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SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography

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... In brain network models, nodes correspond to grey-matter regions (based on brain atlases or parcellations) while links or edges correspond to structural or functional connections. Structural connections are estimated from diffusion weighted imaging [7,62] data by modeling white matter pathways through tractography algorithms [52,54,61]. Functional connections represent statistical dependencies between brain regions time series while subjects are either at rest or performing a task during functional MRI (fMRI) sessions [65]. ...
... The HCP DWI data were processed following the MRtrix3 [62] guidelines (http: //mrtrix.readthedocs.io/en/latest/tutorials/hcp_connectome.html). In summary, we first generated a tissue-segmented image appropriate for anatomically constrained tractography (ACT [52], MRtrix command 5ttgen); we then estimated the multishell multi-tissue response function ( [11], MRtrix command dwi2response msmt 5tt) and performed the multi-shell, multi-tissue constrained spherical deconvolution ( [35], MRtrix dwi2fod msmt csd); afterwards, we generated the initial tractogram (MRtrix command tckgen, 10 million streamlines, maximum tract length = 250, FA cutoff = 0.06) and applied the successor of Spherical-deconvolution Informed Filtering of Tractograms (SIFT2, [54]) methodology (MRtrix command tcksift2). Both SIFT [53] and SIFT2 [54] methods provides more biologically meaningful estimates of structural connection density. ...
... In summary, we first generated a tissue-segmented image appropriate for anatomically constrained tractography (ACT [52], MRtrix command 5ttgen); we then estimated the multishell multi-tissue response function ( [11], MRtrix command dwi2response msmt 5tt) and performed the multi-shell, multi-tissue constrained spherical deconvolution ( [35], MRtrix dwi2fod msmt csd); afterwards, we generated the initial tractogram (MRtrix command tckgen, 10 million streamlines, maximum tract length = 250, FA cutoff = 0.06) and applied the successor of Spherical-deconvolution Informed Filtering of Tractograms (SIFT2, [54]) methodology (MRtrix command tcksift2). Both SIFT [53] and SIFT2 [54] methods provides more biologically meaningful estimates of structural connection density. SIFT2 allows for a more logically direct and computationally efficient solution to the streamlines connectivity quantification problem: by determining an appropriate cross-sectional area multiplier for each streamline rather than removing streamlines altogether, measures of fiber connectivity are obtained whilst making use of the complete streamlines reconstruction [54]. ...
Preprint
A key question in modern neuroscience is how cognitive changes in a human brain can be quantified and captured by functional connectomes (FC) . A systematic approach to measure pairwise functional distance at different brain states is lacking. This would provide a straight-forward way to quantify differences in cognitive processing across tasks; also, it would help in relating these differences in task-based FCs to the underlying structural network. Here we propose a framework, based on the concept of Jensen-Shannon divergence, to map the task-rest connectivity distance between tasks and resting-state FC. We show how this information theoretical measure allows for quantifying connectivity changes in distributed and centralized processing in functional networks. We study resting-state and seven tasks from the Human Connectome Project dataset to obtain the most distant links across tasks. We investigate how these changes are associated to different functional brain networks, and use the proposed measure to infer changes in the information processing regimes. Furthermore, we show how the FC distance from resting state is shaped by structural connectivity, and to what extent this relationship depends on the task. This framework provides a well grounded mathematical quantification of connectivity changes associated to cognitive processing in large-scale brain networks.
... tutorials/hcp connectome.html), as done in recent paper [2]. In summary, we first generated a tissue-segmented image appropriate for anatomically constrained tractography (ACT [28], MRtrix command 5ttgen); we then estimated the multi-shell multi-tissue response function [6](MRtrix command dwi2response msmt 5tt) and performed the multishell, multi-tissue constrained spherical deconvolution [21] (MRtrix dwi2fod msmt csd); afterwards, we generated the initial tractogram (MRtrix command tckgen, 10 million streamlines, maximum tract length = 250, FA cutoff = 0.06) and applied the successor of Spherical-deconvolution Informed Filtering of Tractograms (SIFT2, [30]) methodology (MRtrix command tcksift2). Both SIFT [30] and SIFT2 [29] methods provides more biologically meaningful estimates of structural connection density. ...
... In summary, we first generated a tissue-segmented image appropriate for anatomically constrained tractography (ACT [28], MRtrix command 5ttgen); we then estimated the multi-shell multi-tissue response function [6](MRtrix command dwi2response msmt 5tt) and performed the multishell, multi-tissue constrained spherical deconvolution [21] (MRtrix dwi2fod msmt csd); afterwards, we generated the initial tractogram (MRtrix command tckgen, 10 million streamlines, maximum tract length = 250, FA cutoff = 0.06) and applied the successor of Spherical-deconvolution Informed Filtering of Tractograms (SIFT2, [30]) methodology (MRtrix command tcksift2). Both SIFT [30] and SIFT2 [29] methods provides more biologically meaningful estimates of structural connection density. SIFT2 allows for a more logically direct and computationally efficient solution to the streamlines connectivity quantification problem: by determining an appropriate cross-sectional area multiplier for each streamline rather than removing streamlines altogether, biologically accurate measures of fibre connectivity are obtained whilst making use of the complete streamlines reconstruction [30]. ...
... Both SIFT [30] and SIFT2 [29] methods provides more biologically meaningful estimates of structural connection density. SIFT2 allows for a more logically direct and computationally efficient solution to the streamlines connectivity quantification problem: by determining an appropriate cross-sectional area multiplier for each streamline rather than removing streamlines altogether, biologically accurate measures of fibre connectivity are obtained whilst making use of the complete streamlines reconstruction [30]. Finally, we mapped the SIFT2 outputted streamlines onto the 164 chosen brain regions [13], [9] to produce a structural connectome (MRtrix command tck2connectome). ...
Preprint
Communication processes within the human brain at different cognitive states are neither well understood nor completely characterized. We assess communication processes in the human connectome using ant colony-inspired cooperative learning algorithm, starting from a source with no a priori information about the network topology, and cooperatively searching for the target through a pheromone-inspired model. This framework relies on two parameters, namely pheromone perception and edge perception, to define the cognizance and subsequent behaviour of the ants on the network and, overall, the communication processes happening between source and target nodes. Simulations obtained through different configurations allow the identification of path-ensembles that are involved in the communication between node pairs. These path-ensembles may contain different number of paths depending on the perception parameters and the node pair. In order to assess the different communication regimes displayed on the simulations and their associations with functional connectivity, we introduce two network measurements, effective path-length and arrival rate. These communication features are tested as individual as well as combined predictors of functional connectivity during different tasks. Finally, different communication regimes are found in different specialized functional networks. Overall, this framework may be used as a test-bed for different communication regimes on top of an underlaying topology.
... The streamlines were filtered from the tractogram based on the spherical deconvolution of the diffusion signal. We estimated the streamline weights using the command tcksift2 ( R. E. Smith et al., 2015). Next, the SC matrix was constructed by tck2connectome based on the Schaefer-400 atlas for each participant. ...
... Using diffusion MRI data, we reconstructed whole-brain white matter tracts of individual participants using probabilistic fiber tractography with multishell, multitissue constrained spherical deconvolution (CSD) ( Jeurissen et al., 2014). Anatomically constrained tractography (ACT) ( R. E. Smith et al., 2012) and spherical deconvolution informed filtering of tractograms (SIFT) ( R. E. Smith et al., 2015) have been applied to improve the biological accuracy of fiber reconstruction. For each participant, we quantified the number of streamlines connecting each pair of cortical regions using the Schaefer atlas to construct a structural connectome of the streamline counts. ...
... First, precisely reconstructing individuals' white matter SC is challenging because of the inherent limitations of diffusion MRI-based fiber tractography. In this study, we used state-of-the-art probabilistic fiber tractography with multishell, multitissue constrained spherical deconvolution ( Jeurissen et al., 2014) and applied anatomically constrained tractography ( R. E. Smith et al., 2012) and spherical deconvolution-informed filtering of tractograms ( R. E. Smith et al., 2015) to improve biological accuracy. Moreover, consistency-based thresholding was used to reduce the influence of false-positive connections ( Baum et al., 2020). ...
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The human cerebral cortex is organized into functionally segregated but synchronized regions bridged by the structural connectivity of white matter pathways. While structure–function coupling has been implicated in cognitive development and neuropsychiatric disorders, it remains unclear to what extent the structure–function coupling reflects a group-common characteristic or varies across individuals, at both the global and regional brain levels. By leveraging two independent, high-quality datasets, we found that the graph neural network accurately predicted unseen individuals’ functional connectivity from structural connectivity, reflecting a strong structure–function coupling. This coupling was primarily driven by network topology and was substantially stronger than that of the correlation approaches. Moreover, we observed that structure–function coupling was dominated by group-common effects, with subtle yet significant individual-specific effects. The regional group and individual effects of coupling were hierarchically organized across the cortex along a sensorimotor-association axis, with lower group and higher individual effects in association cortices. These findings emphasize the importance of considering both group and individual effects in understanding cortical structure–function coupling, suggesting insights into interpreting individual differences of the coupling and informing connectivity-guided therapeutics.
... The resulting tractographies were visually inspected. Finally, SC matrices were generated in two different ways: [1] by spherical-deconvolution informed filtering of the tractograms (SIFT2, command tcksift2) (R. E. Smith et al., 2015b). That is, streamline weights were estimated to reduce common biases in tractography and the connectivity between two regions corresponds to the SIFT2-weighted sum of the streamlines that connect the two regions. ...
... It is well known that tractographies are subject to certain biases, for example low interhemispheric connectivity (Watson & Andrews, 2024), underrepresentation of long-range connections (de Reus & van den Heuvel, 2013), or the presence of false positive connections (Maier-Hein et al., 2017). While using SIFT2 provides a biologically more plausible estimate of SC (R. E. Smith et al., 2015aSmith et al., , 2015b, tractography is still not a direct measurement of white matter fibers, but a probability-based estimate (Sotiropoulos & Zalesky, 2019). For example, in a study using a phantom ground truth, it was shown that the weights obtained with SIFT2 were improved by approximately 15% compared to using no filtering algorithm, outperforming other filtering methods (Sarwar et al., 2023). ...
... We initiated the tractogram with 40 million streamlines (maximum tract length, 250; fractional anisotropy cutoff, 0.06). We applied spherical deconvolution informed filtering of tractograms (SIFT2) to reconstruct whole-brain streamlines weighted by cross-sectional multipliers 119 . The reconstructed cross-section streamlines were averaged within 400 spatially contiguous, functionally defined parcels 120 , also warped to DWI space. ...
... Fiber orientation distributions (that is, the apparent density of fibers as a function of orientation) were modeled from the diffusion-weighted MRI with multishell multitissue spherical deconvolution 118 , then values were normalized in the log domain to optimize the sum of all tissue compartments toward 1, under constraints of spatial smoothness. Anatomically constrained tractography was performed systematically by generating streamlines using second-order integration over fiber orientation distributions with dynamic seeding 119,129 . Streamline generation was aborted when 40 million streamlines had been accepted. ...
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The default mode network (DMN) is implicated in many aspects of complex thought and behavior. Here, we leverage postmortem histology and in vivo neuroimaging to characterize the anatomy of the DMN to better understand its role in information processing and cortical communication. Our results show that the DMN is cytoarchitecturally heterogenous, containing cytoarchitectural types that are variably specialized for unimodal, heteromodal and memory-related processing. Studying diffusion-based structural connectivity in combination with cytoarchitecture, we found the DMN contains regions receptive to input from sensory cortex and a core that is relatively insulated from environmental input. Finally, analysis of signal flow with effective connectivity models showed that the DMN is unique amongst cortical networks in balancing its output across the levels of sensory hierarchies. Together, our study establishes an anatomical foundation from which accounts of the broad role the DMN plays in human brain function and cognition can be developed.
... Next, the ODFs were normalized for each subject to be able to obtain quantitative measures of density (Dhollander et al. 2021;Tournier et al. 2019). Further, ACT (R. E. Smith et al. 2012;Tournier, Calamante, and Connelly 2010) with dynamic seeding (R. E. Smith et al. 2015) was used to generate 10 million streamlines. To ensure that the streamlines' densities within the white matter closely approximate the fiber densities estimated through the spherical deconvolution diffusion model, SIFT2 (R. E. Smith et al. 2015;Tournier et al. 2019) was used. ...
... Further, ACT (R. E. Smith et al. 2012;Tournier, Calamante, and Connelly 2010) with dynamic seeding (R. E. Smith et al. 2015) was used to generate 10 million streamlines. To ensure that the streamlines' densities within the white matter closely approximate the fiber densities estimated through the spherical deconvolution diffusion model, SIFT2 (R. E. Smith et al. 2015;Tournier et al. 2019) was used. Further, the fiber density SIFT2 proportionality coefficient (μ) was calculated for each subject to achieve inter-subject connection density normalization. ...
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The human brain connectome is characterized by the duality of highly modular structure and efficient integration, supporting information processing. Newborns with congenital heart disease (CHD), prematurity, or spina bifida aperta (SBA) constitute a population at risk for altered brain development and developmental delay (DD). We hypothesize that, independent of etiology, alterations of connectomic organization reflect neural circuitry impairments in cognitive DD. Our study aim is to address this knowledge gap by using a multi‐etiologic neonatal dataset to reveal potential commonalities and distinctions in the structural brain connectome and their associations with DD. We used diffusion tensor imaging of 187 newborns (42 controls, 51 with CHD, 51 with prematurity, and 43 with SBA). Structural weighted connectomes were constructed using constrained spherical deconvolution‐based probabilistic tractography and the Edinburgh Neonatal Atlas. Assessment of brain network topology encompassed the analysis of global graph features, network‐based statistics, and low‐dimensional representation of global and local graph features. The Cognitive Composite Score of the Bayley scales of Infant and Toddler Development 3rd edition was used as outcome measure at corrected 2 years for the preterm born individuals and SBA patients, and at 1 year for the healthy controls and CHD. We detected differences in the connectomic structure of newborns across the four groups after visualizing the connectomes in a two‐dimensional space defined by network integration and segregation. Further, analysis of covariance analyses revealed differences in global efficiency (p < 0.0001), modularity (p < 0.0001), mean rich club coefficient (p = 0.017), and small‐worldness (p = 0.016) between groups after adjustment for postmenstrual age at scan and gestational age at birth. Moreover, small‐worldness was significantly associated with poorer cognitive outcome, specifically in the CHD cohort (r = −0.41, p = 0.005). Our cross‐etiologic study identified divergent structural brain connectome profiles linked to deviations from optimal network integration and segregation in newborns at risk for DD. Small‐worldness emerges as a key feature, associating with early cognitive outcomes, especially within the CHD cohort, emphasizing small‐worldness' crucial role in shaping neurodevelopmental trajectories. Neonatal connectomic alterations associated with DD may serve as a marker identifying newborns at‐risk for DD and provide early therapeutic interventions. Trial Registration: ClinicalTrials.gov identifier: NCT 00313946
... 45,46 We then used the spherical deconvolution-informed filtering of tractograms (SIFT2) algorithm to reduce biases. 47 ...
... We did not apply a threshold to connectivity matrices in accordiance with SIFT2 guidance. 47 The regional network parameters were weighted degrees for networks. ...
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Background: Cortical morphometry is an intermediate phenotype that is closely related to the genetics and onset of major depressive disorder (MDD), and cortical morphometric networks are considered more relevant to disease mechanisms than brain regions. We sought to investigate changes in cortical morphometric networks in MDD and their relationship with genetic risk in healthy controls. Methods: We recruited healthy controls and patients with MDD of Han Chinese descent. Participants underwent DNA extraction and magnetic resonance imaging, including T 1-weighted and diffusion tensor imaging. We calculated polygenic risk scores (PRS) based on previous summary statistics from a genome-wide association study of the Chinese Han population. We used a novel method based on Kullback-Leibler divergence to construct the morphometric inverse divergence (MIND) network, and we included the classic morphometric similarity network (MSN) as a complementary approach. Considering the relationship between cortical and white matter networks, we also constructed a streamlined density network. We conducted group comparison and PRS correlation analyses at both the regional and network level. Results: We included 130 healthy controls and 195 patients with MDD. The results indicated enhanced connectivity in the MIND network among patients with MDD and people with high genetic risk, particularly in the somatomotor (SMN) and default mode networks (DMN). We did not observe significant findings in the MSN. The white matter network showed disruption among people with high genetic risk, also primarily in the SMN and DMN. The MIND network outperformed the MSN network in distinguishing MDD status. Limitations: Our study was cross-sectional and could not explore the causal relationships between cortical morphological changes, white matter connectivity, and disease states. Some patients had received antidepressant treatment, which may have influenced brain morphology and white matter network structure. Conclusion: The genetic mechanisms of depression may be related to white matter disintegration, which could also be associated with decoupling of the SMN and DMN. These findings provide new insights into the genetic mechanisms and potential biomarkers of MDD.
... The default settings were applied with the exception of the maximal track length reduced to 100 mm (compared to the default 170 mm, given the data resolution) and the cut-off value set to 0.06. As the last step, the generated streamlines were refined with the SIFT2 algorithm (Smith et al. 2015) to allow for the use of the number of streamlines as a valid index of the structural connection density. ...
... In each subject, the resulting nodes were inspected visually and, if required, corrected manually to ensure that they were restricted to the areas of interest. The structural connectivity matrix was created by summing up the SIFT2-generated (Smith et al. 2015) weights of all the streamlines connecting every pair of nodes. The edges of the structural network were defined in the same manner as for the functional data. ...
Article
Maladaptive forms of guilt, such as excessive self-blame, are common characteristics of anxiety and depressive disorders. The underlying network consists of multiple associative areas, including the superior anterior temporal lobe (sATL), underlying the conceptual representations of social meaning, and fronto-subcortical areas involved in the affective dimension of guilt. Nevertheless, despite understanding the circuitry’s anatomy, network-level changes related to subclinical anxiety and self-blaming behaviour have not been depicted. To fill this gap, we used graph theory analyses on a resting-state functional and diffusion-weighted magnetic resonance imaging dataset of 78 healthy adults (20 females, 20–35 years old). Within the guilt network, we found increased functional contributions of the left sATL for individuals with higher self-blaming, while functional isolation of the left pars opercularis and insula was related to higher trait anxiety. Trait anxiety was also linked to the structural network’s mean clustering coefficient, with the circuitry’s architecture favouring increased local information processing in individuals with increased anxiety levels, however, only when a highly specific subset of connections was considered. Previous research suggests that aberrant interactions between conceptual (sATL) and affective (fronto-limbic) regions underlie maladaptive guilt, and the current results align and expand on this theory by detailing network changes associated with self-blame and trait anxiety.
... The tractogram was initialized with 40 million streamlines and constrained with a maximum tract length of 250 and a fractional anisotropy cutoff of 0.06. A spherical deconvolution-informed filtering procedure (SIFT2) was then applied following Smith et al. 30 to estimate streamline-wise cross-section multipliers. Additional information regarding MRI data preprocessing and network reconstruction is available at ref. 116. ...
... The tractogram was initialized with 40 million streamlines and constrained with a maximum tract length of 250 and a fractional anisotropy cutoff of 0.06. A spherical deconvolution-informed filtering procedure (SIFT2) was then applied followingSmith et al. (2015) to estimate streamlinewise cross-section multipliers. For further details about data preprocessing, please refer to https://doi.org/10.1016/ ...
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Scientific discovery in connectomics relies on network null models. The prominence of network features is conventionally evaluated against null distributions estimated using randomized networks. Modern imaging technologies provide an increasingly rich array of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here we propose a simulated annealing procedure for generating randomized networks that preserve weighted degree (strength) sequences. We show that the procedure outperforms other rewiring algorithms and generalizes to multiple network formats, including directed and signed networks, as well as diverse real-world networks. Throughout, we use morphospace representation to assess the sampling behavior of the algorithm and the variability of the resulting ensemble. Finally, we show that accurate strength preservation yields different inferences about brain network organization. Collectively, this work provides a simple but powerful method to analyze richly detailed next-generation connectomics datasets.
... ) was used to reconstruct whole-brain streamlines weighted by cross-section multipliers (R. E. Smith, Tournier, Calamante, & Connelly, 2015). More information regarding the individual network reconstructions is available in Park et al. (2021). ...
... E. Smith, Tournier, Calamante, & Connelly, 2012;S. M. Smith, 2002;Zhang, Brady, & Smith, 2001), spherical deconvolution (Jeurissen et al., 2014;Tournier, Calamante, Gadian, & Connelly, 2004), and probabilistic tractography (Tournier, Calamante, Connelly, et al., 2011) utilizing Anatomically Constrained Tractography (R. E. Smith et al., 2012) and dynamic seeding (R. E. Smith et al., 2015). The resulting fiber track files were subsequently converted into streamline counts by counting the number of streamlines that passed through one of the 400 cortical regions according to the Schaefer functional atlas (Schaefer et al., 2018). ...
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The human brain is a complex system with high metabolic demands and extensive connectivity that requires control to balance energy consumption and functional efficiency over time. How this control is manifested on a whole-brain scale is largely unexplored, particularly what the associated costs are. Using the network control theory, here, we introduce a novel concept, time-averaged control energy (TCE), to quantify the cost of controlling human brain dynamics at rest, as measured from functional and diffusion MRI. Importantly, TCE spatially correlates with oxygen metabolism measures from the positron emission tomography, providing insight into the bioenergetic footing of resting-state control. Examining the temporal dimension of control costs, we find that brain state transitions along a hierarchical axis from sensory to association areas are more efficient in terms of control costs and more frequent within hierarchical groups than between. This inverse correlation between temporal control costs and state visits suggests a mechanism for maintaining functional diversity while minimizing energy expenditure. By unpacking the temporal dimension of control costs, we contribute to the neuroscientific understanding of how the brain governs its functionality while managing energy expenses.
... Probabilistic tractography with anatomical constraints 66 was performed to generate 15 million streamlines per subject. Streamline weights were estimated using the SIFT2 algorithm 67 . Using the cortical surfaces the 7 Networks 400 Parcels Schaefer parcellation was projected into subject space 25 . ...
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The capacity of the brain to process input across temporal scales is exemplified in human narrative, which requires integration of information ranging from words, over sentences to long paragraphs. It has been shown that this processing is distributed in a hierarchy across multiple areas in the brain with areas close to the sensory cortex, processing on a faster time scale than areas in associative cortex. In this study we used reservoir computing with human derived connectivity to investigate the effect of the structural connectivity on time scales across brain regions during a narrative task paradigm. We systematically tested the effect of removal of selected fibre bundles (IFO, ILF, MLF, SLF I/II/III, UF, AF) on the processing time scales across brain regions. We show that long distance pathways such as the IFO provide a form of shortcut whereby input driven activation in the visual cortex can directly impact distant frontal areas. To validate our model we demonstrated significant correlation of our predicted time scale ordering with empirical results from the intact/scrambled narrative fMRI task paradigm. This study emphasizes structural connectivity’s role in brain temporal processing hierarchies, providing a framework for future research on structure and neural dynamics across cognitive tasks.
... Probabilistic anatomically constrained tractography (ACT) was performed using the 121 GM regions of interest (ROIs) from the DKT atlas, generating 10 million streamlines to construct a tractogram for each subject. Spherical-deconvolution Informed Filtering of Tractograms 2 (SIFT2) was additionally applied to improve fiber accuracy [36]. Fractional anisotropy (FA) maps were generated with dwitensor. ...
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Cognitive impairment (CI) in multiple sclerosis (MS) is only partially explained by whole-brain volume measures, but independent component analysis (ICA) can extract regional patterns of damage in grey matter (GM) or white matter (WM) that have proven more closely associated with CI. Pathology in GM and WM occurs in parallel, and so patterns can span both. This study assessed whether joint-ICA of GM and WM features better explained cognitive function compared to single-tissue ICA. 89 people with MS underwent cognitive testing and magnetic resonance imaging. Structural T1 and diffusion-weighted images were used to measure GM volumes and WM connectomes (based on fractional anisotropy weighted by the number of streamlines). ICA was performed for each tissue type separately and as joint-ICA. For each tissue type and joint-ICA, 20 components were extracted. In stepwise linear regression models, joint-ICA components were significantly associated with all cognitive domains. Joint-ICA showed the highest variance explained for executive function (Adjusted R² = 0.35) and visual memory (Adjusted R² = 0.30), while WM-ICA explained the highest variance for working memory (Adjusted R² = 0.23). No significant differences were found between joint-ICA and single-tissue ICA in information processing speed or verbal memory. This is the first MS study to explore GM and WM features in a joint-ICA approach and shows that joint-ICA outperforms single-tissue analysis in some, but not all cognitive domains. This highlights that cognitive domains are differentially affected by tissue-specific features in MS and that processes spanning GM and WM should be considered when explaining cognition. Supplementary Information The online version contains supplementary material available at 10.1007/s00415-024-12795-2.
... Quantitative MRI offers another approach, focusing on absolute unit measurements inherently comparable across sites (Cooper et al., 2020;Smith, Tournier, Calamante, & Connelly, 2015). Improving access to high-quality MRI scanners through resource sharing and targeted funding for technology upgrades ensures broader participation in comprehensive network neuroscience studies. ...
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Translational network neuroscience aims to integrate advanced neuroimaging and data analysis techniques into clinical practice to better understand and treat neurological disorders. Despite the promise of technologies such as functional MRI and diffusion MRI combined with network analysis tools, the field faces several challenges that hinder its swift clinical translation. We have identified 9 key roadblocks that impede this process: (1) Theoretical and basic science foundations; (2) Network construction, data interpretation, and validation; (3) MRI access, data variability, and protocol standardization; (4) Data sharing; (5) Computational resources and expertise; (6) Interdisciplinary collaboration; (7) Industry collaboration and commercialization; (8) Operational efficiency, integration and training; and (9) Ethical and legal considerations. To address these challenges, we propose several possible solution strategies. By aligning scientific goals with clinical realities and establishing a sound ethical framework, translational network neuroscience can achieve meaningful advances in personalized medicine and ultimately improve patient care. We advocate for an interdisciplinary commitment to overcoming translational hurdles in network neuroscience and integrating advanced technologies into routine clinical practice.
... [22]. The "spherical-deconvolution informed filtering of tractograms" (SIFT) method was performed for optimization of the reconstructed streamlines by matching the tractography streamlines with the fiber densities using the constrained spherical deconvolution (CSD) model [23]. Preprocessing of the diffusion-weighted data was done using the default procedure of the FSL eddy tool for multi-shell DTI (FMRIB Software Library, Analysis Group, FMRIB, Oxford, UK; Version 6) [24]. ...
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Background Temporal lobe epilepsy (TLE) can lead to structural brain abnormalities, with thalamus atrophy being the most common extratemporal alteration. This study used probabilistic tractography to investigate the structural connectivity between individual thalamic nuclei and the hippocampus in TLE. Methods Thirty‐six TLE patients who underwent pre‐surgical 3 Tesla magnetic resonance imaging (MRI) and 18 healthy controls were enrolled in this study. Patients were subdivided into TLE with HS (TLE‐HS) and MRI‐negative TLE (TLE‐MRneg). Tractography and whole brain segmentation, including thalamus parcellation, were performed to determine the number of streamlines per mm³ between the thalamic nuclei and hippocampus. Connectivity strength and volume of regions were correlated with clinical data. Results The volume of the entire thalamus ipsilateral to seizure onset was significantly decreased in TLE‐HS compared to controls (Mann–Whitney‐U test: pFDR < 0.01) with the anterior thalamic nuclei (ANT) as important contributor. Furthermore, decreased ipsilateral connectivity strength between the hippocampus and ANT was detected in TLE‐HS (pFDR < 0.01) compared to TLE‐MRneg and controls which correlated negatively with the duration of epilepsy (ρ = −0.512, p = 0.025) and positively with seizure frequency (ρ = 0.603, p = 0.006). Moreover, ANT volume correlated negatively with epilepsy duration in TLE‐HS (ρ = −0.471, p = 0.042). Conclusions ANT showed atrophy and decreased connectivity in TLE‐HS, which correlated with epilepsy duration and seizure frequency. Understanding the dynamics of epileptogenic networks has the potential to shed light on surgery‐resistant epilepsy and refine the selection process for ideal neurosurgical candidates, consequently enhancing post‐surgical outcomes.
... We generated a tractogram with 40M streamlines (maximum tract length=250; fractional anisotropy cutoff=0.06). We applied spherical deconvolution informed filtering of tractograms (SIFT2) to reconstruct whole brain streamlines weighted by cross-sectional multipliers (134). The reconstructed cross-section streamlines were mapped to the Schaefer-200 parcellation, which was also warped to DWI space using the previously generated transforms (130). ...
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The brain is a physically embedded and heavily interconnected system that expresses neural rhythms across multiple time scales. While these dynamics result from the complex interplay of local and inter-regional factors, the relative contribution of such mechanisms across the cortex remains unclear. Our study explored geometric, microstructural, and connectome-level constraints on cortex-wide neural activity. We leveraged intracranial electroencephalography recordings to derive an intrinsic coordinate system of human cortical dynamics. Using multimodal neuroimaging, we could then demonstrate that these patterns were largely explainable by geometric properties indexed by inter-regional distance. However, dynamics in transmodal association regions were additionally explainable by incorporation of inter-regional microstructural similarity and connectivity information. Our findings were consistent when cross-referencing electroencephalography and imaging data from large-scale atlases and when using data obtained in the same individuals, suggesting subject-specificity and population-level generalizability. Together, our results support gradual shifts in the balance of local and macroscale constraints on cortical dynamics and highlight a key role of transmodal networks in inter-regional cortical coordination.
... In particular, we employed multi-shell multi-tissue constrained spherical deconvolution (mrtrix multishell msmt ACT-hsvs) for estimating the fiber orientation distribution using the Dhollander algorithm . We then applied tckgen, specifically iFOD2, a probabilistic tracking method that generates 10 7 streamlines, with the weights for each streamline calculated using SIFT2 (Smith et al., 2015). We set the T1w segmentation reconstructed through Freesurfer as an anatomical constraint. ...
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The intricate structural and functional architecture of the brain enables a wide range of cognitive processes ranging from perception and action to higher order abstract thinking. Despite important progress, the relationship between the brain’s structural and functional properties is not yet fully established. In particular, the way the brain’s anatomy shapes its electrophysiological dynamics remains elusive. The electroencephalography (EEG) activity recorded during naturalistic tasks is thought to exhibit patterns of coupling with the underlying brain structure that vary as a function of behavior. Yet these patterns have not yet been sufficiently quantified. We address this gap by jointly examining individual Diffusion-Weighted Imaging (DWI) scans and continuous EEG recorded during video watching and resting state, using a Graph Signal Processing (GSP) framework. By decomposing the structural graph into eigenmodes and expressing the EEG activity as an extension of anatomy, GSP provides a way to quantify the structure–function coupling. We elucidate how the structure shapes function during naturalistic tasks such as movie watching and how this association is modulated by tasks. We quantify the coupling relationship in a region-, time-, and frequency-resolved manner. First of all, our findings indicate that the EEG activity in the sensorimotor cortex is strongly coupled with brain structure, while the activity in higher order systems is less constrained by anatomy, that is, shows more flexibility. In addition, we found that watching videos was associated with stronger structure–function coupling in the sensorimotor cortex, as compared with resting-state data. Second, time-resolved analysis revealed that the unimodal systems undergo minimal temporal fluctuation in structure–function association, and the transmodal system displays the highest temporal fluctuations, with the exception of PCC seeing low fluctuations. Lastly, our frequency-resolved analysis revealed a consistent topography across different EEG rhythms, suggesting a similar relationship with the anatomical structure across frequency bands. Together, this unprecedented characterization of the link between structure and function using continuous EEG during naturalistic behavior underscores the role of anatomy in shaping ongoing cognitive processes. Taken together, by combining the temporal and spectral resolution of EEG and the methodological advantages of GSP, our work sheds new light on the anatomo-functional organization of the brain.
... Finally, weights for each streamline were calculated using SIFT2 [Smith et al. 2015] and a 114 x 114 SC matrix was filled with the sums of weights of streamlines connecting each node's pair. In addition, structural connectivity matrices were log 10transformed to better account for differences at different magnitudes and to make the distribution of edges' weight more comparable to other layers [Buchanan et al. 2020]. ...
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Disruptions to brain networks, measured using structural (sMRI), diffusion (dMRI), or functional (fMRI) MRI, have been shown in people with multiple sclerosis (PwMS), highlighting the relevance of regions in the core of the connectome but yielding mixed results depending on the studied connectivity domain. Using a multilayer network approach, we integrated these three modalities to portray an enriched representation of the brain's core‐periphery organization and explore its alterations in PwMS. In this retrospective cross‐sectional study, we selected PwMS and healthy controls with complete multimodal brain MRI acquisitions from 13 European centers within the MAGNIMS network. Physical disability and cognition were assessed with the Expanded Disability Status Scale (EDSS) and the symbol digit modalities test (SDMT), respectively. SMRI, dMRI, and resting‐state fMRI data were parcellated into 100 cortical and 14 subcortical regions to obtain networks of morphological covariance, structural connectivity, and functional connectivity. Connectivity matrices were merged in a multiplex, from which regional coreness—the probability of a node being part of the multiplex core—and coreness disruption index (κ)—the global weakening of the core‐periphery structure—were computed. The associations of κ with disease status (PwMS vs. healthy controls), clinical phenotype, level of physical disability (EDSS ≥ 4 vs. EDSS < 4), and cognitive impairment (SDMT z‐score < −1.5) were tested within a linear model framework. Using random forest permutation feature importance, we assessed the relative contribution of κ in the multiplex and single‐layer domains, in addition to conventional MRI measures (brain and lesion volumes), in predicting disease status, physical disability, and cognitive impairment. We studied 1048 PwMS (695F, mean ± SD age: 43.3 ± 11.4 years) and 436 healthy controls (250F, mean ± SD age: 38.3 ± 11.8 years). PwMS showed significant disruption of the multiplex core‐periphery organization (κ = −0.14, Hedges' g = 0.49, p < 0.001), correlating with clinical phenotype (F = 3.90, p = 0.009), EDSS (Hedges' g = 0.18, p = 0.01), and SDMT (Hedges' g = 0.30, p < 0.001). Multiplex κ was the only connectomic measure adding to conventional MRI in predicting disease status and cognitive impairment, while physical disability also depended on single‐layer contributions. In conclusion, we show that multilayer networks represent a biologically and clinically meaningful framework to model multimodal MRI data, with disruption of the core‐periphery structure emerging as a potential connectomic biomarker for disease severity and cognitive impairment in PwMS.
... For each participant, probabilistic (iFOD2) [36], anatomically constrained tractography (ACT) [37] algorithm with voxel-wise directional uncertainty fractional anisotropy (FA) > 0.2, direction change < 60° were used to create the individual, whole-brain tractograms containing 0.2 million streamlines. The SC between any two regions was the SIFT2-weighted [38] sum of streamlines connecting those regions divided by the sum of the gray-matter volume of those regions, resulting a symmetric 412 × 412 ROI-volume SC matrix for each subject. Then, we calculated the separate SC strengths at the regional and the network level, respectively. ...
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Background Parkinson’s disease (PD) is a progressive neurodegenerative disease associated with functional and structural alterations beyond the nigrostriatal dopamine projection. However, the structural-functional (SC-FC) coupling changes in combination with subcortical regions at the network level are rarely investigated in PD. Methods SC-FC coupling networks were systematically constructed using the structural connectivity obtained by diffusion tensor imaging and the functional connectivity obtained by resting-state functional magnetic resonance imaging in 53 PD and 72 age- and sex-matched healthy controls (HCs). Then, we explored how SC-FC coupling varied within and between several well-defined functional domains. Results Results showed that the SC-FC coupling in patients with PD was globally reduced in comparison with HCs. Specifically, regional SC-FC decoupled in the inferior parietal lobule, occipitotemporal cortex, motor cortex, and higher-order association cortex in patients with PD. Moreover, PD showed intranetwork SC-FC decoupling in the visual network (VIS), limbic and higher-order association networks. Furthermore, internetwork decoupling mainly linked to the VIS, the somatomotor network (SOM), the dorsal attention network, and the default mode network, was observed, increased internetwork coupling was found between the subcortical network and the SOM in PD (all p < 0.05, FDR corrected). Conclusions These findings suggest that PD is characterized by SC-FC decoupling in topological organization of multiscale brain networks, providing insights into the brain network mechanisms in PD.
... Then, biases were reduced using the spherical-deconvolution informed filtering of tractograms2 (SIFT2) algorithm, which determines an appropriate cross-sectional area multiplier for each streamline in both human and macaque data. 117 Mapping Connectivity Patterns at the voxel level After a whole-brain probabilistic tractography, for each subject, tractograms were generated and mapped onto the volume to construct the connectivity profiles, where the intensity of each voxel in the gray matter represented the number of streamlines (or called connection strength) with one given ROI (e.g., MST). 118 Subsequently, we binarized the connectivity profiles of each individual without setting any thresholds, with the voxel intensity indicating the presence (defined as 1) or absence (defined as 0) of streamlines. ...
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MT+ is pivotal in the dorsal visual stream, encoding tool-use characteristics such as motion speed and direction. Despite its conservation between humans and monkeys, differences in MT+ spatial location and organization may lead to divergent, yet unexplored, connectivity patterns and functional characteristics. Using diffusion tensor imaging, we examined the structural connectivity of MT+ subregions in macaques and humans. We also employed graph-theoretical analyses on the constructed homologous tool-use network to assess their functional roles. Our results revealed location-dependent connectivity in macaques, with MST, MT, and FST predominantly connected to dorsal, middle, and ventral surfaces, respectively. Humans showed similar connectivity across all subregions. Differences in connectivity between MST and FST are more pronounced in macaques. In humans, the entire MT+ region, especially MST, exhibited stronger information transmission capabilities. Our findings suggest that the differences in tool use between humans and macaques may originate earlier than previously thought, particularly within the MT+ region.
... SIFT2 allows for a more logically direct and computationally efficient solution to the streamlines connectivity quantification problem: by determining an appropriate cross-sectional area multiplier for each streamline rather than removing streamlines altogether, biologically accurate measures of fibre connectivity are obtained whilst making use of the complete streamlines reconstruction (R. E. Smith et al., 2015). Then, we mapped the SIFT2 outputted streamlines onto the 374 chosen brain regions (360 from Glasser et al. (Glasser et al., 2016) brain atlas plus 14 subcortical regions, see Brain Atlases section) to produce a structural connectome (MRtrix command tck2connectome). ...
Preprint
One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework, to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common hybrid connectivity patterns that represent the connectivity fingerprint of a subject. We test this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracted two main task-sensitive hybrid traits. The first, encompassing the within and between connections of dorsal attentional and visual areas, as well as fronto-parietal circuits. The second, mainly encompassing the connectivity between visual, attentional, DMN and subcortical networks. Overall, these findings confirms the potential ofthe hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks.
... QC was performed during preprocessing by visual inspection of images before and after registration. MRtrix 3.0 (https://www .mrtrix.org/) was used to derive an anatomically constrained probabilistic tractography (2 million streamlines, step = 0.3 mm, maximum length = 300 mm, and backtracking) filtered with SIFT2 (Smith, Tournier, Calamante, & Connelly, 2015). Subject-specific brain parcellations from T1weighted images were derived using FreeSurfer (https://surfer.nmr.mgh.harvard.edu/) ...
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Amyloid-β (Aβ) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer’s disease. It is well-known that the identification of individuals with Aβ positivity could enable early diagnosis. In this work, we aim at capturing the Aβ positivity status in an unbalanced cohort enclosing subjects at different disease stages, exploiting the underlying structural and connectivity disease-induced modulations as revealed by structural, functional, and diffusion MRI. Of note, due to the unbalanced cohort, the outcomes may be guided by those factors rather than amyloid accumulation. The partial views provided by each modality are integrated in the model, allowing to take full advantage of their complementarity in encoding the effects of the Aβ accumulation, leading to an accuracy of 0.762 ± 0.04. The specificity of the information brought by each modality is assessed by post hoc explainability analysis (guided backpropagation), highlighting the underlying structural and functional changes. Noteworthy, well-established biomarker key regions related to Aβ deposition could be identified by all modalities, including the hippocampus, thalamus, precuneus, and cingulate gyrus, witnessing in favor of the reliability of the method as well as its potential in shedding light on modality-specific possibly unknown Aβ deposition signatures.
... 1101 Orientation Distributions (iFOD2). Streamlines were weighted using SIFT2 (Smith et al., 2015), and the group consensus connectivity matrix was created using a distance dependant thresholding algorithm. Further details are available in Hasnen et al., (2023). ...
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Generative network models (GNMs) have been proposed to identify the mechanisms/constraints that shape the organisation of the connectome. These models most commonly parameterise the formation of inter-regional axonal connections using a trade-off between connection cost and some measure of topological complexity or functional value. Despite its simplicity, GNMs can generate synthetic networks that capture many topological properties of empirical brain networks. However, current models often fail to capture the spatial embedding––i.e., the topography––of many such properties, such as the anatomical location of network hubs. In this study, we investigate a diverse array of generative network model formulations and find that none can accurately capture empirical patterns of long-range connectivity. We demonstrate that the spatial embedding of long-range connections is critical in defining hub locations and that it is precisely these connections that are poorly captured by extant models. We further show how standard metrics used for model optimisation and evaluation fail to capture the true correspondence between synthetic and empirical brain networks. Overall, our findings demonstrate common failure modes of GNMs, identify why these models do not fully capture brain network organisation, and suggest ways the field can move forward to address these challenges. Author summary Generative network models aim to explain the organisation of connectomes using simple wiring rules. While these models replicate topological features of brain networks, they do not to capture key topographical properties, like the anatomical location of network hubs. We show that this failure occurs because the models are unable to accurately capture the spatial position of long-range inter-regional connections. Standard measures used for model evaluation also fail to accurately quantify the similarity between model and empirical networks. This study identifies how and why limitations of current generative models occur and suggests ways forward for improved practices.
... Subsequently, the fiber orientation distribution for each voxel was determined by performing a multishell, multitissue-constrained spherical deconvolution (MSMT-CSD; Jeurissen, Tournier, Dhollander, Connelly, & Sijbers, 2014). ACT was performed by randomly seeding 100 million fibers within the white matter to construct a tractogram, and spherical-deconvolution informed filtering of tractograms (SIFT, SIFT2 method in MRtrix3; Smith, Tournier, Calamante, & Connelly, 2015) was then performed to improve the accuracy of the reconstructed streamlines and reduce false positives. For every participant, their respective 3D T1-weighted image was used to parcellate the brain into the 78 cortical regions. ...
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Brain tumors can induce pathological changes in neuronal dynamics that are reflected in functional connectivity measures. Here, we use a whole-brain modeling approach to investigate pathological alterations to neuronal activity in glioma patients. By fitting a Hopf whole-brain model to empirical functional connectivity, we investigate glioma-induced changes in optimal model parameters. We observe considerable differences in neuronal dynamics between glioma patients and healthy controls, both on an individual and population-based level. In particular, model parameter estimation suggests that local tumor pathology causes changes in brain dynamics by increasing the influence of interregional interactions on global neuronal activity. Our approach demonstrates that whole-brain models provide valuable insights for understanding glioma-associated alterations in functional connectivity.
... Here, I performed tractography with a probabilistic, anatomically constrained streamline tractography using MRtrix 73 , employing the iFOD2 (Second-order Integration over Fiber Orientation Distributions) algorithm [74][75][76] . The selected parameters of the algorithm were : a) the minimum and maximum streamline lengths were ranged between 30 mm and 250mm, b) the maximum angle between successive steps was defined to 50°, and c) the FOD amplitude cut-off was set-up to 0.06. ...
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It has been proposed that the estimation of the normalized graph Laplacian over a brain network's spectral decomposition can reveal the connectome harmonics (eigenvectors) corresponding to certain frequencies (eigenvalues). Here, I used test-retest dMRI data from the Human Connectome Project to explore the repeatability, and the influence of graph construction schemes on a) graph Laplacian spectrum, b) topological properties, c) high-order interactions, and d) their associations on from structural brain network (SBN). Additionally, I investigated the performance of subject’s identification accuracy (brain fingerprinting) of the graph Laplacian spectrum, the topological properties, and the high-order interactions. Normalized Laplacian eigenvalues were found to be subject-specific and repeatable across the five graph construction schemes. The repeatability of connectome harmonics is lower than that of the Laplacian eigenvalues and shows a heavy dependency on the graph construction scheme. A repeatable relationship between specific topological properties of the SBN with the Laplacian spectrum was also revealed. The identification accuracy of normalized Laplacian eigenvalues was absolute (100%) across the graph construction schemes, while a similar performance was observed for a combination of topological properties of SBN (communities,3,4-motifs) only for the 9m-OMST. Collectively, Laplacian spectrum, topological properties, and high-order interactions characterized uniquely SBN.
... We then used a deterministic tractography algorithm to construct the tractogram from the estimated bers 174 , employing the algorithm's default settings with modi cations only to the maximum length (250 mm) and the number of streamlines (5 million). To enhance accuracy in the tractogram, we applied a ltering method 175 , which minimizes spurious ber tracking. SC matrices were then generated by counting the stream lines between brain regions based on two cortical parcellations: Schaefer-100 and MMP-360. ...
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Understanding the principle of information flow across distributed brain networks is of paramount importance in neuroscience. Here, we introduce a novel neuroimaging framework, leveraging integrated effective connectivity (iEC) and unconstrained signal flow mapping for data-driven discovery of the human cerebral functional hierarchy. Simulation and empirical validation demonstrated the high fidelity of iEC in recovering connectome directionality and its potential relationship with histologically defined feedforward and feedback pathways. Notably, the iEC-derived hierarchy displayed a monotonously increasing level along the axis where the sensorimotor, association, and paralimbic areas are sequentially ordered – a pattern supported by the Structural Model of laminar connectivity. This hierarchy was further demonstrated to flexibly reorganize according to brain states, flattening during an externally oriented condition, evidenced by a reduced slope in the hierarchy, and steepening during an internally focused condition, reflecting heightened engagement of interoceptive regions. Our study highlights the unique role of macroscale directed functional connectivity in uncovering a neurobiologically grounded, state-dependent signal flow hierarchy.
... Structural connectivity matrices were generated using tck2connectome in MRtrix3. Each streamline was then assigned a weight, calculated using a spherical deconvolutioninformed filtering of tractograms (SIFT2) (Smith et al. 2015). These weights were then adjusted using the invnodevolume metric implemented in MRtrix3, which weights the connectome matrix according to the inverse of the surface areas of the regions of interest ("nodes"), to compensate for variances in node size in our custom parcellation scheme (see following section). ...
Article
Adolescent Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disabling illness of unknown etiology. Increasing evidence suggests hypothalamic involvement in ME/CFS pathophysiology, which has rarely been explored using magnetic resonance imaging (MRI) in the condition. This work aimed to use MRI to examine hypothalamus connectivity in adolescents with ME/CFS and explore how this relates to fatigue severity and illness duration. 25 adolescents with ME/CFS and 23 healthy controls completed a neuroimaging protocol consisting of structural and multishell diffusion‐weighted imaging sequences, in addition to the PedsQL Multidimensional Fatigue Scale to assess fatigue severity. Information about illness duration was acquired at diagnosis. Preprocessing and streamlines tractography was performed using QSIPrep combined with a custom parcellation scheme to create structural networks. The number (degree) and weight (strength) of connections between lateralized hypothalamus regions and cortical and subcortical nodes were extracted, and relationships between connectivity measures, fatigue severity, and illness duration were performed using Bayesian regression models. We observed weak‐to‐moderate evidence of increased degree, but not strength, of connections from the bilateral anterior‐inferior (left: pd [%] = 99.18, median [95% CI] = −22.68[−40.96 to 4.45]; right: pd [%] = 99.86, median [95% CI] = −23.35[−38.47 to 8.20]), left anterior‐superior ( pd [%] = 99.33, median [95% CI] = −18.83[−33.45 to 4.07]) and total left hypothalamus ( pd [%] = 99.44, median [95% CI] = −47.18[−83.74 to 11.03]) in the ME/CFS group compared with controls. Conversely, bilateral posterior hypothalamus degree decreased with increasing ME/CFS illness duration (left: pd [%] = 98.13, median [95% CI]: −0.47[−0.89 to 0.03]; right: pd [%] = 98.50, median [95% CI]:‐0.43[−0.82 to 0.05]). Finally, a weak relationship between right intermediate hypothalamus connectivity strength and fatigue severity was identified in the ME/CFS group ( pd [%] = 99.35, median [95% CI] = −0.28[−0.51 to 0.06]), which was absent in controls. These findings suggest changes in hypothalamus connectivity may occur in adolescents with ME/CFS, warranting further investigation.
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Background The treatment of glioblastomas (GBM) with radiation therapy is extremely challenging due to their invasive nature and high recurrence rate within normal brain tissue. Purpose In this work, we present a new metric called the tumour spread (TS) map, which utilizes diffusion tensor imaging (DTI) to predict the probable direction of tumour cells spread along fiber tracts. We hypothesized that the TS map could serve as a predictive tool for identifying patterns of likely recurrence in patients with GBM and, therefore, be used to modify the delivery of radiation treatment to pre‐emptively target regions at high risk of tumour spread. Methods In this proof‐of‐concept study, we visualized the white matter fiber tract pathways within the brain using diffusion tensor tractography and developed an algorithm which mathematically calculates a relative probability index in each voxel, resulting in the generation of the TS map. Based on the information provided by the TS map, the original radiotherapy target volume was then modified to include areas with a higher probability of tumour spread and exclude other areas with a lower probability of spread. A volumetric modulated arc therapy (VMAT) treatment plan was then developed utilizing the modified target volumes and subsequently compared to that using the original target volumes. Follow‐up anatomical imaging obtained 8 months post‐surgery was assessed to validate our findings. Results A TS map was generated on a glioblastoma patient demonstrating a relative probability of tumour spread along fiber tracts throughout the brain. The modified planning target volume better covered brain regions with a higher risk of tumour spread while still demonstrating a 21% reduction in volume compared to the original planning target volume, resulting in greater preservation of normal tissue. The modified VMAT plan resulted in an average mean dose to four identified recurrences of 80% of the prescription dose, while the original VMAT plan delivered only 63% of the prescription dose as the average mean dose to the recurrences. Conclusion The utilization of tractography and the generation of corresponding TS maps offer a promising approach to predicting patterns of tumour recurrence and optimizing treatment delivery. Further research is needed to validate the predictive value of the TS map on a larger cohort of patients and explore its potential in personalized treatment strategies for GBM patients.
Article
Combination of structural and functional brain connectivity methods provides a more complete and effective avenue into the investigation of cortical network responses to traumatic brain injury (TBI) and subtle alterations in brain connectivity associated with TBI. Structural connectivity (SC) can be measured using diffusion tensor imaging to evaluate white matter integrity, whereas functional connectivity (FC) can be studied by examining functional correlations within or between functional networks. In this study, the alterations of SC and FC were assessed for TBI patients, with and without chronic symptoms (TBIcs/TBIncs), compared with a healthy control group (CG). The correlation between global SC and FC was significantly increased for both TBI groups compared with CG. SC was significantly lower in the TBIcs group compared with CG, and FC changes were seen in the TBIncs group compared with CG. When comparing TBI groups, FC differences were observed in the TBIcs group compared with the TBIncs group. These observations show that the presence of chronic symptoms is associated with a distinct pattern of SC and FC changes including the atrophy of the SC and a mixture of functional hypoconnectivity and hyperconnectivity, as well as loss of segregation of functional networks.
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INTRODUCTION Visual short‐term memory (VSTM) is a critical indicator of Alzheimer's disease (AD), but whether its neural substrates could adapt to early disease progression and contribute to cognitive resilience in amnestic mild cognitive impairment (aMCI) has been unclear. METHODS Fifty‐five aMCI patients and 68 normal controls (NC) performed a change‐detection task and underwent multimodal neuroimaging scanning. RESULTS Among the atrophic brain regions in aMCI, VSTM performance correlated with the volume of the right prefrontal cortex (PFC) but not the medial temporal lobe (MTL), and this correlation was mainly present in patients with greater MTL atrophy. Furthermore, VSTM was primarily correlated with frontal structural connectivity in aMCI but was correlated with more distributed frontal and MTL connectivity in NC. DISCUSSION This study provided evidence on neural adaptation in the precursor stages of AD, highlighting the compensatory role of PFC as MTL deteriorated and suggesting potential targets in early intervention for cognitive preservation. Highlights Atrophic left medial temporal lobe (MTL) no longer correlated with visual short‐term memory (VSTM) in amnestic mild cognitive impairment (aMCI). Atrophic right middle frontal area continued to correlate with VSTM in aMCI. Frontal brain–behavior correlation was mainly present in the aMCI subgroup with greater medial temporal lobe (MTL) atrophy. Reliance of VSTM on frontal connectivity increased in compensation for MTL dysfunction.
Article
Optimal brain function is shaped by a combination of global information integration, facilitated by long-range connections, and local processing, which relies on short-range connections and underlying biological factors. With aging, anatomical connectivity undergoes significant deterioration, which affects the brain’s overall function. Despite the structural loss, previous research has shown that normative patterns of functions remain intact across the lifespan, defined as the compensatory mechanism of the aging brain. However, the crucial components in guiding the compensatory preservation of the dynamical complexity and the underlying mechanisms remain uncovered. Moreover, it remains largely unknown how the brain readjusts its biological parameters to maintain optimal brain dynamics with age; in this work, we provide a parsimonious mechanism using a whole-brain generative model to uncover the role of sub-communities comprised of short-range and long-range connectivity in driving the dynamic compensation process in the aging brain. We utilize two neuroimaging datasets to demonstrate how short- and long-range white matter tracts affect compensatory mechanisms. We unveil their modulation of intrinsic global scaling parameters, such as global coupling strength and conduction delay, via a personalized large-scale brain model. Our key finding suggests that short-range tracts predominantly amplify global coupling strength with age, potentially representing an epiphenomenon of the compensatory mechanism. This mechanistically explains the significance of short-range connections in compensating for the major loss of long-range connections during aging. This insight could help identify alternative avenues to address aging-related diseases where long-range connections are significantly deteriorated.
Chapter
This chapter introduces fiber tractography, a diffusion MRI technique that allows reconstructing the organization of biological tissues characterized by consistently oriented structures, or “fibers.” Examples of fibers are axonal bundles in the brain, or bundles of myocytes in the skeletal muscle. In this chapter, we first present an overview of the tractography process, covering the distinction between deterministic and probabilistic approaches, seeding strategies, the propagation process, and anatomical constraints. Subsequently, we will present an overview of methods that are commonly used to reconstruct local fiber orientations, which provide the local piecewise information that tractography collates in its topographical reconstruction. Finally, the chapter showcases some example applications of fiber tractography to explore brain anatomy, and to study brain structure and connectivity.
Article
Background: Hippocampal avoidance during prophylactic cranial irradiation (HA-PCI) is proposed to reduce neurocognitive decline, while preserving the benefits of PCI. We evaluated whether (HA-)PCI induces changes in white matter (WM) microstructure and whether sparing the hippocampus has an impact on preserving brain network topology. Additionally, we evaluated associations between topological metrics with hippocampal volume and neuropsychological outcomes. Methods: In this multicenter randomized phase 3 trial (NCT01780675), small cell lung cancer (SCLC) patients underwent neuropsychological testing and diffusion tensor imaging (DTI) before, 4 months (33 PCI, 37 HA-PCI) and 1 year (19 PCI, 17 HA-PCI) after (HA-)PCI. Changes in WM microstructure were investigated using whole-brain voxel-based analysis of fractional anisotropy (FA) and mean diffusivity (MD). Both hippocampal and whole-brain graph measures were used to evaluate the topological organization of structural networks. Correlation analysis was performed to associate topological metrics with neuropsychological outcomes and hippocampal volume. Results: Both HA-PCI and PCI were associated with decreased FA in major WM tracts, such as the corpus callosum, at 4 months and 1 year post-treatment. While these FA decreases did not differ significantly between treatment groups, only PCI demonstrated increased MD over time. Additionally, PCI showed decreased global efficiency and increased characteristic path length over time when compared to HA-PCI. Significant correlations were found between whole-brain graph measures and neuropsychological outcomes. Conclusion: While both techniques induce important changes in the WM microstructure, HA-PCI might better preserve the topological organization of brain networks than PCI. The neuroprotective role of hippocampal sparing still needs further investigation.
Article
Background Working memory (WM) deficits are among the most prominent cognitive impairments in attention deficit hyperactivity disorder (ADHD). While functional connectivity is a prevailing approach in brain imaging of ADHD, alterations in WM-related functional brain networks and their malleability by cognitive training are not well known. We examined whole-brain functional connectivity differences between adults with and without ADHD during n-back WM tasks and rest at pretest, as well as the effects of WM training on functional and structural brain connectivity in the ADHD group. Methods Forty-two adults with ADHD and 36 neurotypical controls performed visuospatial and verbal n-back tasks during functional magnetic resonance imaging (fMRI). In addition, seven-minute resting state fMRI data and diffusion-weighted MR images were collected from all participants. The adults with ADHD continued into a 5-week randomized controlled WM training trial (experimental group training on a dual n-back task, n = 21; active control group training on Bejeweled II video game, n = 21), followed by a posttraining MRI. Brain connectivity was examined with Network-Based Statistic. Results At the pretest, adults with ADHD had decreased functional connectivity compared with the neurotypical controls during both n-back tasks in networks encompassing fronto-parietal, temporal, occipital, cerebellar, and subcortical brain regions. Furthermore, WM-related connectivity in widespread networks was associated with performance accuracy in a continuous performance test. Regarding resting state connectivity, no group differences or associations with task performance were observed. WM training did not modulate functional or structural connectivity compared with the active controls. Conclusion Our results indicate large-scale abnormalities in functional brain networks underlying deficits in verbal and visuospatial WM commonly faced in ADHD. Training-induced plasticity in these networks may be limited.
Article
Introduction There is growing evidence suggesting that children with prenatal alcohol exposure (PAE) struggle with cognitively demanding tasks, such as learning, attention, and language. Complex structural network analyses can provide insight into the neurobiological underpinnings of these functions, as they may be sensitive for characterizing the effects of PAE on the brain. However, investigations on how PAE affects brain networks are limited. We aim to compare diffusion magnetic resonance imaging (MRI) tractography-based structural networks between children with low-to-moderate PAE in trimester 1 only (T1) or throughout all trimesters (T1-T3) with those without alcohol exposure prenatally. Methods Our cohort included three groups of children aged 6 to 8 years: 1) no PAE (n = 24), 2) low-to-moderate PAE during T1 only (n = 30), 3) low-to-moderate PAE throughout T1-T3 (n = 36). Structural networks were constructed using the multi-shell multi-tissue constrained spherical deconvolution tractography technique. Quantitative group-wise analyses were conducted at three levels: (a) at the whole-brain network level, using both network-based statistical analyses and network centrality; and then using network centrality at (b) the modular level, and (c) per-region level, including the regions identified as brain hubs. Results Compared with the no PAE group, widespread brain network alterations were observed in the PAE T1-T3 group using network-based statistics, but no alterations were observed for the PAE T1 group. Network alterations were also detected at the module level in the PAE T1-T3 compared with the no PAE group, with lower eigenvector centrality in the module that closely represented the right cortico-basal ganglia-thalamo-cortical network. No significant group differences were found in network centrality at the per-region level, including the hub regions. Conclusions This study demonstrated that low-to-moderate PAE throughout pregnancy may alter brain structural connectivity, which may explain the neurodevelopmental deficits associated with PAE. It is possible that timing and duration of alcohol exposure are crucial, as PAE in T1 only did not appear to alter brain structural connectivity.
Article
BACKGROUND Predicting function from structure is central to optimizing the onco-functional balance in neurosurgical planning: if we can predict changes in function due to changes in structure, we may be able to predict the functional outcomes of surgery. In recent years, this challenge has been formulated in terms of predicting Functional Connectivity (FC) from Structural Connectivity (SC), both derived from advanced MRI. Several recent studies claim high individual-level accuracy in predicting FC from SC on healthy subjects. We investigated whether these approaches could be extended to the prediction of FC from SC in glioma patients. MATERIAL AND METHODS Three recent studies with promising results were considered for adaptation. To ensure correct implementation, we reproduced their methods on 1000 healthy subjects of the Human Connectome Project (HCP), a data set used in all three studies. We also retrospectively included 242 glioma patients from the Elisabeth-TweeSteden hospital (Tilburg, The Netherlands) to investigate if the studies’ methods could be translated to this population. SC was derived from tractography on diffusion-weighted MRI, while FC was derived from resting-state functional MRI. Performance was calculated as the average individual-level correlation between FC predicted from SC and FC derived from fMRI, and assessed with 10-fold cross-validation, using 90% of the data for training and 10% for validation. To compare performance on the two different data sets in a meaningful way, we constructed a baseline predictor that calculates the group average fMRI-derived FC of the training set and uses this single matrix as predictor for the validation set. RESULTS We replicated the prediction performance of 0.71 reported by the first of the three studies using HCP data. On the glioma patient data, we achieved a performance of 0.43. However, we found that for both data sets, the group average predictor outperformed the prediction model presented by the study, achieving a performance of 0.84 and 0.56 respectively. Subsequently, we found that the group average predictor matched the prediction performance reported by the other two studies. CONCLUSION None of the three studies improved upon the basic group average predictor, indicating that none of the predictive models represent a meaningful individual mapping from SC to FC. We consider it likely that the construction of SC or FC is currently not accurate enough at individual level to capture meaningful variation in individuals, either due to excessive noise in resting-state fMRI or due to inadequate accuracy in individual-level parcellations. In conclusion, a failure of promising methods to work on clinical populations may not be caused by population-specific difficulties, but by subtle problems with the methods themselves, only revealed upon careful scrutiny of the implications of the presented quantitative results.
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Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE). These PVEs can be caused both by complex (e.g. crossing) WM fiber configurations and non-WM tissue, such as gray matter (GM) and cerebrospinal fluid. High angular resolution diffusion imaging methods have been developed to correctly characterize complex WM fiber configurations, but significant non-WM PVEs are also present in a large proportion of WM voxels. In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice, the RF is modified based on tissue fractions estimated from high-resolution anatomical data. Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.
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Tractography algorithms provide us with the ability to non-invasively reconstruct fiber pathways in the white matter (WM) by exploiting the directional information described with diffusion magnetic resonance. These methods could be divided into two major classes, local and global. Local methods reconstruct each fiber tract iteratively by considering only directional information at the voxel level and its neighborhood. Global methods, on the other hand, reconstruct all the fiber tracts of the whole brain simultaneously by solving a global energy minimization problem. The latter have shown improvements compared to previous techniques but these algorithms still suffer from an important shortcoming that is crucial in the context of brain connectivity analyses. As no anatomical priors are usually considered during the reconstruction process, the recovered fiber tracts are not guaranteed to connect cortical regions and, as a matter of fact, most of them stop prematurely in the WM; this violates important properties of neural connections, which are known to originate in the gray matter (GM) and develop in the WM. Hence, this shortcoming poses serious limitations for the use of these techniques for the assessment of the structural connectivity between brain regions and, de facto, it can potentially bias any subsequent analysis. Moreover, the estimated tracts are not quantitative, every fiber contributes with the same weight toward the predicted diffusion signal. In this work, we propose a novel approach for global tractography that is specifically designed for connectivity analysis applications which: (i) explicitly enforces anatomical priors of the tracts in the optimization and (ii) considers the effective contribution of each of them, i.e., volume, to the acquired diffusion magnetic resonance imaging (MRI) image. We evaluated our approach on both a realistic diffusion MRI phantom and in vivo data, and also compared its performance to existing tractography algorithms.
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Diffusion-weighted imaging coupled with tractography is currently the only method for in vivo mapping of human white-matter fascicles. Tractography takes diffusion measurements as input and produces the connectome, a large collection of white-matter fascicles, as output. We introduce a method to evaluate the evidence supporting connectomes. Linear fascicle evaluation (LiFE) takes any connectome as input and predicts diffusion measurements as output, using the difference between the measured and predicted diffusion signals to quantify the prediction error. We use the prediction error to evaluate the evidence that supports the properties of the connectome, to compare tractography algorithms and to test hypotheses about tracts and connections.
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Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple non-parallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNRs), fiber configurations, and tissue fractions. Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35-50% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM-GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500-3000 s/mm(2), reasonable SNR (~30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.
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A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lanrange multipliers. The solution is obtained using the gradient-projection method. This amounts to solving a time dependent partial differential equation on a manifold determined by the constraints. As t → ∞ the solution converges to a steady state which is the denoised image. The numerical algorithm is simple and relatively fast. The results appear to be state-of-the-art for very noisy images. The method is noninvasive, yielding sharp edges in the image. The technique could be interpreted as a first step of moving each level set of the image normal to itself with velocity equal to the curvature of the level set divided by the magnitude of the gradient of the image, and a second step which projects the image back onto the constraint set.
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We have recently introduced a novel MRI methodology, so-called super resolution track-density imaging (TDI), which produces high-quality white matter images, with high spatial resolution and exquisite anatomical contrast not available from other MRI modalities. This method achieves super resolution by utilising the long-range information contained in the diffusion MRI fibre tracks. In this study, we validate the super resolution property of the TDI method by using in vivo diffusion MRI data acquired at ultra-high magnetic field strength (7 T), and in silico diffusion MRI data from a well-characterised numerical phantom. Furthermore, an alternative version of the TDI technique is described, which mitigates the track length weighting of the TDI map intensity. For the in vivo data, high-resolution diffusion images were down-sampled to simulate low-resolution data, for which the high-resolution images serve as a gold standard. For the in silico data, the gold standard is given by the known simulated structures of the numerical phantom. Both the in vivo and in silico data show that the structures that could be identified in the TDI maps only after using super resolution were consistent with the corresponding structures identified in the reference maps. This supports the claim that the structures identified by the super resolution step are accurate, thus providing further evidence for the important potential role of the super resolution TDI methodology in neuroscience.
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The connection matrix of the human brain (the human "connectome") represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact.
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Tractography is a class of algorithms aiming at in-vivo mapping the major neuronal pathways in the white matter from diffusion MRI data. These techniques offer a powerful tool to non-invasively investigate at the macroscopic scale the architecture of the neuronal connections of the brain. However, unfortunately, the reconstructions recovered with existing tractography algorithms are not really quantitative even though diffusion MRI is a quantitative modality by nature. As a matter of fact, several techniques have been proposed in recent years to estimate, at the voxel level, intrinsic micro-structural features of the tissue, such as axonal density and diameter, by using multi-compartment models. In this article, we present a novel framework to re-establish the link between tractography and tissue micro-structure. Starting from an input set of candidate fiber-tracts, which are estimated from the data using standard fiber-tracking techniques, we model the diffusion MRI signal in each voxel of the image as a linear combination of the restricted and hindered contributions generated in every location of the brain by these candidate tracts. Then, we seek for the global weight of each of them, i.e. the effective contribution or volume, such that they globally fit the measured signal at best. We demonstrate that these weights can be easily recovered by solving a global convex optimization problem and using efficient algorithms. The effectiveness of our approach has been evaluated both on a realistic phantom with known ground-truth and in-vivo brain data. Results clearly demonstrate the benefits of the proposed formulation, opening new perspectives for a more quantitative and biologically-plausible assessment of the structural connectivity of the brain.
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In recent years, diffusion-weighted magnetic resonance imaging has attracted considerable attention due to its unique potential to delineate the white matter pathways of the brain. However, methodologies currently available and in common use among neuroscientists and clinicians are typically based on the diffusion tensor model, which has comprehensively been shown to be inadequate to characterize diffusion in brain white matter. This is due to the fact that it is only capable of resolving a single fiber orientation per voxel, causing incorrect fiber orientations, and hence pathways, to be estimated through these voxels. Given that the proportion of affected voxels has been recently estimated at 90%, this is a serious limitation. Furthermore, most implementations use simple “deterministic” streamlines tracking algorithms, which have now been superseded by “probabilistic” approaches. In this study, we present a robust set of tools to perform tractography, using fiber orientations estimated using the validated constrained spherical deconvolution method, coupled with a probabilistic streamlines tracking algorithm. This methodology is shown to provide superior delineations of a number of known white matter tracts, in a manner robust to crossing fiber effects. These tools have been compiled into a software package, called MRtrix, which has been made freely available for use by the scientific community. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 53–66, 2012
Article
Constrained spherical deconvolution (CSD) has become one of the most widely used methods to extract white matter (WM) fibre orientation information from diffusion-weighted MRI (DW-MRI) data, overcoming the crossing fibre limitations inherent in the diffusion tensor model. It is routinely used to obtain high quality fibre orientation distribution function (fODF) estimates and fibre tractograms and is increasingly used to obtain apparent fibre density (AFD) measures. Unfortunately, CSD typically only supports data acquired on a single shell in q-space. With multi-shell data becoming more and more prevalent, there is a growing need for CSD to fully support such data. Furthermore, CSD can only provide high quality fODF estimates in voxels containing WM only. In voxels containing other tissue types such as grey matter (GM) and cerebrospinal fluid (CSF), the WM response function may no longer be appropriate and spherical deconvolution produces unreliable, noisy fODF estimates. The aim of this study is to incorporate support for multi-shell data into the CSD approach as well as to exploit the unique b-value dependencies of the different tissue types to estimate a multi-tissue ODF. The resulting approach is dubbed multi-shell, multi-tissue CSD (MSMT-CSD) and is compared to the state-of-the-art single-shell, single-tissue CSD (SSST-CSD) approach. Using both simulations and real data, we show that MSMT-CSD can produce reliable WM/GM/CSF volume fraction maps, directly from the DW data, whereas SSST-CSD has a tendency to overestimate the WM volume in voxels containing GM and/or CSF. In addition, compared to SSST-CSD, MSMT-CSD can substantially increase the precision of the fODF fibre orientations and reduce the presence ofspurious fODF peaks in voxels containing GM and/or CSF. Both effects translate into more reliable AFD measures and tractography results with MSMT-CSD compared to SSST-CSD.
Article
There is accumulating evidence that at current acquisition resolutions for diffusion-weighted (DW) MRI, the vast majority of white matter voxels contains "crossing fibers", referring to complex fiber configurations in which multiple and distinctly differently oriented fiber populations exist. Spherical deconvolution based techniques are appealing to characterize this DW intra-voxel signal heterogeneity, as they provide a balanced trade-off between constraints on the required hardware performance and acquisition time on the one hand, and the reliability of the reconstructed fiber orientation distribution function (fODF) on the other hand. Recent findings, however, suggest that an inaccurate calibration of the response function (RF), which represents the DW signal profile of a single fiber orientation, can lead to the detection of spurious fODF peaks which, in turn, can have a severe impact on tractography results. Currently, the computation of this RF is either model-based or estimated from selected voxels that have a fractional anisotropy (FA) value above a predefined threshold. For both approaches, however, there are user-defined settings that affect the RF and, consequently, fODF estimation and tractography. Moreover, these settings still rely on the second-rank diffusion tensor, which may not be the appropriate model, especially at high b-values. In this work, we circumvent these issues for RF calibration by excluding "crossing fibers" voxels in a recursive framework. Our approach is evaluated with simulations and applied to in vivo and ex vivo data sets with different acquisition settings. The results demonstrate that with the proposed method the RF can be calibrated in a robust and automated way without needing to define ad-hoc FA threshold settings. Our framework facilitates the use of spherical deconvolution approaches in data sets in which it is not straightforward to define RF settings a priori.
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Diffusion tensor imaging is often performed by acquiring a series of diffusion-weighted spin-echo echo-planar images with different direction diffusion gradients. A problem of echo-planar images is the geometrical distortions that obtain near junctions between tissues of differing magnetic susceptibility. This results in distorted diffusion-tensor maps. To resolve this we suggest acquiring two images for each diffusion gradient; one with bottom-up and one with top-down traversal of k-space in the phase-encode direction. This achieves the simultaneous goals of providing information on the underlying displacement field and intensity maps with adequate spatial sampling density even in distorted areas. The resulting DT maps exhibit considerably higher geometric fidelity, as assessed by comparison to an image volume acquired using a conventional 3D MR technique.
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A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B -spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as ??N4ITK,?? available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized 3He lung image data, and 9.4T postmortem hippocampus data.
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S ummary A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.
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The diffusion of water molecules inside organic tissues is often anisotropic (1). Namely, if there are aligned structures in the tissue, the apparent diffusion coefficient (ADC) of water may vary depending on the orientation along which the diffusion-weighted (DW) measurements are taken. In the late 1980s, diffusion-weighted imaging (DWI) became possible by combining MR diffusion measurements with imaging, enabling the mapping of both diffusion constants and diffusion anisotropy inside the brain and revealing valuable information about axonal architectures (2-14). In the beginning of the 1990s, the diffusion tensor model was introduced to describe the degree of anisotropy and the structural orientation information quantitatively (15,16). This diffusion tensor imaging (DTI) approach provided a simple and elegant way to model this complex neuroanatomical information using only six parameters. Since then, we have witnessed a tremendous amount of growth in this research field, including more sophisticated nontensor models to describe diffusion properties and to extract finer anatomical information from each voxel. Three-dimensional (3D) reconstruction technologies for white matter tracts are also developing beyond the initial deterministic line-propagation models (17-20). As these new reconstruction methods are an area of very active research, it is important to remember that the theory cannot be dissociated from practical aspects of the technology. Importantly, DWI is inherently a noise-sensitive and artifact-prone technique (Fig. 1). Thus, we cannot overemphasize the importance of image quality assurance and robust image analysis techniques. Last but not least, data acquisition technologies have also been steadfastly evolving. In this article, we review the recent advances in these areas since 2000. FIG. 1 Examples of typical artifacts: (i) signal/slice dropouts, (ii) eddy-current induced geometric distortions, (iii) systematic vibration artifacts, and (iv) ghosting (insufficient/incorrect fat-suppression).
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Automatic segmentation of subcortical structures in human brain MR images is an important but difficult task due to poor and variable intensity contrast. Clear, well-defined intensity features are absent in many places along typical structure boundaries and so extra information is required to achieve successful segmentation. A method is proposed here that uses manually labelled image data to provide anatomical training information. It utilises the principles of the Active Shape and Appearance Models but places them within a Bayesian framework, allowing probabilistic relationships between shape and intensity to be fully exploited. The model is trained for 15 different subcortical structures using 336 manually-labelled T1-weighted MR images. Using the Bayesian approach, conditional probabilities can be calculated easily and efficiently, avoiding technical problems of ill-conditioned covariance matrices, even with weak priors, and eliminating the need for fitting extra empirical scaling parameters, as is required in standard Active Appearance Models. Furthermore, differences in boundary vertex locations provide a direct, purely local measure of geometric change in structure between groups that, unlike voxel-based morphometry, is not dependent on tissue classification methods or arbitrary smoothing. In this paper the fully-automated segmentation method is presented and assessed both quantitatively, using Leave-One-Out testing on the 336 training images, and qualitatively, using an independent clinical dataset involving Alzheimer's disease. Median Dice overlaps between 0.7 and 0.9 are obtained with this method, which is comparable or better than other automated methods. An implementation of this method, called FIRST, is currently distributed with the freely-available FSL package.
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Neuroimaging advances have given rise to major progress in neurosciences and neurology, as ever more subtle and specific imaging methods reveal new aspects of the brain. One major limitation of current methods is the spatial scale of the information available. We present an approach to gain spatial resolution using post-processing methods based on diffusion MRI fiber-tracking, to reveal structures beyond the resolution of the acquired imaging voxel; we term such a method as super-resolution track-density imaging (TDI). A major unmet challenge in imaging is the identification of abnormalities in white matter as a cause of illness; super-resolution TDI is shown to produce high-quality white matter images, with high spatial resolution and outstanding anatomical contrast. A unique property of these maps is demonstrated: their spatial resolution and signal-to-noise ratio can be tailored depending on the chosen image resolution and total number of fiber-tracks generated. Super-resolution TDI should greatly enhance the study of white matter in disorders of the brain and mind.
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A new three-dimensional imaging technique which is applicable for 3D MR imaging throughout the body is introduced. In our preliminary investigations we have acquired high-quality 3D image sets of the abdomen showing minimal respiratory artifacts in just over 7 min (voxel size 2.7 X 2.7 X 2.7 mm3), and 3D image sets of the head showing excellent gray/white contrast in less than 6 min (voxel size 1.0 X 2.0 X 1.4 mm3).
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Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging.
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The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation--no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. We show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, we show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.
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An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods.
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Image distortion due to field gradient eddy currents can create image artifacts in diffusion-weighted MR images. These images, acquired by measuring the attenuation of NMR signal due to directionally dependent diffusion, have recently been shown to be useful in the diagnosis and assessment of acute stroke and in mapping of tissue structure. This work presents an improvement on the spin-echo (SE) diffusion sequence that displays less distortion and consequently improves image quality. Adding a second refocusing pulse provides better image quality with less distortion at no cost in scanning efficiency or effectiveness, and allows more flexible diffusion gradient timing. By adjusting the timing of the diffusion gradients, eddy currents with a single exponential decay constant can be nulled, and eddy currents with similar decay constants can be greatly reduced. This new sequence is demonstrated in phantom measurements and in diffusion anisotropy images of normal human brain.
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The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).
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In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
Article
Diffusion-weighted (DW) MR images contain information about the orientation of brain white matter fibres that potentially can be used to study human brain connectivity in vivo using tractography techniques. Currently, the diffusion tensor model is widely used to extract fibre directions from DW-MRI data, but fails in regions containing multiple fibre orientations. The spherical deconvolution technique has recently been proposed to address this limitation. It provides an estimate of the fibre orientation distribution (FOD) by assuming the DW signal measured from any fibre bundle is adequately described by a single response function. However, the deconvolution is ill-conditioned and susceptible to noise contamination. This tends to introduce artefactual negative regions in the FOD, which are clearly physically impossible. In this study, the introduction of a constraint on such negative regions is proposed to improve the conditioning of the spherical deconvolution. This approach is shown to provide FOD estimates that are robust to noise whilst preserving angular resolution. The approach also permits the use of super-resolution, whereby more FOD parameters are estimated than were actually measured, improving the angular resolution of the results. The method provides much better defined fibre orientation estimates, and allows orientations to be resolved that are separated by smaller angles than previously possible. This should allow tractography algorithms to be designed that are able to track reliably through crossing fibre regions.
How to correct susceptibility distortions in
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modeling for micro-structure informed tractography
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