Bringing Proportional Recovery into Proportion: Bayesian Modelling of Post-Stroke Motor Impairment

Accurate predictions of motor impairment after stroke are of cardinal importance for the patient, clinician, and health care system. More than ten years ago, the proportional recovery rule was introduced by promising just that: high-fidelity predictions of recovery following stroke based only on the initially lost motor function, at least for a specific fraction of patients. However, emerging evidence suggests that this recovery rule is subject to various confounds and may apply less universally than previously assumed. Here, we systematically revisited stroke outcome predictions by applying strategies to avoid confounds and fitting hierarchical Bayesian models. We jointly analyzed n=385 post-stroke trajectories from six separate studies – one of the currently largest overall datasets of upper limb motor recovery. We addressed confounding ceiling effects by introducing a subset approach and ensured correct model estimation through synthetic data simulations. Subsequently, we used model comparisons to assess the underlying nature of recovery within our empirical recovery data. The first model comparison, relying on the conventional fraction of patients called fitters, pointed to a combination of proportional to lost function and constant recovery. Proportional to lost here describes the original notion of proportionality, indicating greater recovery in case of a more severe initial impairment. This combination explained only 32% of the variance in recovery, which is in stark contrast to previous reports of >80%. When instead analyzing the complete spectrum of subjects, fitters and non-fitters, a combination of proportional to spared function and constant recovery was favoured, implying a more significant improvement in case of more preserved function. Explained variance was at 53%. Therefore, our quantitative findings suggest that motor recovery post-stroke may exhibit some characteristics of proportionality. However, the variance explained was substantially reduced compared to what has previously been reported. This finding motivates future research moving beyond solely behavior scores to explain stroke recovery and establish robust and discriminating single-subject predictions

State-dependent differences between functional and effective connectivity of the human cortical motor system.

Neural processing is based on interactions between functionally specialized areas that can be described in terms of functional or effective connectivity. Functional connectivity is often assessed by task-free, resting-state functional magnetic resonance imaging (fMRI), whereas effective connectivity is usually estimated from task-based fMRI time-series. To investigate whether different connectivity approaches assess similar network topologies in the same subjects, we scanned 36 right-handed volunteers with resting-state fMRI followed by active-state fMRI involving a hand movement task. Time-series information was extracted from identical locations defined from individual activation maxima during the motor task. Dynamic causal modeling (DCM) was applied to the motor task time-series to estimate endogenous and context-dependent effective connectivity. In addition, functional connectivity was computed for both the rest and the motor task condition by means of inter-regional time-series correlations. At the group-level, we found strong interactions between the motor areas of interest in all three connectivity analyses. However, although the sample size warranted 90% power to detect correlations of medium effect size, resting-state functional connectivity was only weakly correlated with both task-based functional and task-based effective connectivity estimates for corresponding region-pairs of the subjects. By contrast, task-based functional connectivity showed strong positive correlations with DCM effective connectivity parameters. In conclusion, resting-state and task-based connectivity reflect different components of functional integration that particularly depend on the functional state in which the subject is being scanned. Therefore, resting-state fMRI and DCM should be used as complementary measures when assessing functional brain networks

Functional resting-state connectivity of the human motor network: Differences between right- and left-handers

Handedness is associated with differences in activation levels in various motor tasks performed with the dominant or non-dominant hand. Here we tested whether handedness is reflected in the functional architecture of the motor system even in the absence of an overt motor task. Using resting-state functional magnetic resonance imaging we investigated 18 right- and 18 left-handers. Whole-brain functional connectivity maps of the primary motor cortex (M1), supplementary motor area (SMA), dorsolateral premotor cortex (PMd), pre-SMA, inferior frontal junction and motor putamen were compared between right- and left-handers. We further used a multivariate linear support vector machine (SVM) classifier to reveal the specificity of brain regions for classifying handedness based on individual resting-state maps. Using left M1 as seed region, functional connectivity analysis revealed stronger interhemispheric functional connectivity between left M1 and right PMd in right-handers as compared to left-handers. This connectivity cluster contributed to the individual classification of right- and left-handers with 86.2% accuracy. Consistently, also seeding from right PMd yielded a similar handedness-dependent effect in left M1, albeit with lower classification accuracy (78.1%). Control analyses of the other resting-state networks including the speech and the visual network revealed no significant differences in functional connectivity related to handedness. In conclusion, our data revealed an intrinsically higher functional connectivity in right-handers. These results may help to explain that hand preference is more lateralized in right-handers than in left-handers. Furthermore, enhanced functional connectivity between left M1 and right PMd may serve as an individual marker of handedness

Handedness and effective connectivity of the motor system

Handedness denotes the individual predisposition to consistently use the left or right hand for most types of skilled movements. A putative neurobiological mechanism for handedness consists in hemisphere-specific differences in network dynamics that govern unimanual movements.We, therefore, used functional magnetic resonance imaging and dynamic causal modeling to investigate effective connectivity between key motor areas during fist closures of the dominant or non-dominant hand performed by 18 right- and 18 left-handers. Handedness was assessed employing the Edinburgh-Handedness-Inventory (EHI). The network of interest consisted of key motor regions in both hemispheres including the primary motor cortex (M1), supplementary motor area (SMA), ventral premotor cortex (PMv), motor putamen (Put) and motor cerebellum (Cb).The connectivity analysis revealed that in right-handed subjects movements of the dominant hand were associated with significantly stronger coupling of contralateral (left, i.e., dominant) SMA with ipsilateral SMA, ipsilateral PMv, contralateral motor putamen and contralateral M1 compared to equivalent connections in left-handers. The degree of handedness as indexed by the individual EHI scores also correlated with coupling parameters of these connections. In contrast, we found no differences between right- and left-handers when testing for the effect of movement speed on effective connectivity.In conclusion, the data show that handedness is associated with differences in effective connectivity within the human motor network with a prominent role of SMA in right-handers. Left-handers featured less asymmetry in effective connectivity implying different hemispheric mechanisms underlying hand motor control compared to right-handers

Network dynamics engaged in the modulation of motor behavior in healthy subjects

Motor skills are mediated by a dynamic and finely regulated interplay of the primary motor cortex (M1) with various cortical and subcortical regions engaged in movement preparation and execution. To date, data elucidating the dynamics in the motor network that enable movements at different levels of behavioral performance remain scarce. We here used functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) to investigate effective connectivity of key motor areas at different movement frequencies performed by right-handed subjects (n = 36) with the left or right hand. The network of interest consisted of motor regions in both hemispheres including M1, supplementary motor area (SMA), ventral premotor cortex (PMv), motor putamen, and motor cerebellum. The connectivity analysis showed that performing hand movements at higher frequencies was associated with a linear increase in neural coupling strength from premotor areas (SMA, PMv) contralateral to the moving hand and ipsilateral cerebellum towards contralateral, active M1. In addition, we found hemispheric differences in the amount by which the coupling of premotor areas and M1 was modulated, depending on which hand was moved. Other connections were not modulated by changes in motor performance. The results suggest that a stronger coupling, especially between contralateral premotor areas and M1, enables increased motor performance of simple unilateral hand movements. (c) 2013 Elsevier Inc. All rights reserved

Behavioral and neuroanatomical correlates of facial emotion processing in post-stroke depression

Background: Emotion processing deficits are known to accompany depressive symptoms and are often seen in stroke patients. Little is known about the influence of post-stroke depressive (PSD) symptoms and specific brain lesions on altered emotion processing abilities and how these phenomena develop over time. This potential relationship may impact post-stroke rehabilitation of neurological and psychosocial function. To address this scientific gap, we investigated the relationship between PSD symptoms and emotion processing abilities in a longitudinal study design from the first days post-stroke into the early chronic phase. Methods: Twenty-six ischemic stroke patients performed an emotion processing task on videos with emotional faces ('happy,' 'sad,' 'anger,' 'fear,' and 'neutral') at different intensity levels (20%, 40%, 60%, 80%, 100%). Recognition accuracies and response times were measured, as well as scores of depressive symptoms (Montgomery-Åsberg Depression Rating Scale). Twenty-eight healthy participants matched in age and sex were included as a control group. Whole-brain support-vector regression lesion-symptom mapping (SVR-LSM) analyses were performed to investigate whether specific lesion locations were associated with the recognition accuracy of specific emotion categories. Results: Stroke patients performed worse in overall recognition accuracy compared to controls, specifically in the recognition of happy, sad, and fearful faces. Notably, more depressed stroke patients showed an increased processing towards specific negative emotions, as they responded significantly faster to angry faces and recognized sad faces of low intensities significantly more accurately. These effects obtained for the first days after stroke partly persisted to follow-up assessment several months later. SVR-LSM analyses revealed that inferior and middle frontal regions (IFG/MFG) and insula and putamen were associated with emotion-recognition deficits in stroke. Specifically, recognizing happy facial expressions was influenced by lesions affecting the anterior insula, putamen, IFG, MFG, orbitofrontal cortex, and rolandic operculum. Lesions in the posterior insula, rolandic operculum, and MFG were also related to reduced recognition accuracy of fearful facial expressions, whereas recognition deficits of sad faces were associated with frontal pole, IFG, and MFG damage. Conclusion: PSD symptoms facilitate processing negative emotional stimuli, specifically angry and sad facial expressions. The recognition accuracy of different emotional categories was linked to brain lesions in emotion-related processing circuits, including insula, basal ganglia, IFG, and MFG. In summary, our study provides support for psychosocial and neural factors underlying emotional processing after stroke, contributing to the pathophysiology of PSD

Improved nTMS- and DTI-derived CST tractography through anatomical ROI seeding on anterior pontine level compared to internal capsule

Imaging of the course of the corticospinal tract (CST) by diffusion tensor imaging (DTI) is useful for function-preserving tumour surgery. The integration of functional localizer data into tracking algorithms offers to establish a direct structure–function relationship in DTI data. However, alterations of MRI signals in and adjacent to brain tumours often lead to spurious tracking results. We here compared the impact of subcortical seed regions placed at different positions and the influences of the somatotopic location of the cortical seed and clinical co-factors on fibre tracking plausibility in brain tumour patients.The CST of 32 patients with intracranial tumours was investigated by means of deterministic DTI and neuronavigated transcranial magnetic stimulation (nTMS). The cortical seeds were defined by the nTMS hot spots of the primary motor area (M1) of the hand, the foot and the tongue representation. The CST originating from the contralesional M1 hand area was mapped as intra-individual reference. As subcortical region of interests (ROI), we used the posterior limb of the internal capsule (PLIC) and/or the anterior inferior pontine region (aiP). The plausibility of the fibre trajectories was assessed by a-priori defined anatomical criteria. The following potential co-factors were analysed: Karnofsky Performance Scale (KPS), resting motor threshold (RMT), T1-CE tumour volume, T2 oedema volume, presence of oedema within the PLIC, the fractional anisotropy threshold (FAT) to elicit a minimum amount of fibres and the minimal fibre length.The results showed a higher proportion of plausible fibre tracts for the aiP-ROI compared to the PLIC-ROI. Low FAT values and the presence of peritumoural oedema within the PLIC led to less plausible fibre tracking results. Most plausible results were obtained when the FAT ranged above a cut-off of 0.105. In addition, there was a strong effect of somatotopic location of the seed ROI; best plausibility was obtained for the contralateral hand CST (100%), followed by the ipsilesional hand CST (>95%), the ipsilesional foot (>85%) and tongue (>75%) CST. In summary, we found that the aiP-ROI yielded better tracking results compared to the IC-ROI when using deterministic CST tractography in brain tumour patients, especially when the M1 hand area was tracked. In case of FAT values lower than 0.10, the result of the respective CST tractography should be interpreted with caution with respect to spurious tracking results. Moreover, the presence of oedema within the internal capsule should be considered a negative predictor for plausible CST tracking

Neuroanatomy of post-stroke depression: the association between symptom clusters and lesion location

Post-stroke depression affects about 30% of stroke patients and often hampers functional recovery. The diagnosis of depression encompasses heterogeneous symptoms at emotional, motivational, cognitive, behavioural or somatic levels. Evidence indicates that depression is caused by disruption of bio-aminergic fibre tracts between prefrontal and limbic or striatal brain regions comprising different functional networks. Voxel-based lesion-symptom mapping studies reported discrepant findings regarding the association between infarct locations and depression. Inconsistencies may be due to the usage of sum scores, thereby mixing different symptoms of depression. In this cross-sectional study, we used multivariate support vector regression for lesion-symptom mapping to identify regions significantly involved in distinct depressive symptom domains and global depression. MRI lesion data were included from 200 patients with acute first-ever ischaemic stroke (mean 0.9 ± 1.5 days of post-stroke). The Montgomery-Åsberg Depression Rating interview assessed depression severity in five symptom domains encompassing motivational, emotional and cognitive symptoms deficits, anxiety and somatic symptoms and was examined 8.4 days of post-stroke (±4.3). We found that global depression severity, irrespective of individual symptom domains, was primarily linked to right hemispheric lesions in the dorsolateral prefrontal cortex and inferior frontal gyrus. In contrast, when considering distinct symptom domains individually, the analyses yielded much more sensitive results in regions where the correlations with the global depression score yielded no effects. Accordingly, motivational deficits were associated with lesions in orbitofrontal cortex, dorsolateral prefrontal cortex, pre- and post-central gyri and basal ganglia, including putamen and pallidum. Lesions affecting the dorsal thalamus, anterior insula and somatosensory cortex were significantly associated with emotional symptoms such as sadness. Damage to the dorsolateral prefrontal cortex was associated with concentration deficits, cognitive symptoms of guilt and self-reproach. Furthermore, somatic symptoms, including loss of appetite and sleep disturbances, were linked to the insula, parietal operculum and amygdala lesions. Likewise, anxiety was associated with lesions impacting the central operculum, insula and inferior frontal gyrus. Interestingly, symptoms of anxiety were exclusively left hemispheric, whereas the lesion-symptom associations of the other domains were lateralized to the right hemisphere. In conclusion, this large-scale study shows that in acute stroke patients, differential post-stroke depression symptom domains are associated with specific structural correlates. Our findings extend existing concepts on the neural underpinnings of depressive symptoms, indicating that differential lesion patterns lead to distinct depressive symptoms in the first weeks of post-stroke. These findings may facilitate the development of personalized treatments to improve post-stroke rehabilitation.Keywords: MADRS; SVR-LSM; large-scale; multivariate; neural substrates

Inter-individual variability in cortical excitability and motor network connectivity following multiple blocks of rTMS

The responsiveness to non-invasive neuromodulation protocols shows high inter-individual variability, the reasons of which remain poorly understood. We here tested whether the response to intermittent theta-burst stimulation (iTBS) – an effective repetitive transcranial magnetic stimulation (rTMS) protocol for increasing cortical excitability – depends on network properties of the cortical motor system. We furthermore investigated whether the responsiveness to iTBS is dose-dependent.To this end, we used a sham-stimulation controlled, single-blinded within-subject design testing for the relationship between iTBS aftereffects and (i) motor-evoked potentials (MEPs) as well as (ii) resting-state functional connectivity (rsFC) in 16 healthy subjects. In each session, three blocks of iTBS were applied, separated by 15 min.We found that non-responders (subjects not showing an MEP increase of ≥ 10% after one iTBS block) featured stronger rsFC between the stimulated primary motor cortex (M1) and premotor areas before stimulation compared to responders. However, only the group of responders showed increases in rsFC and MEPs, while most non-responders remained close to baseline levels after all three blocks of iTBS. Importantly, there was still a large amount of variability in both groups.Our data suggest that responsiveness to iTBS at the local level (i.e., M1 excitability) depends upon the pre-interventional network connectivity of the stimulated region. Of note, increasing iTBS dose did not turn non-responders into responders. The finding that higher levels of pre-interventional connectivity precluded a response to iTBS could reflect a ceiling effect underlying non-responsiveness to iTBS at the systems lev