Separate neural systems support representations for actions and objects during narrative speech in post-stroke aphasia.

BACKGROUND: Representations of objects and actions in everyday speech are usually materialized as nouns and verbs, two grammatical classes that constitute the core elements of language. Given their very distinct roles in singling out objects (nouns) or referring to transformative actions (verbs), they likely rely on distinct brain circuits. METHOD: We tested this hypothesis by conducting network-based lesion-symptom mapping in 38 patients with chronic stroke to the left hemisphere. We reconstructed the individual brain connectomes from probabilistic tractography applied to magnetic resonance imaging and obtained measures of production of words referring to objects and actions from narrative discourse elicited by picture naming tasks. RESULTS: Words for actions were associated with a frontal network strongly engaging structures involved in motor control and programming. Words for objects, instead, were related to a posterior network spreading across the occipital, posterior inferior temporal, and parietal regions, likely related with visual processing and imagery, object recognition, and spatial attention/scanning. Thus, each of these networks engaged brain areas typically involved in cognitive and sensorimotor experiences equivalent to the function served by each grammatical class (e.g. motor areas for verbs, perception areas for nouns). CONCLUSIONS: The finding that the two major grammatical classes in human speech rely on two dissociable networks has both important theoretical implications for the neurobiology of language and clinical implications for the assessment and potential rehabilitation and treatment of patients with chronic aphasia due to stroke.

Preservation of structural brain network hubs is associated with less severe post-stroke aphasia.

PURPOSE: Post-stroke aphasia is typically associated with ischemic damage to cortical areas or with loss of connectivity among spared brain regions. It remains unclear whether the participation of spared brain regions as networks hubs affects the severity of aphasia. METHODS: We evaluated language performance and magnetic resonance imaging from 44 participants with chronic aphasia post-stroke. The individual structural brain connectomes were constructed from diffusion tensor. Hub regions were defined in accordance with the rich club classification and studied in relation with language performance. RESULTS: Number of remaining left hemisphere rich club nodes was associated with aphasia, including comprehension, repetition and naming sub-scores. Importantly, among participants with relative preservation of regions of interest for language, aphasia severity was lessened if the region was not only spared, but also participated in the remaining network as a rich club node: Brodmann area (BA) 44/45 - repetition (p = 0.009), BA 39 - repetition (p = 0.045) and naming (p <  0.01), BA 37 - fluency (p <  0.001), comprehension (p = 0.025), repetition (p <  0.001) and naming (p <  0.001). CONCLUSIONS: Disruption of language network structural hubs is directly associated with aphasia severity after stroke.

Success of Anomia Treatment in Aphasia Is Associated With Preserved Architecture of Global and Left Temporal Lobe Structural Networks.

BACKGROUND AND OBJECTIVE: Targeted speech therapy can lead to substantial naming improvement in some subjects with anomia following dominant-hemisphere stroke. We investigated whether treatment-induced improvement in naming is associated with poststroke preservation of structural neural network architecture. METHODS: Twenty-four patients with poststroke chronic aphasia underwent 30 hours of speech therapy over a 2-week period and were assessed at baseline and after therapy. Whole brain maps of neural architecture were constructed from pretreatment diffusion tensor magnetic resonance imaging to derive measures of global brain network architecture (network small-worldness) and regional network influence (nodal betweenness centrality). Their relationship with naming recovery was evaluated with multiple linear regressions. RESULTS: Treatment-induced improvement in correct naming was associated with poststroke preservation of global network small worldness and of betweenness centrality in temporal lobe cortical regions. Together with baseline aphasia severity, these measures explained 78% of the variability in treatment response. CONCLUSIONS: Preservation of global and left temporal structural connectivity broadly explains the variability in treatment-related naming improvement in aphasia. These findings corroborate and expand on previous classical lesion-symptom mapping studies by elucidating some of the mechanisms by which brain damage may relate to treated aphasia recovery. Favorable naming outcomes may result from the intact connections between spared cortical areas that are functionally responsive to treatment.

Reproducibility of the Structural Brain Connectome Derived from Diffusion Tensor Imaging.

RATIONALE: Disruptions of brain anatomical connectivity are believed to play a central role in several neurological and psychiatric illnesses. The structural brain connectome is typically derived from diffusion tensor imaging (DTI), which may be influenced by methodological factors related to signal processing, MRI scanners and biophysical properties of neuroanatomical regions. In this study, we evaluated how these variables affect the reproducibility of the structural connectome. METHODS: Twenty healthy adults underwent 3 MRI scanning sessions (twice in the same MRI scanner and a third time in a different scanner unit) within a short period of time. The scanning sessions included similar T1 weighted and DTI sequences. Deterministic or probabilistic tractography was performed to assess link weight based on the number of fibers connecting gray matter regions of interest (ROI). Link weight and graph theory network measures were calculated and reproducibility was assessed through intra-class correlation coefficients, assuming each scanning session as a rater. RESULTS: Connectome reproducibility was higher with data from the same scanner. The probabilistic approach yielded larger reproducibility, while the individual variation in the number of tracked fibers from deterministic tractography was negatively associated with reproducibility. Links connecting larger and anatomically closer ROIs demonstrated higher reproducibility. In general, graph theory measures demonstrated high reproducibility across scanning sessions. DISCUSSION: Anatomical factors and tractography approaches can influence the reproducibility of the structural connectome and should be factored in the interpretation of future studies. Our results demonstrate that connectome mapping is a largely reproducible technique, particularly as it relates to the geometry of network architecture measured by graph theory methods.

Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data.

The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve our objective, a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome, and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier. Using a 10-fold cross validation strategy, the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80% accuracy, and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70% accuracy. Compared to existing state-of-the-art methods that use VBM data, the proposed two-stage connectome-based prediction framework is a suitable alternative with comparable prediction performance. Our results additionally show that machine learning algorithms that exclusively use structural connectome data can predict treatment outcomes in epilepsy with similar accuracy compared with "expert-based" clinical decision. In summary, using the unprecedented information provided in the brain connectome, machine learning algorithms may uncover pathological changes in brain network organization and improve outcome forecasting in the context of epilepsy.

Individual variability in the anatomical distribution of nodes participating in rich club structural networks.

With recent advances in computational analyses of structural neuroimaging, it is possible to comprehensively map neural connectivity, i.e., the brain connectome. The architectural organization of the connectome is believed to play an important role in several biological processes. Central to the conformation of the connectome are connectivity hubs, which are likely to be organized in accordance with the rich club phenomenon, as evidenced by graph theory analyses of neural architecture. It is yet unclear whether rich club connectivity hubs are consistently organized in the same anatomical framework across healthy adults. We constructed the brain connectome from 43 healthy adults, based on T1-weighted and diffusion tensor MRI data. Probabilistic fiber tractography was used to evaluate connectivity between each possible pair of cortical anatomical regions of interest. Connectivity hubs were identified in accordance with the rich club phenomenon applied to binarized matrices, and the variability in frequency of hub participation was assessed node-wise across all subjects. The anatomical location of nodes participating in rich club networks was fairly consistent across subjects. The most common locations for rich club nodes were identified in integrative areas, such as the cingulate and pericingulate regions, medial aspect of the occipital areas and precuneus; or else, they were found in important and specialized brain regions (such as the oribitofrontal cortex, caudate, fusiform gyrus, and hippocampus). Marked anatomical consistency exists across healthy brains in terms of nodal participation and location of rich club networks. The consistency of connections between integrative areas and specialized brain regions highlights a fundamental connectivity pattern shared among healthy brains. We propose that approaching brain connectivity with this framework of anatomical consistencies may have clinical implications for early detection of individual variability.

The brain connectome as a personalized biomarker of seizure outcomes after temporal lobectomy.

OBJECTIVE: We examined whether individual neuronal architecture obtained from the brain connectome can be used to estimate the surgical success of anterior temporal lobectomy (ATL) in patients with temporal lobe epilepsy (TLE). METHODS: We retrospectively studied 35 consecutive patients with TLE who underwent ATL. The structural brain connectome was reconstructed from all patients using presurgical diffusion MRI. Network links in patients were standardized as Z scores based on connectomes reconstructed from healthy controls. The topography of abnormalities in linkwise elements of the connectome was assessed on subnetworks linking ipsilateral temporal with extratemporal regions. Predictive models were constructed based on the individual prevalence of linkwise Z scores >2 and based on presurgical clinical data. RESULTS: Patients were more likely to achieve postsurgical seizure freedom if they exhibited fewer abnormalities within a subnetwork composed of the ipsilateral hippocampus, amygdala, thalamus, superior frontal region, lateral temporal gyri, insula, orbitofrontal cortex, cingulate, and lateral occipital gyrus. Seizure-free surgical outcome was predicted by neural architecture alone with 90% specificity (83% accuracy), and by neural architecture combined with clinical data with 94% specificity (88% accuracy). CONCLUSIONS: Individual variations in connectome topography, combined with presurgical clinical data, may be used as biomarkers to better estimate surgical outcomes in patients with TLE.

Gray matter axonal connectivity maps.

Structural brain connectivity is generally assessed through methods that rely on pre-defined regions of interest (e.g., Brodmann's areas), thus preventing analyses that are largely free from a priori anatomical assumptions. Here, we introduce a novel and practical technique to evaluate a voxel-based measure of axonal projections connecting gray matter tissue [gray matter axonal connectivity map (GMAC)]. GMACs are compatible with voxel-based statistical approaches, and can be used to assess whole brain, scale-free, gray matter connectivity. In this study, we demonstrate how whole-brain GMACs can be generated from conventional structural connectome methodology, describing each step in detail, as well as providing tools to allow for the calculation of GMAC. To illustrate the utility of GMAC, we demonstrate the relationship between age and gray matter connectivity, using voxel-based analyses of GMAC. We discuss the potential role of GMAC in further analyses of cortical connectivity in healthy and clinical populations.

Human brain asymmetry in microstructural connectivity demonstrated by diffusional kurtosis imaging.

Structural asymmetry of whole brain white matter (WM) pathways, i.e., the connectome, has been demonstrated using fiber tractography based on diffusion tensor imaging (DTI). However, DTI-based tractography fails to resolve axonal fiber bundles that intersect within an imaging voxel, and therefore may not fully characterize the extent of asymmetry. The goal of this study was to assess structural asymmetry with tractography based on diffusional kurtosis imaging (DKI), which improves upon DTI-based tractography by delineating intravoxel crossing fibers. DKI images were obtained from 42 healthy subjects. By using automatic segmentation, gray matter (GM) was parcellated into anatomically defined regions of interest (ROIs). WM pathways were reconstructed with both DKI- and DTI-based tractography. The connectivity between the ROIs was quantified with the streamlines connecting the ROIs. The asymmetry index (AI) was utilized to quantify hemispheric differences in the connectivity of cortical ROIs and of links interconnecting cortical ROIs. Our results demonstrated that leftward asymmetrical ROIs and links were observed in frontal, parietal, temporal lobes, and insula. Rightward asymmetrical ROI and links were observed in superior frontal lobe, cingulate cortex, fusiform, putamen, and medial temporal lobe. Interestingly, these observed structural asymmetries were incompletely identified with DTI-based tractography. These results suggest that DKI-based tractography can improve the identification of asymmetrical connectivity patterns, thereby serving as an additional tool in the evaluation of the structural bases of functional lateralization.

Mapping remote subcortical ramifications of injury after ischemic strokes.

BACKGROUND: The extent of brain damage in chronic stroke patients is traditionally defined as the necrotic tissue observed on magnetic resonance image (MRI). However, patients often exhibit symptoms suggesting that functional impairment may affect areas beyond the cortical necrotic lesion, for example, when cortical symptoms ensue after subcortical damage. This observation suggests that disconnection or diaschisis can lead to remote cortical dysfunction that can be functionally equivalent to direct cortical lesions. Objective. To directly measure subcortical disconnection after stroke. METHODS: We describe a principled approach utilizing the whole brain connectome reconstructed from diffusion MRI to evaluate the reduction of apparent white matter fiber density in the hemisphere affected by the stroke compared with the spared hemisphere. RESULTS: In eight chronic stroke patients, we observed subcortical disconnection extending beyond the location of tissue necrosis and affecting major white matter pathways underlying the necrotic area. CONCLUSIONS: We suggest that it is possible to detect and quantify previously unappreciated areas of subcortical and cortical disconnection. Specifically, this method can be used to evaluate the relationship between lesion location and symptoms, with emphasis on a connectivity-based approach.

Asymmetry of the structural brain connectome in healthy older adults.

BACKGROUND: It is now possible to map neural connections in vivo across the whole brain (i.e., the brain connectome). This is a promising development in neuroscience since many health and disease processes are believed to arise from the architecture of neural networks. OBJECTIVE: To describe the normal range of hemispheric asymmetry in structural connectivity in healthy older adults. MATERIALS AND METHODS: We obtained high-resolution structural magnetic resonance images (MRI) from 17 healthy older adults. For each subject, the brain connectome was reconstructed by parcelating the probabilistic map of gray matter into anatomically defined regions of interested (ROIs). White matter fiber tractography was reconstructed from diffusion tensor imaging and streamlines connecting gray matter ROIs were computed. Asymmetry indices were calculated regarding ROI connectivity (representing the sum of connectivity weight of each cortical ROI) and for regional white matter links. All asymmetry measures were compared to a normal distribution with mean = 0 through one-sample t-tests. RESULTS: Leftward cortical ROI asymmetry was observed in medial temporal, dorsolateral frontal, and occipital regions. Rightward cortical ROI asymmetry was observed in middle temporal and orbito-frontal regions. Link-wise asymmetry revealed stronger connections in the left hemisphere between the medial temporal, anterior, and posterior peri-Sylvian and occipito-temporal regions. Rightward link asymmetry was observed in lateral temporal, parietal, and dorsolateral frontal connections. CONCLUSION: We postulate that asymmetry of specific connections may be related to functional hemispheric organization. This study may provide reference for future studies evaluating the architecture of the connectome in health and disease processes in older individuals.

Presurgical connectome and postsurgical seizure control in temporal lobe epilepsy.

OBJECTIVES: The objective of this study was to evaluate whether patients with surgically refractory medial temporal lobe epilepsy (MTLE) exhibit a distinct pattern of structural network organization involving the temporal lobes and extratemporal regions. METHODS: We retrospectively studied 18 healthy controls and 20 patients with medication refractory unilateral MTLE who underwent anterior temporal lobectomy for treatment of seizures. Patients were classified as seizure-free or not seizure-free at least 1 year after surgery. The presurgical brain connectome was calculated through probabilistic connectivity from MRI-diffusion tensor imaging from 83 anatomically defined regions of interest encompassing the whole brain. The connectivity patterns were analyzed regarding group differences in regional connectivity and network graph properties. RESULTS: Compared with controls, patients exhibited a decrease in connectivity involving ipsilateral thalamocortical regions, with a pathologic increase in ipsilateral medial temporal lobe, insular, and frontal connectivity. Among patients, those not seizure-free exhibited a higher connectivity between structures in 1) the ipsilateral medial and lateral temporal lobe, 2) the ipsilateral medial temporal and parietal lobe, and 3) the contralateral temporal pole and parietal lobe. Patients not seizure-free also exhibited lower small-worldness in the subnetwork within the ipsilateral temporal lobe, with higher subnetwork integration at the expense of segregation. CONCLUSIONS: MTLE is associated with network rearrangement within, but not restricted to, the temporal lobe ipsilateral to the onset of seizures. Networks involving key components of the medial temporal lobe and structures traditionally not removed during surgery may be associated with seizure control after surgical treatment of MTLE.

Medial temporal lobe epilepsy is associated with neuronal fibre loss and paradoxical increase in structural connectivity of limbic structures.

BACKGROUND: It has been hypothesised that seizure induced neuronal loss and axonal damage in medial temporal lobe epilepsy (MTLE) may lead to the development of aberrant connections between limbic structures and eventually result in the reorganisation of the limbic network. In this study, limbic structural connectivity in patients with MTLE was investigated, using diffusion tensor MRI, probabilistic tractography and graph theory based network analysis. METHODS: 12 patients with unilateral MTLE and hippocampal sclerosis (five left and seven right MTLE) and 26 healthy controls were studied. The connectivity of 10 bilateral limbic regions of interest was mapped with probabilistic tractography, and the probabilistic fibre density between each pair of regions was used as the measure of their weighted structural connectivity. Binary connectivity matrices were then obtained from the weighted connectivity matrix using a range of fixed density thresholds. Graph theory based properties of nodes (degree, local efficiency, clustering coefficient and betweenness centrality) and the network (global efficiency and average clustering coefficient) were calculated from the weight and binary connectivity matrices of each subject and compared between patients and controls. RESULTS: MTLE was associated with a regional reduction in fibre density compared with controls. Paradoxically, patients exhibited (1) increased limbic network clustering and (2) increased nodal efficiency, degree and clustering coefficient in the ipsilateral insula, superior temporal region and thalamus. There was also a significant reduction in clustering coefficient and efficiency of the ipsilateral hippocampus, accompanied by increased nodal degree. CONCLUSIONS: These results suggest that MTLE is associated with reorganisation of the limbic system. These results corroborate the concept of MTLE as a network disease, and may contribute to the understanding of network excitability dynamics in epilepsy and MTLE.

A translational behavioral model of mood-based impulsivity: Implications for substance abuse.

BACKGROUND: Laboratory tasks that measure various facets of impulsivity derived from self-report questionnaires are important for elucidating the behavioral consequences of impulsivity in humans and for back-translating these facets to non-human species. Negative urgency, or mood-based rash action, is a self-report facet of impulsivity linked to problem substance use; however, a valid behavioral task is lacking. METHODS: The current studies were designed to bridge self-report questionnaire and behavioral measures of negative urgency in humans and to determine if this could be back-translated to rats. RESULTS: Humans scoring high in negative urgency showed greater behavioral responding and increased frustration following unexpected reward omission on a monetary-based task compared to subjects low in negative urgency. Rats also showed elevated responding for either sucrose pellets or intravenous amphetamine following unexpected reward omission. CONCLUSION: These results suggest that impulsive behavior engendered by unexpected reward omission may represent a valid behavioral model of negative urgency linked to substance abuse.