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Preserved Anatomical Bypasses Predict Variance in Language Functions After Stroke
Abstract
The severity of post-stroke aphasia is related to damage to white matter connections. However, neural signaling can route not only through direct connections, but also along multi-step network paths. When brain networks are damaged by stroke, paths can bypass around the damage to restore communication. The shortest network paths between regions could be the most efficient routes for mediating bypasses. We examined how shortest-path bypasses after left hemisphere strokes were related to language performance. Regions within and outside of the canonical language network could be important in aphasia recovery. Therefore, we innovated methods to measure the influence of bypasses in the whole brain.
Distinguishing bypasses from all residual shortest paths is difficult without pre-stroke imaging. We identified bypasses by finding shortest paths in subjects with stroke that were longer than the most reliably observed connections in age-matched control networks. We tested whether features of those bypasses predicted scores in four orthogonal dimensions of language performance derived from a principal components analysis of a battery of language tasks. The features were the length of each bypass in steps, and how many bypasses overlapped on each individual direct connection. We related these bypass features to language factors using support vector regression, a technique that extracts robust relationships in high-dimensional data analysis. The support vector regression parameters were tuned using grid-search cross-validation.
We discovered that the length of bypasses reliably predicted variance in lexical production (R² = .576) and auditory comprehension scores (R² = .164). Bypass overlaps reliably predicted variance in Lexical Production scores (R² = .247). The predictive elongation features revealed that bypass efficiency along the dorsal stream and ventral stream were most related to Lexical Production and Auditory Comprehension, respectively. Among the predictive bypass overlaps, increased bypass routing through the right hemisphere putamen was negatively related to lexical production ability.
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Background: White matter disconnection of language-specific brain regions associates with worse aphasia recovery. Despite a loss of direct connections, many stroke survivors may maintain indirect connections between brain regions.
Objective:To determine (1) whether preserved direct connections between language-specific brain regions relate to better poststroke naming treatment outcomes compared to no direct connections and (2) whether for individuals with a loss of direct connections, preserved indirect connections are associated with better treatment outcomes compared to individuals with no connections.
Methods: We computed structural whole-brain connectomes from 69 individuals with chronic left-hemisphere stroke and aphasia who completed a 3-week-long language treatment that was supplemented by either anodal transcranial direct current stimulation (A-tDCS) or sham stimulation (S-tDCS). We determined differences in naming improvement between individuals with direct, indirect, and no connections using 1-way analyses of covariance and multivariable linear regressions.ResultsIndependently of tDCS modality, direct or indirect connections between the inferior frontal gyrus pars opercularis and angular gyrus were both associated with a greater increase in correct naming compared to no connections (P = .027 and P = .039, respectively). Participants with direct connections between the inferior frontal gyrus pars opercularis and middle temporal gyrus who received S-tDCS and participants with indirect connections who received A-tDCS significantly improved in naming accuracy.
Conclusions: Poststroke preservation of indirect white matter connections is associated with better treated naming improvement in aphasia even when direct connections are damaged. This mechanistic information can be used to stratify and predict treated naming recovery in individuals with aphasia.
Human cognition is dynamic, alternating over time between externally-focused states and more abstract, often self-generated, patterns of thought. Although cognitive neuroscience has documented how networks anchor particular modes of brain function, mechanisms that describe transitions between distinct functional states remain poorly understood. Here, we examined how time-varying changes in brain function emerge within the constraints imposed by macroscale structural network organization. Studying a large cohort of healthy adults (n = 326), we capitalized on manifold learning techniques that identify low dimensional representations of structural connectome organization and we decomposed neurophysiological activity into distinct functional states and their transition patterns using Hidden Markov Models. Structural connectome organization predicted dynamic transitions anchored in sensorimotor systems and those between sensorimotor and transmodal states. Connectome topology analyses revealed that transitions involving sensorimotor states traversed short and intermediary distances and adhered strongly to communication mechanisms of network diffusion. Conversely, transitions between transmodal states involved spatially distributed hubs and increasingly engaged long-range routing. These findings establish that the structure of the cortex is optimized to allow neural states the freedom to vary between distinct modes of processing, and so provides a key insight into the neural mechanisms that give rise to the flexibility of human cognition.
Recent developments in semi-global tractogram optimisation algorithms have opened the field of diffusion MRI to the possibility of performing quantitative assessment of structural fibre connectivity. The proper application of these methods in neuroscience research has however been limited by a lack of awareness, understanding, or appreciation for the consequences of these methods; furthermore, particular steps necessary to use these tools in an appropriate manner to fully exploit their quantitative properties have not yet been described. This article therefore serves three purposes: to increase awareness of the fact that there are existing tools that attempt to address the well-known non-quantitative nature of streamlines counts; to illustrate why these algorithms work the way they do to give quantitative results; and to explain how to properly utilise these results for quantitative tractography analysis across subjects.
Background and Purpose—
It is uncertain what determines the potential for cognitive recovery after ischemic stroke. The extent to which strategic areas of the brain network, so-called hubs, are affected by the infarct could be a key factor. We developed a lesion impact score, which estimates the damage to network hubs by integrating information on infarct size with healthy brain network topology. We verified whether the lesion impact score indeed reflects global network disturbances in patients and assessed if it could predict cognitive recovery.
Methods—
Seventy-five ischemic stroke patients without signs of a prestroke cognitive disorder were included, all with evidence of a cognitive disorder during hospitalization. A brain magnetic resonance imaging and neuropsychological assessment were performed 5 weeks (±1 week) after stroke. Neuropsychological testing was repeated after 1 year to assess cognitive recovery. Brain networks were reconstructed from diffusion-weighted data and consisted of 90 gray matter regions (ie, network nodes). A standard brain network map, indicating the hub-score of each node, was obtained from network data of 44 cognitively healthy adults. For each patient, we calculated the lesion impact score by multiplying the percentage of node volume affected by the infarct with the node’s corresponding hub-score. The patients’ maximum lesion impact score was used as outcome predictor.
Results—
A higher lesion impact score in patients, indicating an increasing infarct size in nodes with a higher hub-score, was related to lower global brain network efficiency (β=−0.528 [−0.776 to −0.277]; P <0.001), independent of age, brain volume, infarct volume, and white matter hyperintensity severity. A lower lesion impact score, however, was an independent predictor of cognitive recovery 1 year after stroke (odds ratio=0.434 [0.193–0.978]; P =0.044).
Conclusions—
We introduced a lesion impact score that combines information on infarct size and network topology to predict long-term recovery after stroke. This score can potentially be used in a clinical setting, also without availability of high-resolution diffusion-weighted magnetic resonance imaging.
Connectome-based lesion symptom mapping (CLSM) can be used to relate disruptions of brain network connectivity with clinical measures. We present a novel method that extends current CLSM approaches by introducing a fast reliable and accurate way for computing disconnectomes, i.e. identifying damaged or lesioned connections. We introduce a new algorithm that finds the maximally disconnected subgraph containing regions and region pairs with the greatest shared connectivity loss. After normalizing a stroke patient's segmented MRI lesion into template space, probability weighted structural connectivity matrices are constructed from shortest paths found in white matter voxel graphs of 210 subjects from the Human Connectome Project. Percent connectivity loss matrices are constructed by measuring the proportion of shortest-path probability weighted connections that are lost because of an intersection with the patient's lesion. Maximally disconnected subgraphs of the overall connectivity loss matrix are then derived using a computationally fast greedy algorithm that closely approximates the exact solution. We illustrate the approach in eleven stroke patients with hemiparesis by identifying expected disconnections of the corticospinal tract (CST) with cortical sensorimotor regions. Major disconnections are found in the thalamus, basal ganglia, and inferior parietal cortex. Moreover, the size of the maximally disconnected subgraph quantifies the extent of cortical disconnection and strongly correlates with multiple clinical measures. The methods provide a fast, reliable approach for both visualizing and quantifying the disconnected portion of a patient's structural connectome based on their routine clinical MRI, without reliance on concomitant diffusion weighted imaging. The method can be extended to large databases of stroke patients, multiple sclerosis or other diseases causing focal white matter injuries helping to better characterize clinically relevant white matter lesions and to identify biomarkers for the recovery potential of individual patients. Keywords: Diffusion MRI, Tractography, Lesion symptom mapping, Disconnection, Brain injury, Spatial normalization, Brain networks, Connectomes, Disconnectome, Graphs
Brain structure reflects the influence of evolutionary processes that pit the costs of its anatomical wiring against the computational advantages conferred by its complexity. We show that cost-neutral 'mutations' of the human connectome almost inevitably degrade its complexity and disconnect high-strength connections to prefrontal network hubs. Conversely, restoring the peripheral location and strong connectivity of empirically observed hubs confers a wiring cost that the brain appears to minimize. Progressive cost-neutral randomization yields daughter networks that differ substantially from one another and results in a topologically unstable phenomenon consistent with a phase transition in complex systems. The fragility of hubs to disconnection shows a significant association with the acceleration of gray matter loss in schizophrenia. Together with effects on wiring cost, we suggest that fragile prefrontal hub connections and topological volatility act as evolutionary influences on brain networks whose optimal set point may be perturbed in neuropsychiatric disorders.
Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.
Lesion-symptom mapping is often employed to define brain structures that are crucial for human behavior. Even though poststroke deficits result from gray matter damage as well as secondary white matter loss, the impact of structural disconnection is overlooked by conventional lesion-symptom mapping because it does not measure loss of connectivity beyond the stroke lesion. This study describes how traditional lesion mapping can be combined with structural connectome lesion symptom mapping (CLSM) and connectome dynamics lesion symptom mapping (CDLSM) to relate residual white matter networks to behavior. Using data from a large cohort of stroke survivors with aphasia, we observed improved prediction of aphasia severity when traditional lesion symptom mapping was combined with CLSM and CDLSM. Moreover, only CLSM and CDLSM disclosed the importance of temporal-parietal junction connections in aphasia severity. In summary, connectome measures can uniquely reveal brain networks that are necessary for function, improving the traditional lesion symptom mapping approach.
The putamen is a subcortical structure that forms part of the dorsal striatum of basal
ganglia, and has traditionally been associated with reinforcement learning and motor
control, including speech articulation. However, recent studies have shown
involvement of the left putamen in other language functions such as bilingual language
processing (Abutalebi et al., 2013) and production, with some authors arguing for
functional segregation of anterior and posterior putamen (Oberhuber et al., 2013). A
further step in exploring the role of putamen in language would involve identifying the
network of coactivations of left, but also right putamen, given the involvement of right
hemisphere in high order language functions (Vigneau et al., 2011). Here, a meta-analytic
connectivity modeling technique was used to determine the patterns of
coactivation of anterior and bilateral putamen in the language domain. Based on
previous evidence, we hypothesized that left putamen coactivations would encompass
brain regions directly associated with language processing, whereas right putamen
coactivations would comprise regions involved in broader semantic processes, such
as memory and visual imagery. The results showed left anterior putamen coactivations
in a network located mostly in left hemisphere, including regions directly
associated with language processing, a left posterior putamen network spanning both
hemispheres, and cerebellum. In right hemisphere, coactivations were in both
hemispheres, in regions associated with visual and orthographic processing. These
results confirm the differential involvement of right and left putamen in different
language components, thus highlighting the need for further research into the role of
the striatum in language.
Damage to the white matter underlying the left posterior
temporal lobe leads to deficits in multiple language functions. The
posterior temporal white matter may correspond to a bottleneck where both
dorsal and ventral language pathways are vulnerable to simultaneous
damage. Damage to a second putative white matter bottleneck in the left
deep prefrontal white matter involving projections associated with
ventral language pathways and thalamo-cortical projections has recently
been proposed as a source of semantic deficits after stroke. Here, we
first used white matter atlases to identify the previously described
white matter bottlenecks in the posterior temporal and deep prefrontal
white matter. We then assessed the effects of damage to each region on
measures of verbal fluency, picture naming, and auditory semantic
decision-making in 43 chronic left hemispheric stroke patients. Damage to
the posterior temporal bottleneck predicted deficits on all tasks, while
damage to the anterior bottleneck only significantly predicted deficits
in verbal fluency. Importantly, the effects of damage to the bottleneck
regions were not attributable to lesion volume, lesion loads on the
tracts traversing the bottlenecks, or damage to nearby cortical language
areas. Multivariate lesion-symptom mapping revealed additional lesion
predictors of deficits. Post-hoc fiber tracking of the peak white matter
lesion predictors using a publicly available tractography atlas revealed
evidence consistent with the results of the bottleneck analyses.
Together, our results provide support for the proposal that spatially
specific white matter damage affecting bottleneck regions, particularly
in the posterior temporal lobe, contributes to chronic language deficits
after left hemispheric stroke. This may reflect the simultaneous
disruption of signaling in dorsal and ventral language processing
streams.
The study of language network plasticity following left hemisphere stroke is foundational to the understanding of aphasia recovery and neural plasticity in general. Damage in different language nodes may influence whether local plasticity is possible and whether right hemisphere recruitment is beneficial. However, the relationships of both lesion size and location to patterns of remapping are poorly understood. In the context of a picture naming fMRI task, we tested whether lesion size and location relate to activity in surviving left hemisphere language nodes, as well as homotopic activity in the right hemisphere during covert name retrieval and overt name production. We found that lesion size was positively associated with greater right hemisphere activity during both phases of naming, a pattern that has frequently been suggested but has not previously been clearly demonstrated. During overt naming, lesions in the inferior frontal gyrus led to deactivation of contralateral frontal areas, while lesions in motor cortex led to increased right motor cortex activity. Furthermore, increased right motor activity related to better naming performance only when left motor cortex was lesioned, suggesting compensatory takeover of speech or language function by the homotopic node. These findings demonstrate that reorganization of language function, and the degree to which reorganization facilitates aphasia recovery, is dependent on the size and site of the lesion.
Unlabelled:
Language processing relies on a widespread network of brain regions. Univariate post-stroke lesion-behavior mapping is a particularly potent method to study brain-language relationships. However, it is a concern that this method may overlook structural disconnections to seemingly spared regions and may fail to adjudicate between regions that subserve different processes but share the same vascular perfusion bed. For these reasons, more refined structural brain mapping techniques may improve the accuracy of detecting brain networks supporting language. In this study, we applied a predictive multivariate framework to investigate the relationship between language deficits in human participants with chronic aphasia and the topological distribution of structural brain damage, defined as post-stroke necrosis or cortical disconnection. We analyzed lesion maps as well as structural connectome measures of whole-brain neural network integrity to predict clinically applicable language scores from the Western Aphasia Battery (WAB). Out-of-sample prediction accuracy was comparable for both types of analyses, which revealed spatially distinct, albeit overlapping, networks of cortical regions implicated in specific aspects of speech functioning. Importantly, all WAB scores could be predicted at better-than-chance level from the connections between gray-matter regions spared by the lesion. Connectome-based analysis highlighted the role of connectivity of the temporoparietal junction as a multimodal area crucial for language tasks. Our results support that connectome-based approaches are an important complement to necrotic lesion-based approaches and should be used in combination with lesion mapping to fully elucidate whether structurally damaged or structurally disconnected regions relate to aphasic impairment and its recovery.
Significance statement:
We present a novel multivariate approach of predicting post-stroke impairment of speech and language from the integrity of the connectome. We compare it with multivariate prediction of speech and language scores from lesion maps, using cross-validation framework and a large (n = 90) database of behavioral and neuroimaging data from individuals with post-stroke aphasia. Connectome-based analysis was similar to lesion-based analysis in terms of predictive accuracy and provided additional details about the importance of specific connections (in particular, between parietal and posterior temporal areas) for preserving speech functions. Our results suggest that multivariate predictive analysis of the connectome is a useful complement to multivariate lesion analysis, being less dependent on the spatial constraints imposed by underlying vasculature.
Computational analysis of communication efficiency of brain networks often relies on graph-theoretic measures based on the shortest paths between network nodes. Here, we explore a communication scheme that relaxes the assumption that information travels exclusively through optimally short paths. The scheme assumes that communication between a pair of brain regions may take place through a path ensemble comprising the k-shortest paths between those regions. To explore this approach, we map path ensembles in a set of anatomical brain networks derived from diffusion imaging and tractography. We show that while considering optimally short paths excludes a significant fraction of network connections from participating in communication, considering k-shortest path ensembles allows all connections in the network to contribute. Path ensembles enable us to assess the resilience of communication pathways between brain regions, by measuring the number of alternative, disjoint paths within the ensemble, and to compare generalized measures of path length and betweenness centrality to those that result when considering only the single shortest path between node pairs. Furthermore, we find a significant correlation, indicative of a trade-off, between communication efficiency and resilience of communication pathways in structural brain networks. Finally, we use k-shortest path ensembles to demonstrate hemispherical lateralization of efficiency and resilience.
Quantitative descriptions of network structure can provide fundamental insights into the function of interconnected complex systems. Small-world structure, diagnosed by high local clustering yet short average path length between any two nodes, promotes information flow in coupled systems, a key function that can differ across conditions or between groups. However, current techniques to quantify small-worldness are density dependent and neglect important features such as the strength of network connections, limiting their application in real-world systems. Here, we address both limitations with a novel metric called the Small-World Propensity (SWP). In its binary instantiation, the SWP provides an unbiased assessment of small-world structure in networks of varying densities. We extend this concept to the case of weighted brain networks by developing (i) a standardized procedure for generating weighted small-world networks, (ii) a weighted extension of the SWP, and (iii) a method for mapping observed brain network data onto the theoretical model. In applying these techniques to compare real-world brain networks, we uncover the surprising fact that the canonical biological small-world network, the C. elegans neuronal network, has strikingly low SWP. These metrics, models, and maps form a coherent toolbox for the assessment and comparison of architectural properties in brain networks.
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The mechanisms underlying recovery of language after left hemisphere stroke are incompletely understood. Xing et al. report increases in grey matter volumes in the right temporoparietal cortex in stroke survivors relative to healthy controls, and a positive correlation with spontaneous speech, naming, and repetition scores.
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The mechanisms underlying recovery of language after left hemisphere stroke are incompletely understood. Xing et al. report increases in grey matter volumes in the right temporoparietal cortex in stroke survivors relative to healthy controls, and a positive correlation with spontaneous speech, naming, and repetition scores.
Disruptions of brain anatomical connectivity are believed to play a central role in several neurological and psychiatric illnesses. The structural brain connectome is typically derived from diffusion tensor imaging (DTI), which may be influenced by methodological factors related to signal processing, MRI scanners and biophysical properties of neuroanatomical regions. In this study, we evaluated how these variables affect the reproducibility of the structural connectome.
Twenty healthy adults underwent 3 MRI scanning sessions (twice in the same MRI scanner and a third time in a different scanner unit) within a short period of time. The scanning sessions included similar T1 weighted and DTI sequences. Deterministic or probabilistic tractography was performed to assess link weight based on the number of fibers connecting gray matter regions of interest (ROI). Link weight and graph theory network measures were calculated and reproducibility was assessed through intra-class correlation coefficients, assuming each scanning session as a rater.
Connectome reproducibility was higher with data from the same scanner. The probabilistic approach yielded larger reproducibility, while the individual variation in the number of tracked fibers from deterministic tractography was negatively associated with reproducibility. Links connecting larger and anatomically closer ROIs demonstrated higher reproducibility. In general, graph theory measures demonstrated high reproducibility across scanning sessions.
Anatomical factors and tractography approaches can influence the reproducibility of the structural connectome and should be factored in the interpretation of future studies. Our results demonstrate that connectome mapping is a largely reproducible technique, particularly as it relates to the geometry of network architecture measured by graph theory methods.
Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.
Connections between neurons are affected within 3 min of stroke onset by massive ischemic depolarization and then delayed cell death. Some connections can recover with prompt reperfusion; others associated with the dying infarct do not. Disruption in functional connectivity is due to direct tissue loss and indirect disconnections of remote areas known as diaschisis. Stroke is devastating, yet given the brain's redundant design, collateral surviving networks and their connections are well-positioned to compensate. Our perspective is that new treatments for stroke may involve a rational functional and structural connections-based approach. Surviving, affected, and at-risk networks can be identified and targeted with scenario-specific treatments. Strategies for recovery may include functional inhibition of the intact hemisphere, rerouting of connections, or setpoint-mediated network plasticity. These approaches may be guided by brain imaging and enabled by patient- and injury-specific brain stimulation, rehabilitation, and potential molecule-based strategies to enable new connections.
The Objective was to describe the contributions of Joseph Jules Dejerine and his wife Augusta Dejerine-Klumpke to our understanding of cerebral association fiber tracts and language processing. The Dejerines (and not Constantin von Monakow) were the first to describe the superior longitudinal fasciculus/arcuate fasciculus (SLF/AF) as an association fiber tract uniting Broca's area, Wernicke's area, and a visual image center in the angular gyrus of a left hemispheric language zone. They were also the first to attribute language-related functions to the fasciculi occipito-frontalis (FOF) and the inferior longitudinal fasciculus (ILF) after describing aphasia patients with degeneration of the SLF/AF, ILF, uncinate fasciculus (UF), and FOF. These fasciculi belong to a functional network known as the Dejerines' language zone, which exceeds the borders of the classically defined cortical language centers. The Dejerines provided the first descriptions of the anatomical pillars of present-day language models (such as the SLF/AF). Their anatomical descriptions of fasciculi in aphasia patients provided a foundation for our modern concept of the dorsal and ventral streams in language processing.
The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges
The present study examined the crowding hypothesis in a cohort of epileptic patients whose left-hemisphere dysfunction occurred either before or aft 12 mo of age. The results reveal that the crowding effect depends not only on interhemispheric speech transfer but also on a lesion that occurs in the pre- or perinatal period. Right-hemisphere speech dominance alone does not provide a sufficient condition. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
An undirected weighted graph may be constructed from diffusion weighted magnetic resonance imaging data. Every node represents a voxel and the edge weights between nodes represent the white matter connectivity between neighboring voxels. In this paper we propose and test a new method for calculating trajectories of fiber bundles in the brain by applying Dijkstra's shortest path algorithm to the weighted graph. Subsequently, the resulting tree structure is pruned, showing the main white matter structures of the brain. The time consumption of this method is in the order of seconds.
The brain is expensive, incurring high material and metabolic costs for its size--relative to the size of the body--and many aspects of brain network organization can be mostly explained by a parsimonious drive to minimize these costs. However, brain networks or connectomes also have high topological efficiency, robustness, modularity and a 'rich club' of connector hubs. Many of these and other advantageous topological properties will probably entail a wiring-cost premium. We propose that brain organization is shaped by an economic trade-off between minimizing costs and allowing the emergence of adaptively valuable topological patterns of anatomical or functional connectivity between multiple neuronal populations. This process of negotiating, and re-negotiating, trade-offs between wiring cost and topological value continues over long (decades) and short (millisecond) timescales as brain networks evolve, grow and adapt to changing cognitive demands. An economical analysis of neuropsychiatric disorders highlights the vulnerability of the more costly elements of brain networks to pathological attack or abnormal development.
Scikit-learn is a Python module integrating a wide range of state-of-the-art
machine learning algorithms for medium-scale supervised and unsupervised
problems. This package focuses on bringing machine learning to non-specialists
using a general-purpose high-level language. Emphasis is put on ease of use,
performance, documentation, and API consistency. It has minimal dependencies
and is distributed under the simplified BSD license, encouraging its use in
both academic and commercial settings. Source code, binaries, and documentation
can be downloaded from http://scikit-learn.sourceforge.net.
The human brain is a complex network of interlinked regions. Recent studies have demonstrated the existence of a number of highly connected and highly central neocortical hub regions, regions that play a key role in global information integration between different parts of the network. The potential functional importance of these "brain hubs" is underscored by recent studies showing that disturbances of their structural and functional connectivity profile are linked to neuropathology. This study aims to map out both the subcortical and neocortical hubs of the brain and examine their mutual relationship, particularly their structural linkages. Here, we demonstrate that brain hubs form a so-called "rich club," characterized by a tendency for high-degree nodes to be more densely connected among themselves than nodes of a lower degree, providing important information on the higher-level topology of the brain network. Whole-brain structural networks of 21 subjects were reconstructed using diffusion tensor imaging data. Examining the connectivity profile of these networks revealed a group of 12 strongly interconnected bihemispheric hub regions, comprising the precuneus, superior frontal and superior parietal cortex, as well as the subcortical hippocampus, putamen, and thalamus. Importantly, these hub regions were found to be more densely interconnected than would be expected based solely on their degree, together forming a rich club. We discuss the potential functional implications of the rich-club organization of the human connectome, particularly in light of its role in information integration and in conferring robustness to its structural core.
Split brain patients who are initially unable to produce speech in their right hemispheres sometimes develop the ability to do so. Patient J.W., the subject of this report, is such a patient. At the time of his callosotomy, J.W. had a language dominant left hemisphere; his right hemisphere could understand both spoken and written language, but he was unable to speak. Fourteen years after his surgery, we found that J.W. was capable of naming approximately 25% of the stimuli presented to his left visual field (LVF). Now, 1 year later, we find that he can name about 60% of such stimuli. This late-developing speech ability appears to be consequence of long-term neural plasticity. However, the subject's extended verbal responses to LVF stimuli seem to result from a collaboration between the hemispheres and to involve the left hemisphere interpreter.
Modelling behavioural deficits based on structural lesion imaging is a popular approach to map functions in the human brain, and efforts to translationally apply lesion-behaviour modelling to predict post-stroke outcomes are on the rise. The high-dimensional complexity of lesion data, however, evokes challenges in both lesion behaviour mapping and post stroke outcome prediction. This paper aims to deepen the understanding of this complexity by reframing it from the perspective of causal and non-causal dependencies in the data, and by discussing what this complexity implies for different data modelling approaches. By means of theoretical discussion and empirical examination, several common strategies and views are challenged, and future research perspectives are outlined. A main conclusion is that lesion-behaviour inference is subject to a lesion-anatomical bias that cannot be overcome by using multivariate models or any other algorithm that is blind to causality behind relations in the data. This affects the validity of lesion behaviour mapping and might even wrongfully identify paradoxical effects of lesion-induced functional facilitation - but, as this paper argues, only to a minor degree. Thus, multivariate lesion-brain inference appears to be a valuable tool to deepen our understanding of the human brain, but only because it takes into account the functional relation between brain areas. The perspective of causality and inter-variable dependence is further used to point out challenges in improving lesion behaviour models. Firstly, the dependencies in the data open up different possible strategies of data reduction, and considering those might improve post-stroke outcome prediction. Secondly, the role of non-topographical causal predictors of post stroke behaviour is discussed. The present article argues that, given these predictors, different strategies are required in the evaluation of model quality in lesion behaviour mapping and post stroke outcome prediction.
Purpose
Understanding the brain basis of language and cognitive outcomes is a major goal of aphasia research. Prior studies have not often considered the many ways that brain features can relate to behavioral outcomes or the mechanisms underlying these relationships. The purpose of this review article is to provide a new framework for understanding the ways that brain features may relate to language and cognitive outcomes from stroke.
Method
Brain–behavior relationships that may be important for aphasia outcomes are organized into a taxonomy, including features of the lesion and features of brain tissue spared by the lesion. Features of spared brain tissue are categorized into those that change after stroke and those that do not. Features that change are further subdivided, and multiple mechanisms of brain change after stroke are discussed.
Results
Features of the stroke, including size, location, and white matter damage, relate to many behavioral outcomes and likely account for most of the variance in outcomes. Features of the spared brain tissue that are unchanged by stroke, such as prior ischemic disease in the white matter, contribute to outcomes. Many different neurobiological and behavioral mechanisms may drive changes in the brain after stroke in association with behavioral recovery. Changes primarily driven by neurobiology are likely to occur in brain regions with a systematic relationship to the stroke distribution. Changes primarily driven by behavior are likely to occur in brain networks related to the behavior driving the change.
Conclusions
Organizing the various hypothesized brain–behavior relationships according to this framework and considering the mechanisms that drive these relationships may help investigators develop specific experimental designs and more complete statistical models to explain language and cognitive abilities after stroke. Eight main recommendations for future research are provided.
Presentation Video
https://doi.org/10.23641/asha.10257578
MRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualisation, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.
Researchers in fields ranging from biology and medicine to the social sciences, law, and economics regularly encounter variables that are discrete or categorical in nature. While there is no dearth of books on the analysis and interpretation of such data, these generally focus on large sample methods. When sample sizes are not large or the data are otherwise sparse, exact methods--methods not based on asymptotic theory--are more accurate and therefore preferable. This book introduces the statistical theory, analysis methods, and computation techniques for exact analysis of discrete data. After reviewing the relevant discrete distributions, the author develops the exact methods from the ground up in a conceptually integrated manner. The topics covered range from univariate discrete data analysis, a single and several 2 × 2 tables, a single and several 2 × K tables, incidence density and inverse sampling designs, unmatched and matched case -control studies, paired binary and trinomial response models, and Markov chain data. While most chapters focus on statistical theory and applications, three chapters deal exclusively with computational issues. Detailed worked examples appear throughout the book, and each chapter includes an extensive problem set. Written at an elementary to intermediate level, Exact Analysis of Discrete Data is accessible to anyone having taken a basic course in statistics or biostatistics, bringing to them valuable material previously buried in specialized journals.
Lesion‐symptom mapping has become a cornerstone of neuroscience research seeking to localize cognitive function in the brain by examining the sequelae of brain lesions. Recently, multivariate lesion‐symptom mapping methods have emerged, such as support vector regression, which simultaneously consider many voxels at once when determining whether damaged regions contribute to behavioral deficits (Zhang, Kimberg, Coslett, Schwartz, & Wang, 2014). Such multivariate approaches are capable of identifying complex dependences that traditional mass‐univariate approach cannot. Here, we provide a new toolbox for support vector regression lesion‐symptom mapping (SVR‐LSM) that provides a graphical interface and enhances the flexibility and rigor of analyses that can be conducted using this method. Specifically, the toolbox provides cluster‐level family‐wise error correction via permutation testing, the capacity to incorporate arbitrary nuisance models for behavioral data and lesion data and makes available a range of lesion volume correction methods including a new approach that regresses lesion volume out of each voxel in the lesion maps. We demonstrate these new tools in a cohort of chronic left‐hemisphere stroke survivors and examine the difference between results achieved with various lesion volume control methods. A strong bias was found toward brain wide lesion‐deficit associations in both SVR‐LSM and traditional mass‐univariate voxel‐based lesion symptom mapping when lesion volume was not adequately controlled. This bias was corrected using three different regression approaches; among these, regressing lesion volume out of both the behavioral score and the lesion maps provided the greatest sensitivity in analyses.
In most cases, aphasia is caused by strokes involving the left hemisphere, with more extensive damage typically being associated with more severe aphasia. The classical model of aphasia commonly adhered to in the Western world is the Wernicke-Lichtheim model. The model has been in existence for over a century, and classification of aphasic symptomatology continues to rely on it. However, far more detailed models of speech and language localization in the brain have been formulated. In this regard, the dual stream model of cortical brain organization proposed by Hickok and Poeppel is particularly influential. Their model describes two processing routes, a dorsal stream and a ventral stream, that roughly support speech production and speech comprehension, respectively, in normal subjects. Despite the strong influence of the dual stream model in current neuropsychological research, there has been relatively limited focus on explaining aphasic symptoms in the context of this model. Given that the dual stream model represents a more nuanced picture of cortical speech and language organization, cortical damage that causes aphasic impairment should map clearly onto the dual processing streams. Here, we present a follow-up study to our previous work that used lesion data to reveal the anatomical boundaries of the dorsal and ventral streams supporting speech and language processing. Specifically, by emphasizing clinical measures, we examine the effect of cortical damage and disconnection involving the dorsal and ventral streams on aphasic impairment. The results reveal that measures of motor speech impairment mostly involve damage to the dorsal stream, whereas measures of impaired speech comprehension are more strongly associated with ventral stream involvement. Equally important, many clinical tests that target behaviours such as naming, speech repetition, or grammatical processing rely on interactions between the two streams. This latter finding explains why patients with seemingly disparate lesion locations often experience similar impairments on given subtests. Namely, these individuals' cortical damage, although dissimilar, affects a broad cortical network that plays a role in carrying out a given speech or language task. The current data suggest this is a more accurate characterization than ascribing specific lesion locations as responsible for specific language deficits.awx363media15705668782001.
Background:
Understanding the relationships between clinical tests, the processes they measure, and the brain networks underlying them, is critical in order for clinicians to move beyond aphasia syndrome classification toward specification of individual language process impairments.
Objective:
To understand the cognitive, language, and neuroanatomical factors underlying scores of commonly used aphasia tests.
Methods:
Twenty-five behavioral tests were administered to a group of 38 chronic left hemisphere stroke survivors and a high-resolution magnetic resonance image was obtained. Test scores were entered into a principal components analysis to extract the latent variables (factors) measured by the tests. Multivariate lesion-symptom mapping was used to localize lesions associated with the factor scores.
Results:
The principal components analysis yielded 4 dissociable factors, which we labeled Word Finding/Fluency, Comprehension, Phonology/Working Memory Capacity, and Executive Function. While many tests loaded onto the factors in predictable ways, some relied heavily on factors not commonly associated with the tests. Lesion symptom mapping demonstrated discrete brain structures associated with each factor, including frontal, temporal, and parietal areas extending beyond the classical language network. Specific functions mapped onto brain anatomy largely in correspondence with modern neural models of language processing.
Conclusions:
An extensive clinical aphasia assessment identifies 4 independent language functions, relying on discrete parts of the left middle cerebral artery territory. A better understanding of the processes underlying cognitive tests and the link between lesion and behavior may lead to improved aphasia diagnosis, and may yield treatments better targeted to an individual's specific pattern of deficits and preserved abilities.
The exploration of brain networks with resting-state fMRI (rs-fMRI) combined with graph theoretical approaches has become popular, with the perspective of finding network graph metrics as biomarkers in the context of clinical studies. A preliminary requirement for such findings is to assess the reliability of the graph based connectivity metrics. In previous test-retest (TRT) studies, this reliability has been explored using intraclass correlation coefficient (ICC) with heterogeneous results. But the issue of sample size has not been addressed. Using the large TRT rs-fMRI dataset from the Human Connectome Project (HCP), we computed ICCs and their corresponding p-values (applying permutation and bootstrap techniques) and varied the number of subjects (from 20 to 100), the scan duration (from 400 to 1200 time points), the cost and the graph metrics, using the Anatomic-Automatic Labelling (AAL) parcellation scheme. We quantified the reliability of the graph metrics computed both at global and regional level depending, at optimal cost, on two key parameters, the sample size and the number of time points or scan duration. In the cost range between 20% to 35%, most of the global graph metrics are reliable with 40 subjects or more with long scan duration (14 min 24 s). In large samples (for instance, 100 subjects) most global and regional graph metrics are reliable for a minimum scan duration of 7 min 14 s. Finally, for 40 subjects and long scan duration (14 min 24 s), the reliable regions are located in the main areas of the default mode network (DMN), the motor and the visual networks.
In this study, models based on quantitative imaging biomarkers of post-stroke structural connectome disruption were used to predict six-month outcomes in various domains. Demographic information and clinical MRIs were collected from 40 ischemic stroke subjects (age: 68.1 ± 13.2 years, 17 female, NIHSS: 6.8 ± 5.6). Diffusion-weighted images were used to create lesion masks, which were uploaded to the Network Modification (NeMo) Tool. The NeMo Tool, using only clinical MRIs, allows estimation of connectome disruption at three levels: whole brain, individual gray matter regions and between pairs of gray matter regions. Partial Least Squares Regression models were constructed for each level of connectome disruption and for each of the three six-month outcomes: applied cognitive, basic mobility and daily activity. Models based on lesion volume were created for comparison. Cross-validation, bootstrapping and multiple comparisons corrections were implemented to minimize over-fitting and Type I errors. The regional disconnection model best predicted applied cognitive (R(2) = 0.56) and basic mobility outcomes (R(2) = 0.70), while the pairwise disconnection model best predicted the daily activity measure (R(2) = 0.72). These results demonstrate that models based on connectome disruption metrics were more accurate than ones based on lesion volume and that increasing anatomical specificity of disconnection metrics does not always increase model accuracy, likely due to statistical adjustments for concomitant increases in data dimensionality. This work establishes that the NeMo Tool's measures of baseline connectome disruption, acquired using only routinely collected MRI scans, can predict 6-month post-stroke outcomes in various functional domains including cognition, motor function and daily activities. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.
Purpose:
Post-stroke aphasia is typically associated with ischemic damage to cortical areas or to loss of connectivity among spared brain regions. It remains unclear whether the participation of spared brain regions as networks hubs affects the severity of aphasia.
Methods:
We evaluated language performance and magnetic resonance imaging from 44 participants with chronic aphasia post-stroke. The individual structural brain connectomes were constructed from diffusion tensor tractography. Hub regions were defined in accordance with the rich club classification and studied in relation with language performance.
Results:
The number of remaining left hemisphere rich club nodes was strongly associated with severity of aphasia, including impairments fluency, comprehension, repetition and naming sub-scores. Importantly, among participants with relative preservation of regions of interest for language, aphasia severity was lessened if the region was not only spared, but also participated in the remaining network as a rich club node: Brodmann area (BA) 44/45 - repetition (p = 0.009), BA 39 - repetition (p = 0.045) and naming (p < 0.01), BA 37 - fluency (p < 0.001), comprehension (p = 0.025), repetition (p < 0.001) and naming (p < 0.001).
Conclusions:
Disruption of language network structural hubs is directly associated with aphasia severity after stroke.
The aim of this work was to quantitatively model cross-sectional relationships between structural connectome disruptions caused by cerebral infarction and measures of clinical performance. Imaging biomarkers of 41 ischemic stroke patients (72.0 ± 12.0 years, 20 female) were related to their baseline performance in 18 cognitive, physical and daily life activity assessments. Individual estimates of structural connectivity disruption in gray matter regions were computed using the Change in Connectivity (ChaCo) score. ChaCo scores were utilized because they can be calculated using routinely collected clinical magnetic resonance imagings. Partial Least Squares Regression (PLSR) was used to predict various acute impairment and activity measures from ChaCo scores and patient demographics. Statistical methods of cross-validation, bootstrapping and multiple comparisons correction were implemented to minimize over-fitting and Type I errors. Multiple linear regression models based on lesion volume and lateralization information were constructed for comparison. All models based on connectivity disruption had lower Akaike Information Criterion and almost all had better goodness-of-fit values (R2: 0.26–0.92) than models based on lesion characteristics (R2: 0.06–0.50). Confidence intervals of PLSR coefficients identified brain regions important in predicting each clinical assessment. Appropriate mapping of eloquent functions, that is, language and motor, and replication of results across pathologies provided validation of this method. Models of complex functions provided new insights into brain-behavior relationships. In addition to the potential applications in prognostication and rehabilitation development, this quantitative approach provides insight into the structural networks underlying complex functions like activities of daily living and cognition. Quantitative analysis of big data will be invaluable in understanding complex brain-behavior relationships.
Aphasia is an acquired neurologic disorder that impairs an individual's ability to use and/or understand language. It commonly occurs after stroke or other injury to the brain's language network. The authors present the current methods of diagnosis and treatment of aphasia. They include a review of the evidence for the benefits of speech-language therapy, the most widespread approach to aphasia treatment, and a discussion of newer interventions such as medication and brain stimulation. These methods hold much promise for improving patient outcomes in aphasia; however, additional research regarding the best approaches to aphasia treatment will greatly improve our clinical approach.
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Constrained spherical deconvolution (CSD) has become one of the most widely used methods to extract white matter (WM) fibre orientation information from diffusion-weighted MRI (DW-MRI) data, overcoming the crossing fibre limitations inherent in the diffusion tensor model. It is routinely used to obtain high quality fibre orientation distribution function (fODF) estimates and fibre tractograms and is increasingly used to obtain apparent fibre density (AFD) measures. Unfortunately, CSD typically only supports data acquired on a single shell in q-space. With multi-shell data becoming more and more prevalent, there is a growing need for CSD to fully support such data. Furthermore, CSD can only provide high quality fODF estimates in voxels containing WM only. In voxels containing other tissue types such as grey matter (GM) and cerebrospinal fluid (CSF), the WM response function may no longer be appropriate and spherical deconvolution produces unreliable, noisy fODF estimates. The aim of this study is to incorporate support for multi-shell data into the CSD approach as well as to exploit the unique b-value dependencies of the different tissue types to estimate a multi-tissue ODF. The resulting approach is dubbed multi-shell, multi-tissue CSD (MSMT-CSD) and is compared to the state-of-the-art single-shell, single-tissue CSD (SSST-CSD) approach. Using both simulations and real data, we show that MSMT-CSD can produce reliable WM/GM/CSF volume fraction maps, directly from the DW data, whereas SSST-CSD has a tendency to overestimate the WM volume in voxels containing GM and/or CSF. In addition, compared to SSST-CSD, MSMT-CSD can substantially increase the precision of the fODF fibre orientations and reduce the presence ofspurious fODF peaks in voxels containing GM and/or CSF. Both effects translate into more reliable AFD measures and tractography results with MSMT-CSD compared to SSST-CSD.
Structural MR connectomics holds promise for the diagnosis, outcome prediction and treatment monitoring of many common neurodevelopmental, psychiatric and neurodegenerative disorders for which there is currently no clinical utility for MR imaging. Before computational network metrics from the human connectome can be applied in a clinical setting, their precision and their normative inter-subject variation must be understood in order to guide study design and the interpretation of longitudinal data. In this work, the reproducibility of commonly used graph theoretic measures is investigated, as applied to the structural connectome of healthy adult volunteers. Two datasets are examined, one consisting of ten subjects scanned twice at one MR imaging facility and one consisting of five subjects scanned once each at two different facilities using the same imaging platform. Global graph metrics are calculated for unweighted and weighted connectomes and two levels of granularity of the connectome are evaluated, one based on the 82-node cortical and subcortical parcellation from FreeSurfer and one based on an atlas-free parcellation of the gray-white matter boundary consisting of 1000 cortical nodes. The consistency of the unweighted and weighted edges and the module assignments are also computed for the 82-nodes connectomes. Overall, the results demonstrate good to excellent test-retest reliability for the entire connectome processing pipeline, including the graph analytics, in both the intrasite and intersite datasets. These findings indicate that measurements of computational network metrics derived from the structural connectome have sufficient precision to be tested as potential biomarkers for diagnosis, prognosis, and monitoring of interventions in neurological and psychiatric diseases.
In studies of patients with focal brain lesions, it is often useful to coregister an image of the patient's brain to that of another subject or a standard template. We refer to this process as spatial normalization. Spatial normalization can improve the presentation and analysis of lesion location in neuropsychological studies; it can also allow other data, for example from functional imaging, to be compared to data from other patients or normal controls. In functional imaging, the standard procedure for spatial normalization is to use an automated algorithm, which minimizes a measure of difference between image and template, based on image intensity values. These algorithms usually optimize both linear (translations, rotations, zooms, and shears) and nonlinear transforms. In the presence of a focal lesion, automated algorithms attempt to reduce image mismatch between template and image at the site of the lesion. This can lead to significant inappropriate image distortion, especially when nonlinear transforms are used. One solution is to use cost-function masking—masking the areas used in the calculation of image difference—to exclude the area of the lesion, so that the lesion does not bias the transformations. We introduce and evaluate this technique using normalizations of a selection of brains with focal lesions and normal brains with simulated lesions. Our results suggest that cost-function masking is superior to the standard approach to this problem, which is affine-only normalization; we propose that cost-function masking should be used routinely for normalizations of brains with focal lesions.
Selecting relevant features for support vector machine (SVM) classifiers is important for a variety of reasons such as generalization performance, computational efficiency, and feature interpretability. Traditional SVM approaches to feature selection typically extract features and learn SVM parameters independently. Independently performing these two steps might result in a loss of information related to the classification process. This paper proposes a convex energy-based framework to jointly perform feature selection and SVM parameter learning for linear and non-linear kernels. Experiments on various databases show significant reduction of features used while maintaining classification performance.
The global structural connectivity of the brain, the human connectome, is now accessible at millimeter scale with the use of MRI. In this paper, we describe an approach to map the connectome by constructing normalized whole-brain structural connection matrices derived from diffusion MRI tractography at 5 different scales. Using a template-based approach to match cortical landmarks of different subjects, we propose a robust method that allows (a) the selection of identical cortical regions of interest of desired size and location in different subjects with identification of the associated fiber tracts (b) straightforward construction and interpretation of anatomically organized whole-brain connection matrices and (c) statistical inter-subject comparison of brain connectivity at various scales. The fully automated post-processing steps necessary to build such matrices are detailed in this paper. Extensive validation tests are performed to assess the reproducibility of the method in a group of 5 healthy subjects and its reliability is as well considerably discussed in a group of 20 healthy subjects.
Neurologists and aphasiologists have debated for over a century whether right hemisphere recruitment facilitates or impedes recovery from aphasia. Here we present a well-characterized patient with sequential left and right hemisphere strokes whose case substantially informs this debate. A 72-year-old woman with chronic nonfluent aphasia was enrolled in a trial of transcranial magnetic stimulation (TMS). She underwent 10 daily sessions of inhibitory TMS to the right pars triangularis. Brain activity was measured during picture naming using functional magnetic resonance imaging (fMRI) prior to TMS exposure and before and after TMS on the first day of treatment. Language and cognition were tested behaviorally three times prior to treatment, and at 2 and 6 months afterward. Inhibitory TMS to the right pars triangularis induced immediate improvement in naming, which was sustained 2 months later. fMRI confirmed a local reduction in activity at the TMS target, without expected increased activity in corresponding left hemisphere areas. Three months after TMS, the patient suffered a right hemisphere ischemic stroke, resulting in worsening of aphasia without other clinical deficits. Behavioral testing 3 months later confirmed that language function was impacted more than other cognitive domains. The paradoxical effects of inhibitory TMS and the stroke to the right hemisphere demonstrate that even within a single patient, involvement of some right hemisphere areas may support recovery, while others interfere. The behavioral evidence confirms that compensatory reorganization occurred within the right hemisphere after the original stroke. No support is found for interhemispheric inhibition, the theoretical framework on which most therapeutic brain stimulation protocols for aphasia are based.