scholarly journals Mapping the human praxis network: an investigation of white matter disconnection in limb apraxia

2020 ◽  
Author(s):  
Hannah Rosenzopf ◽  
Daniel Wiesen ◽  
Alexandra Basilakos ◽  
Grigori Yourganov ◽  
Leonardo Bonilha ◽  
...  
Keyword(s):  
White Matter ◽  
Left Hemisphere ◽  
Structural Damage ◽  
Parietal Lobe ◽  
Diffusion Tensor ◽  
Brain Network ◽  
Support Vector ◽  
Precentral Gyrus ◽  
Support Vector Regression Model ◽  
Limb Apraxia

AbstractStroke to the left hemisphere of the brain can cause limb apraxia, a disorder characterised by deficits of higher order motor skills such as the failure to accurately produce meaningful gestures. This disorder provides unique insights into the anatomy of the human praxis system. The present study aimed to identify the structural brain network, that when damaged by stroke, causes limb apraxia. We assessed the ability to perform meaningful gestures with the hand in 101 patients with chronic left hemisphere stroke. Structural damage to white matter fibres was assessed by diffusion tensor imaging. A support vector regression model predicting apraxia based on individual topographies of tract-based fractional anisotropy was utilised to obtain multivariate topographical inference. We found pathological white matter alterations in a densely connected fronto-temporo-parietal network of short and long association fibres to predict limb apraxia deficits. Major disconnection affected temporo-parietal and temporo-temporal connections. Grey matter areas with a high number of disconnections included inferior parietal lobe, middle and superior temporal gyrus, inferior and middle frontal lobe, precentral gyrus, putamen, and caudate nucleus. These results demonstrate the relevance of frontal and inferior parietal regions in praxis, but they also highlight the temporal lobe and its connections to be an important contributor to the human praxis network.

scholarly journals An Augmented Aging Process in Brain White Matter in HIV

2018 ◽  
Author(s):  
T. Kuhn ◽  
T. Kaufmann ◽  
N.T. Doan ◽  
L.T. Westlye ◽  
J. Jones ◽  
...  
Keyword(s):  
White Matter ◽  
Hiv Infection ◽  
Cognitive Performance ◽  
Aging Process ◽  
Brain Aging ◽  
Diffusion Tensor ◽  
Adult Life ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Brain White Matter

AbstractObjectiveHIV infection and aging are both associated with neurodegeneration. However, whether the aging process alone or other factors associated with advanced age account for the progression of neurodegeneration in the aging HIV-positive (HIV+) population remains unclear.MethodsHIV+ (n=70) and HIV-negative (HIV-, n=34) participants underwent diffusion tensor imaging (DTI) and metrics of microstructural properties were extracted from regions of interest (ROIs). A support vector regression model was trained on two independent datasets of healthy adults across the adult life-span (n=765, Cam-CAN = 588; UiO = 177) to predict participant age from DTI metrics, and applied to the HIV dataset. Predicted brain age gap (BAG) was computed as the difference between predicted age and chronological age, and statistically compared between HIV groups. Regressions assessed the relationship between BAG and HIV severity/medical comorbidities. Finally, correlation analyses tested for associations between BAG and cognitive performance.ResultsBAG was significantly higher in the HIV+ group than the HIV-group F (1, 103) = 12.408, p = 0.001). HIV RNA viral load was significantly associated with BAG, particularly in older HIV+ individuals (R2 = 0.29, F(7, 70) = 2.66, p = 0.021). Further, BAG was negatively correlated with domain-level cognitive function (learning: r = −0.26, p = 0.008; memory: r = −0.21, p = 0.034).ConclusionsHIV infection is associated with augmented white matter aging, and greater brain aging is associated with worse cognitive performance in multiple domains.

scholarly journals White matter in prolonged glucocorticoid response to psychological stress in schizophrenia

2021 ◽  
Author(s):  
Eric L. Goldwaser ◽  
Joshua Chiappelli ◽  
Mark D. Kvarta ◽  
Xiaoming Du ◽  
Zachary B. Millman ◽  
...  
Keyword(s):  
White Matter ◽  
Psychological Stress ◽  
Stress Responses ◽  
Diffusion Tensor ◽  
Brain Network ◽  
White Matter Tracts ◽  
Cortisol Reactivity ◽  
Cortisol Responses ◽  
Psychological Stressor ◽  
Post Stress

AbstractStress is implicated in psychosis etiology and exacerbation, but pathogenesis toward brain network alterations in schizophrenia remain unclear. White matter connects limbic and prefrontal regions responsible for stress response regulation, and white matter tissues are also vulnerable to glucocorticoid aberrancies. Using a novel psychological stressor task, we studied cortisol stress responses over time and white matter microstructural deficits in schizophrenia spectrum disorder (SSD). Cortisol was measured at baseline, 0-, 20-, and 40-min after distress induction by a psychological stressor task in 121 SSD patients and 117 healthy controls (HC). White matter microstructural integrity was measured by 64-direction diffusion tensor imaging. Fractional anisotropy (FA) in white matter tracts were related to cortisol responses and then compared to general patterns of white matter tract deficits in SSD identified by mega-analysis. Differences between 40-min post-stress and baseline, but not acute reactivity post-stress, was significantly elevated in SSD vs HC, time × diagnosis interaction F2.3,499.9 = 4.1, p = 0.013. All SSD white matter tracts were negatively associated with prolonged cortisol reactivity but all tracts were positively associated with prolonged cortisol reactivity in HC. Individual tracts most strongly associated with prolonged cortisol reactivity were also most impacted in schizophrenia in general as established by the largest schizophrenia white matter study (r = −0.56, p = 0.006). Challenged with psychological stress, SSD and HC mount similar cortisol responses, and impairments arise in the resolution timeframe. Prolonged cortisol elevations are associated with the white matter deficits in SSD, in a pattern previously associated with schizophrenia in general.

scholarly journals MRI substrates of sustained attention system and cognitive impairment in pediatric MS patients

Neurology ◽  
2017 ◽  
Vol 89 (12) ◽  
pp. 1265-1273 ◽  
Author(s):  
Ermelinda De Meo ◽  
Lucia Moiola ◽  
Angelo Ghezzi ◽  
Pierangelo Veggiotti ◽  
Ruggero Capra ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
White Matter ◽  
Sustained Attention ◽  
Structural Damage ◽  
Performance Test ◽  
Diffusion Tensor ◽  
Group Analysis ◽  
Anterior Cingulate ◽  
Attention Network ◽  
Attention System

Objective:To explore the structural and functional integrity of the sustained attention system in patients with pediatric multiple sclerosis (MS) and its effect on cognitive impairment.Methods:We enrolled 57 patients with pediatric MS and 14 age- and sex-matched healthy controls (HCs). Patients with >3 abnormal tests at neuropsychological evaluation were classified as cognitively impaired (CI). Sustained attention system activity was studied with fMRI during the Conners Continuous Performance Test (CCPT). Structural integrity of attention network connections was quantified with diffusion tensor (DT) MRI.Results:Within-group analysis showed similar patterns of recruitment of the attention network in HCs and patients with pediatric MS. Diffuse network DT MRI structural abnormalities were found in patients with MS. During CCPT, with increasing task demand, patients with pediatric MS showed increased activation of the left thalamus, anterior insula, and anterior cingulate cortex (ACC) and decreased recruitment of the right precuneus compared to HCs. Thirteen patients (23%) were classified as CI. Compared to cognitively preserved patients, CI patients with pediatric MS had decreased recruitment of several areas located mainly in parietal and occipital lobes and cerebellum and increased deactivation of the ACC, combined with more severe structural damage of white matter tracts connecting these regions.Conclusions:Our results suggest that the age-expected level of sustained attention system functional competence is achieved in patients with pediatric MS. Inefficient regulation of the functional interaction between different areas of this system, due to abnormal white matter integrity, may result in global cognitive impairment in these patients.

scholarly journals Abnormal Functional and Structural networks in Rectal Cancer with Depressive symptoms: A Graph Theory Analysis

2019 ◽  
Author(s):  
Wenwen Zhang ◽  
Ying Zou ◽  
Yuan Li ◽  
Yu Fu ◽  
Jie Shi ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Emotional Disorders ◽  
Parietal Lobe ◽  
Diffusion Tensor ◽  
Brain Network ◽  
Imaging Data ◽  
Network Efficiency ◽  
Limbic Lobe ◽  
Random Node ◽  
Brain Network Reorganization

Abstract Background: Surgery and chemotherapy can cause emotional disorders in patients with rectal cancer (RC). However, few comprehensive studies are conducted on RC patients associated alterations in the topological organization of structural and functional networks. Methods: Resting-state functional MRI and Diffusion tensor imaging data were collected from 36 RC patients with surgery and chemotherapy and 32 healthy controls (HC). Functional network (FN) was constructed from extracting average time courses for 246 regions of interest (ROI) and structural network (SN) was established by deterministic tractography. Graph theoretical analysis was used to calculate small-worldness property, clustering coefficients, shortest path length and network efficiency. Additionally, we assess network resilient on FN and SN. Results: Abnormal small-worldness property of FN and SN were found in RC patients. The FN and SN exhibited increased local efficiency and global efficiency respectively in RC patients.The increased nodal efficiency in RC patients were mainly found in the frontal lobe, parietal lobe and limbic lobe for FN and SN, while the decreased nodal efficiency were distributed in subcortical nuclei, parietal lobe and limbic lobe only for SN. In network resilient analysis, the RC patients showed less resilient to targeted or random node deletion in both networks compared with HC. Moreover, FN is more robust than SN for all participants. Conclusions: This study revealed that topological organizations of the FN and SN may be disrupted in RC patients. Brain network reorganization is a compensation mechanism for brain impairment after surgery and chemotherapy.

Structural integrity of white matter tracts as a predictor of acute ischemic stroke outcome

2020 ◽  
Vol 15 (9) ◽  
pp. 965-972
Author(s):  
Deepthi Rajashekar ◽  
Pauline Mouchès ◽  
Jens Fiehler ◽  
Bijoy K Menon ◽  
Mayank Goyal ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Acute Ischemic Stroke ◽  
Support Vector Regression ◽  
Support Vector ◽  
Stroke Severity ◽  
Lesion Volume ◽  
White Matter Tract ◽  
Support Vector Regression Model ◽  
White Matter Tracts

Background and purpose Clinical assessment scores in acute ischemic stroke are only moderately correlated with lesion volume since lesion location is an important confounding factor. Many studies have investigated gray matter indicators of stroke severity, but the understanding of white matter tract involvement is limited in the early phase after stroke. This study aimed to measure and model the involvement of white matter tracts with respect to 24-h post-stroke National Institutes of Health Stroke Scale (NIHSS). Material and methods A total of 96 patients (50 females, mean age 66.4 ± 14.0 years, median NIHSS 5, interquartile range: 2–9.5) with follow-up fluid-attenuated inversion recovery magnetic resonance imaging data sets acquired one to seven days after acute ischemic stroke onset due to proximal anterior circulation occlusion were included. Lesions were semi-automatically segmented and non-linearly registered to a common reference atlas. The lesion overlap and tract integrity were determined for each white matter tract in the AALCAT atlas and used to model NIHSS outcomes using a supervised linear-kernel support vector regression method, which was evaluated using leave-one-patient-out cross validation. Results The support vector regression model using the tract integrity and tract lesion overlap measurements predicted the 24-h NIHSS score with a high correlation value of r = 0.7. Using the tract overlap and tract integrity feature improved the modeling accuracy of NIHSS significantly by 6% (p < 0.05) compared to using overlap measures only. Conclusion White matter tract integrity and lesion load are important predictors for clinical outcome after an acute ischemic stroke as measured by the NIHSS and should be integrated for predictive modeling.

scholarly journals The network underlying human higher-order motor control: Insights from machine learning-based lesion-behaviour mapping in apraxia

2019 ◽  
Author(s):  
Christoph Sperber ◽  
Daniel Wiesen ◽  
Georg Goldenberg ◽  
Hans-Otto Karnath
Keyword(s):  
Motor Control ◽  
Temporal Cortex ◽  
Neural Correlates ◽  
Brain Network ◽  
Higher Order ◽  
Support Vector ◽  
Precentral Gyrus ◽  
Canonical Correlations ◽  
Neurological Patients ◽  
Left Hemisphere Stroke

AbstractNeurological patients with apraxia of pantomime provide us with a unique opportunity to study the neural correlates of higher-order motor function. Previous studies using lesion-behaviour mapping methods led to inconsistent anatomical results, reporting various lesion locations to induce this symptom. We hypothesised that the inconsistencies might arise from limitations of mass-univariate lesion-behaviour mapping approaches if our ability to pantomime the use of objects is organised in a brain network. Thus, we investigated apraxia of pantomime by using multivariate lesion behaviour mapping based both on support vector regression and sparse canonical correlations in a sample of 130 left-hemisphere stroke patients. Both multivariate methods identified multiple areas to underlie high-order motor control, including inferior parietal lobule, precentral gyrus, posterior parts of middle temporal cortex, and insula. Further, long association fibres were affected, such as the superior longitudinal fascicle, inferior occipito-frontal fascicle, uncinated fascicle, and superior occipito-frontal fascicle. The findings thus not only underline the benefits of multivariate lesion-behaviour mapping in brain networks, but they also uncovered that higher-order motor control indeed is based on a common anatomical network.

scholarly journals Machine Learning for Predicting Individual Severity of Blepharospasm Using Diffusion Tensor Imaging

2021 ◽  
Vol 15 ◽  
Author(s):  
Gang Liu ◽  
Yanan Gao ◽  
Ying Liu ◽  
Yaomin Guo ◽  
Zhicong Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Disease Severity ◽  
Rating Scale ◽  
Diffusion Tensor ◽  
Support Vector ◽  
Learning Scheme ◽  
The Individual ◽  
Jankovic Rating Scale

Accumulating diffusion tensor imaging (DTI) evidence suggests that white matter abnormalities evaluated by local diffusion homogeneity (LDH) or fractional anisotropy (FA) occur in patients with blepharospasm (BSP), both of which are significantly correlated with disease severity. However, whether the individual severity of BSP can be identified using these DTI metrics remains unknown. We aimed to investigate whether a combination of machine learning techniques and LDH or FA can accurately identify the individual severity of BSP. Forty-one patients with BSP were assessed using the Jankovic Rating Scale and DTI. The patients were assigned to non-functionally and functionally limited groups according to their Jankovic Rating Scale scores. A machine learning scheme consisting of beam search and support vector machines was designed to identify non-functionally versus functionally limited outcomes, with the input features being LDH or FA in 68 white matter regions. The proposed machine learning scheme with LDH or FA yielded an overall accuracy of 88.67 versus 85.19% in identifying non-functionally limited versus functionally limited outcomes. The scheme also identified a sensitivity of 91.40 versus 85.87% in correctly identifying functionally limited outcomes, a specificity of 83.33 versus 83.67% in accurately identifying non-functionally limited outcomes, and an area under the curve of 93.7 versus 91.3%. These findings suggest that a combination of LDH or FA measurements and a sophisticated machine learning scheme can accurately and reliably identify the individual disease severity in patients with BSP.

Application of Diffusion Tensor Imaging in Modeling Diffuse Axonal Injury

2009 ◽  
Author(s):  
Rika M. Wright ◽  
K. T. Ramesh
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Fiber Orientation ◽  
Structural Damage ◽  
Diffusion Tensor ◽  
Axonal Injury ◽  
Diffuse Axonal Injury ◽  
Material Model ◽  
Orientation Data ◽  
The Brain

Traumatic brain injury (TBI) is a debilitating injury that has received a lot of attention within the past few years partly as a result of the increased number of TBI incidents arising from military conflicts. Of the incidences of TBI, diffuse axonal injury (DAI) accounts for the second largest percentage of deaths [1]. DAI is caused by sudden inertial loads to the head, and it is characterized by damage to neural cells [2]. These inertial loads at the macroscale result in functional and structural damage at the cellular level. To understand the coupling between the mechanical forces and the functional damage of neurons, an analytical model that accurately represents the mechanics of brain deformation under inertial loads must be developed. It has been shown in clinical and experimental studies that the deep white matter of the brain is highly susceptible to injury [2]. Unlike the gray matter of the brain, the white matter structures contain an organized arrangement of neural axons and therefore can be considered anisotropic (Figure 1). To account for the anisotropic nature of the white matter in finite element simulations, the orientation of the neural axons must be incorporated into a material model for brain tissue. In this study, the use of diffusion tensor imaging (DTI) as a tool to provide fiber orientation information to continuum models is investigated. By incorporating fiber orientation data into a material model for white matter, the strains experienced by neural axons in the white matter tracts of the brain are computed, and this strain is related to cellular stretch thresholds of diffuse axonal injury.

Fiber Dissection and Diffusion Tensor Imaging Tractography Study of the Temporoparietal Fiber Intersection Area

2012 ◽  
Vol 72 (1) ◽  
pp. ons87-ons98 ◽  
Author(s):  
Juan Martino ◽  
Rousinelle da Silva-Freitas ◽  
Hugo Caballero ◽  
Enrique Marco de Lucas ◽  
Juan A. García-Porrero ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Parietal Lobe ◽  
Diffusion Tensor ◽  
Angular Gyrus ◽  
Posterior Portion ◽  
Parietal Lobes ◽  
Intersection Area ◽  
Horizontal Portion ◽  
Diffusion Tensor Imaging Tractography

Abstract Background: Lesion studies and recent surgical series report important sequelae when the inferior parietal lobe and posterior temporal lobe are damaged. Millions of axons cross through the white matter underlying these cortical areas; however, little is known about the complex organization of these connections. Objective: To analyze the subcortical anatomy of a specific region within the parietal and temporal lobes where 7 long-distances tracts intersect, ie, the temporoparietal fiber intersection area (TPFIA). Methods: Four postmortem human hemispheres were dissected, and 4 healthy hemispheres were analyzed through the use of diffusion tensor imaging-based tractography software. The different tracts that intersect at the posterior temporal and parietal lobes were isolated, and the relations with the surrounding structures were analyzed. Results: Seven tracts pass through the TPFIA: horizontal portion of the superior longitudinal fasciculus, arcuate fasciculus, middle longitudinal fasciculus, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, optic radiations, and tapetum. The TPFIA was located deep to the angular gyrus, posterior portion of the supramarginal gyrus, and posterior portion of the superior, middle, and inferior temporal gyri. Conclusion: The TPFIA is a critical neural crossroad; it is traversed by 7 white matter tracts that connect multiple areas of the ipsilateral and contralateral hemisphere. It is also a vulnerable part of the network in that a lesion within this area will produce multiple disconnections. This is valuable information when a surgical approach through the parieto-temporo-occipital junction is planned. To decrease surgical risks, a detailed diffusion tensor imaging tractography reconstruction of the TPFIA should be performed, and intraoperative electric stimulation should be strongly considered.

scholarly journals Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

2016 ◽  
Author(s):  
David M Schnyer ◽  
Peter C. Clasen ◽  
Christopher Gonzalez ◽  
Christopher G Beevers
Keyword(s):  
Major Depressive Disorder ◽  
White Matter ◽  
Depressive Disorder ◽  
Right Hemisphere ◽  
Diffusion Tensor ◽  
Support Vector ◽  
Healthy Controls ◽  
Major Depressive ◽  
Predictive Information ◽  
Brain White Matter

AbstractUsing MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n = 25) and healthy controls (n = 25), SVM learning accurately (70%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.

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