scholarly journals Post-stroke deficit prediction from lesion and indirect structural and functional disconnection

Brain ◽  
2020 ◽  
Vol 143 (7) ◽  
pp. 2173-2188 ◽  
Author(s):  
Alessandro Salvalaggio ◽  
Michele De Filippo De Grazia ◽  
Marco Zorzi ◽  
Michel Thiebaut de Schotten ◽  
Maurizio Corbetta
Keyword(s):  
Functional Connectivity ◽  
Functional Mri ◽  
Verbal Memory ◽  
Structural Connectivity ◽  
Indirect Estimation ◽  
Post Stroke ◽  
Brain Connectome ◽  
Left And Right ◽  
Direct Measures ◽  
Behavioural Deficits

Abstract Behavioural deficits in stroke reflect both structural damage at the site of injury, and widespread network dysfunction caused by structural, functional, and metabolic disconnection. Two recent methods allow for the estimation of structural and functional disconnection from clinical structural imaging. This is achieved by embedding a patient’s lesion into an atlas of functional and structural connections in healthy subjects, and deriving the ensemble of structural and functional connections that pass through the lesion, thus indirectly estimating its impact on the whole brain connectome. This indirect assessment of network dysfunction is more readily available than direct measures of functional and structural connectivity obtained with functional and diffusion MRI, respectively, and it is in theory applicable to a wide variety of disorders. To validate the clinical relevance of these methods, we quantified the prediction of behavioural deficits in a prospective cohort of 132 first-time stroke patients studied at 2 weeks post-injury (mean age 52.8 years, range 22–77; 63 females; 64 right hemispheres). Specifically, we used multivariate ridge regression to relate deficits in multiple functional domains (left and right visual, left and right motor, language, spatial attention, spatial and verbal memory) with the pattern of lesion and indirect structural or functional disconnection. In a subgroup of patients, we also measured direct alterations of functional connectivity with resting-state functional MRI. Both lesion and indirect structural disconnection maps were predictive of behavioural impairment in all domains (0.16 < R2 < 0.58) except for verbal memory (0.05 < R2 < 0.06). Prediction from indirect functional disconnection was scarce or negligible (0.01 < R2 < 0.18) except for the right visual field deficits (R2 = 0.38), even though multivariate maps were anatomically plausible in all domains. Prediction from direct measures of functional MRI functional connectivity in a subset of patients was clearly superior to indirect functional disconnection. In conclusion, the indirect estimation of structural connectivity damage successfully predicted behavioural deficits post-stroke to a level comparable to lesion information. However, indirect estimation of functional disconnection did not predict behavioural deficits, nor was a substitute for direct functional connectivity measurements, especially for cognitive disorders.

scholarly journals A functional-structural connectivity metric detects ipsilateral connections with distinct functional specialisation in each hemisphere

2020 ◽  
Author(s):  
Oren Civier ◽  
Marion Sourty ◽  
Fernando Calamante
Keyword(s):  
Functional Connectivity ◽  
Functional Mri ◽  
Measurement Errors ◽  
Diffusion Mri ◽  
Right Hemisphere ◽  
Structural Connectivity ◽  
Joint Analysis ◽  
Fibre Bundles ◽  
Human Connectome Project ◽  
The Brain

AbstractWe introduce a connectomics metric that integrates information on structural connectivity (SC) from diffusion MRI tractography and functional connectivity (FC) from resting-state functional MRI, at individual subject level. The metric is based on the ability of SC to broadly predict FC using a simple linear predictive model; for each connection in the brain, the metric quantifies the deviation from that model. For the metric to capture underlying physiological properties, we minimise systematic measurement errors and processing biases in both SC and FC, and address several challenges with the joint analysis. This also includes a data-driven normalisation approach. The combined metric may provide new information by indirectly assessing white matter structural properties that cannot be inferred from diffusion MRI alone, and/or complex interregional neural interactions that cannot be inferred from functional MRI alone. To demonstrate the utility of the metric, we used young adult data from the Human Connectome Project to examine all bilateral pairs of ipsilateral connections, i.e. each left-hemisphere connection in the brain was paired with its right-hemisphere homologue. We detected a minority of bilateral pairs where the metric value is significantly different across hemispheres, which we suggest reflects cases of ipsilateral connections that have distinct functional specialisation in each hemisphere. The pairs with significant effects spanned all cortical lobes, and also included several cortico-subcortical connections. Our findings highlight the potential in a joint analysis of structural and functional measures of connectivity, both for clinical applications and to help in the interpretation of results from standard functional connectivity analysis.Significance StatementBased on the notion that structure predicts function, the scientific community sought to demonstrate that structural information on fibre bundles that connect brain regions is sufficient to estimate the strength of interregional interactions. However, an accurate prediction using MRI has proved elusive. This paper posits that the failure to predict function from structure originates from limitations in measurement or interpretation of either diffusion MRI (to assess fibre bundles), fMRI (to assess functional interactions), or both. We show that these limitations can be nevertheless beneficial, as the extent of divergence between the two modalities may reflect hard-to-measure properties of interregional connections, such as their functional role in the brain. This provides many insights, including into the division of labour between hemispheres.

scholarly journals The Distinct Disrupted Plasticity in Structural and Functional Network in Mild Stroke With Basal Ganglia Region Infarcts

2021 ◽  
Author(s):  
Hua Zhu ◽  
Lijun Zuo ◽  
Wanlin Zhu ◽  
Jing Jing ◽  
Zhe Zhang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Basal Ganglia ◽  
Functional Connectivity ◽  
Network Analysis ◽  
Structural Connectivity ◽  
Functional Network ◽  
Imaging Biomarkers ◽  
Functional Networks ◽  
Stroke Patients ◽  
Post Stroke

Abstract ObjectiveTo characterize brain structural and functional networks in post-stroke patients with or without cognitive impairment. MethodsGraph theory analysis was applied to diffusion-weighted imaging (DWI) data and resting-state functional MRI (fMRI) data from 23 post-stroke patients with cognitive impairment (PSCI), 17 post-stroke patients without cognitive impairment (NPSCI), and 29 healthy controls (HC). Structural and functional connectivity between 90 cortical and subcortical brain regions was estimated and thresholded to construct a set of undirected graphs. Network-based statistics (NBS) was used to characterize altered connectivity patterns among the three groups. ResultsCompared to HC, the PSCI group demonstrated substantial reductions in all three types of connections - rich club, feeder, and local - in structural and functional networks. Specifically, in structural network analysis, reduced connections were observed within basal ganglia and basal ganglia-frontal networks, whereas in the functional network analysis, reduced connections were observed in fronto-parietal network (FPN) and cingulo-opercular networks (CON). Meanwhile, compared to HC, the NPSCI group demonstrated reductions in both feeder and local connections only within occipital area and occipital-temporal structural networks. ConclusionsThe findings of reduced structural connectivity in regions stemming from a basal ganglia core and reduced functional connectivity in FPN and CON may indicate a bottom-up cognitive impairment induced by stroke. Graph analysis and connectomics may aid clinical diagnosis and serve as potential imaging biomarkers for post-stroke patients with cognitive impairment.

scholarly journals Brainstem Functional Connectivity Disturbances in Epilepsy may Recover After Successful Surgery

Neurosurgery ◽  
2019 ◽  
Vol 86 (3) ◽  
pp. 417-428 ◽  
Author(s):  
Hernán F J González ◽  
Sarah E Goodale ◽  
Monica L Jacobs ◽  
Kevin F Haas ◽  
Bennett A Landman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Epilepsy Surgery ◽  
Verbal Memory ◽  
Severe Disease ◽  
Structural Connectivity ◽  
Pedunculopontine Nucleus ◽  
Brain Network ◽  
Seizure Freedom ◽  
Spearman's Rho ◽  
Spearman’S Rho

Abstract BACKGROUND Focal seizures in temporal lobe epilepsy (TLE) are associated with widespread brain network perturbations and neurocognitive problems. OBJECTIVE To determine whether brainstem connectivity disturbances improve with successful epilepsy surgery, as recent work has demonstrated decreased brainstem connectivity in TLE that is related to disease severity and neurocognitive profile. METHODS We evaluated 15 adult TLE patients before and after (>1 yr; mean, 3.4 yr) surgery, and 15 matched control subjects using magnetic resonance imaging to measure functional and structural connectivity of ascending reticular activating system (ARAS) structures, including cuneiform/subcuneiform nuclei (CSC), pedunculopontine nucleus (PPN), and ventral tegmental area (VTA). RESULTS TLE patients who achieved long-term postoperative seizure freedom (10 of 15) demonstrated increases in functional connectivity between ARAS structures and fronto-parietal-insular neocortex compared to preoperative baseline (P = .01, Kruskal–Wallis), with postoperative connectivity patterns resembling controls’ connectivity. No functional connectivity changes were detected in 5 patients with persistent seizures after surgery (P = .9, Kruskal–Wallis). Among seizure-free postoperative patients, larger increases in CSC, PPN, and VTA functional connectivity were observed in individuals with more frequent seizures before surgery (P < .05 for each, Spearman's rho). Larger postoperative increases in PPN functional connectivity were seen in patients with lower baseline verbal IQ (P = .03, Spearman's rho) or verbal memory (P = .04, Mann–Whitney U). No changes in ARAS structural connectivity were detected after successful surgery. CONCLUSION ARAS functional connectivity disturbances are present in TLE but may recover after successful epilepsy surgery. Larger increases in postoperative connectivity may be seen in individuals with more severe disease at baseline.

scholarly journals A functional MRI study of the relationship between naming treatment outcomes and resting state functional connectivity in post-stroke aphasia

2014 ◽  
Vol 35 (8) ◽  
pp. 3919-3931 ◽  
Author(s):  
Sophia van Hees ◽  
Katie McMahon ◽  
Anthony Angwin ◽  
Greig de Zubicaray ◽  
Stephen Read ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Treatment Outcomes ◽  
Functional Mri ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Post Stroke ◽  
Mri Study ◽  
The Relationship

scholarly journals A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Federico Calesella ◽  
Alberto Testolin ◽  
Michele De Filippo De Grazia ◽  
Marco Zorzi
Keyword(s):  
Feature Extraction ◽  
Functional Connectivity ◽  
Dimensionality Reduction ◽  
Verbal Memory ◽  
Resting State ◽  
Component Analysis ◽  
Model Complexity ◽  
Stroke Patients ◽  
Prediction Ability ◽  
The Impact

AbstractMultivariate prediction of human behavior from resting state data is gaining increasing popularity in the neuroimaging community, with far-reaching translational implications in neurology and psychiatry. However, the high dimensionality of neuroimaging data increases the risk of overfitting, calling for the use of dimensionality reduction methods to build robust predictive models. In this work, we assess the ability of four well-known dimensionality reduction techniques to extract relevant features from resting state functional connectivity matrices of stroke patients, which are then used to build a predictive model of the associated deficits based on cross-validated regularized regression. In particular, we investigated the prediction ability over different neuropsychological scores referring to language, verbal memory, and spatial memory domains. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) were the two best methods at extracting representative features, followed by Dictionary Learning (DL) and Non-Negative Matrix Factorization (NNMF). Consistent with these results, features extracted by PCA and ICA were found to be the best predictors of the neuropsychological scores across all the considered cognitive domains. For each feature extraction method, we also examined the impact of the regularization method, model complexity (in terms of number of features that entered in the model) and quality of the maps that display predictive edges in the resting state networks. We conclude that PCA-based models, especially when combined with L1 (LASSO) regularization, provide optimal balance between prediction accuracy, model complexity, and interpretability.

scholarly journals Effect of corpus callosum agenesis on the language network in children and adolescents

2021 ◽  
Author(s):  
Lisa Bartha-Doering ◽  
Ernst Schwartz ◽  
Kathrin Kollndorfer ◽  
Florian Ph. S. Fischmeister ◽  
Astrid Novak ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Corpus Callosum ◽  
Network Connectivity ◽  
Functional Language ◽  
Healthy Controls ◽  
Language Abilities ◽  
Verbal Abilities ◽  
Left And Right ◽  
Language Network

AbstractThe present study is interested in the role of the corpus callosum in the development of the language network. We, therefore, investigated language abilities and the language network using task-based fMRI in three cases of complete agenesis of the corpus callosum (ACC), three cases of partial ACC and six controls. Although the children with complete ACC revealed impaired functions in specific language domains, no child with partial ACC showed a test score below average. As a group, ACC children performed significantly worse than healthy controls in verbal fluency and naming. Furthermore, whole-brain ROI-to-ROI connectivity analyses revealed reduced intrahemispheric and right intrahemispheric functional connectivity in ACC patients as compared to controls. In addition, stronger functional connectivity between left and right temporal areas was associated with better language abilities in the ACC group. In healthy controls, no association between language abilities and connectivity was found. Our results show that ACC is associated not only with less interhemispheric, but also with less right intrahemispheric language network connectivity in line with reduced verbal abilities. The present study, thus, supports the excitatory role of the corpus callosum in functional language network connectivity and language abilities.

scholarly journals Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain

2021 ◽  
Author(s):  
Nestor Timonidis ◽  
Alberto Llera ◽  
Paul H. E. Tiesinga
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Structural Connectivity ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Synaptic Function ◽  
Reticular Nucleus ◽  
Sources Of Information ◽  
Independent Components ◽  
Brain Connectome

AbstractFinding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.

scholarly journals M146. NEUROCHEMICAL MODULATION OF AUDITORY CORTEX FUNCTIONAL CONNECTIVITY IN PATIENTS WITH AUDITORY VERBAL HALLUCINATIONS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S191-S191
Author(s):  
Sarah Weber ◽  
Helene Hjelmervik ◽  
Alexander R Craven ◽  
Erik Johnsen ◽  
Rune Kroken ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Temporal Lobe ◽  
Superior Temporal Gyrus ◽  
Underlying Mechanism ◽  
Auditory Hallucinations ◽  
Anterior Cingulate ◽  
Gamma Aminobutyric Acid ◽  
Auditory Verbal Hallucinations ◽  
Left And Right ◽  
Neurochemical Correlates

Abstract Background Auditory hallucinations have been linked to aberrant functioning of the left superior temporal gyrus (STG) and are associated with impaired cognitive control regulated by areas in the prefrontal cortex. However, the mechanisms behind these dysfunctions are still unclear. Methods The current study combined resting state connectivity fMRI with MR spectroscopy (MRS) in a sample of 81 psychosis patients to explore how neurochemical correlates of auditory hallucinations modulate left STG functioning. The analyses were focused on glutamate (Glu) and gamma-aminobutyric acid (GABA), two neurotransmitters with excitatory and inhibitory functions, respectively, since these have previously been implicated in psychosis. Results Glu and GABA showed differential relationships with left STG connectivity in patients with and without hallucinations. Specifically, Glu concentration in the anterior cingulate cortex (ACC) was positively related to functional connectivity between the left and right temporal lobe in hallucinating patients only. In contrast, GABA concentration in the ACC was negatively related to connectivity between the left and right temporal lobe in non-hallucinating patients only. Discussion These findings support a recently proposed model of interhemispheric temporal lobe miscommunication in auditory hallucinations and indicate prefrontal neurochemical modulation as a potential underlying mechanism. The results can further be integrated with previously suggested excitatory/inhibitory imbalances as neurochemical modulators in AVH.

P1-057: Lithium Carbonate and Verbal Memory Improvement in Post-Stroke Patients: Preliminary Report

2016 ◽  
Vol 12 ◽  
pp. P423-P424
Author(s):  
Yue Ran Sun ◽  
Nathan Herrmann ◽  
Nadia Reider ◽  
Sandra E. Black ◽  
Alexander Kiss ◽  
...  
Keyword(s):  
Verbal Memory ◽  
Preliminary Report ◽  
Lithium Carbonate ◽  
Stroke Patients ◽  
Post Stroke ◽  
Memory Improvement
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