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The effects of SIFT on the reproducibility and biological accuracy of the structural connectome

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... Rights reserved. the reconstructed tracts by discarding streamlines that do not correspond well to the underlying diffusion signal [41]. To balance the risk of an overabundance of false positive fibers (weak filtering) against the risk of artificially sparse tractograms (strong filtering) and to address limitations of computational demand (large amounts of streamline creation or strong filtering), an overall 20 million streamlines were created and subsequently filtered down to 5 million streamlines, gaining connectome accuracy comparable to previously published literature [17,37,41]. ...
... the reconstructed tracts by discarding streamlines that do not correspond well to the underlying diffusion signal [41]. To balance the risk of an overabundance of false positive fibers (weak filtering) against the risk of artificially sparse tractograms (strong filtering) and to address limitations of computational demand (large amounts of streamline creation or strong filtering), an overall 20 million streamlines were created and subsequently filtered down to 5 million streamlines, gaining connectome accuracy comparable to previously published literature [17,37,41]. ...
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Background Anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is characterized by distinct structural and functional brain alterations, predominantly affecting the medial temporal lobes and the hippocampus. Structural connectome analysis with graph-based investigations of network properties allows for an in-depth characterization of global and local network changes and their relationship with clinical deficits in NMDAR encephalitis. Methods Structural networks from 61 NMDAR encephalitis patients in the post-acute stage (median time from acute hospital discharge: 18 months) and 61 age- and sex-matched healthy controls (HC) were analyzed using diffusion-weighted imaging (DWI)-based probabilistic anatomically constrained tractography and volumetry of a selection of subcortical and white matter brain volumes was performed. We calculated global, modular, and nodal graph measures with special focus on default-mode network, medial temporal lobe, and hippocampus. Pathologically altered metrics were investigated regarding their potential association with clinical course, disease severity, and cognitive outcome. Results Patients with NMDAR encephalitis showed regular global graph metrics, but bilateral reductions of hippocampal node strength (left: p = 0.049; right: p = 0.013) and increased node strength of right precuneus (p = 0.013) compared to HC. Betweenness centrality was decreased for left-sided entorhinal cortex (p = 0.042) and left caudal middle frontal gyrus (p = 0.037). Correlation analyses showed a significant association between reduced left hippocampal node strength and verbal long-term memory impairment (p = 0.021). We found decreased left (p = 0.013) and right (p = 0.001) hippocampal volumes that were associated with hippocampal node strength (left p = 0.009; right p < 0.001). Conclusions Focal network property changes of the medial temporal lobes indicate hippocampal hub failure that is associated with memory impairment in NMDAR encephalitis at the post-acute stage, while global structural network properties remain unaltered. Graph theory analysis provides new pathophysiological insight into structural network changes and their association with persistent cognitive deficits in NMDAR encephalitis.
... No reuse allowed without permission. We then filtered the tractogram using SIFT [34], which has been shown to improve biological plausibility of the reconstructed tracts by discarding streamlines that do not correspond well to the underlying diffusion signal [38]. To balance the risk of an overabundance of false positive fibers (weak filtering) against the risk of artificially sparse tractograms (strong filtering) and to address limitations of computational demand (large amounts of streamline creation or strong filtering), an overall 20 million streamlines were created and subsequently filtered down to 5 million streamlines, gaining connectome accuracy comparable to previously published literature [12,34,38]. ...
... We then filtered the tractogram using SIFT [34], which has been shown to improve biological plausibility of the reconstructed tracts by discarding streamlines that do not correspond well to the underlying diffusion signal [38]. To balance the risk of an overabundance of false positive fibers (weak filtering) against the risk of artificially sparse tractograms (strong filtering) and to address limitations of computational demand (large amounts of streamline creation or strong filtering), an overall 20 million streamlines were created and subsequently filtered down to 5 million streamlines, gaining connectome accuracy comparable to previously published literature [12,34,38]. ...
Preprint
Introduction Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is characterized by distinct structural and functional brain alterations, predominantly affecting the medial temporal lobes and the hippocampus. Structural connectome analysis with graph-based investigations of network properties allows for an in-depth characterization of global and local network changes and their relationship with clinical deficits in NMDAR encephalitis. Objective To investigate changes in structural connectivity and network efficiency in NMDAR encephalitis by use of probabilistic whole-brain tractography and graph theoretical analysis of structural brain networks. Methods Structural networks from sixty-one NMDAR encephalitis patients in the post-acute stage (median time from acute hospital discharge: 18 months) and sixty-one age- and sex-matched healthy controls (HC) were analyzed using diffusion-weighted imaging (DWI)-based probabilistic anatomically-constrained tractography and spherical deconvolution-informed filtering of tractograms. We calculated global, modular, and nodal graph measures indicative of structural connectivity and network reorganization with special focus on default-mode network, medial temporal lobe, and hippocampus. Pathologically altered metrics were included in multiple regression analyses to investigate their potential association with clinical course, disease severity, and cognitive outcome. Results Patients with NMDAR encephalitis showed regular global graph metrics, but bilateral reductions of hippocampal node strength (left: p =0.049; right: p =0.013) and increased node strength of right precuneus ( p =0.013) compared to HC. Betweenness centrality was decreased for left-sided entorhinal cortex ( p =0.042) and left caudal middle frontal gyrus (p = 0.037). Correlation analyses showed a significant association between reduced left hippocampal node strength and verbal long-term memory impairment ( p =0.021) Conclusion Focal network property changes of the medial temporal lobes indicate hippocampal hub failure that is associated with memory impairment in NMDAR encephalitis at the post-acute stage, while global structural network properties remain unaltered. Graph theory analysis provides new pathophysiological insight into structural network changes and their association with persistent cognitive deficits in NMDAR encephalitis.
... 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). ...
... The parcellation was transferred to the subject space using the T1 linear co-registration and the UK-biobank warp field. It was finally dilated and masked to be used in MRtrix3 along the SIFT [90] for the connectome extraction. Fiber assignment was done with a radial search of 3mm and the resulting connectomes were symmetric with a zero diagonal. ...
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Identifying the driver nodes of a network has crucial implications in biological systems from unveiling causal interactions to informing effective intervention strategies. Despite recent advances in network control theory, results remain inaccurate as the number of drivers becomes too small compared to the network size, thus limiting the concrete usability in many real-life applications. To overcome this issue, we introduced a framework that integrates principles from spectral graph theory and output controllability to project the network state into a smaller topological space formed by the Laplacian network structure. Through extensive simulations on synthetic and real networks, we showed that a relatively low number of projected components can significantly improve the control accuracy. By introducing a new low-dimensional controllability metric we experimentally validated our method on N = 6134 human connectomes obtained from the UK-biobank cohort. Results revealed previously unappreciated influential brain regions, enabled to draw directed maps between differently specialized cerebral systems, and yielded new insights into hemispheric lateralization. Taken together, our results offered a theoretically grounded solution to deal with network controllability and provided insights into the causal interactions of the human brain.
... For each subject from the Cam-CAN cohort, the atlas was transformed to diffusion-weighted space with SPM12, and the reconstructed streamlines were assigned to the parcels by a radial search outwards from the streamline termination point, out to a maximum radius of 2 mm as in Smith et al. 48 To extract the structural connectome, a binary backbone network was estimated by testing each possible connection for its significance using the non-parametric sign test proposed in Gong et al. 49 False discovery rate correction was applied to correct for multiple comparisons. The weighted network was then constructed by assigning to each edge a weight computed as the average across all subjects of the total number of tracked fibres between two regions divided by the mean volume of the two regions. ...
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Four important imaging biomarkers of Alzheimer’s disease, namely grey matter atrophy, glucose hypometabolism and amyloid-β and tau deposition, follow stereotypical spatial distributions shaped by the brain network of structural and functional connections. In this case-control study, we combined several predictors reflecting various possible mechanisms of spreading through structural and functional pathways to predict the topography of the four biomarkers in amyloid-positive patients while controlling for the effect of spatial distance along the cortex. For each biomarker, we quantified the relative contribution of each predictor to the variance explained by the model. We also compared the contribution between apolipoprotein E-ɛ4 carriers and non-carriers. We found that topological proximity to areas of maximal pathology through the functional connectome explained significant parts of variance for all biomarkers and that functional pathways totalized more than 30% of contributions for hypometabolism and amyloid load. By contrast, atrophy and tau load were mainly predicted by structural pathways, with major contributions from inter-regional diffusion. The ɛ4 allele modulated contributions to the four biomarkers in a way consistent with compromised brain connectomics in carriers. Our approach can be used to assess the contribution of concurrent mechanisms in other neurodegenerative diseases and the possible modifying impact of relevant factors on this contribution.
... Fiber orientation were estimated with constrained spherical deconvolution, and 2.5 · 10 6 streamlines were obtained by probabilistic tractography. We used the ACT and SIFT framework to improve reproducibility and biological accuracy (48). ...
Preprint
Recent studies have shown that seizures can spread and terminate across brain areas via a rich diversity of spatiotemporal patterns. In particular, while the location of the seizure onset area is usually in-variant across seizures in a same patient, the source of traveling (2-3 Hz) spike-and-wave discharges (SWDs) during seizures can either move with the slower propagating ictal wavefront or remain stationary at the seizure onset area. In addition, although most focal seizures terminate quasi-synchronously across brain areas, some evolve into distinct ictal clusters and terminate asynchronously. To provide a unifying perspective on the observed diversity of spatiotemporal dynamics for seizure spread and termination, we introduce here the Epileptor neural field model. Two mechanisms play an essential role. First, while the slow ictal wavefront propagates as a front in excitable neural media, the faster SWDs propagation results from coupled-oscillator dynamics. Second, multiple time scales interact during seizure spread, allowing for low-voltage fast-activity (>10 Hz) to hamper seizure spread and for SWD propagation to affect the way a seizure terminates. These dynamics, together with variations in short and long-range connectivity strength, play a central role on seizure spread, maintenance and termination. We demonstrate how Epileptor field models incorporating the above mechanisms predict the previously reported diversity in seizure spread patterns. Furthermore, we confirm the predictions for synchronous or asynchronous (clustered) seizure termination in human seizures recorded via stereotactic EEG. Our new insights into seizure spatiotemporal dynamics may also contribute to the development of new closed-loop neuromodulation therapies for focal epilepsy.
... However, studies using SSMT-CSD technique have shown that single-shell DWI data produces similar results compared to multi-shell DWI data 97 . The biological accuracy of single-shell data processed with CSD has also been confirmed in postmortem histological studies 98 . We believe that the biological interpretation of our results conveys biologically relevant and reliable findings, even though they rely on less optimal data acquisition parameters. ...
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Patients with schizophrenia exhibit structural and functional dysconnectivity but the relationship to the well-documented cognitive impairments is less clear. This study investigates associations between structural and functional connectivity and executive functions in antipsychotic-naïve patients experiencing schizophrenia. Sixty-four patients with schizophrenia and 95 matched controls underwent cognitive testing, diffusion weighted imaging and resting state functional magnetic resonance imaging. In the primary analyses, groupwise interactions between structural connectivity as measured by fixel-based analyses and executive functions were investigated using multivariate linear regression analyses. For significant structural connections, secondary analyses examined whether functional connectivity and associations with executive functions also differed for the two groups. In group comparisons, patients exhibited cognitive impairments across all executive functions compared to controls (p < 0.001), but no group difference were observed in the fixel-based measures. Primary analyses revealed a groupwise interaction between planning abilities and fixel-based measures in the left anterior thalamic radiation (p = 0.004), as well as interactions between cognitive flexibility and fixel-based measures in the isthmus of corpus callosum and cingulum (p = 0.049). Secondary analyses revealed increased functional connectivity between grey matter regions connected by the left anterior thalamic radiation (left thalamus with pars opercularis p = 0.018, and pars orbitalis p = 0.003) in patients compared to controls. Moreover, a groupwise interaction was observed between cognitive flexibility and functional connectivity between contralateral regions connected by the isthmus (precuneus p = 0.028, postcentral p = 0.012), all p-values corrected for multiple comparisons. We conclude that structural and functional connectivity appear to associate with executive functions differently in antipsychotic-naïve patients with schizophrenia compared to controls.
... With the FOD data of each individual, whole-brain probabilistic tractography was performed 53 . A parcellated canine brain atlas 54 was registered to each individual's space to create a connectome matrix using tck2connectome 55 . Regions of interest include the caudal colliculus, medial geniculate nucleus, and middle ectosylvian cortex. ...
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Presbycusis, or age-related hearing loss, affects both elderly humans and dogs, significantly impairing their social interactions and cognition. In humans, presbycusis involves changes in peripheral and central auditory systems, with central changes potentially occurring independently. While peripheral presbycusis in dogs is well-documented, research on central changes remains limited. Diffusion tensor imaging (DTI) is a useful tool for detecting and quantifying cerebral white matter abnormalities. This study used DTI to explore the central auditory pathway of senior dogs, aiming to enhance our understanding of canine presbycusis. Dogs beyond 75% of their expected lifespan were recruited and screened with brainstem auditory evoked response testing to select dogs without severe peripheral hearing loss. Sixteen dogs meeting the criteria were scanned using a 3 T magnetic resonance scanner. Tract-based spatial statistics was used to analyze the central auditory pathways. A significant negative correlation between fractional lifespan and fractional anisotropy was found in the acoustic radiation, suggesting age-related white matter changes in the central auditory system. These changes, observed in dogs without severe peripheral hearing loss, may contribute to central presbycusis development.
... FOD is a non-negative function defined on the unit sphere with its values representing the probability of fiber tracts along each direction (Tournier et al., 2004); (9). Initial probabilistic tractography (10 million streamlines) was performed using Anatomically Constrained Tractography (ACT) method in the masking area following the parameters recommended by the software developers (Tournier et al., 2019); (10) Spherical-deconvolution informed filtering of tracts (SIFT) algorithms (Smith et al., , 2015b were applied to generate more biologically meaningful estimates of structural connection density (1 million streamlines); (11) A structural connectivity (SC) matrix was created (Smith et al., 2015a) based on the SIFT2 outputted streamlines for the 84 parcels (or nodes) including nodes from both hemispheres (42 each). The connectivity is the sum of streamline weighting factors between node pairs (Smith et al., 2015b). ...
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Diffusion properties from diffusion tensor imaging (DTI) are exquisitely sensitive to white matter abnormalities incurred during traumatic brain injury (TBI), especially for those patients with chronic post-TBI symptoms such as headaches, dizziness, fatigue, etc. The evaluation of structural and functional connectivity using DTI has become a promising method for identifying subtle alterations in brain connectivity associated with TBI that are otherwise not visible with conventional imaging. This study assessed whether TBI patients with (n = 17) or without (n = 16) chronic symptoms (TBIcs/TBIncs) exhibit any changes in structural connectivity (SC) and mean fractional anisotropy (mFA) of intra- and inter-hemispheric connections when compared to a control group (CG) (n = 13). Reductions in SC and mFA were observed for TBIcs compared to CG, but not for TBIncs. More connections were found to have mFA reductions than SC reductions. On the whole, SC is dominated by ipsilateral connections for all the groups after the comparison of contralateral and ipsilateral connections. More contra-ipsi reductions of mFA were found for TBIcs than TBIncs compared to CG. These findings suggest that TBI patients with chronic symptoms not only demonstrate decreased global and regional mFA but also reduced structural network connectivity.
... There are many different approaches for propagating streamlines through white matter, from deterministic methods proposed in the late 1990s 44,45 to probabilistic methods that better account for the inherent noise levels of the signal and allow multiple sampling opportunities in each voxel. 30,46,47 Together with recent approaches for filtering streamlines, [48][49][50][51][52][53] probabilistic approaches produce overall more accurate representations of the brain connectome than deterministic approaches. However, . ...
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Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.
... Streamlines were probabilistically generated with whole-brain seeding using DiPy and an NVIDIA GPU [54]. Spherical-deconvolution informed filtering of tractograms (SIFT-2) determined the cross-sectional area multiplier for each streamline such that the streamline densities in each voxel are close to the fiber density estimated using spherical deconvolution [55]. The Desikan-Killiany atlas with 86 regions was linearly and non-linearly registered to DWI space using ANTS with GenericLabel interpolation, which parcellated streamlines into region-to-region structural connectivity [40]. ...
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Resting state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain's functional organization and to examine if it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being under-utilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here, we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysical signal generation model followed by graph spectral (i.e. eigen) decomposition. We call this model a Spectral Graph Model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal's spectral and spatial features into a small number of biophysically-interpretable parameters. We expect this model-based inference of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relations in a variety of brain disorders.
... However, a backbone of these models, the map of structural connections, typically estimated from diffusion MRI data, has received less attention. How this map is estimated and processed can lead to less or more faithful representations of true biophysical features of brain's connectivity; e.g., distributions of connection strengths and path lengths [5][6][7]. Here, we use a modular framework to explore the influence of such features in whole-brain models of functional dynamics. We show that they can have significant effects on modelling performance, when comparing forward predictions with empirical data. ...
... Developing metrics to measure the integrity and strength of white matter connections, e.g. building upon methods such as SIFT (Smith et al., 2015a(Smith et al., , 2015b or investigating alternative microstructure measures (Chamberland et al., 2019;Raffelt et al., 2012), may strongly improve the individual-level information in SC. ...
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Several studies predicting Functional Connectivity (FC) from Structural Connectivity (SC) at an individual level have been published in recent years, each promising increased performance and utility. We investigated three of these studies, analyzing whether the results truly represent a meaningful individual-level mapping from SC to FC. Using data from the Human Connectome Project shared accross the three studies, we constructed a predictor by averaging FC of training data and analyzed its performance in the same way. In each case, we found that this group average FC is an equivalent or better predictor of individual FC than the predictive models in terms of raw prediction performance. Furthermore, we showed that additional analyses performed by the authors of the three studies, in which they attempt to show that their predicted FC has value beyond raw prediction performance, could also be reproduced using the group average FC predictor. This makes it unclear whether any of the three methods represent a meaningful individual-level predictive model. We conclude that either the applied methods are not appropriate for the data, that the sample size is too small, or that the data itself does not contain sufficient information to learn a mapping from SC to FC e.g. due to the amount of noise in MRI measurements. We advise future individual-level studies to always explicitly report their results in comparison to the performance of the group average, and carefully demonstrate that their predictions contain meaningful individual-level information. Finally, we believe that investigating alternatives for the construction of SC and FC may improve the chances of developing a meaningful individual-level mapping from SC to FC.
... Since streamlines are generated seeding from the gray-white matter interface and the predefined parcellation scheme only covers cortical gray matter, the template was expanded adding voxels towards the gray-white matter boundary so that all regions also include the seeding points. To increase the biological accuracy of SC, the SIFT-2 method was applied (Smith et al., 2015). Here, each streamline is weighted with an estimate of its effective cross-sectional area, so that the streamline density matches the white matter fiber density computed directly from the diffusion signal. ...
... com/ NotaCS/ scilpy. Such a procedure is similar to tractography toolboxes when building connectomes, like the tck2connectome function from MRtrix3 (Smith et al. 2015;Tournier et al. 2019). However, the current method differs from these approaches as they usually only allow the association of a streamline to a whole grey matter region, while our method was specifically tailored to our needs by associating the streamlines to the grey matter voxels they reach. ...
Article
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Integrating the underlying brain circuit's structural and functional architecture is required to explore the functional organization of cognitive networks. In that regard, we recently introduced the Functionnectome. This structural–functional method combines an fMRI acquisition with tractography-derived white matter connectivity data to map cognitive processes onto the white matter. However, this multimodal integration faces three significant challenges: (1) the necessarily limited overlap between tractography streamlines and the grey matter, which may reduce the amount of functional signal associated with the related structural connectivity; (2) the scrambling effect of crossing fibers on functional signal, as a single voxel in such regions can be structurally connected to several cognitive networks with heterogeneous functional signals; and (3) the difficulty of interpretation of the resulting cognitive maps, as crossing and overlapping white matter tracts can obscure the organization of the studied network. In the present study, we tackled these problems by developing a streamline-extension procedure and dividing the white matter anatomical priors between association, commissural, and projection fibers. This approach significantly improved the characterization of the white matter involvement in the studied cognitive processes. The new Functionnectome priors produced are now readily available, and the analysis workflow highlighted here should also be generalizable to other structural–functional approaches. Graphical abstract We improved the Functionnectome approach to better study the involvement of white matter in brain function by separating the analysis of the three classes of white matter fibers (association, commissural, and projection fibers). This step successfully clarified the activation maps and increased their statistical significance.
... All the diffusion tensor images and diffusion metrics such as fractional anisotropy (FA), mean diffusivity (MD), etc., were generated from the FOD images and response functions in the masking area;(9). Initial probabilistic tractography (10 million streamlines) was performed using Anatomically Constrained Tractography (ACT) method in the masking area following the parameters recommended by the software developers (Tournier et al., 2019); (10) Spherical-deconvolution informed ltering of tracts (SIFT) algorithms(Smith et al., , 2015b were applied to generate more biologically meaningful estimates of structural connection density (1 million streamlines); (11) A structural connectivity (SC) was created(Smith et al., 2015a) based on the SIFT2 outputted streamlines for the 84 parcels (or nodes). The connectivity is the sum of streamline weighting factors between node pairs(Smith et al., 2015b). ...
Preprint
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Diffusion properties from diffusion tensor imaging (DTI) are exquisitely sensitive to white matter abnormalities incurred during traumatic brain injury (TBI), especially for those patients with chronic post-TBI symptoms such as headaches, dizziness, fatigue, etc. The evaluation of structural and functional connectivity using DTI has become a promising method for identifying subtle alterations in brain connectivity associated with TBI that are otherwise not visible with conventional imaging. This study assessed whether TBI patients with or without chronic symptoms (TBIcs/TBIncs) exhibit any changes in structural connectivity (SC) and mean fractional anisotropy (mFA) of intra- and inter-hemispheric connections when compared to a control group (CG). Reductions in SC and mFA were observed for TBIcs compared to CG, but not for TBIncs. More connections were found to have mFA reductions than SC reductions. On the whole, SC is dominated by ipsilateral connections for all the groups after the comparison of contralateral and ipsilateral connections. More contra-ipsi reductions of mFA were found for TBIcs than TBIncs compared to CG. These findings suggest that TBI patients with chronic symptoms not only demonstrate decreased global and regional mFA but also reduced structural network efficiency.
... 1. White matter ( WM): voxels coded as white matter are randomly chosen as streamline seeds. 2. Gray matter-white matter interface (GMWMI): voxels containing a gradient between gray matter and white matter are chosen as streamline seeds, with the aim of improving the tractography of shorter fibers (Smith et al., 2013(Smith et al., , 2015a. 3. Dynamic: the relative difference between the predicted fiber density (based on the diffusion model) and the current density is used to inform the probability of choosing a particular location as a seed, with the aim of correcting for under-or oversampling of a given fiber tract (Smith et al., 2015b). ...
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Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization.
... The generated tractograms were filtered using Spherical-deconvolution informed filtering of tracks (SIFT2, Smith et al., 2015). ...
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The human structural brain network, or connectome, has a rich-club organization with a small number of brain regions showing high network connectivity, called hubs. Hubs are centrally located in the network, energy costly, and critical for human cognition. Aging has been associated with changes in brain structure, function, and cognitive decline, such as processing speed. At a molecular level, the aging process is a progressive accumulation of oxidative damage, which leads to subsequent energy depletion in the neuron and causes cell death. However, it is still unclear how age affects hub connections in the human connectome. The current study aims to address this research gap by constructing structural connectome using fiber bundle capacity (FBC). FBC is derived from Constrained Spherical Deconvolution (CSD) modeling of white-matter fiber bundles, which represents the capacity of a fiber bundle to transfer information. Compared to the raw number of streamlines, FBC is less bias for quantifying connection strength within biological pathways. We found that hubs exhibit longer-distance connections and higher metabolic rates compared to peripheral brain regions, suggesting that hubs are biologically costly. Although the landscape of structural hubs was relatively age-invariant, there were wide-spread age effects on FBC in the connectome. Critically, these age effects were larger in connections within hub compared to peripheral brain connections. These findings were supported by both a cross-sectional sample with wide age-range (N=137) and a longitudinal sample across 5 years (N=83). Moreover, our results demonstrated that associations between FBC and processing speed were more concentrated in hub connections than chance level, and FBC in hub connections mediated the age-effects on processing speed. Overall, our findings indicate that structural connections of hubs, which demonstrate greater energy demands, are particular vulnerable to aging. The vulnerability may contribute to age-related impairments in processing speed among older adults.
... et al., 2019). Streamlines were assigned to the nearest parcel within a 2mm search radius of each streamline endpoint(Smith et al., 2015b). Following the application of SIFT2, the sum of streamline weights was used as the quantitative connectivity metric for each edge. ...
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The network-based statistic (NBS) is a popular method for performing edge-wise statistical inference on brain networks, with a known limitation in the form of a need for the user to pre-define an arbitrary cluster-forming threshold. Recently a new method, the “Threshold Free Network Based Statistic” (TFNBS), was proposed to attempt to overcome this necessity. While TFNBS does not require the a priori definition of a hard cluster-forming threshold to generate edge-wise significance values, it does require definition of the statistical enhancement parameters intrinsic to the method. In this work, we explore the practical consequences of parameter choice on reported results using both methods, and assess whether TFNBS indeed provides the research community with a significant increase in the fidelity of results. We do so by applying both NBS and TFNBS to a previously well-characterized cohort with temporal lobe epilepsy in a case-control study of diffusion MRI-derived connectivity, and observing the variation of statistical inference outcomes depending on the values of enhancement parameters utilised. Our results exhibit substantial variability for both TFNBS and NBS, indicating that the choice of parameters for both methods influences the extent of the inferred network changes; this therefore imposes a restriction on the precision with which the outcomes of statistical inference using either method may be interpreted.
... Tracking parameters were included a step size of 0.1 mm, FOD cutoff threshold of 0.2, and maximum angle of 45 degrees. Next, Spherical-deconvolution informed filtering of tractograms (SIFT) algorithm 34 was applied to obtain a better match between the reconstructed streamlines and the underlying WM structures, provided a highly biologically relevant measure of 'structural connectivity' 35 . The edge weight was determined by the number of streamlines (NOS). ...
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Anxiety and fear are dysfunctional behaviors commonly observed in domesticated dogs. Although dogs and humans share psychopathological similarities, little is known about how dysfunctional fear behaviors are represented in brain networks in dogs diagnosed with anxiety disorders. A combination of diffusion tensor imaging (DTI) and graph theory was used to investigate the underlying structural connections of dysfunctional anxiety in anxious dogs and compared with healthy dogs with normal behavior. The degree of anxiety was assessed using the Canine Behavioral Assessment & Research Questionnaire (C-BARQ), a widely used, validated questionnaire for abnormal behaviors in dogs. Anxious dogs showed significantly decreased clustering coefficient ([Formula: see text]), decreased global efficiency ([Formula: see text]), and increased small-worldness (σ) when compared with healthy dogs. The nodal parameters that differed between the anxious dogs and healthy dogs were mainly located in the posterior part of the brain, including the occipital lobe, posterior cingulate gyrus, hippocampus, mesencephalon, and cerebellum. Furthermore, the nodal degree ([Formula: see text]) of the left cerebellum was significantly negatively correlated with "excitability" in the C-BARQ of anxious dogs. These findings could contribute to the understanding of a disrupted brain structural connectome underlying the pathological mechanisms of anxiety-related disorders in dogs.
... To minimise this source of error, we used among the best methods currently available, 5 which include modelling crossing fibres, 39 partial volume between tissue structures, 40 probabilistic tractography incorporating anatomical constraints, 27 and tractography quantitative modelling 28 for rigorous computation of the connectome. 41 Another potential limitation is the computational demands of the proposed regularisation method which may make it challenging to process large-scale clinical data. So CONN-NLM was applied after every 12 subiterations to speed up the overall processing speed. ...
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Positron emission tomography (PET) molecular biomarkers and diffusion magnetic resonance imaging (dMRI) derived information show associations and highly complementary information in a number of neurodegenerative conditions, including Alzheimer's disease. Diffusion MRI provides valuable information about the microstructure and structural connectivity of the brain which could guide and improve the PET image reconstruction when such associations exist. However, this potental has not been previously explored. In the present study, we propose a CONNectome-based Non-Local Means One-Step Late Maximum A Posteriori (CONN-NLM-OSLMAP) method, which allows diffusion MRI-derived connectivity information to be incorporated into the PET iterative image reconstruction process, thus regularising the estimated PET images. The proposed method was evaluated using a realistic PET/MRI simulated phantom, demonstrating more effective noise reduction and lesion contrast improvement, as well as the lowest overall bias compared with both a median filter applied as an alternative regulariser and CONN-NLM as a post-reconstruction filter. By adding complementary structural connectivity information from diffusion MRI, the proposed regularisation method offers more useful and targeted denoising and regularisation, demonstrating the feasibility and effectiveness of integrating connectivity information into PET image reconstruction.
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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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Resting-state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain’s functional organization and to examine whether it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being underutilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysics-informed signal generation model followed by graph spectral (i.e., eigen) decomposition. We call this model a spectral graph model (SGM) for fMRI, using which we can not only quantify the structure–function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal’s spectral and spatial features into a small number of biophysically interpretable parameters. We expect this model-based analysis of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure–function relationships in a variety of brain disorders.
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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.
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Structural brain network topology can be altered in case of a brain tumor, due to both the tumor itself and its treatment. In this study, we explored the role of structural whole-brain and nodal network metrics and their association with cognitive functioning. Fifty WHO grade 2–3 adult glioma survivors (> 1-year post-therapy) and 50 matched healthy controls underwent a cognitive assessment, covering six cognitive domains. Raw cognitive assessment scores were transformed into w-scores, corrected for age and education. Furthermore, based on multi-shell diffusion-weighted MRI, whole-brain tractography was performed to create weighted graphs and to estimate whole-brain and nodal graph metrics. Hubs were defined based on nodal strength, betweenness centrality, clustering coefficient and shortest path length in healthy controls. Significant differences in these metrics between patients and controls were tested for the hub nodes (i.e. n = 12) and non-hub nodes (i.e. n = 30) in two mixed-design ANOVAs. Group differences in whole-brain graph measures were explored using Mann–Whitney U tests. Graph metrics that significantly differed were ultimately correlated with the cognitive domain-specific w-scores. Bonferroni correction was applied to correct for multiple testing. In survivors, the bilateral putamen were significantly less frequently observed as a hub (pbonf < 0.001). These nodes’ assortativity values were positively correlated with attention (r(90) > 0.573, pbonf < 0.001), and proxy IQ (r(90) > 0.794, pbonf < 0.001). Attention and proxy IQ were significantly more often correlated with assortativity of hubs compared to non-hubs (pbonf < 0.001). Finally, the whole-brain graph measures of clustering coefficient (r = 0.685), global (r = 0.570) and local efficiency (r = 0.500) only correlated with proxy IQ (pbonf < 0.001). This study demonstrated potential reorganization of hubs in glioma survivors. Assortativity of these hubs was specifically associated with cognitive functioning, which could be important to consider in future modeling of cognitive outcomes and risk classification in glioma survivors.
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Several studies predicting Functional Connectivity (FC) from Structural Connectivity (SC) at individual level have been published in recent years, each promising increased performance and utility. We investigated three of these studies, analyzing whether the results truly represent a meaningful individual-level mapping from SC to FC. Using data from the Human Connectome Project shared accross the three studies, we constructed a predictor by averaging FC of training data and analyzed its performance in the same way. In each case, we found that group average FC is an equivalent or better predictor of individual FC than the predictive models in terms of raw prediction performance. Furthermore, we showed that additional analyses performed by the authors of the three studies, in which they attempt to show that their predicted FC has value beyond raw prediction performance, could also be reproduced using the group average FC predictor. This makes it unclear whether any of the three methods represent a meaningful individual-level predictive model. We conclude that either the methods are not appropriate for the data, that the sample size is too small, or that the data does not contain sufficient information to learn a mapping from SC to FC. We advise future individual-level studies to explicitly report results in comparison to the performance of the group average, and carefully demonstrate that their predictions contain meaningful individual-level information. Finally, we believe that investigating alternatives for the construction of SC and FC may improve the chances of developing a meaningful individual-level mapping from SC to FC.
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Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or ‘oscillatoriness’ per se. Here we introduce a new approach, the phase-autocorrelation function (pACF), for the direct quantification of rhythmicity. We applied pACF to human intracerebral stereoelectroencephalography (SEEG) and magnetoencephalography (MEG) data and uncovered a spectrally and anatomically fine-grained cortical architecture in the rhythmicity of single- and multi-frequency neuronal oscillations. Evidencing the functional significance of rhythmicity, we found it to be a prerequisite for long-range synchronization in resting-state networks and to be dynamically modulated during event-related processing. We also extended the pACF approach to measure ’burstiness’ of oscillatory processes and characterized regions with stable and bursty oscillations. These findings show that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.
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Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or "oscillatoriness" per se . Here we introduce a new method, the phase-autocorrelation functon(pACF), for direct quantification of rhythmicity. We applied pACF to human intracerebral stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) data to quantify rhythmicity and uncovered a spectrally and anatomically fine-grained cortical architecture of single- and multi-frequency neuronal oscillations. We also extended the pACF approach to measure "burstiness" of oscillatory processes and characterized regions with stable and bursty oscillations. We found that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.
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Neuronal oscillations are commonly analyzed with power spectral methods that quantify signal amplitude, but not rhythmicity or "oscillatoriness" per se. Here we introduce a new method, the phase-autocorrelation functon(pACF), for direct quantification of rhythmicity. We applied pACF to human intracerebral stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) data to quantify rhythmicity and uncovered a spectrally and anatomically fine-grained cortical architecture of single- and multi-frequency neuronal oscillations. We also extended the pACF approach to measure "burstiness" of oscillatory processes and characterized regions with stable and bursty oscillations. We found that rhythmicity is double-dissociable from amplitude and constitutes a functionally relevant and dynamic characteristic of neuronal oscillations.
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The human connectome has recently become a popular research topic in neuroscience, and many new algorithms have been applied to analyze brain networks. In particular, network topology measures from graph theory have been adapted to analyze network efficiency and 'small-world' properties. While there has been a surge in the number of papers examining connectivity through graph theory, questions remain about its test-retest reliability (TRT). In particular, the reproducibility of structural connectivity measures has not been assessed. We examined the TRT of global connectivity measures generated from graph theory analyses of 17 young adults who underwent two high-angular resolution diffusion (HARDI) scans approximately 3 months apart. Of the measures assessed, modularity had the highest TRT, and it was stable across a range of sparsities (a thresholding parameter used to define which network edges are retained). These reliability measures underline the need to develop network descriptors that are robust to acquisition parameters.
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Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks - such as small-world topology, highly connected hubs and modularity - both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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There is ongoing debate whether using a higher spatial resolution (sampling k-space) or a higher angular resolution (sampling q-space angles) is the better way to improve diffusion MRI (dMRI) based tractography results in living humans. In both cases, the limiting factor is the signal-to-noise ratio (SNR), due to the restricted acquisition time. One possible way to increase the spatial resolution without sacrificing either SNR or angular resolution is to move to a higher magnetic field strength. Nevertheless, dMRI has not been the preferred application for ultra-high field strength (7 T). This is because single-shot echo-planar imaging (EPI) has been the method of choice for human in vivo dMRI. EPI faces several challenges related to the use of a high resolution at high field strength, for example, distortions and image blurring. These problems can easily compromise the expected SNR gain with field strength. In the current study, we introduce an adapted EPI sequence in conjunction with a combination of ZOOmed imaging and Partially Parallel Acquisition (ZOOPPA). We demonstrate that the method can produce high quality diffusion-weighted images with high spatial and angular resolution at 7 T. We provide examples of in vivo human dMRI with isotropic resolutions of 1 mm and 800 μm. These data sets are particularly suitable for resolving complex and subtle fiber architectures, including fiber crossings in the white matter, anisotropy in the cortex and fibers entering the cortex.
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Minimization of the wiring cost of white matter fibers in the human brain appears to be an organizational principle. We investigate this aspect in the human brain using whole brain connectivity networks extracted from high resolution diffusion MRI data of 14 normal volunteers. We specifically address the question of whether brain anatomy determines its connectivity or vice versa. Unlike previous studies we use weighted networks, where connections between cortical nodes are real-valued rather than binary off-on connections. In one set of analyses we found that the connectivity structure of the brain has near optimal wiring cost compared to random networks with the same number of edges, degree distribution and edge weight distribution. A specifically designed minimization routine could not find cheaper wiring without significantly degrading network performance. In another set of analyses we kept the observed brain network topology and connectivity but allowed nodes to freely move on a 3D manifold topologically identical to the brain. An efficient minimization routine was written to find the lowest wiring cost configuration. We found that beginning from any random configuration, the nodes invariably arrange themselves in a configuration with a striking resemblance to the brain. This confirms the widely held but poorly tested claim that wiring economy is a driving principle of the brain. Intriguingly, our results also suggest that the brain mainly optimizes for the most desirable network connectivity, and the observed brain anatomy is merely a result of this optimization.
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Recent research on Alzheimer's disease (AD) has shown that the decline of cognitive and memory functions is accompanied by a disrupted neuronal connectivity characterized by white matter (WM) degeneration. However, changes in the topological organization of WM structural network in AD remain largely unknown. Here, we used diffusion tensor image tractography to construct the human brain WM networks of 25 AD patients and 30 age- and sex-matched healthy controls, followed by a graph theoretical analysis. We found that both AD patients and controls had a small-world topology in WM network, suggesting an optimal balance between structurally segregated and integrative organization. More important, the AD patients exhibited increased shortest path length and decreased global efficiency in WM network compared with controls, implying abnormal topological organization. Furthermore, we showed that the WM network contained highly connected hub regions that were predominately located in the precuneus, cingulate cortex, and dorsolateral prefrontal cortex, which was consistent with the previous diffusion-MRI studies. Specifically, AD patients were found to have reduced nodal efficiency predominantly located in the frontal regions. Finally, we showed that the alterations of various network properties were significantly correlated with the behavior performances. Together, the present study demonstrated for the first time that the Alzheimer's brain was associated with disrupted topological organization in the large-scale WM structural networks, thus providing the structural evidence for abnormalities of systematic integrity in this disease. This work could also have implications for understanding how the abnormalities of structural connectivity in AD underlie behavioral deficits in the patients.
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To what extent cortical pathways show significant weight differences and whether these differences are consistent across animals (thereby comprising robust connectivity profiles) is an important and unresolved neuroanatomical issue. Here we report a quantitative retrograde tracer analysis in the cynomolgus macaque monkey of the weight consistency of the afferents of cortical areas across brains via calculation of a weight index (fraction of labeled neurons, FLN). Injection in 8 cortical areas (3 occipital plus 5 in the other lobes) revealed a consistent pattern: small subcortical input (1.3% cumulative FLN), high local intrinsic connectivity (80% FLN), high-input form neighboring areas (15% cumulative FLN), and weak long-range corticocortical connectivity (3% cumulative FLN). Corticocortical FLN values of projections to areas V1, V2, and V4 showed heavy-tailed, lognormal distributions spanning 5 orders of magnitude that were consistent, demonstrating significant connectivity profiles. These results indicate that 1) connection weight heterogeneity plays an important role in determining cortical network specificity, 2) high investment in local projections highlights the importance of local processing, and 3) transmission of information across multiple hierarchy levels mainly involves pathways having low FLN values.
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