants at the third time point (≈14 years) using the Developmental Coordination Disorder Questionnaire (DCD-Q). Following pre-processing of diffusion MRI scans, fixel-based analysis was performed, and the bilateral CST was delineated using TractSeg. Mean fiber density (FD) and fiber cross-section (FC) were extracted for each tract at each time-point. To investigate longitudinal trajectories of fiber development, linear mixed models were performed separately for the left and right CST, controlling for nuisance variables. To examine possible variations in fiber development between groups, we tested whether the inclusion of group and the interaction between age and group improved model fit. At ≈10 years, those with ADHD presented with lower FD within the bilateral CST relative to controls, irrespective of their prospective motor status. While these microstructural abnormalities persisted into adolescence for individuals with ADHD and co-occurring motor problems, they resolved for those with ADHD alone. Divergent maturational pathways of motor networks (i.e., the CST) may, at least partly, explain motor problems individuals with ADHD.
View
... A mask of the preprocessed dMRI volumes was extracted (Dhollander et al., 2016). The diffusion tensor (DT) was estimated at the voxels defined within the brain mask following the ordinary least squares method implemented in FSL software (Jenkinson et al., 2012). ...
Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies
Article
Full-text available
  • Jul 2023
The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.
View
The prefrontal cortex of the bottlenose dolphin (Tursiops truncatus Montagu, 1821): a tractography study and comparison with the human
Article
Full-text available
Cetaceans are well known for their remarkable cognitive abilities including self-recognition, sound imitation and decision making. In other mammals, the prefrontal cortex (PFC) takes a key role in such cognitive feats. In cetaceans, however, a PFC could up to now not be discerned based on its usual topography. Classical in vivo methods like tract tracing are legally not possible to perform in Cetacea, leaving diffusion-weighted imaging (DWI) as the most viable alternative. This is the first investigation focussed on the identification of the cetacean PFC homologue. In our study, we applied the constrained spherical deconvolution (CSD) algorithm on 3 T DWI scans of three formalin-fixed brains of bottlenose dolphins (Tursiops truncatus) and compared the obtained results to human brains, using the same methodology. We first identified fibres related to the medio-dorsal thalamic nuclei (MD) and then seeded the obtained putative PFC in the dolphin as well as the known PFC in humans. Our results outlined the dolphin PFC in areas not previously studied, in the cranio-lateral, ectolateral and opercular gyri, and furthermore demonstrated a similar connectivity pattern between the human and dolphin PFC. The antero-lateral rotation of the PFC, like in other areas, might be the result of the telescoping process which occurred in these animals during evolution.
View
White matter associations with spelling performance
Preprint
Full-text available
  • Aug 2023
Multiple neurocognitive processes are involved in the highly complex task of producing written words. Yet, little is known about the neural pathways that support spelling in healthy adults. We assessed the associations between performance on a difficult spelling-to-dictation task and microstructural properties of language-related white matter pathways, in a sample of 73 native English-speaking neurotypical adults. Participants completed a diffusion magnetic resonance imaging (dMRI) scan and a cognitive assessment battery. Using constrained spherical deconvolution modeling and probabilistic tractography, we reconstructed dorsal and ventral white matter tracts of interest, bilaterally, in individual participants. Spelling associations were found in both dorsal and ventral stream pathways. In high-performing spellers, spelling scores significantly correlated with fractional anisotropy (FA) within the left inferior longitudinal fasciculus, a ventral stream pathway. In low-performing spellers, spelling scores significantly correlated with FA within the third branch of the right superior longitudinal fasciculus, a dorsal pathway. An automated analysis of spelling errors revealed that high- and low- performing spellers also differed in their error patterns, diverging primarily in terms of the orthographic distance between their errors and the correct spelling, compared to the phonological plausibility of their spelling responses. The results demonstrate the complexity of the neurocognitive architecture of spelling. The distinct white matter associations and error patterns detected in low- and high- performing spellers suggest that they rely on different cognitive processes, such that high-performing spellers rely more on lexical-orthographic representations, while low-performing spellers rely more on phoneme-to-grapheme conversion.
View
Sleep Differentially and Profoundly Impairs Recall Memory in a Patient with Fornix Damage
Article
In March 2020, C.T., a kind, bright, and friendly young woman underwent surgery for a midline tumor involving her septum pellucidum and extending down into her fornices bilaterally. Following tumor diagnosis and surgery, C.T. experienced significant memory deficits: C.T.'s family reported that she could remember things throughout the day, but when she woke up in the morning or following a nap, she would expect to be in the hospital, forgetting all the information that she had learned before sleep. The current study aimed to empirically validate C.T.'s pattern of memory loss and explore its neurological underpinnings. On two successive days, C.T. and age-matched controls watched an episode of a TV show and took a nap or stayed awake before completing a memory test. Although C.T. performed numerically worse than controls in both conditions, sleep profoundly exacerbated her memory impairment, such that she could not recall any details following a nap. This effect was replicated in a second testing session. In high-resolution MRI scans, we observed evidence of the trans-callosal surgical approach's impact on the mid-anterior corpus callosum, showed that C.T. had perturbed white matter particularly in the right fornix column, and demonstrated that C.T.'s hippocampal volumes did not differ from controls. These findings suggest that the fornix is important for processing episodic memories during sleep. As a key output pathway of the hippocampus, the fornix may ensure that specific memories are replayed during sleep, maintain the balance of sleep stages, or allow for the retrieval of memories following sleep.
View
Human white matter myelinates faster in utero than ex utero
Article
  • Aug 2023
The formation of myelin, the fatty sheath that insulates nerve fibers, is critical for healthy brain function. A fundamental open question is what impact being born has on myelin growth. To address this, we evaluated a large ( n = 300) cross-sectional sample of newborns from the Developing Human Connectome Project (dHCP). First, we developed software for the automated identification of 20 white matter bundles in individual newborns that is well suited for large samples. Next, we fit linear models that quantify how T1w/T2w (a myelin-sensitive imaging contrast) changes over time at each point along the bundles. We found faster growth of T1w/T2w along the lengths of all bundles before birth than right after birth. Further, in a separate longitudinal sample of preterm infants ( N = 34), we found lower T1w/T2w than in full-term peers measured at the same age. By applying the linear models fit on the cross-section sample to the longitudinal sample of preterm infants, we find that their delay in T1w/T2w growth is well explained by the amount of time they spent developing in utero and ex utero. These results suggest that white matter myelinates faster in utero than ex utero. The reduced rate of myelin growth after birth, in turn, explains lower myelin content in individuals born preterm and could account for long-term cognitive, neurological, and developmental consequences of preterm birth. We hypothesize that closely matching the environment of infants born preterm to what they would have experienced in the womb may reduce delays in myelin growth and hence improve developmental outcomes.
View
Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks
Article
The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.
View
ResearchGate has not been able to resolve any references for this publication.