Ling E. Wang

Bio: Ling E. Wang is an academic researcher from University of Hong Kong. The author has contributed to research in topics: Premotor cortex & Functional magnetic resonance imaging. The author has an hindex of 8, co-authored 10 publications receiving 2268 citations. Previous affiliations of Ling E. Wang include RWTH Aachen University & University of Texas at Austin.

Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty

Abstract: A widely used technique for coordinate-based meta-analyses of neuroimaging data is activation likelihood estimation (ALE). ALE assesses the overlap between foci based on modeling them as probability distributions centered at the respective coordinates. In this Human Brain Project/Neuroinformatics research, the authors present a revised ALE algorithm addressing drawbacks associated with former implementations. The first change pertains to the size of the probability distributions, which had to be specified by the used. To provide a more principled solution, the authors analyzed fMRI data of 21 subjects, each normalized into MNI space using nine different approaches. This analysis provided quantitative estimates of between-subject and between-template variability for 16 functionally defined regions, which were then used to explicitly model the spatial uncertainty associated with each reported coordinate. Secondly, instead of testing for an above-chance clustering between foci, the revised algorithm assesses above-chance clustering between experiments. The spatial relationship between foci in a given experiment is now assumed to be fixed and ALE results are assessed against a null-distribution of random spatial association between experiments. Critically, this modification entails a change from fixed- to random-effects inference in ALE analysis allowing generalization of the results to the entire population of studies analyzed. By comparative analysis of real and simulated data, the authors showed that the revised ALE-algorithm overcomes conceptual problems of former meta-analyses and increases the specificity of the ensuing results without loosing the sensitivity of the original approach. It may thus provide a methodologically improved tool for coordinate-based meta-analyses on functional imaging data.

Degeneration of corpus callosum and recovery of motor function after stroke: A multimodal magnetic resonance imaging study

Abstract: Animal models of stroke demonstrated that white matter ischemia may cause both axonal damage and myelin degradation distant from the core lesion, thereby impacting on behavior and functional outcome after stroke. We here used parameters derived from diffusion magnetic resonance imaging (MRI) to investigate the effect of focal white matter ischemia on functional reorganization within the motor system. Patients (n = 18) suffering from hand motor deficits in the subacute or chronic stage after subcortical stroke and healthy controls (n = 12) were scanned with both diffusion MRI and functional MRI while performing a motor task with the left or right hand. A laterality index was employed on activated voxels to assess functional reorganization across hemispheres. Regression analyses revealed that diffusion MRI parameters of both the ipsilesional corticospinal tract (CST) and corpus callosum (CC) predicted increased activation of the unaffected hemisphere during movements of the stroke-affected hand. Changes in diffusion MRI parameters possibly reflecting axonal damage and/or destruction of myelin sheath correlated with a stronger bilateral recruitment of motor areas and poorer motor performance. Probabilistic fiber tracking analyses revealed that the region in the CC correlating with the fMRI laterality index and motor deficits connected to sensorimotor cortex, supplementary motor area, ventral premotor cortex, superior parietal lobule, and temporoparietal junction. The results suggest that degeneration of transcallosal fibers connecting higher order sensorimotor regions constitute a relevant factor influencing cortical reorganization and motor outcome after subcortical stroke.

Noradrenergic enhancement improves motor network connectivity in stroke patients.

Abstract: Objective Both animal and human data suggest that noradrenergic stimulation may enhance motor performance after brain damage. We conducted a placebo-controlled, double-blind and crossover design study to investigate the effects of noradrenergic stimulation on the cortical motor system in hemiparetic stroke patients. Methods Stroke patients (n = 11) in the subacute or chronic stage with mild-to-moderate hand paresis received a single oral dose of 6mg reboxetine (RBX), a selective noradrenaline reuptake inhibitor. We used functional magnetic resonance imaging and dynamic causal modeling to assess changes in neural activity and interregional effective connectivity while patients moved their paretic hand. Results RBX stimulation significantly increased maximum grip power and index finger-tapping frequency of the paretic hand. Enhanced motor performance was associated with a reduction of cortical “hyperactivity” toward physiological levels as observed in healthy control subjects, especially in the ipsilesional ventral premotor cortex (vPMC) and supplementary motor area (SMA), but also in the temporoparietal junction and prefrontal cortex. Connectivity analyses revealed that in stroke patients neural coupling with SMA or vPMC was significantly reduced compared with healthy controls. This “hypoconnectivity” was partially normalized when patients received RBX, especially for the coupling of ipsilesional SMA with primary motor cortex. Interpretation The data suggest that noradrenergic stimulation by RBX may help to modulate the pathologically altered motor network architecture in stroke patients, resulting in increased coupling of ipsilesional motor areas and thereby improved motor function. Ann Neurol 2011.

The connectomics of brain disorders

Abstract: Pathological perturbations of the brain are rarely confined to a single locus; instead, they often spread via axonal pathways to influence other regions. Patterns of such disease propagation are constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture; the so-called connectome. Thus, network organization fundamentally influences brain disease, and a connectomic approach grounded in network science is integral to understanding neuropathology. Here, we consider how brain-network topology shapes neural responses to damage, highlighting key maladaptive processes (such as diaschisis, transneuronal degeneration and dedifferentiation), and the resources (including degeneracy and reserve) and processes (such as compensation) that enable adaptation. We then show how knowledge of network topology allows us not only to describe pathological processes but also to generate predictive models of the spread and functional consequences of brain disease.

Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions

Abstract: Classic cognitive theory conceptualizes executive functions as involving multiple specific domains, including initiation, inhibition, working memory, flexibility, planning, and vigilance. Lesion and neuroimaging experiments over the past two decades have suggested that both common and unique processes contribute to executive functions during higher cognition. It has been suggested that a superordinate fronto–cingulo–parietal network supporting cognitive control may also underlie a range of distinct executive functions. To test this hypothesis in the largest sample to date, we used quantitative meta-analytic methods to analyze 193 functional neuroimaging studies of 2,832 healthy individuals, ages 18–60, in which performance on executive function measures was contrasted with an active control condition. A common pattern of activation was observed in the prefrontal, dorsal anterior cingulate, and parietal cortices across executive function domains, supporting the idea that executive functions are supported by a superordinate cognitive control network. However, domain-specific analyses showed some variation in the recruitment of anterior prefrontal cortex, anterior and midcingulate regions, and unique subcortical regions such as the basal ganglia and cerebellum. These results are consistent with the existence of a superordinate cognitive control network in the brain, involving dorsolateral prefrontal, anterior cingulate, and parietal cortices, that supports a broad range of executive functions.