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 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.

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 White Matter Integrity Predicts Delay Discounting Behavior in 9- to 23-Year-Olds: A Diffusion Tensor Imaging Study

2009 ◽  
Vol 21 (7) ◽  
pp. 1406-1421 ◽  
Author(s):  
Elizabeth A. Olson ◽  
Paul F. Collins ◽  
Catalina J. Hooper ◽  
Ryan Muetzel ◽  
Kelvin O. Lim ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Delay Discounting ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Imaging Study ◽  
Temporal Lobes ◽  
Brain White Matter ◽  
Independent Effects ◽  
Age Range

Healthy participants (n = 79), ages 9–23, completed a delay discounting task assessing the extent to which the value of a monetary reward declines as the delay to its receipt increases. Diffusion tensor imaging (DTI) was used to evaluate how individual differences in delay discounting relate to variation in fractional anisotropy (FA) and mean diffusivity (MD) within whole-brain white matter using voxel-based regressions. Given that rapid prefrontal lobe development is occurring during this age range and that functional imaging studies have implicated the prefrontal cortex in discounting behavior, we hypothesized that differences in FA and MD would be associated with alterations in the discounting rate. The analyses revealed a number of clusters where less impulsive performance on the delay discounting task was associated with higher FA and lower MD. The clusters were located primarily in bilateral frontal and temporal lobes and were localized within white matter tracts, including portions of the inferior and superior longitudinal fasciculi, anterior thalamic radiation, uncinate fasciculus, inferior fronto-occipital fasciculus, corticospinal tract, and splenium of the corpus callosum. FA increased and MD decreased with age in the majority of these regions. Some, but not all, of the discounting/DTI associations remained significant after controlling for age. Findings are discussed in terms of both developmental and age-independent effects of white matter organization on discounting behavior.

Human Brain Diffusion Tensor Imaging Visualization With Virtual Reality

2010 ◽  
Author(s):  
Bin Chen ◽  
John Moreland
Keyword(s):  
Virtual Reality ◽  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Biological Tissues ◽  
Head Tracking ◽  
Fiber Tractography ◽  
Brain White Matter ◽  
Diffusion Tensors ◽  
Visualization Techniques

Magnetic resonance diffusion tensor imaging (DTI) is sensitive to the anisotropic diffusion of water exerted by its macromolecular environment and has been shown useful in characterizing structures of ordered tissues such as the brain white matter, myocardium, and cartilage. The water diffusivity inside of biological tissues is characterized by the diffusion tensor, a rank-2 symmetrical 3×3 matrix, which consists of six independent variables. The diffusion tensor contains much information of diffusion anisotropy. However, it is difficult to perceive the characteristics of diffusion tensors by looking at the tensor elements even with the aid of traditional three dimensional visualization techniques. There is a need to fully explore the important characteristics of diffusion tensors in a straightforward and quantitative way. In this study, a virtual reality (VR) based MR DTI visualization with high resolution anatomical image segmentation and registration, ROI definition and neuronal white matter fiber tractography visualization and fMRI activation map integration is proposed. The VR application will utilize brain image visualization techniques including surface, volume, streamline and streamtube rendering, and use head tracking and wand for navigation and interaction, the application will allow the user to switch between different modalities and visualization techniques, as well making point and choose queries. The main purpose of the application is for basic research and clinical applications with quantitative and accurate measurements to depict the diffusivity or the degree of anisotropy derived from the diffusion tensor.

scholarly journals Acute ex vivo changes in brain white matter diffusion tensor metrics

PLoS ONE ◽  
2019 ◽  
Vol 14 (9) ◽  
pp. e0223211
Author(s):  
Matthew R. Walker ◽  
Jidan Zhong ◽  
Adam C. Waspe ◽  
Thomas Looi ◽  
Karolina Piorkowska ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Tensor ◽  
Ex Vivo ◽  
Brain White Matter

scholarly journals Cerebral Mitochondrial Function and Cognitive Performance during Aging: A Longitudinal Study in NMRI Mice

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Martina Reutzel ◽  
Rekha Grewal ◽  
Benjamin Dilberger ◽  
Carmina Silaidos ◽  
Aljoscha Joppe ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Cognitive Performance ◽  
Mitochondrial Biogenesis ◽  
Mitochondrial Function ◽  
Aging Process ◽  
Brain Aging ◽  
Model Organisms ◽  
Nmri Mice ◽  
Aged Mice ◽  
The Brain

Brain aging is one of the major risk factors for the development of several neurodegenerative diseases. Therefore, mitochondrial dysfunction plays an important role in processes of both, brain aging and neurodegeneration. Aged mice including NMRI mice are established model organisms to study physiological and molecular mechanisms of brain aging. However, longitudinal data evaluated in one cohort are rare but are important to understand the aging process of the brain throughout life, especially since pathological changes early in life might pave the way to neurodegeneration in advanced age. To assess the longitudinal course of brain aging, we used a cohort of female NMRI mice and measured brain mitochondrial function, cognitive performance, and molecular markers every 6 months until mice reached the age of 24 months. Furthermore, we measured citrate synthase activity and respiration of isolated brain mitochondria. Mice at the age of three months served as young controls. At six months of age, mitochondria-related genes (complex IV, creb-1, β-AMPK, and Tfam) were significantly elevated. Brain ATP levels were significantly reduced at an age of 18 months while mitochondria respiration was already reduced in middle-aged mice which is in accordance with the monitored impairments in cognitive tests. mRNA expression of genes involved in mitochondrial biogenesis (cAMP response element-binding protein 1 (creb-1), peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1-α), nuclear respiratory factor-1 (Nrf-1), mitochondrial transcription factor A (Tfam), growth-associated protein 43 (GAP43), and synaptophysin 1 (SYP1)) and the antioxidative defense system (catalase (Cat) and superoxide dismutase 2 (SOD2)) was measured and showed significantly decreased expression patterns in the brain starting at an age of 18 months. BDNF expression reached, a maximum after 6 months. On the basis of longitudinal data, our results demonstrate a close connection between the age-related decline of cognitive performance, energy metabolism, and mitochondrial biogenesis during the physiological brain aging process.

scholarly journals Aberrant White Matter Networks Mediate Cognitive Impairment in Patients with Silent Lacunar Infarcts in Basal Ganglia Territory

2015 ◽  
Vol 35 (9) ◽  
pp. 1426-1434 ◽  
Author(s):  
Jinfu Tang ◽  
Suyu Zhong ◽  
Yaojing Chen ◽  
Kewei Chen ◽  
Junying Zhang ◽  
...  
Keyword(s):  
White Matter ◽  
Basal Ganglia ◽  
Cognitive Deficits ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Lacunar Infarcts ◽  
Healthy Elderly ◽  
Brain White Matter ◽  
Diffusion Tensor Imaging Tractography

Silent lacunar infarcts, which are present in over 20% of healthy elderly individuals, are associated with subtle deficits in cognitive functions. However, it remains largely unclear how these silent brain infarcts lead to cognitive deficits and even dementia. Here, we used diffusion tensor imaging tractography and graph theory to examine the topological organization of white matter networks in 27 patients with silent lacunar infarcts in the basal ganglia territory and 30 healthy controls. A whole-brain white matter network was constructed for each subject, where the graph nodes represented brain regions and the edges represented interregional white matter tracts. Compared with the controls, the patients exhibited a significant reduction in local efficiency and global efficiency. In addition, a total of eighteen brain regions showed significantly reduced nodal efficiency in patients. Intriguingly, nodal efficiency–behavior associations were significantly different between the two groups. The present findings provide new aspects into our understanding of silent infarcts that even small lesions in subcortical brain regions may affect large-scale cortical white matter network, as such may be the link between subcortical silent infarcts and the associated cognitive impairments. Our findings highlight the need for network-level neuroimaging assessment and more medical care for individuals with silent subcortical infarcts.

Anatomy-Based Visualizations of Diffusion Tensor Images of Brain White Matter

2006 ◽  
pp. 155-163 ◽  
Author(s):  
James C. Gee ◽  
Hui Zhang ◽  
Abraham Dubb ◽  
Brian B. Avants ◽  
Paul A. Yushkevich ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Tensor ◽  
Brain White Matter ◽  
Diffusion Tensor Images
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