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Establishing a link between sex-related differences in the structural connectome and behaviour

Recent years have witnessed an increased attention to studies of sex differences, partly because such differences offer important considerations for personalized medicine. While the presence of sex differences in human behaviour is well documented, our knowledge of their anatomical foundations in the brain is still relatively limited. As a natural gateway to fathom the human mind and behaviour, studies concentrating on the human brain network constitute an important segment of the research effort to investigate sex differences. Using a large sample of healthy young individuals, each assessed with diffusion MRI and a computerized neurocognitive battery, we conducted a comprehensive set of experiments examining sex-related differences in the meso-scale structures of the human connectome and elucidated how these differences may relate to sex differences at the level of behaviour. Our results suggest that behavioural sex differences, which indicate complementarity of males and females, are accompanied by related differences in brain structure across development. When using subnetworks that are defined over functional and behavioural domains, we observed increased structural connectivity related to the motor, sensory and executive function subnetworks in males. In females, subnetworks associated with social motivation, attention and memory tasks had higher connectivity. Males showed higher modularity compared to females, with females having higher inter-modular connectivity. Applying multivariate analysis, we showed an increasing separation between males and females in the course of development, not only in behavioural patterns but also in brain structure. We also showed that these behavioural and structural patterns correlate with each other, establishing a reliable link between brain and behaviour.

Automated tract extraction via atlas based Adaptive Clustering

Advancements in imaging protocols such as the high angular resolution diffusion-weighted imaging (HARDI) and in tractography techniques are expected to cause an increase in the tract-based analyses. Statistical analyses over white matter tracts can contribute greatly towards understanding structural mechanisms of the brain since tracts are representative of connectivity pathways. The main challenge with tract-based studies is the extraction of the tracts of interest in a consistent and comparable manner over a large group of individuals without drawing the inclusion and exclusion regions of interest. In this work, we design a framework for automated extraction of white matter tracts. The framework introduces three main components, namely a connectivity based fiber representation, a fiber bundle atlas, and a clustering approach called Adaptive Clustering. The fiber representation relies on the connectivity signatures of fibers to establish an easy correspondence between different subjects. A group-wise clustering of these fibers that are represented by the connectivity signatures is then used to generate a fiber bundle atlas. Finally, Adaptive Clustering incorporates the previously generated clustering atlas as a prior, to cluster the fibers of a new subject automatically. Experiments on the HARDI scans of healthy individuals acquired repeatedly, demonstrate the applicability, reliability and the repeatability of our approach in extracting white matter tracts. By alleviating the seed region selection and the inclusion/exclusion ROI drawing requirements that are usually handled by trained radiologists, the proposed framework expands the range of possible clinical applications and establishes the ability to perform tract-based analyses with large samples.

Harmonization of multi-site diffusion tensor imaging data.

Abstract Diffusion tensor imaging (DTI) is a well‐established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between‐scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site‐specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, may be counter‐productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch‐effect correction tool used in genomics, performs best at modeling and removing the unwanted inter‐site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies. HighlightsSignificant site and scanner effects exist in DTI scalar maps.Several multi‐site harmonization methods are proposed.ComBat performs the best at removing site effects in FA and MD.Voxels associated with age in FA and MD are more replicable after ComBat.ComBat is generalizable to other imaging modalities.

Individualized Map of White Matter Pathways: Connectivity-Based Paradigm for Neurosurgical Planning.

BACKGROUND Advances in white matter tractography enhance neurosurgical planning and glioma resection, but white matter tractography is limited by biological variables such as edema, mass effect, and tract infiltration or selection biases related to regions of interest or fractional anisotropy values. OBJECTIVE To provide an automated tract identification paradigm that corrects for artifacts created by tumor edema and infiltration and provides a consistent, accurate method of fiber bundle identification. METHODS An automated tract identification paradigm was developed and evaluated for glioma surgery. A fiber bundle atlas was generated from 6 healthy participants. Fibers of a test set (including 3 healthy participants and 10 patients with brain tumors) were clustered adaptively with this atlas. Reliability of the identified tracts in both groups was assessed by comparison with 2 experts with the Cohen κ used to quantify concurrence. We evaluated 6 major fiber bundles: cingulum bundle, fornix, uncinate fasciculus, arcuate fasciculus, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus, the last 3 tracts mediating language function. RESULTS The automated paradigm demonstrated a reliable and practical method to identify white mater tracts, despite mass effect, edema, and tract infiltration. When the tumor demonstrated significant mass effect or shift, the automated approach was useful for providing an initialization to guide the expert with identification of the specific tract of interest. CONCLUSION We report a reliable paradigm for the automated identification of white matter pathways in patients with gliomas. This approach should enhance the neurosurgical objective of maximal safe resections. ABBREVIATIONS AF, arcuate fasciculusDTI, diffusion tensor imagingIFOF, inferior fronto-occipital fasciculusILF, inferior longitudinal fasciculusROI, region of interestWM, white matter.

Multinomial probabilistic fiber representation for connectivity driven clustering

The clustering of fibers into bundles is an important task in studying the structure and function of white matter. Existing technology mostly relies on geometrical features, such as the shape of fibers, and thus only provides very limited information about the neuroanatomical function of the brain. We advance this issue by proposing a multinomial representation of fibers decoding their connectivity to gray matter regions. We then simplify the clustering task by first deriving a compact encoding of our representation via the logit transformation. Furthermore, we define a distance between fibers that is in theory invariant to parcellation biases and is equivalent to a family of Riemannian metrics on the simplex of multinomial probabilities. We apply our method to longitudinal scans of two healthy subjects showing high reproducibility of the resulting fiber bundles without needing to register the corresponding scans to a common coordinate system. We confirm these qualitative findings via a simple statistical analyse of the fiber bundles.

Whole brain white matter connectivity analysis using machine learning: An application to autism

&NA; In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data‐driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross‐validation study. We investigate the discriminative diffusion features based on a two‐tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along‐tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering.

Assessing connectivity related injury burden in diffuse traumatic brain injury: Connectivity Related Injury Burden in TBI

Many of the clinical and behavioral manifestations of traumatic brain injury (TBI) are thought to arise from disruption to the structural network of the brain due to diffuse axonal injury (DAI). However, a principled way of summarizing diffuse connectivity alterations to quantify injury burden is lacking. In this study, we developed a connectome injury score, Disruption Index of the Structural Connectome (DISC), which summarizes the cumulative effects of TBI‐induced connectivity abnormalities across the entire brain. Forty patients with moderate‐to‐severe TBI examined at 3 months postinjury and 35 uninjured healthy controls underwent magnetic resonance imaging with diffusion tensor imaging, and completed behavioral assessment including global clinical outcome measures and neuropsychological tests. TBI patients were selected to maximize the likelihood of DAI in the absence of large focal brain lesions. We found that hub‐like regions, with high betweenness centrality, were most likely to be impaired as a result of diffuse TBI. Clustering of participants revealed a subgroup of TBI patients with similar connectivity abnormality profiles who exhibited relatively poor cognitive performance. Among TBI patients, DISC was significantly correlated with post‐traumatic amnesia, verbal learning, executive function, and processing speed. Our experiments jointly demonstrated that assessing structural connectivity alterations may be useful in development of patient‐oriented diagnostic and prognostic tools. Hum Brain Mapp 38:2913–2922, 2017.

The impact of in-scanner head motion on structural connectivity derived from diffusion MRI

&NA; Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in‐scanner head motion on structural connectivity using a sample of 949 participants (ages 8‐23 years old) who passed a rigorous quality assessment protocol for diffusion magnetic resonance imaging (dMRI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in‐scanner head motion significantly impacted the strength of structural connectivity in a consistency‐ and length‐dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for network edges with high inter‐subject consistency, which included both short‐ and long‐range connections. In contrast, motion inflated estimates of structural connectivity for low‐consistency network edges that were primarily shorter‐range. Finally, we demonstrate that age‐related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion‐related confounds in studies of structural brain network development.

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome

Abstract. The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Unifying Inference of Meso-Scale Structures in Networks.

Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery).