Headache and Migraine Miniatlas


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Although depression is a common symptom across schizophrenia, affective disorders, and mTBI, direct comparison between findings from the study by Rockstroh and colleagues and the present study is difficult due to the following two factors: 1 these are three different brain disorders; 2 all subjects with schizophrenia and affective disorder in the study by Rockstroh and colleagues were inpatients treated by a variety of medications including the neuroleptics, whereas all of our mTBI outpatients were free of sedative, neuroleptic, and hypnotic medications see exclusion criteria.

Future studies in which the effects of medications are controlled will be needed to address the correlation between abnormal slow-wave generation and common symptomology such as depression across different disorders. We speculate that as a common cause in the former group, blast may contribute to our findings. However, future study is needed to confirm or disprove this hypothesis. The present study also revealed the diffuse nature of the neuronal injuries in TBI patients Fig.

Such findings are consistent with the mechanism of diffuse axonal injury in TBI due to a combination of linear and rotational acceleration and deceleration Adams et al. The results are also consistent with our previous findings that abnormal MEG slow-waves are generated from cortical gray-matter areas that connect to white-matter fibers with reduced DTI fractional anisotropy due to axonal injury in patients with mTBI Huang et al.

Although the location of slow-wave generation is highly variable across mTBI patients see Fig. Multiple regions in the frontal lobes i. In addition, the posterior parietal lobe, inferior temporal lobes, hippocampus, and cerebella also have a relatively higher likelihood for generating abnormal MEG slow-waves than other brain areas, indicating that these regions are also particularly vulnerable to head trauma. A forthcoming study that correlates the MEG slow-wave with cognitive functions in mTBI will examine the connection of slow-wave generation and abnormal brain function Robb et al.

Neurophysiological studies in animals have shown that cortical deafferentation caused by axonal lesions in WM is an important factor in pathological delta-wave production in GW Ball et al. We believe that the cortical deafferentation caused by axonal injury is the main mechanism for abnormal MEG slow-wave generation in mTBI. However, pathological delta-wave production can also be induced by deafferentation following the administration of atropine in WM in animals Schaul et al. So the electrophysiological similarity of lesion-induced and atropine-induced slow waves raises the possibility that a defect in cholinergic pathways plays a role in pathological slow-wave generation Schaul, In the human brain, the projections of cholinergic pathways highly overlap with the WM fiber tracts Selden et al.

Abnormal slow waves are not the only abnormal findings in TBI. A recent MEG study in a group with mixing mild, moderate, and severe TBI patients showed reduced functional connectivity primarily in bilateral frontal and left greater than right parieto-temporo-occipital regions as well as the right thalamus Tarapore et al. In a future study, we will examine the relationships between MEG slow-wave generation and functional connectivity in different frequency bands in mTBI.

The spatial-sensitivity of the voxel-based approach is more uniformly distributed across the brain volume whereas the sizes of 96 cortical ROIs in previous ROI-based approach varied substantially from one ROI to another.

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Second, as shown in Fig. In mTBI patients, it was common that multiple regions generated abnormal slow-waves. Generators of abnormal slow-waves in mTBI patients can be in one or more of the above spatial-and-temporal profiles Huang et al. In the present study we have shown that the spatial smoothing factor in MEG source imaging plays an important role in the positive detection rate of abnormal slow waves.

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This finding suggests that the abnormal MEG slow-wave generation may be more of a local effect, and MEG source analysis methods with high spatial resolution may be essential in detecting abnormal slow waves in mTBI. In the present study, a MEG source imaging method with high spatial resolution i. Differences in positive detection rates may be due to the smearing effect of the skull tissue, which with its poor conductivity substantially distorted the electric fields and reduced the spatial resolution of the EEG signal during scalp recording; whereas, head tissues are essentially transparent to MEG signals.

It was shown that Fast-VESTAL can: 1 provide high resolution source images for multiple correlated sources; 2 faithfully recover source time-courses; 3 perform robustly in poor SNR conditions; 4 handle correlated brain noise; and 5 effectively create resting-state MEG source images that are highly consistent with known neurophysiology findings Huang et al.

This is likely due to beamformer's intrinsic limitation which assumes that the neuronal sources are uncorrelated Robinson and Vrba, , Sekihara et al. Wienbruch introduced a different voxel-based resting-state MEG source analysis approach, in which a sequential single dipole model was used to fit MEG signal for each time point i. Then, voxel-based dipole location density measure was used to establish a normative database, and a Z-score statistics was used to assess abnormalities. First, the approach by Wienbruch is less able to handle time points where multiple sources contribute simultaneously to the MEG measures.

For example, in many such cases, the GoF with a single sequential dipole model would be less than the 0. Second, the dipole location density measure from Wienbruch's approach does not directly take into consideration of the strength differences in the sequential dipoles. For example, two dipoles with different strengths e. In contrast, Fast-VESTAL directly assesses the source magnitude differences at all grid locations, which is also a key feature that differentiates the MEG signals from one subject to another. In the dipole-fitting approach, the basic assumption is that the neuronal generators of MEG signals are focal and can be modeled by one or a few dipoles.

The dipole location and dipole moment parameters are determined by an over-determined non-linear optimization procedure. However, all dipole modeling techniques require the number of dipoles to be pre-estimated, and the non-linear optimization procedure becomes extremely high in computational cost and may be trapped into local minima when the number of dipoles increases. Usually, 8—10 dipoles are the upper limit that the dipole-fitting methods can handle Huang et al. In the Fast-VESTAL approach, the brain volume, or just the cortex is pre-divided into a source grid with several thousand nodes, and a dipole is assigned to each grid node.

Fast-VESTAL identifies the grid nodes with neuronal activity with high resolution, and suppresses the magnitude at the grid nodes without neuronal activity to essentially zero Huang et al. In the MEG responses that are known to contain a few focal neuronal generators e.


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The results show that this MEG slow-wave source imaging method achieves a positive detection rate of The results showed that although abnormal MEG slow-wave generations in individual mTBI patients were highly variable in space with a diffuse characteristic, the prefrontal lobe, posterior parietal lobe, inferior temporal lobe, hippocampus, and cerebella were particularly vulnerable to head trauma. In addition, we found that a high spatial smoothing factor can reduce the positive detection rate of abnormal MEG slow-waves in mTBI, which suggests that MEG source analysis methods with high spatial resolution may be essential for mTBI study.

This study provides support for using MEG slow-wave source imaging to localize affected areas and highlights the potential use of this methodology for the clinical diagnosis of mTBI. Huang and R. Ghajar, site PIs: R. Lee and M. Baker, M. Geyer, M. Huang, V. We also thank the Marine and veteran volunteers for military service and participation in this study.


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National Center for Biotechnology Information , U. Journal List Neuroimage Clin v.

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Neuroimage Clin. Published online Jun Theilmann , b Mithun Diwakar , b Victoria B. Risbrough , a, d, e Zhengwei Ji , b Charles W.

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Huang , i Douglas G. Chang , j Deborah L. Harrington , a, b Laura Muzzatti , b Jose M. Canive , k, l J. Lee a, b. Dewleen G. Kate A. Rebecca J. Victoria B.

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Charles W. Douglas G. Deborah L. Jose M.

Christopher Edgar. Roland R. Author information Article notes Copyright and License information Disclaimer. Ming-Xiong Huang: ude. Copyright notice. This article has been cited by other articles in PMC. Abstract Traumatic brain injury TBI is a leading cause of sustained impairment in military and civilian populations. Introduction Traumatic brain injury TBI is a leading cause of sustained physical, cognitive, emotional, and behavioral deficits in the civilian population due to motor vehicle accidents, sports, falls, and assaults and military personnel with blast injury as an additional cause.


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Methods and materials 2. Assessing the spatial distribution of abnormal MEG slow-wave generation to identify the brain areas that are vulnerable to mTBI In addition to the single-subject-based analysis, we also performed an analysis to identify common brain areas that were likely to generate abnormal MEG slow-waves in mTBI. Assessing the effect of different spatial smoothing factors The spatial smoothing with a Gaussian smoothing kernel may also play an important role to the positive detection rates of abnormal MEG slow-wave source imaging. Results 3.

Headache and Migraine Miniatlas Headache and Migraine Miniatlas
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