Glymphatic System Function in Patients with Transient Global Amnesia

Lee, Dong Ah; Park, Bong Soo; Park, Si Hyung; Lee, Yoo Jin; Ko, Junghae; Park, Kang Min

doi:10.31083/j.jin2104117

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…This is a common finding in healthy volunteers and in neurodegenerative diseases such as Parkinson's and Alzheimer's disease. However, in cases of brain tumors [9][10][11], REM sleep disorder [46], neuromyelitis optica [47], renal disease [48,49], global amnesia [50], and migraine [36] did not find significant correlation was found between DTI-ALPS and age. In our research, we did also not find any correlation between patients' age and DTI-ALPS index.…”

Section: Discussionmentioning

confidence: 87%

Evaluating the glymphatic system via magnetic resonance diffusion tensor imaging along the perivascular spaces in brain tumor patients

Villacis,

Schmidt,

Rudolf

et al. 2024

Jpn J Radiol

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Purpose To investigate glymphatic system function in patients with brain tumors, including both primary and secondary tumors, using diffusion tensor imaging along perivascular spaces (DTI-ALPS). Methods We retrospectively analyzed the MR DTI of 24 patients with unilateral brain tumors and compared them with age and sex-matched controls. We compared the DTI-ALPS index of the ipsi- and contralateral brain hemispheres. The region of interest was placed in the periventricular vessels adjacent to the lateral ventricles. Differences between sex, age, and kind of tumor (primary or brain metastasis) were evaluated. Correlations between DTI-ALPS index and age and the tumor's apparent diffusion coefficient (ADC) were also investigated. Results The DTI-ALPS index was significantly lower (p < 0.05) in the tumor-affected hemisphere (mean = 1.26 ± 0.24) than contralateral (mean = 1.43 ± 0.28). A comparison with healthy controls revealed no significant difference on the matched ipsilateral side. However, the DTI-ALPS index of the contralateral side of the patients was larger than the HC. Additionally, no statistically significant differences were found when analyzing the DTI-ALPS index vs. age, sex, and tumor entity. Additionally, we did not find a correlation between the DTI-ALPS index and patient age or tumor ADC. Conclusion The decreased DTI-ALPS index in the tumor-affected hemisphere may be related to impaired glymphatic system function. However, cancer is often a systemic disease; thus, the DTI-ALPS index from the contralateral brain hemisphere may not generally be considered as a normal control. Nonetheless, the DTI-ALPS index does not only reflect diffusion in the perivascular spaces but it can also be influenced by factors such as axonal degeneration. Therefore, it does not directly reflect brain waste clearance and changes in the index should be interpreted carefully.

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Section: Discussionmentioning

confidence: 87%

Evaluating the glymphatic system via magnetic resonance diffusion tensor imaging along the perivascular spaces in brain tumor patients

Villacis,

Schmidt,

Rudolf

et al. 2024

Jpn J Radiol

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“…This might indicate that there is a relationship between the brain network alterations and EEG findings in patients with TGA. Furthermore, we recently demonstrated that the glymphatic system function differed significantly according to TGA event recurrence, 35 being higher in patients with recurrent TGA events than in those with a single event. These previous findings along with the present results suggest that the pathophysiology of patients with recurrent TGA events differ from that of patients with one TGA event.…”

Section: Discussionmentioning

confidence: 99%

Can Artificial Intelligence Diagnose Transient Global Amnesia Using Electroencephalography Data?

2023

Self Cite

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Background and Purpose This study aimed to determine the ability of deep learning using convolutional neural networks (CNNs) to diagnose transient global amnesia (TGA) based on electroencephalography (EEG) data, and to differentiate between patients with recurrent TGA events and those with a single TGA event. Methods We retrospectively enrolled newly diagnosed patients with TGA and healthy controls. All patients with TGA and the healthy controls underwent EEG. The EEG signals were converted into images using time-frequency analysis with short-time Fourier transforms. We employed two CNN models (AlexNet and VGG19) to classify the patients with TGA and the healthy controls, and for further classification of patients with recurrent TGA events and those with a single TGA event. Results We enrolled 171 patients with TGA and 68 healthy controls. The accuracy and area under the curve (AUC) of the AlexNet and VGG19 models in classifying patients with TGA and healthy controls were 70.4% and 71.8%, and 0.718 and 0.743, respectively. In addition, the accuracy and AUC of the AlexNet and VGG19 models in classifying patients with recurrent TGA events and those with a single TGA event were 71.1% and 88.4%, and 0.773 and 0.873, respectively. Conclusions We have successfully demonstrated the feasibility of deep learning in diagnosing TGA based on EEG data, and used two different CNN models to distinguish between patients with recurrent TGA events and those with a single TGA event.

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Glymphatic imaging: a critical look at the DTI-ALPS index

Ringstad

2024

Neuroradiology

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Glymphatic System Function in Patients with Transient Global Amnesia

Cited by 6 publications

References 43 publications

Evaluating the glymphatic system via magnetic resonance diffusion tensor imaging along the perivascular spaces in brain tumor patients

Evaluating the glymphatic system via magnetic resonance diffusion tensor imaging along the perivascular spaces in brain tumor patients

Can Artificial Intelligence Diagnose Transient Global Amnesia Using Electroencephalography Data?

Glymphatic imaging: a critical look at the DTI-ALPS index

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