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The white matter tract-specific correlates of neuropsychological deficits are not fully established in patients with subcortical vascular cognitive impairment (SVCI), where white matter tract damage may be a critical factor in cognitive impairment. The purpose of this study is to investigate the tract-specific correlates of neuropsychological deficits in SVCI patients using tract-specific statistical analysis (TSSA). We prospectively recruited 114 SVCI patients, and 55 age-, gender-, and education-matched individuals with normal cognition (NC). All participants underwent diffusion weighted imaging and neuropsychological testing. We classified tractography results into fourteen major fiber tracts and analyzed group comparison and correlation with cognitive impairments. Relative to NC subjects, SVCI patients showed decreased fractional anisotropy values in bilateral anterior-thalamic radiation, cingulum, superior-longitudinal fasciculus, uncinate fasciculus, corticospinal tract, and left inferior-longitudinal fasciculus. Focal disruptions in specific tracts were associated with specific cognitive impairments. Our findings suggest that disconnection of specific white matter tracts, especially those neighboring and providing connections between gray matter regions important to certain cognitive functions, may contribute to specific cognitive impairments in SVCI.

Temporal lobe epilepsy (TLE) is a prevalent neurological disorder resulting in disruptive seizures. In the case of drug resistant epilepsy resective surgery is often considered. This is a procedure hampered by unpredictable success rates, with many patients continuing to have seizures even after surgery. In this study we apply a computational model of epilepsy to patient specific structural connectivity derived from diffusion tensor imaging (DTI) of 22 individuals with left TLE and 39 healthy controls. We validate the model by examining patient-control differences in simulated seizure onset time and network location. We then investigate the potential of the model for surgery prediction by performing in silico surgical resections, removing nodes from patient networks and comparing seizure likelihood post-surgery to pre-surgery simulations. We find that, first, patients tend to transit from non-epileptic to epileptic states more often than controls in the model. Second, regions in the left hemisphere (particularly within temporal and subcortical regions) that are known to be involved in TLE are the most frequent starting points for seizures in patients in the model. In addition, our analysis also implicates regions in the contralateral and frontal locations which may play a role in seizure spreading or surgery resistance. Finally, the model predicts that patient-specific surgery (resection areas chosen on an individual, model-prompted, basis and not following a predefined procedure) may lead to better outcomes than the currently used routine clinical procedure. Taken together this work provides a first step towards patient specific computational modelling of epilepsy surgery in order to inform treatment strategies in individuals.

Network science provides a general framework for analysing the large-scale brain networks that naturally arise from modern neuroimaging studies, and a key goal in theoretical neuroscience is to understand the extent to which these neural architectures influence the dynamical processes they sustain. To date, brain network modelling has largely been conducted at the macroscale level (i.e. white-matter tracts), despite growing evidence of the role that local grey matter architecture plays in a variety of brain disorders. Here, we present a new model of intrinsic grey matter connectivity of the human connectome. Importantly, the new model incorporates detailed information on cortical geometry to construct 'shortcuts' through the thickness of the cortex, thus enabling spatially distant brain regions, as measured along the cortical surface, to communicate. Our study indicates that structures based on human brain surface information differ significantly, both in terms of their topological network characteristics and activity propagation properties, when compared against a variety of alternative geometries and generative algorithms. In particular, this might help explain histological patterns of grey matter connectivity, highlighting that observed connection distances may have arisen to maximise information processing ability, and that such gains are consistent with (and enhanced by) the presence of short-cut connections.

Human brain maturation is characterized by the prolonged development of structural and functional properties of large-scale networks that extends into adulthood. However, it is not clearly understood which features change and which remain stable over time. Here, we examined structural connectivity based on diffusion tensor imaging (DTI) in 121 participants between 4 and 40 years of age. DTI data were analyzed for small-world parameters, modularity, and the number of fiber tracts at the level of streamlines. First, our findings showed that the number of fiber tracts, small-world topology, and modular organization remained largely stable despite a substantial overall decrease in the number of streamlines with age. Second, this decrease mainly affected fiber tracts that had a large number of streamlines, were short, within modules and within hemispheres; such connections were affected significantly more often than would be expected given their number of occurrences in the network. Third, streamline loss occurred earlier in females than in males. In summary, our findings suggest that core properties of structural brain connectivity, such as the small-world and modular organization, remain stable during brain maturation by focusing streamline loss to specific types of fiber tracts., (© The Author 2013. Published by Oxford University Press.)

Cognitive reserve is the ability to sustain cognitive function even with a certain amount of brain damages. Here we investigate the neural compensation mechanism of cognitive reserve from the perspective of structural brain connectivity. Our goal was to show that normal people with high education levels (i.e., cognitive reserve) maintain abundant pathways connecting any two brain regions, providing better compensation or resilience after brain damage. Accordingly, patients with high education levels show more deterioration in structural brain connectivity than those with low education levels before symptoms of Alzheimer's disease (AD) become apparent. To test this hypothesis, we use network flow measuring the number of alternative paths between two brain regions in the brain network. The experimental results show that for normal aging, education strengthens network reliability, as measured through flow values, in a subnetwork centered at the supramarginal gyrus. For AD, a subnetwork centered at the left middle frontal gyrus shows a negative correlation between flow and education, which implies more collapse in structural brain connectivity for highly educated patients. We conclude that cognitive reserve may come from the ability of network reorganization to secure the information flow within the brain network, therefore making it more resistant to disease progress.

Background: Previous studies reported reduced volumes of many brain regions for temporal lobe epilepsy (TLE). It has also been suggested that there may be widespread changes in network features of TLE patients. It is not fully understood, however, how these two observations are related., Methods: Using magnetic resonance imaging data, we perform parcellation of the brains of 22 patients with left TLE and 39 non-epileptic controls. In each parcellated region of interest (ROI) we computed the surface area and, using diffusion tensor imaging and deterministic tractography, infer the number of streamlines and their average length between each pair of connected ROIs. For comparison to previous studies, we use a connectivity 'weight' and investigate how ROI surface area, number of streamlines & mean streamline length contribute to such weight., Results: We find that although there are widespread significant changes in surface area and position of ROIs in patients compared to controls, the changes in connectivity are much more subtle. Significant changes in connectivity weight can be accounted for by decreased surface area and increased streamline count., Conclusion: Changes in the surface area of ROIs can be a reliable biomarker for TLE with a large influence on connectivity. However, changes in structural connectivity via white matter streamlines are more subtle with a relatively lower influence on connection weights.

Evidence from anatomical and functional imaging studies have highlighted major modifications of cortical circuits during adolescence. These include reductions of gray matter (GM), increases in the myelination of cortico-cortical connections and changes in the architecture of large-scale cortical networks. It is currently unclear, however, how the ongoing developmental processes impact upon the folding of the cerebral cortex and how changes in gyrification relate to maturation of GM/WM-volume, thickness and surface area. In the current study, we acquired high-resolution (3 Tesla) magnetic resonance imaging (MRI) data from 79 healthy subjects (34 males and 45 females) between the ages of 12 and 23 years and performed whole brain analysis of cortical folding patterns with the gyrification index (GI). In addition to GI-values, we obtained estimates of cortical thickness, surface area, GM and white matter (WM) volume which permitted correlations with changes in gyrification. Our data show pronounced and widespread reductions in GI-values during adolescence in several cortical regions which include precentral, temporal and frontal areas. Decreases in gyrification overlap only partially with changes in the thickness, volume and surface of GM and were characterized overall by a linear developmental trajectory. Our data suggest that the observed reductions in GI-values represent an additional, important modification of the cerebral cortex during late brain maturation which may be related to cognitive development.

Graph theoretical approaches have successfully revealed abnormality in brain connectivity, in particular, for contrasting patients from healthy controls. Besides the group comparison analysis, a correlational study is also challenging. In studies with patients, for example, finding brain connections that indeed deepen specific symptoms is interesting. The correlational study is also beneficial since it does not require controls, which are often difficult to find, especially for old-age patients with cognitive impairment where controls could also have cognitive deficits due to normal ageing. However, one of the major difficulties in such correlational studies is too conservative multiple comparison correction. In this paper, we propose a novel method for identifying brain connections that are correlated with a specific cognitive behavior by employing cluster-based statistics, which is less conservative than other methods, such as Bonferroni correction, false discovery rate procedure, and extreme statistics. Our method is based on the insight that multiple brain connections, rather than a single connection, are responsible for abnormal behaviors. Given brain connectivity data, we first compute a partial correlation coefficient between every edge and the behavioral measure. Then we group together neighboring connections with strong correlation into clusters and calculate their maximum sizes. This procedure is repeated for randomly permuted assignments of behavioral measures. Significance levels of the identified sub-networks are estimated from the null distribution of the cluster sizes. This method is independent of network construction methods: either structural or functional network can be used in association with any behavioral measures. We further demonstrated the efficacy of our method using patients with subcortical vascular cognitive impairment. We identified sub-networks that are correlated with the disease severity by exploiting diffusion tensor imaging techniques. The identified sub-networks were consistent with the previous clinical findings having valid significance level, while other methods did not assert any significant findings.

Background: Arm nonuse, defined as the difference between what the individual can do when constrained to use the paretic arm and what the individual does when given a free choice to use either arm, has not yet been quantified in individuals poststroke., Objectives: (1) To quantify nonuse poststroke and (2) to develop and test a novel, simple, objective, reliable, and valid instrument, the Bilateral Arm Reaching Test (BART), to quantify arm use and nonuse poststroke., Methods: First, we quantify nonuse with the Quality of Movement (QOM) subscale of the Actual Amount of Use Test (AAUT) by subtracting the AAUT QOM score in the spontaneous use condition from the AAUT QOM score in a subsequent constrained use condition. Second, we quantify arm use and nonuse with BART by comparing reaching performance to visual targets projected over a 2D horizontal hemi-work space in a spontaneous-use condition (in which participants are free to use either arm at each trial) with reaching performance in a constrained-use condition., Results: All participants (N = 24) with chronic stroke and with mild to moderate impairment exhibited nonuse with the AAUT QOM. Nonuse with BART had excellent test-retest reliability and good external validity., Conclusions: BART is the first instrument that can be used repeatedly and practically in the clinic to quantify the effects of neurorehabilitation on arm use and nonuse and in the laboratory for advancing theoretical knowledge about the recovery of arm use and the development of nonuse and "learned nonuse" after stroke.

"Use it and improve it, or lose it" is one of the axioms of motor therapy after stroke. There is, however, little understanding of the interactions between arm function and use in humans post-stroke. Here, we explored putative non-linear interactions between upper extremity function and use by developing a first-order dynamical model of stroke recovery with longitudinal data from participants receiving constraint induced movement therapy (CIMT) in the EXCITE clinical trial. Using a Bayesian regression framework, we systematically compared this model with competitive models that included, or not, interactions between function and use. Model comparisons showed that the model with the predicted interactions between arm function and use was the best fitting model. Furthermore, by comparing the model parameters before and after CIMT intervention in participants receiving the intervention one year after randomization, we found that therapy increased the parameter that controls the effect of arm function on arm use. Increase in this parameter, which can be thought of as the confidence to use the arm for a given level of function, lead to increase in spontaneous use after therapy compared to before therapy.

Human brain networks can be characterized at different temporal or spatial scales given by the age of the subject or the spatial resolution of the neuroimaging method. Integration of data across scales can only be successful if the combined networks show a similar architecture. One way to compare networks is to look at spatial features, based on fiber length, and topological features of individual nodes where outlier nodes form single node motifs whose frequency yields a fingerprint of the network. Here, we observe how characteristic single node motifs change over age (12-23 years) and network size (414, 813, and 1615 nodes) for diffusion tensor imaging structural connectivity in healthy human subjects. First, we find the number and diversity of motifs in a network to be strongly correlated. Second, comparing different scales, the number and diversity of motifs varied across the temporal (subject age) and spatial (network resolution) scale: certain motifs might only occur at one spatial scale or for a certain age range. Third, regions of interest which show one motif at a lower resolution may show a range of motifs at a higher resolution which may or may not include the original motif at the lower resolution. Therefore, both the type and localization of motifs differ for different spatial resolutions. Our results also indicate that spatial resolution has a higher effect on topological measures whereas spatial measures, based on fiber lengths, remain more comparable between resolutions. Therefore, spatial resolution is crucial when comparing characteristic node fingerprints given by topological and spatial network features. As node motifs are based on topological and spatial properties of brain connectivity networks, these conclusions are also relevant to other studies using connectome analysis.

According to the cerebellar learning hypothesis, the inferior olive neurons, despite their low firing rates, are thought to transmit high-fidelity error signals to the cerebellar cortex. "Chaotic resonance", via moderate electrical coupling between inferior olive neurons, has been proposed to realize efficient transmission of the error signal by desynchronizing spiking. Here, we first show that chaotic resonance is a robust phenomenon, as it does not depend upon the details of the inferior olive neuronal model. Second, we show that chaotic resonance enhances learning of a neural controller for fast arm movements. Furthermore, when both coupling and noise are considered simultaneously, we found that chaotic resonance widens the range of noise intensity within which efficient learning can be realized. We suggest that, from an energetic viewpoint, the spiking activity induced by chaos can be more economical than that induced by noise., (Copyright 2010 Elsevier Ltd. All rights reserved.)

Although there is converging experimental and clinical evidences suggesting that mental training with motor imagery can improve motor performance, it is unclear how humans can learn movements through mental training despite the lack of sensory feedback from the body and the environment. In a first experiment, we measured the trial-by-trial decrease in durations of executed movements (physical training group) and mentally simulated movements (motor-imagery training group), by means of training on a multiple-target arm-pointing task requiring high accuracy and speed. Movement durations were significantly lower in posttest compared with pretest after both physical and motor-imagery training. Although both the posttraining performance and the rate of learning were smaller in motor-imagery training group than in physical training group, the change in movement duration and the asymptotic movement duration after a hypothetical large number of trials were identical. The two control groups (eye-movement training and rest groups) did not show change in movement duration. In the second experiment, additional kinematic analyses revealed that arm movements were straighter and faster both immediately and 24 h after physical and motor-imagery training. No such improvements were observed in the eye-movement training group. Our results suggest that the brain uses state estimation, provided by internal forward model predictions, to improve motor performance during mental training. Furthermore, our results suggest that mental practice can, at least in young healthy subjects and if given after a short bout of physical practice, be successfully substituted to physical practice to improve motor performance.

Background: Although spontaneous use of the more-affected arm and hand after stroke is an important determinant of participation and quality of life, a number of patients exhibit decreases in use following rehabilitative therapy. A previous neurocomputational model predicted that if the dose of therapy is sufficient to bring performance above a certain threshold, training can be stopped., Objective: The aim of this study was to test the hypothesis that there exists a threshold for function of the paretic arm and hand after therapy. If function is above this threshold, spontaneous use will increase in the months following therapy. In contrast, if function is below this threshold, spontaneous use will decrease., Methods: New computer simulations are presented showing that changes in arm use following therapy depend on a performance threshold. This prediction was tested by reanalyzing the data from the Extremity Constraint-Induced Therapy Evaluation (EXCITE) trial, a phase III randomized controlled trial in which participants received constraint-induced movement therapy for 2 weeks and were tested both 1 week and 1 year after therapy., Results: The results demonstrate that arm and hand function measured immediately after therapy predicts, on average, the long-term change of arm use. Above a functional threshold, use improves. Below this threshold, use decreases., Limitations: The reanalysis of the EXCITE trial data provides a "group" threshold above which a majority of patients, but not all, improve spontaneously. A goal of future research is to provide the means to assess when patients reach their individual threshold., Conclusion: Understanding of the causal and nonlinear relationship between limb function and daily use is important for the future development of cost-effective interventions and prevention of "rehabilitation in vain."

Motor training with the upper limb affected by stroke partially reverses the loss of cortical representation after lesion and has been proposed to increase spontaneous arm use. Moreover, repeated attempts to use the affected hand in daily activities create a form of practice that can potentially lead to further improvement in motor performance. We thus hypothesized that if motor retraining after stroke increases spontaneous arm use sufficiently, then the patient will enter a virtuous circle in which spontaneous arm use and motor performance reinforce each other. In contrast, if the dose of therapy is not sufficient to bring spontaneous use above threshold, then performance will not increase and the patient will further develop compensatory strategies with the less affected hand. To refine this hypothesis, we developed a computational model of bilateral hand use in arm reaching to study the interactions between adaptive decision making and motor relearning after motor cortex lesion. The model contains a left and a right motor cortex, each controlling the opposite arm, and a single action choice module. The action choice module learns, via reinforcement learning, the value of using each arm for reaching in specific directions. Each motor cortex uses a neural population code to specify the initial direction along which the contralateral hand moves towards a target. The motor cortex learns to minimize directional errors and to maximize neuronal activity for each movement. The derived learning rule accounts for the reversal of the loss of cortical representation after rehabilitation and the increase of this loss after stroke with insufficient rehabilitation. Further, our model exhibits nonlinear and bistable behavior: if natural recovery, motor training, or both, brings performance above a certain threshold, then training can be stopped, as the repeated spontaneous arm use provides a form of motor learning that further bootstraps performance and spontaneous use. Below this threshold, motor training is "in vain": there is little spontaneous arm use after training, the model exhibits learned nonuse, and compensatory movements with the less affected hand are reinforced. By exploring the nonlinear dynamics of stroke recovery using a biologically plausible neural model that accounts for reversal of the loss of motor cortex representation following rehabilitation or the lack thereof, respectively, we can explain previously hard to reconcile data on spontaneous arm use in stroke recovery. Further, our threshold prediction could be tested with an adaptive train-wait-train paradigm: if spontaneous arm use has increased in the "wait" period, then the threshold has been reached, and rehabilitation can be stopped. If spontaneous arm use is still low or has decreased, then another bout of rehabilitation is to be provided.

Critical to our many daily choices between larger delayed rewards, and smaller more immediate rewards, are the shape and the steepness of the function that discounts rewards with time. Although research in artificial intelligence favors exponential discounting in uncertain environments, studies with humans and animals have consistently shown hyperbolic discounting. We investigated how humans perform in a reward decision task with temporal constraints, in which each choice affects the time remaining for later trials, and in which the delays vary at each trial. We demonstrated that most of our subjects adopted exponential discounting in this experiment. Further, we confirmed analytically that exponential discounting, with a decay rate comparable to that used by our subjects, maximized the total reward gain in our task. Our results suggest that the particular shape and steepness of temporal discounting is determined by the task that the subject is facing, and question the notion of hyperbolic reward discounting as a universal principle.

Chordin is a bone morphogenetic protein (BMP) inhibitor that has been identified as a factor dorsalizing the Xenopus embryo. A novel secreted protein, CHL (for chordin-like), with significant homology to chordin, was isolated from mouse bone marrow stromal cells. Injection of CHL RNA into Xenopus embryos induced a secondary axis. Recombinant CHL protein inhibited the BMP4-dependent differentiation of embryonic stem cells in vitro and interacted directly with BMPs, similar to chordin. However, CHL also weakly bound to TGFbetas. In situ hybridization revealed that the mouse CHL gene, located on the X chromosome, was expressed predominantly in mesenchyme-derived cell types: (1) the dermatome and limb bud mesenchyme and, later, the subdermal mesenchyme and the chondrocytes of the developing skeleton during embryogenesis and (2) a layer of fibroblasts/connective tissue cells in the gastrointestinal tract, the thick straight segments of kidney tubules, and the marrow stromal cells in adults. An exception was expression in the neural cells of the olfactory bulb and cerebellum. Interestingly, the spatiotemporal expression patterns of CHL were distinct from those of chordin in many areas examined. Thus, CHL may serve as an important BMP regulator for differentiating mesenchymal cells, especially during skeletogenesis, and for developing specific neurons., (Copyright 2001 Academic Press.)