Ten problems and solutions when predicting individual outcome from lesion site after stroke

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual's functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve the predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients

The impact of sample size on the reproducibility of voxel-based lesion-deficit mappings

This study investigated how sample size affects the reproducibility of findings from univariate voxel-based lesion-deficit analyses (e.g., voxel-based lesion-symptom mapping and voxel-based morphometry). Our effect of interest was the strength of the mapping between brain damage and speech articulation difficulties, as measured in terms of the proportion of variance explained. First, we identified a region of interest by searching on a voxel-by-voxel basis for brain areas where greater lesion load was associated with poorer speech articulation using a large sample of 360 right-handed English-speaking stroke survivors. We then randomly drew thousands of bootstrap samples from this data set that included either 30, 60, 90, 120, 180, or 360 patients. For each resample, we recorded effect size estimates and p values after conducting exactly the same lesion-deficit analysis within the previously identified region of interest and holding all procedures constant. The results show (1) how often small effect sizes in a heterogeneous population fail to be detected; (2) how effect size and its statistical significance varies with sample size; (3) how low-powered studies (due to small sample sizes) can greatly over-estimate as well as under-estimate effect sizes; and (4) how large sample sizes (N ≥ 90) can yield highly significant p values even when effect sizes are so small that they become trivial in practical terms. The implications of these findings for interpreting the results from univariate voxel-based lesion-deficit analyses are discussed.</p

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Large-scale inference in the focally damaged human brain

Clinical outcomes in focal brain injury reflect the interactions between two distinct anatomically distributed patterns: the functional organisation of the brain and the structural distribution of injury. The challenge of understanding the functional architecture of the brain is familiar; that of understanding the lesion architecture is barely acknowledged. Yet, models of the functional consequences of focal injury are critically dependent on our knowledge of both. The studies described in this thesis seek to show how machine learning-enabled high-dimensional multivariate analysis powered by large-scale data can enhance our ability to model the relation between focal brain injury and clinical outcomes across an array of modelling applications. All studies are conducted on internationally the largest available set of MR imaging data of focal brain injury in the context of acute stroke (N=1333) and employ kernel machines at the principal modelling architecture. First, I examine lesion-deficit prediction, quantifying the ceiling on achievable predictive fidelity for high-dimensional and low-dimensional models, demonstrating the former to be substantially higher than the latter. Second, I determine the marginal value of adding unlabelled imaging data to predictive models within a semi-supervised framework, quantifying the benefit of assembling unlabelled collections of clinical imaging. Third, I compare high- and low-dimensional approaches to modelling response to therapy in two contexts: quantifying the effect of treatment at the population level (therapeutic inference) and predicting the optimal treatment in an individual patient (prescriptive inference). I demonstrate the superiority of the high-dimensional approach in both settings

Mechanisms and Perspectives of Post-Stroke Depression: Neuroanatomical Substrates, Incentive Motivation, and Emotional Processing

Post-stroke depression (PSD) is a prevalent affective condition after a stroke, which can impair functional and motor rehabilitation. The present dissertation and the included studies aim to further our understanding of how different mechanisms after stroke affect the emergence and maintenance of depressive symptoms on different pathological levels. For this, three separate studies were executed using several methodological applications, including different lesion-symptom mapping approaches, experimental task designs with different behavioral parameters, interviews, and test batteries. Study 1 investigated the link between anatomical brain lesions and specific PSD symptoms in a large stroke sample using a support-vector regression lesion-symptom mapping approach. Rather than treating depression as a single global score, lesion locations contributing to distinct PSD symptom domains were identified and analyzed. Study 2 examined the relationship between motor impairment and incentive motivation early after stroke and PSD symptoms, specifically motivational deficits. We were specifically interested in whether differences in motivation for physically demanding tasks could predict the development of PSD in patients with residual motor impairments. As disrupted emotion processing abilities can deteriorate social relationships and promote and maintain depressive symptoms study 3 examined emotion processing deficits after stroke and their relationship with PSD. Taken together, our findings contribute to a further understanding of the pathophysiology of PSD and how psychological and neurological influences interact in the development, maintenance, and treatment of depression after stroke. This should help to advance personalized therapeutic approaches and alleviate the burden of PSD characteristics for patients and caregivers

On the Viability of Diffusion MRI-Based Microstructural Biomarkers in Ischemic Stroke

Recent tract-based analyses provided evidence for the exploitability of 3D-SHORE microstructural descriptors derived from diffusion MRI (dMRI) in revealing white matter (WM) plasticity. In this work, we focused on the main open issues left: (1) the comparative analysis with respect to classical tensor-derived indices, i.e., Fractional Anisotropy (FA) and Mean Diffusivity (MD); and (2) the ability to detect plasticity processes in gray matter (GM). Although signal modeling in GM is still largely unexplored, we investigated their sensibility to stroke-induced microstructural modifications occurring in the contralateral hemisphere. A more complete picture could provide hints for investigating the interplay of GM and WM modulations. Ten stroke patients and ten age/gender-matched healthy controls were enrolled in the study and underwent diffusion spectrum imaging (DSI). Acquisitions at three and two time points (tp) were performed on patients and controls, respectively. For all subjects and acquisitions, FA and MD were computed along with 3D-SHORE-based indices [Generalized Fractional Anisotropy (GFA), Propagator Anisotropy (PA), Return To the Axis Probability (RTAP), Return To the Plane Probability (RTPP), and Mean Square Displacement (MSD)]. Tract-based analysis involving the cortical, subcortical and transcallosal motor networks and region-based analysis in GM were successively performed, focusing on the contralateral hemisphere to the stroke. Reproducibility of all the indices on both WM and GM was quantitatively proved on controls. For tract-based, longitudinal group analyses revealed the highest significant differences across the subcortical and transcallosal networks for all the indices. The optimal regression model for predicting the clinical motor outcome at tp3 included GFA, PA, RTPP, and MSD in the subcortical network in combination with the main clinical information at baseline. Region-based analysis in the contralateral GM highlighted the ability of anisotropy indices in discriminating between groups mainly at tp1, while diffusivity indices appeared to be altered at tp2. 3D-SHORE indices proved to be suitable in probing plasticity in both WM and GM, further confirming their viability as a novel family of biomarkers in ischemic stroke in WM and revealing their potential exploitability in GM. Their combination with tensor-derived indices can provide more detailed insights of the different tissue modulations related to stroke pathology

The dimensionalities of lesion-deficit mapping

Lesion-deficit mapping remains the most powerful method for localising function in the human brain. As the highest court of appeal where competing theories of cerebral function conflict, it ought to be held to the most stringent inferential standards. Though at first sight elegantly transferable, the mass-univariate statistical framework popularized by functional imaging is demonstrably ill-suited to the task, both theoretically and empirically. The critical difficulty lies with the handling of the data's intrinsically high dimensionality. Conceptual opacity and computational complexity lead lesion-deficit mappers to neglect two distinct sets of anatomical interactions: those between areas unified by function, and those between areas unified by the natural pattern of pathological damage. Though both are soluble through high-dimensional multivariate analysis, the consequences of ignoring them are radically different. The former will bleach and coarsen a picture of the functional anatomy that is nonetheless broadly faithful to reality; the latter may alter it beyond all recognition. That the field continues to cling to mass-univariate methods suggests the latter problem is misidentified with the former, and that their distinction is in need of elaboration. We further argue that the vicious effects of lesion-driven interactions are not limited to anatomical localisation but will inevitably degrade purely predictive models of function such as those conceived for clinical prognostic use. Finally, we suggest there is a great deal to be learnt about lesion-mapping by simulation-based modelling of lesion data, for the fundamental problems lie upstream of the experimental data themselves

Visuospatial neglect after stroke:heterogeneity, diagnosis and treatment.

The general objective of this thesis was to better understand and treat visuospatial neglect, a frequent and disabling disorder in lateralized attention. The first aim was to further unravel visuospatial neglect by focusing on several subtypes. In neglect research, mostly patients with left-sided neglect are included. Knowledge regarding neglect is, therefore, mainly based on a subset of patients. This is unfortunate, however, as our study showed that left- and right-sided neglect are both common after stroke. Furthermore, both patients with left- and right-sided neglect are less independent in mobility and self-care. It is, therefore, of great importance to adequately diagnose and treat both subtypes. Another subtype regards region-specific neglect. We studied neural substrates of peripersonal (i.e. within arm length) and extrapersonal (i.e. beyond arm length) neglect, and found that several right temporal and thalamic regions were related to both peripersonal and extrapersonal neglect, and several additional right temporal, parietal and occipital regions were only related to extrapersonal neglect. None of the brain regions were only related to peripersonal neglect. It seems that mostly shared anatomical regions are related to peripersonal and extrapersonal neglect. Today’s diagnosis of neglect lacks sensitivity, and discrepancies exist between performance on paper-and-pencil tasks and patient functioning in daily life. This is problematic for accurate diagnosis of neglect and for proper evaluation of rehabilitation interventions. We evaluated a dynamic multitask to assess neglect in a sensitive manner: the Mobility Assessment Course. An association existed between performance on the Mobility Assessment Course and performance on standard paper-and-pencil neglect tasks. Especially patients who were ‘recovered’, based on the paper-and-pencil tasks, showed neglect during the Mobility Assessment Course. This fits the hypothesis that this task may detect neglect in patients who do not show neglect during standard paper-and-pencil tasks. Next, we evaluated the potential of digitized neuropsychological testing. Next to the attentional bias, other cognitive processes that may relate to attention can be evaluated in more detail, such as search organization, involved in many daily processes and often disturbed after stroke. We studied search organization in stroke patients and found that, although disorganized search is related to neglect, this is only a weak relation, and it might be a separate cognitive construct. We conclude that analysing measures of search provides useful additional insights into the lower-order visuospatial processes of stroke patients. Finally, we evaluated the long-term effects of early treatment with prism adaptation compared to sham adaptation on neglect behaviour in daily life.Both patient groups (i.e., receiving sham adaptation and prism adaptation) improved on dynamic and static outcome measures of neglect. However, no differences were seen between groups. One of the main reasons for these neutral results could relate to the heterogeneity of the disorder, enhanced by the spontaneous neurobiological recovery in especially the subacute phase post-stroke onset or standard treatment effects (care as usual). They could have overshadowed the potential effects of prism adaptation. To conclude, prism adaptation is not effective for all patients with neglect, and, based on these results, should not be implemented in clinical practice