Language processing relies on a widespread network of brain regions. Univariate post-stroke lesion-behavior mapping is a particularly potent method to study brain-language relationships. However, it ...is a concern that this method may overlook structural disconnections to seemingly spared regions and may fail to adjudicate between regions that subserve different processes but share the same vascular perfusion bed. For these reasons, more refined structural brain mapping techniques may improve the accuracy of detecting brain networks supporting language. In this study, we applied a predictive multivariate framework to investigate the relationship between language deficits in human participants with chronic aphasia and the topological distribution of structural brain damage, defined as post-stroke necrosis or cortical disconnection. We analyzed lesion maps as well as structural connectome measures of whole-brain neural network integrity to predict clinically applicable language scores from the Western Aphasia Battery (WAB). Out-of-sample prediction accuracy was comparable for both types of analyses, which revealed spatially distinct, albeit overlapping, networks of cortical regions implicated in specific aspects of speech functioning. Importantly, all WAB scores could be predicted at better-than-chance level from the connections between gray-matter regions spared by the lesion. Connectome-based analysis highlighted the role of connectivity of the temporoparietal junction as a multimodal area crucial for language tasks. Our results support that connectome-based approaches are an important complement to necrotic lesion-based approaches and should be used in combination with lesion mapping to fully elucidate whether structurally damaged or structurally disconnected regions relate to aphasic impairment and its recovery.
We present a novel multivariate approach of predicting post-stroke impairment of speech and language from the integrity of the connectome. We compare it with multivariate prediction of speech and language scores from lesion maps, using cross-validation framework and a large (n = 90) database of behavioral and neuroimaging data from individuals with post-stroke aphasia. Connectome-based analysis was similar to lesion-based analysis in terms of predictive accuracy and provided additional details about the importance of specific connections (in particular, between parietal and posterior temporal areas) for preserving speech functions. Our results suggest that multivariate predictive analysis of the connectome is a useful complement to multivariate lesion analysis, being less dependent on the spatial constraints imposed by underlying vasculature.
Spatial normalization reshapes an individual's brain to match the shape and size of a template image. This is a crucial step required for group-level statistical analyses. The most popular standard ...templates are derived from MRI scans of young adults. We introduce specialized templates that allow normalization algorithms to be applied to stroke-aged populations. First, we developed a CT template: while this is the dominant modality for many clinical situations, there are no modern CT templates and popular algorithms fail to successfully normalize CT scans. Importantly, our template was based on healthy individuals with ages similar to what is commonly seen in stroke (mean 65years old). This template allows studies where only CT scans are available. Second, we derived a MRI template that approximately matches the shape of our CT template as well as processing steps that aid the normalization of scans from older individuals (including lesion masking and the ability to generate high quality cortical renderings despite brain injury). The benefit of this strategy is that the resulting templates can be used in studies where mixed modalities are present. We have integrated these templates and processing algorithms into a simple SPM toolbox (http://www.mricro.com/clinical-toolbox/spm8-scripts).
Measures of brain activation (e.g., changes in scalp electrical potentials) have become the most popular method for inferring brain function. However, examining brain disruption (e.g., examining ...behavior after brain injury) can complement activation studies. Activation techniques identify regions involved with a task, whereas disruption techniques are able to discover which regions are crucial for a task. Voxel-based lesion mapping can be used to determine relationships between behavioral measures and the location of brain injury, revealing the function of brain regions. Lesion mapping can also correlate the effectiveness of neurosurgery with the location of brain resection, identifying optimal surgical targets. Traditionally, voxel-based lesion mapping has employed the chi-square test when the clinical measure is binomial and the Student's t test when measures are continuous. Here we suggest that the Liebermeister approach for binomial data is more sensitive than the chi-square test. We also suggest that a test described by Brunner and Munzel is more appropriate than the t test for nonbinomial data because clinical and neuropsychological data often violate the assumptions of the t test. We test our hypotheses comparing statistical tests using both simulated data and data obtained from a sample of stroke patients with disturbed spatial perception. We also developed software to implement these tests (MRIcron), made freely available to the scientific community.
Abstract Recent years have witnessed a paradigm shift in the study and conceptualization of epilepsy, which is increasingly understood as a network-level disorder. An emblematic case is temporal lobe ...epilepsy (TLE), the most common drug-resistant epilepsy that is electroclinically defined as a focal epilepsy and pathologically associated with hippocampal sclerosis. In this review, we will summarize histopathological, electrophysiological, and neuroimaging evidence supporting the concept that the substrate of TLE is not limited to the hippocampus alone, but rather is broadly distributed across multiple brain regions and interconnecting white matter pathways. We will introduce basic concepts of graph theory, a formalism to quantify topological properties of complex systems that has recently been widely applied to study networks derived from brain imaging and electrophysiology. We will discuss converging graph theoretical evidence indicating that networks in TLE show marked shifts in their overall topology, providing insight into the neurobiology of TLE as a network-level disorder. Our review will conclude by discussing methodological challenges and future clinical applications of this powerful analytical approach.
We used left-hemisphere stroke as a model to examine how damage to sensorimotor brain networks impairs vocal auditory feedback processing and control. Individuals with post-stroke aphasia and matched ...neurotypical control subjects vocalized speech vowel sounds and listened to the playback of their self-produced vocalizations under normal (NAF) and pitch-shifted altered auditory feedback (AAF) while their brain activity was recorded using electroencephalography (EEG) signals. Event-related potentials (ERPs) were utilized as a neural index to probe the effect of vocal production on auditory feedback processing with high temporal resolution, while lesion data in the stroke group was used to determine how brain abnormality accounted for the impairment of such mechanisms. Results revealed that ERP activity was aberrantly modulated during vocalization vs. listening in aphasia, and this effect was accompanied by the reduced magnitude of compensatory vocal responses to pitch-shift alterations in the auditory feedback compared with control subjects. Lesion-mapping revealed that the aberrant pattern of ERP modulation in response to NAF was accounted for by damage to sensorimotor networks within the left-hemisphere inferior frontal, precentral, inferior parietal, and superior temporal cortices. For responses to AAF, neural deficits were predicted by damage to a distinguishable network within the inferior frontal and parietal cortices. These findings define the left-hemisphere sensorimotor networks implicated in auditory feedback processing, error detection, and vocal motor control. Our results provide translational synergy to inform the theoretical models of sensorimotor integration while having clinical applications for diagnosis and treatment of communication disabilities in individuals with stroke and other neurological conditions.
Revealing the dual streams of speech processing Fridriksson, Julius; Yourganov, Grigori; Bonilha, Leonardo ...
Proceedings of the National Academy of Sciences - PNAS,
12/2016, Volume:
113, Issue:
52
Journal Article
Peer reviewed
Open access
Several dual route models of human speech processing have been proposed suggesting a large-scale anatomical division between cortical regions that support motor–phonological aspects vs. ...lexical–semantic aspects of speech processing. However, to date, there is no complete agreement on what areas subserve each route or the nature of interactions across these routes that enables human speech processing. Relying on an extensive behavioral and neuroimaging assessment of a large sample of stroke survivors, we used a data-driven approach using principal components analysis of lesion-symptom mapping to identify brain regions crucial for performance on clusters of behavioral tasks without a priori separation into task types. Distinct anatomical boundaries were revealed between a dorsal frontoparietal stream and a ventral temporal–frontal stream associated with separate components. Collapsing over the tasks primarily supported by these streams, we characterize the dorsal stream as a form-to-articulation pathway and the ventral stream as a form-to-meaning pathway. This characterization of the division in the data reflects both the overlap between tasks supported by the two streams as well as the observation that there is a bias for phonological production tasks supported by the dorsal stream and lexical–semantic comprehension tasks supported by the ventral stream. As such, our findings show a division between two processing routes that underlie human speech processing and provide an empirical foundation for studying potential computational differences that distinguish between the two routes.
Lesion-symptom mapping is a key tool in understanding the relationship between structure and function in neuroscience as it can provide objective evidence about which regions are
for a given process. ...Initial limitations with this approach were largely overcome by voxel-based lesion-symptom mapping (VLSM), a method introduced in the early 2000s, which allows for a whole-brain approach to study the association between damaged areas and behavioral impairment by applying an independent statistical test at every voxel. By doing so, this technique eliminated the need to predefine regions of interest or classify patients into groups based on arbitrary cutoff scores. VLSM has nonetheless its own limitations; chiefly, a bias towards recognizing cortical necrosis/gliosis but with poor sensitivity for detecting injury along long white matter tracts, thus ignoring cortical disconnection, which can per se lead to behavioral impairment. Here, we propose a complementary method that, instead, establishes a statistical relationship between the strength of connections between all brain regions of the brain (as defined by a standard brain atlas) and the array of behavioral performance seen in patients with brain injury: connectome-based lesion-symptom mapping (CLSM). Whole-brain CLSM therefore has the potential to identify key connections for behavior independently of a priori assumptions with applicability across a broad spectrum of neurological and psychiatric diseases. We propose that this approach can further our understanding of brain-structure relationships and is worth exploring in clinical and theoretical contexts.
Studies assessing the relationship between chronic poststroke language impairment (aphasia) and ischemic brain damage usually rely on measuring the extent of brain necrosis observed on MRI. ...Nonetheless, clinical observation suggests that patients can exhibit deficits that are more severe than what would be expected based on lesion location and size. This phenomenon is commonly explained as being the result of cortical disconnection. To understand whether disconnection contributes to clinical symptoms, we assessed the relationship between language impairments and structural brain connectivity (the connectome) in patients with chronic aphasia after a stroke.
Thirty-nine patients with chronic aphasia underwent language assessment and MRI scanning. Relying on MRI data, we reconstructed the individual connectome from T1-weighted and diffusion tensor imaging. Deterministic fiber tractography was used to assess connectivity between each possible pair of cortical Brodmann areas. Multiple linear regression analyses were performed to evaluate the relationship between language performance and cortical necrosis and cortical disconnection.
We observed that structural disconnection of Brodmann area 45 (spared by the necrotic tissue) was independently associated with naming performance, controlling for the extent of Brodmann area 45 necrosis (F=4.62; P<0.01; necrosis: β=0.43; P=0.03; disconnection β=1.21; P<0.001).
We suggest that cortical disconnection, as measured by the structural connectome, is an independent predictor of naming impairment in patients with chronic aphasia. The full extent of clinically relevant brain damage after an ischemic stroke may be underappreciated by visual inspection of cortical necrosis alone.
Summary
Objective
We evaluated whether deep learning applied to whole‐brain presurgical structural connectomes could be used to predict postoperative seizure outcome more accurately than inference ...from clinical variables in patients with mesial temporal lobe epilepsy (TLE).
Methods
Fifty patients with unilateral TLE were classified either as having persistent disabling seizures (SZ) or becoming seizure‐free (SZF) at least 1 year after epilepsy surgery. Their presurgical structural connectomes were reconstructed from whole‐brain diffusion tensor imaging. A deep network was trained based on connectome data to classify seizure outcome using 5‐fold cross‐validation.
Results
Classification accuracy of our trained neural network showed positive predictive value (PPV; seizure freedom) of 88 ± 7% and mean negative predictive value (NPV; seizure refractoriness) of 79 ± 8%. Conversely, a classification model based on clinical variables alone yielded <50% accuracy. The specific features that contributed to high accuracy classification of the neural network were located not only in the ipsilateral temporal and extratemporal regions, but also in the contralateral hemisphere.
Significance
Deep learning demonstrated to be a powerful statistical approach capable of isolating abnormal individualized patterns from complex datasets to provide a highly accurate prediction of seizure outcomes after surgery. Features involved in this predictive model were both ipsilateral and contralateral to the clinical foci and spanned across limbic and extralimbic networks.
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