Natural resonance frequency of the brain depends on only intracranial pressure: clinical research

Goto, Tetsuya; Kenji, Furihata; Hongo, Kazuhiro

doi:10.1038/s41598-020-59376-7

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…This form of two components mainly reflects the frequency of micromovements inside MTL and, thus, could be compared to quadratic function expression of AD probability. Goto et al [49] found a quadratic function relationship between the intracranial pressure value and the pulse waveform frequency inside the brain. Seen in the Goto et al [49] study, the lowest frequency of pulse waveforms was higher than the frequency values of the high-end spectra in our study.…”

Section: Discussionmentioning

confidence: 99%

“…Goto et al [49] found a quadratic function relationship between the intracranial pressure value and the pulse waveform frequency inside the brain. Seen in the Goto et al [49] study, the lowest frequency of pulse waveforms was higher than the frequency values of the high-end spectra in our study. Although we cannot link our results to the Goto et al [49] findings directly, Goto et al provide an insight that high frequency also could have a pathophysiological meaning.…”

Section: Discussionmentioning

confidence: 99%

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Ultrasonic Assessment of the Medial Temporal Lobe Tissue Displacements in Alzheimer’s Disease

et al. 2020

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We aim to estimate brain tissue displacements in the medial temporal lobe (MTL) using backscattered ultrasound radiofrequency (US RF) signals, and to assess the diagnostic ability of brain tissue displacement parameters for the differentiation of patients with Alzheimer’s disease (AD) from healthy controls (HC). Standard neuropsychological evaluation and transcranial sonography (TCS) for endogenous brain tissue motion data collection are performed for 20 patients with AD and for 20 age- and sex-matched HC in a prospective manner. Essential modifications of our previous method in US waveform parametrization, raising the confidence of micrometer-range displacement signals in the presence of noise, are done. Four logistic regression models are constructed, and receiver operating characteristic (ROC) curve analyses are applied. All models have cut-offs from 61.0 to 68.5% and separate AD patients from HC with a sensitivity of 89.5% and a specificity of 100%. The area under a ROC curve of predicted probability in all models is excellent (from 95.2 to 95.7%). According to our models, AD patients can be differentiated from HC by a sharper morphology of some individual MTL spatial point displacements (i.e., by spreading the spectrum of displacements to the high-end frequencies with higher variability across spatial points within a region), by lower displacement amplitude differences between adjacent spatial points (i.e., lower strain), and by a higher interaction of these attributes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Ultrasonic Assessment of the Medial Temporal Lobe Tissue Displacements in Alzheimer’s Disease

et al. 2020

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“…An excellent correlation was observed between the best NRF and the inserted CSF volume (r 2 = 0.96) as a surrogate for the ICP. An interesting study from Japan [ 126 ] suggested that the NRF depends only on the ICP and that the relationship between the ICP and the NRF in the brain can be calculated using a quadratic function (ICP = 0.0329 × NRF 2 + 0.0842 × NRF), with an excellent correlation (R 2 = 0.9952). Therefore, the individual NRF depends only on the ICP value.…”

Section: Approaches Of Non-invasive Measurementsmentioning

confidence: 99%

Non-Invasive Intracranial Pressure Monitoring

Müller

Henkes

Gounis³

et al. 2023

JCM

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(1) Background: Intracranial pressure (ICP) monitoring plays a key role in the treatment of patients in intensive care units, as well as during long-term surgeries and interventions. The gold standard is invasive measurement and monitoring via ventricular drainage or a parenchymal probe. In recent decades, numerous methods for non-invasive measurement have been evaluated but none have become established in routine clinical practice. The aim of this study was to reflect on the current state of research and shed light on relevant techniques for future clinical application. (2) Methods: We performed a PubMed search for “non-invasive AND ICP AND (measurement OR monitoring)” and identified 306 results. On the basis of these search results, we conducted an in-depth source analysis to identify additional methods. Studies were analyzed for design, patient type (e.g., infants, adults, and shunt patients), statistical evaluation (correlation, accuracy, and reliability), number of included measurements, and statistical assessment of accuracy and reliability. (3) Results: MRI-ICP and two-depth Doppler showed the most potential (and were the most complex methods). Tympanic membrane temperature, diffuse correlation spectroscopy, natural resonance frequency, and retinal vein approaches were also promising. (4) Conclusions: To date, no convincing evidence supports the use of a particular method for non-invasive intracranial pressure measurement. However, many new approaches are under development.

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“…Similar studies in this field have successfully developed methods to predict the occurrence of hypertensive events or the progression of intracranial hypertension (IH) into a potentially lifethreatening condition (LTH) [15]- [18]. Other works have developed deep-learning algorithms to forecast the ICP waveform [19,20]. However, all of these studies rely on using ICP as an input and therefore do not offer the potential of an extracranial, non-invasive methodology for measuring ICP.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Deep Learning Approach for Inferring Intracranial Pressure from Routinely Measured Extracranial Waveforms in the Intensive Care Unit

Nair,

Guo,

Boen

et al. 2023

Preprint

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Objective.Intracranial pressure (ICP) is a vital waveform used to assess the conditions of patients with intracranial pathologies, such as traumatic brain injury. The current standard for monitoring ICP requires a catheter to be inserted into the brain, which is extremely invasive and increases the risk of hemorrhage and infection. We hypothesize that ICP can be inferred from extracranial waveforms routinely measured in the Intensive Care Unit (ICU), such as invasive arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG).Methods.We extracted 600 hours of simultaneous ABP, EKG, PPG, and ICP waveforms (125 Hz) across 10 different patients from the MIMIC III Waveform Database. These concurrent recordings were broken into 10-second windows to train on long-short term memory (LSTM) and temporal convolutional network (TCN) models to predict ICP using ABP, EKG, and PPG as input features. These models were evaluated in both a single-patient analysis and multi-patient analysis study.Results.In the single-patient analysis study, TCN's median mean average error (MAE) test loss was 1.60 mmHg in comparison to a median MAE test loss of 1.88 mmHg for LSTM. TCN was used in the multi-patient analysis study, reporting a test MAE loss of 5.44 mmHg.Conclusions.These novel results indicate that it is possible to predict ICP waveforms from extracranial waveforms in a real-time, continuous, and non-invasive manner. This approach could potentially eliminate the risks associated with invasive monitoring and allow for more timely treatment of patients with intracranial pathologies with further follow-up studies.

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Natural resonance frequency of the brain depends on only intracranial pressure: clinical research

Cited by 8 publications

References 25 publications

Ultrasonic Assessment of the Medial Temporal Lobe Tissue Displacements in Alzheimer’s Disease

Ultrasonic Assessment of the Medial Temporal Lobe Tissue Displacements in Alzheimer’s Disease

Non-Invasive Intracranial Pressure Monitoring

A Real-Time Deep Learning Approach for Inferring Intracranial Pressure from Routinely Measured Extracranial Waveforms in the Intensive Care Unit

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