Widening spinal injury research to consider all supraspinal cell types: Why we must and how we can.

The supraspinal connectome consists of dozens of neuronal populations that project axons from the brain to the spinal cord to influence a wide range of motor, autonomic, and sensory functions. The complexity and wide distribution of supraspinal neurons present significant technical challenges, leading most spinal cord injury research to focus on a handful of major pathways such as the corticospinal, rubrospinal, and raphespinal. Much less is known about many additional populations that carry information to modulate or compensate for these main pathways, or which carry pre-autonomic and other information of high value to individuals with spinal injury. A confluence of technical developments, however, now enables a whole-connectome study of spinal cord injury. Improved viral labeling, tissue clearing, and automated registration to 3D atlases can quantify supraspinal neurons throughout the murine brain, offering a practical means to track responses to injury and treatment on an unprecedented scale. Here we discuss the need for expanded connectome-wide analyses in spinal injury research, illustrate the potential by discussing a new web-based resource for brain-wide study of supraspinal neurons, and highlight future prospects for connectome analyses.

The human connectome in Alzheimer disease - relationship to biomarkers and genetics.

The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-ß and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-ß and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.

Investigating the brain structural connectome following working memory training in children born extremely preterm or extremely low birth weight.

Children born extremely preterm (EP, <28 weeks' gestation) or extremely low birth weight (ELBW, <1,000 g) are a vulnerable population at high risk of working memory impairments. We aimed to examine changes in the brain structural connectivity networks thought to underlie working memory performance, after completion of a working memory training program (Cogmed) compared with a placebo program in EP/ELBW children. This was a double-blind, placebo-controlled randomized trial (the Improving Memory in a Preterm Randomised Intervention Trial). Children born EP/ELBW received either the Cogmed or placebo program at 7 years of age (n = 91). A subset of children had magnetic resonance imaging of the brain immediately pre- and 2 weeks post-training (Cogmed n = 28; placebo n = 27). T1 -weighted and diffusion-weighted images were used to perform graph theoretical analysis of structural connectivity networks. Changes from pre-training to post-training in structural connectivity metrics were generally similar between randomized groups. There was little evidence that changes in structural connectivity metrics were related to changes in working memory performance from pre- to post-training. Overall, our results provide little evidence that the Cogmed working memory training program has training-specific effects on structural connectivity networks in EP/ELBW children.

The nonhuman primate neuroimaging and neuroanatomy project.

Multi-modal neuroimaging projects such as the Human Connectome Project (HCP) and UK Biobank are advancing our understanding of human brain architecture, function, connectivity, and their variability across individuals using high-quality non-invasive data from many subjects. Such efforts depend upon the accuracy of non-invasive brain imaging measures. However, 'ground truth' validation of connectivity using invasive tracers is not feasible in humans. Studies using nonhuman primates (NHPs) enable comparisons between invasive and non-invasive measures, including exploration of how "functional connectivity" from fMRI and "tractographic connectivity" from diffusion MRI compare with long-distance connections measured using tract tracing. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-tracing data for cortical and subcortical projections to cortical areas; and (iii) map the distributions of different brain cell types with immunocytochemical stains to better define brain areal boundaries. We are acquiring high-resolution structural, functional, and diffusion MRI data together with behavioral measures from over 100 individual macaques and marmosets in order to generate non-invasive measures of brain architecture such as myelin and cortical thickness maps, as well as functional and diffusion tractography-based connectomes. We are using classical and next-generation anatomical tracers to generate quantitative connectivity maps based on brain-wide counting of labeled cortical and subcortical neurons, providing ground truth measures of connectivity. Advanced statistical modeling techniques address the consistency of both kinds of data across individuals, allowing comparison of tracer-based and non-invasive MRI-based connectivity measures. We aim to develop improved cortical and subcortical areal atlases by combining histological and imaging methods. Finally, we are collecting genetic and sociality-associated behavioral data in all animals in an effort to understand how genetic variation shapes the connectome and behavior.

Development of the brain functional connectome follows puberty-dependent nonlinear trajectories.

Adolescence is a developmental period that dramatically impacts body and behavior, with pubertal hormones playing an important role not only in the morphological changes in the body but also in brain structure and function. Understanding brain development during adolescence has become a priority in neuroscience because it coincides with the onset of many psychiatric and behavioral disorders. However, little is known about how puberty influences the brain functional connectome. In this study, taking a longitudinal human sample of typically developing children and adolescents (of both sexes), we demonstrate that the development of the brain functional connectome better fits pubertal status than chronological age. In particular, centrality, segregation, efficiency, and integration of the brain functional connectome increase after the onset of the pubertal markers. We found that these effects are stronger in attention and task control networks. Lastly, after controlling for this effect, we showed that functional connectivity between these networks is related to better performance in cognitive flexibility. This study points out the importance of considering longitudinal nonlinear trends when exploring developmental trajectories, and emphasizes the impact of puberty on the functional organization of the brain in adolescence.

Virtual brain grafting: Enabling whole brain parcellation in the presence of large lesions.

Brain atlases and templates are at the heart of neuroimaging analyses, for which they facilitate multimodal registration, enable group comparisons and provide anatomical reference. However, as atlas-based approaches rely on correspondence mapping between images they perform poorly in the presence of structural pathology. Whilst several strategies exist to overcome this problem, their performance is often dependent on the type, size and homogeneity of any lesions present. We therefore propose a new solution, referred to as Virtual Brain Grafting (VBG), which is a fully-automated, open-source workflow to reliably parcellate magnetic resonance imaging (MRI) datasets in the presence of a broad spectrum of focal brain pathologies, including large, bilateral, intra- and extra-axial, heterogeneous lesions with and without mass effect. The core of the VBG approach is the generation of a lesion-free T1-weighted image, which enables further image processing operations that would otherwise fail. Here we validated our solution based on Freesurfer recon-all parcellation in a group of 10 patients with heterogeneous gliomatous lesions, and a realistic synthetic cohort of glioma patients (n = 100) derived from healthy control data and patient data. We demonstrate that VBG outperforms a non-VBG approach assessed qualitatively by expert neuroradiologists and Mann-Whitney U tests to compare corresponding parcellations (real patients U(6,6) = 33, z = 2.738, P < .010, synthetic-patients U(48,48) = 2076, z = 7.336, P < .001). Results were also quantitatively evaluated by comparing mean dice scores from the synthetic-patients using one-way ANOVA (unilateral VBG = 0.894, bilateral VBG = 0.903, and non-VBG = 0.617, P < .001). Additionally, we used linear regression to show the influence of lesion volume, lesion overlap with, and distance from the Freesurfer volumes of interest, on labeling accuracy. VBG may benefit the neuroimaging community by enabling automated state-of-the-art MRI analyses in clinical populations using methods such as FreeSurfer, CAT12, SPM, Connectome Workbench, as well as structural and functional connectomics. To fully maximize its availability, VBG is provided as open software under a Mozilla 2.0 license (https://github.com/KUL-Radneuron/KUL_VBG).

Alterations in Functional Connectomics Associated With Neurocognitive Changes Following Glioma Resection.

BACKGROUND: Decline in neurocognitive functioning (NCF) often occurs following brain tumor resection. Functional connectomics have shown how neurologic insults disrupt cerebral networks underlying NCF, though studies involving patients with brain tumors are lacking. OBJECTIVE: To investigate the impact of brain tumor resection upon the connectome and relationships with NCF outcome in the early postoperative period. METHODS: A total of 15 right-handed adults with left perisylvian glioma underwent resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological assessment before and after awake tumor resection. Graph theoretical analysis was applied to rs-fMRI connectivity matrices to calculate network properties. Network properties and NCF measures were compared across the pre- to postoperative periods with matched pairs Wilcoxon signed-rank tests. Associations between pre- to postoperative change in network and NCF measures were determined with Spearman rank-order correlations (ρ). RESULTS: A majority of the sample showed postoperative decline on 1 or more NCF measures. Significant postoperative NCF decline was found across measures of verbal memory, processing speed, executive functioning, receptive language, and a composite index. Regarding connectomic properties, betweenness centrality and assortativity were significantly smaller postoperatively, and reductions in these measures were associated with better NCF outcomes. Significant inverse associations (ρ = -.51 to -.78, all P < .05) were observed between change in language, executive functioning, and learning and memory, and alterations in segregation, centrality, and resilience network properties. CONCLUSION: Decline in NCF was common shortly following resection of glioma involving eloquent brain regions, most frequently in verbal learning/memory and executive functioning. Better postoperative outcomes accompanied reductions in centrality and resilience connectomic measures.

Tractography and the connectome in neurosurgical treatment of gliomas: the premise, the progress, and the potential.

The ability of diffusion tensor MRI to detect the preferential diffusion of water in cerebral white matter tracts enables neurosurgeons to noninvasively visualize the relationship of lesions to functional neural pathways. Although viewed as a research tool in its infancy, diffusion tractography has evolved into a neurosurgical tool with applications in glioma surgery that are enhanced by evolutions in crossing fiber visualization, edema correction, and automated tract identification. In this paper the current literature supporting the use of tractography in brain tumor surgery is summarized, highlighting important clinical studies on the application of diffusion tensor imaging (DTI) for preoperative planning of glioma resection, and risk assessment to analyze postoperative outcomes. The key methods of tractography in current practice and crucial white matter fiber bundles are summarized. After a review of the physical basis of DTI and post-DTI tractography, the authors discuss the methodologies with which to adapt DT image processing for surgical planning, as well as the potential of connectomic imaging to facilitate a network approach to oncofunctional optimization in glioma surgery.

Is brain connectome research the future frontier for subjective cognitive decline? A systematic review.

OBJECTIVE: We performed a systematic literature review on Subjective Cognitive Decline (SCD) in order to examine whether the resemblance of brain connectome and functional connectivity (FC) alterations in SCD with respect to MCI, AD and HC can help us draw conclusions on the progression of SCD to more advanced stages of dementia. METHODS: We searched for studies that used any neuroimaging tool to investigate potential differences/similarities of brain connectome in SCD with respect to HC, MCI, and AD. RESULTS: Sixteen studies were finally included in the review. Apparent FC connections and disruptions were observed in the white matter, default mode and gray matter networks in SCD with regards to HC, MCI, and AD. Interestingly, more apparent connections in SCD were located over the posterior regions, while an increase of FC over anterior regions was observed as the disease progressed. CONCLUSIONS: Elders with SCD display a significant disruption of the brain network, which in most of the cases is worse than HC across multiple network parameters. SIGNIFICANCE: The present review provides comprehensive and balanced coverage of a timely target research activity around SCD with the intention to identify similarities/differences across patient groups on the basis of brain connectome properties.

Targeting age-related differences in brain and cognition with multimodal imaging and connectome topography profiling.

Aging is characterized by accumulation of structural and metabolic changes in the brain. Recent studies suggest transmodal brain networks are especially sensitive to aging, which, we hypothesize, may be due to their apical position in the cortical hierarchy. Studying an open-access healthy cohort (n = 102, age range = 30-89 years) with MRI and Aß PET data, we estimated age-related cortical thinning, hippocampal atrophy and Aß deposition. In addition to carrying out surface-based morphological and metabolic mapping experiments, we stratified effects along neocortical and hippocampal resting-state functional connectome gradients derived from independent datasets. The cortical gradient depicts an axis of functional differentiation from sensory-motor regions to transmodal regions, whereas the hippocampal gradient recapitulates its long-axis. While age-related thinning and increased Aß deposition occurred across the entire cortical topography, increased Aß deposition was especially pronounced toward higher-order transmodal regions. Age-related atrophy was greater toward the posterior end of the hippocampal long-axis. No significant effect of age on Aß deposition in the hippocampus was observed. Imaging markers correlated with behavioral measures of fluid intelligence and episodic memory in a topography-specific manner, confirmed using both univariate as well as multivariate analyses. Our results strengthen existing evidence of structural and metabolic change in the aging brain and support the use of connectivity gradients as a compact framework to analyze and conceptualize brain-based biomarkers of aging.

A cross-disorder connectome landscape of brain dysconnectivity.

Many human brain disorders are associated with characteristic alterations in the structural and functional connectivity of the brain. In this article, we explore how commonalities and differences in connectome alterations can reveal relationships across disorders. We survey recent literature on connectivity changes in neurological and psychiatric disorders in the context of key organizational principles of the human connectome and observe that several disturbances to network properties of the human brain have a common role in a wide range of brain disorders and point towards potentially shared network mechanisms underpinning disorders. We hypothesize that the distinct dimensions along which connectome networks are organized (for example, 'modularity' and 'integration') provide a general coordinate system that allows description and categorization of relationships between seemingly disparate disorders. We outline a cross-disorder 'connectome landscape of dysconnectivity' along these principal dimensions of network organization that may place shared connectome alterations between brain disorders in a common framework.

The impact of white matter hyperintensities on the structural connectome in late-life depression: Relationship to executive functions.

BACKGROUND: White matter hyperintensities (WMH) represent ischemic white matter damage in late-life depression (LLD) and are associated with cognitive control dysfunction. Understanding the impact of WMH on the structural connectivity of gray matter and the cognitive control correlates of WMH-related structural dysconnectivity can provide insight into the pathophysiology of LLD. METHODS: We compared WMH burden and performance on clinical measures of cognitive control in patients with LLD (N = 44) and a control group of non-depressed older adults (N = 59). We used the Network Modification (NeMo) Tool to investigate the impact of WMH on structural dysconnectivity in specific gray matter regions, and how such connectivity was related to cognitive control functions. RESULTS: Compared to the control group, LLD participants had greater WMH burden, poorer performance on Trail Making Test (TMT) A & B, and greater self-reported dysexecutive behavior on the Frosntal Systems Behavior Scale-Executive Function subscale (FrSBe-EF). Within the LLD group, disrupted connectivity in the left supramarginal gyrus, paracentral lobule, thalamus, and pallidum was associated with psychomotor slowing (TMT-A). Altered connectivity in the left supramarginal gyrus, paracentral lobule, precentral gyrus, postcentral gyrus, thalamus, and pallidum was associated with poor attentional set-shifting (TMT-B). A follow-up analysis that isolated set-shifting ability (TMT-B/A ratio) confirmed the association with dysconnectivity in the bilateral paracentral lobule, right thalamus, left precentral gyrus, postcentral gyrus, and pallidum; additionally, it revealed associations with dysconnectivity in the right posterior cingulate, and left anterior cingulate, middle frontal cortex, and putamen. CONCLUSIONS: In LLD, WMH are associated with region-specific disruptions in cortical and subcortical gray matter areas involved in attentional aspects of cognitive control systems and sensorimotor processing, which in turn are associated with slower processing speed, and reduced attentional set-shifting. CLINICAL TRIALS REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT01728194.

Q&A: Why use synchrotron x-ray tomography for multi-scale connectome mapping?

To understand how information flows and is used in the human brain, we must map neural structures at all levels, providing visualizations similar to those of Google Earth for continents, countries, cities, and streets. Unfortunately, the imaging and processing techniques currently used in connectomics projects cannot achieve complete mapping for the brains of large animals within the timespan of a typical research career. However, feasible improvements in x-ray imaging would change this situation. This Q&A discusses synchrotron x-ray tomography, an exciting new approach for in situ mapping of whole-brain wiring diagrams at multiple levels of spatial resolution.

Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity.

Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer's tool kit.

Connectomics and other novel methods for examining neural systems.

Novel approaches for studying the brain and relating its activities to mental phenomena have come into use during the past decade (Bargmann, 2015). These include both new laboratory methods - involving, among others, generation of isolated cells which retain neuronal characteristics in vivo; the selective stimulation of neurons by light in vivo; and direct electrical stimulation of specific brain regions to restore a system's balance of excitation and inhibition - and a new organizing principle, "connectomics", which recognizes that networks, and not simply a key nucleus or region, underlie most brain functions and malfunctions. Its application has already improved our comprehension of how the brain normally functions and our ability to help patients with such poorly treated neurologic and psychiatric diseases as Alzheimer's disease.