Brain networks under attack : robustness properties and the impact of lesions

A growing number of studies approach the brain as a complex network, the so-called ‘connectome’. Adopting this framework, we examine what types or extent of damage the brain can withstand—referred to as network ‘robustness’—and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer’s disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions—and especially those connecting different subnetworks—was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research

Deep Variational Lesion-Deficit Mapping

Causal mapping of the functional organisation of the human brain requires evidence of \textit{necessity} available at adequate scale only from pathological lesions of natural origin. This demands inferential models with sufficient flexibility to capture both the observable distribution of pathological damage and the unobserved distribution of the neural substrate. Current model frameworks -- both mass-univariate and multivariate -- either ignore distributed lesion-deficit relations or do not model them explicitly, relying on featurization incidental to a predictive task. Here we initiate the application of deep generative neural network architectures to the task of lesion-deficit inference, formulating it as the estimation of an expressive hierarchical model of the joint lesion and deficit distributions conditioned on a latent neural substrate. We implement such deep lesion deficit inference with variational convolutional volumetric auto-encoders. We introduce a comprehensive framework for lesion-deficit model comparison, incorporating diverse candidate substrates, forms of substrate interactions, sample sizes, noise corruption, and population heterogeneity. Drawing on 5500 volume images of ischaemic stroke, we show that our model outperforms established methods by a substantial margin across all simulation scenarios, including comparatively small-scale and noisy data regimes. Our analysis justifies the widespread adoption of this approach, for which we provide an open source implementation: https://github.com/guilherme-pombo/vae_lesion_defici

Macroscale imaging: a potential biomarker for post stroke functional outcome?

To determine whether long-term functional outcomes in stroke patients can be predicted by the amount of acutely damaged white matter tracts. We collected acute behavioral and neuroimaging data from a group of first-time stroke patients and add those from the other(s) databases. (n=114 + n) within one week (check with the other DBs) post-stroke. Functional outcome was telephonically evaluated using the Stroke Impact Scale 3.0 at 12 months post-stroke. For each patient, we calculated the absolute number of white matter tracts affected by the ischemic lesion from our anatomical scans. We measured a numerical index that considers white matter tract density (WMTD index). We compared the ability of the WMTD index, considered individually, or within a series of prediction models including demographics and behavioral data), to predict chronic outcomes. Multiple linear regression was used to assess the quality of prediction of the most informative model.To determine whether long-term functional outcomes in stroke patients can be predicted by the amount of acutely damaged white matter tracts. We collected acute behavioral and neuroimaging data from a group of first-time stroke patients and add those from the other(s) databases. (n=114 + n) within one week (check with the other DBs) post-stroke. Functional outcome was telephonically evaluated using the Stroke Impact Scale 3.0 at 12 months post-stroke. For each patient, we calculated the absolute number of white matter tracts affected by the ischemic lesion from our anatomical scans. We measured a numerical index that considers white matter tract density (WMTD index). We compared the ability of the WMTD index, considered individually, or within a series of prediction models including demographics and behavioral data), to predict chronic outcome. Multiple linear regression was used to assess the quality of prediction of the most informative model

Neural and Behavioral Dissociations in Aphasic Verb Retrieval

Verb-retrieval deficits are pervasive impairments that negatively impact communicative function for individuals living with aphasia, a language disorder caused by brain damage. Behavioral treatments can ameliorate these deficits, but the nature of the deficits remains controversial, and the neurocognitive mechanisms supporting treatment are poorly understood. Aphasia accounts traditionally follow cognitive-linguistic theories, which maintain language as a separate system from other cognitive processes and that aphasia results from distinct damage to language. However, recent cognitive neuroscience theories make contrasting predictions. Grounded cognition claims that language is accomplished by a general-purpose cognitive system that operates over distributed representations encoding both linguistic and non-linguistic knowledge. Rational adaptation predicts that people with aphasia adapt to their language impairments by relying more heavily on non-linguistic knowledge in order to communicate. This dissertation examines these predictions in patients with chronic aphasia due to left-hemisphere stroke and age-matched neurotypical controls. The first experiment examined the degree to which conceptual versus lexical action-processing abilities are impaired and may contribute to verb-retrieval deficits in adults with aphasia. The second experiment employed diffusion spectrum imaging and connectometry analyses to identify white-matter tracts associated with verb retrieval and to assess the involvement of conceptual-motor pathways not considered part of standard dual-stream neurocognitive models of language. The third experiment investigated the extent to which adults with aphasia rationally adapt to their language impairments by relying more on conceptual rather than lexical information during verb retrieval, as compared to controls. The results from these experiments indicate that conceptual processing can be impaired and contributes to verb-retrieval deficits in aphasia. However, relatively unimpaired conceptual processing can ameliorate the influence of lexical impairments on verb-retrieval deficits. Furthermore, the structural integrity of classical motor pathways strongly predicts verb retrieval ability. These findings are consistent with rational adaptation and grounded cognition accounts. This research is the first to systematically evaluate grounded cognition accounts of aphasic language impairments, white-matter connectivity contributions to verb retrieval in aphasia, and rational adaptation to rely on conceptual information. This work provides treatment-relevant evidence by assessing the underlying neurocognitive nature of aphasic impairments and cues that are facilitative of verb retrieval

Post-stroke visual impairment: a systematic literature review of types and recovery of visual conditions

Aim: The aim of this literature review was to determine the reported incidence and prevalence of visual impairment due to stroke for all visual conditions including central vision loss, visual field loss, eye movement problems and visual perception problems. A further aim was to document the reported rate and extent of recovery of visual conditions post stroke. Methods: A systematic review of the literature was conducted including all languages and translations obtained. The review covered adult participants (aged 18 years or over) diagnosed with a visual impairment as a direct cause of a stroke. Studies which included mixed populations were included if over 50% of the participants had a diagnosis of stroke. We searched scholarly online resources and hand searched journals and registers of published, unpublished and ongoing trials. Search terms included a variety of MESH terms and alternatives in relation to stroke and visual conditions. The quality of the evidence was assessed using key reporting guidelines, e.g. STROBE, CONSORT. Results: Sixty-one studies (n=25,672) were included in the review. Overall prevalence of visual impairment early after stroke was estimated at 65%, ranging from 19% to 92%. Visual field loss reports ranged from 5.5% to 57%, ocular motility problems from 22% to 54%, visual inattention from 14% to 82% and reduced central vision reported in up to 70%. Recovery of visual field loss varied between 0% and 72%, with ocular motility between 7% and 92% and visual inattention between 29% and 78%. Conclusion: The current literature provides a range of estimates for prevalence of visual impairment after stroke. Visual impairment post stroke is a common problem and has significant relevance to the assessment and care these patients receive. Prospective figures regarding incidence remain unknown

Indirect Structural Connectivity As a Biomarker for Stroke Motor Recovery

In this dissertation project, we demonstrated that diffusion magnetic resonance imaging and measures of indirect structural brain connectivity are sensitive to changes in fiber integrity and connectivity to remote regions in the brain after stroke. Our results revealed new insights into the effects local lesions have on global connectivity—in particular, the cerebellum—and how these changes in connectivity and integrity relate to motor impairment. We tested this methodology on two stroke groups—subacute and chronic—and were able to show that indirect connectivity is sensitive to differences in connectivity during stroke recovery. Our work can inform clinical methods for rehabilitating motor function in stroke individuals. By introducing methodology that extends local damage to remotely connected motor related areas, we can measure Wallerian degeneration in addition to providing the framework to predict improvements in motor impairment score based on structural connectivity at the subacute stage.We used diffusion magnetic resonance imaging (dMRI), probabilistic tractography, and novel graph theory metrics to quantify structural connectivity and integrity after stroke. In the first aim, we improved on a measure of indirect structural connectivity in order to detect remote gray matter regions with reduced connectivity after stroke. In a region-level analysis, we found that indirect connectivity was more sensitive to remote changes in connectivity after stroke than measures of direct connectivity, in particular in cortical, subcortical, and cerebellar gray matter regions that play a central role in sensorimotor function. Adding this information to the integrity of the corticospinal tract (CST) improved our ability to predict motor impairment. In the second aim, we investigated the relationship between white matter integrity, connectivity, and motor impairment by developing a unified measure of white matter structure that extends local changes in white matter integrity along remotely connected fiber tracks. Our measure uniquely identified damaged fiber tracks outside the CST, correlated with motor impairment in the CST better than the FA, and also was able to relate white matter structure in the superior cerebellar peduncle to motor impairment. Our final aim used a novel connectome similarity metric and the measure of indirect structural connectivity in order to identify cross-sectional differences in white matter structure between subacute and chronic stroke. We found more reductions in indirect connectivity in the chronic stroke cerebellar fibers than the subacute group, Additionally, the indirect connectivity of the superior cerebellar peduncle at the subacute stage correlated with the improvement in motor impairment score for the paired participants. In conclusion, indirect connectivity is an important measure of global brain damage and motor impairment after stroke, and can be a useful metric to relate to brain function and stroke recovery

Advances in Primary Progressive Aphasia

Primary progressive aphasia is a clinical syndrome that includes a group of neurodegenerative disorders characterized by progressive language impairment. Our knowledge about this disorder has evolved significantly in recent years. Notably, correlations between clinical findings and pathology have improved, and main clinical, neuroimaging, and genetic features have been described. Furthermore, primary progressive aphasia is a good model for the study of brain–behavior relationships, and has contributed to the knowledge of the neural basis of language functioning. However, there are many open questions remaining. For instance, classification into three variants (non-fluent, semantic, and logopenic) is under debate; further data about epidemiology and natural history of the diseases are needed; and, as in other neurodegenerative disorders, successful therapies are lacking. The Guest Editors expect that this book can be very useful for scholars

Are developmental disorders like cases of adult brain damage? Implications from connectionist modelling

It is often assumed that similar domain-specific behavioural impairments found in cases of adult brain damage and developmental disorders correspond to similar underlying causes, and can serve as convergent evidence for the modular structure of the normal adult cognitive system. We argue that this correspondence is contingent on an unsupported assumption that atypical development can produce selective deficits while the rest of the system develops normally (Residual Normality), and that this assumption tends to bias data collection in the field. Based on a review of connectionist models of acquired and developmental disorders in the domains of reading and past tense, as well as on new simulations, we explore the computational viability of Residual Normality and the potential role of development in producing behavioural deficits. Simulations demonstrate that damage to a developmental model can produce very different effects depending on whether it occurs prior to or following the training process. Because developmental disorders typically involve damage prior to learning, we conclude that the developmental process is a key component of the explanation of endstate impairments in such disorders. Further simulations demonstrate that in simple connectionist learning systems, the assumption of Residual Normality is undermined by processes of compensation or alteration elsewhere in the system. We outline the precise computational conditions required for Residual Normality to hold in development, and suggest that in many cases it is an unlikely hypothesis. We conclude that in developmental disorders, inferences from behavioural deficits to underlying structure crucially depend on developmental conditions, and that the process of ontogenetic development cannot be ignored in constructing models of developmental disorders

Structural and effective connectivity of lexical-semantic and naming networks in patients with chronic aphasia

Given the difficulty in predicting outcomes in persons with stroke-induced aphasia (PWA), neuroimaging-based biomarkers of recovery could provide invaluable predictive power to stroke models. However, the neural patterns that constitute beneficial neural organization of language in PWA remain debated. Thus, in this work, we propose a novel network theory of aphasia recovery and test our overarching hypothesis, i.e., that task-specific language processing in PWA requires the dynamic engagement of intact tissue within a bilateral network of anatomically-segregated but functionally and structurally connected language-specific and domain-general brain regions. We first present two studies in which we examined left frontotemporal connectivity during different language tasks (i.e., picture naming and semantic feature verification). Results suggest that PWA heavily rely on left middle frontal gyrus (LMFG)-driven connectivity for tasks requiring lexical-semantic processing and semantic control whereas controls prefer models with input to either LMFG or left inferior frontal gyrus (LIFG). Both studies also revealed several significant associations between spared tissue, connectivity and language skills in PWA. In the third study, we examined bilateral frontotemporoparietal connectivity and tested a lesion- and connectivity-based hierarchical model of chronic aphasia recovery. Between-group comparisons showed controls exhibited stronger left intra-hemispheric task-modulated connectivity than did PWA. Connectivity and language deficit patterns most closely matched predictions for patients with primarily anterior damage whereas connectivity results for patients with other lesion types were best explained by the nature of the semantic task. In the last study, we investigated the utility of lesion classification based on gray matter (GM) only versus combined GM plus white matter (WM) metrics. Results suggest GM only classification was sufficient for characterizing aphasia and anomia severity but the GM+WM classification better predicted naming treatment outcomes. We also found that fractional anisotropy of left WM association tracts predicted baseline naming and treatment outcomes independent of total lesion volume. Finally, results of a preliminary multimodal prediction analysis suggest that combined structural and functional metrics reflecting the integrity of regions and connections comprise optimal predictive models of behavior in PWA. To conclude this dissertation, we discuss how multimodal network models of aphasia recovery can guide future investigations.2020-10-23T00:00:00