Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future.

In neurointensive care units (NICU), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of pre-admission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

Effects of altered functional connectivity on motor imagery brain-computer interfaces based on the laterality of paralysis in hemiplegia patients.

Motor imagery (MI)-based brain-computer interfaces are widely employed for improving the rehabilitation of paralyzed people and their quality of life. It has been well documented that brain activity patterns in the primary motor cortex and sensorimotor cortex during MI are similar to those of motor execution/imagery. However, individuals paralyzed owing to various neurological disorders have debilitated activation of the motor control region. Therefore, the differences in brain activation based on the paralysis location should be considered. We analyzed brain activation patterns using the electroencephalogram (EEG) acquired while performing MI on the right upper limb to investigate hemiplegia-related brain activation patterns. Participants with hemiplegia of the right upper limb (n=7) and left upper limb (n=4) performed the MI task within the right upper limb. EEG signals were acquired using 14 channels based on a 10-20 global system, and analyzed for event-related desynchronization (ERD) based on event-related spectral perturbation and functional connectivity, using the weighted phase-lag index of both hemispheres at the location of hemiplegia. Enhanced ERD was found in the ipsilateral region, compared to the contralateral region, after MI of the affected limb. The reduced difference in the centrality of the channels was observed in all subjects, likely reflecting an altered brain network from increased interhemispheric connections. Furthermore, the tendency of distinct network-based features depending on the MI task on the affected limb was diluted between the inter-hemispheres. Analysis of interaction between inter-region using functional connectivity could provide avenues for further investigation of BCI strategy through the brain state of individuals with hemiplegia.

Prediction of Serious Intracranial Hypertension from Low-Resolution Neuromonitoring in Traumatic Brain Injury: An Explainable Machine Learning Approach.

There is a strong association between intracranial hypertension (IH) that occurs following the acute phase of traumatic brain injury (TBI) and negative outcomes. This study proposes a pressure-time dose (PTD)-based parameter that may specify a possible serious IH (SIH) event and develops a model to predict SIH. The minute-by-minute signals of arterial blood pressure (ABP) and intracranial pressure (ICP) of 117 TBI patients were utilized as the internal validation dataset. The SIH event was explored through the prognostic power of the IH event variables for the outcome after 6 months, and an IH event with thresholds that included an ICP of 20 mmHg and PTD > 130 mmHg * minutes was considered an SIH event. The physiological characteristics of normal, IH and SIH events were investigated. LightGBM was employed to forecast an SIH event from various time intervals using physiological parameters derived from the ABP and ICP. Training and validation were conducted on 1,921 SIH events. External validation was performed on two multi-center datasets containing 26 and 382 SIH events. The SIH parameters could be used to predict mortality (AUROC = 0.893, p < 0.001) and favorability (AUROC = 0.858, p < 0.001). The trained model robustly forecasted SIH after 5 and 480 minutes with an accuracy of 86.95% and 72.18% in internal validation. External validation also revealed a similar performance. This study demonstrated that the proposed SIH prediction model has reasonable predictive capacities. A future intervention study is required to investigate whether the definition of SIH is maintained in multi-center data and to ensure the effects of the predictive system on TBI patient outcomes at the bedside.

DeepCNAP: A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography.

Arterial blood pressure (ABP) monitoring may permit the early diagnosis and management of cardiovascular disease (CVD); however, existing methods for measuring ABP outside the clinic use inconvenient cuff sphygmomanometry, or do not estimate continuous ABP waveforms. This study proposes a novel deep learning model DeepCNAP for estimating continuous BP waveform from a noninvasively measured photoplethysmography (PPG) signal in real-time. DeepCNAP was designed through the combination of deep convolutional networks and self-attention. The proposed method was constructed via 10-fold cross-validation based on the MIMIC database (the number of subjects = 942, recording time = 374.43 hours). The performance of DeepCNAP was evaluated from two perspectives: estimating ABP from PPG and classifying hemodynamically unstable events (i.e., hypertension, prehypertension, hypotension, and the normal state). The mean absolute errors of the BP estimates were 3.40 ± 4.36 mmHg for systolic BP, 1.75 ± 2.25 mmHg for diastolic BP, and 3.23 ± 2.21 mmHg for the BP waveform, indicating that DeepCNAP satisfies the standards of both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). From the estimated BP, hypertension, prehypertension, hypotension, and the normal state were classified with 99.44, 97.58, 92.23, and 94.64% accuracy, respectively. DeepCNAP enables feasible real-time estimation of invasively measured ABP from noninvasive PPG. With its noninvasive nature, high accuracy, and clinical relevance, the proposed model could be valuable in general wards at hospitals and for wearable devices in daily life.

Autonomic Dysfunction in Sleep Disorders: From Neurobiological Basis to Potential Therapeutic Approaches.

Sleep disorder has been portrayed as merely a common dissatisfaction with sleep quality and quantity. However, sleep disorder is actually a medical condition characterized by inconsistent sleep patterns that interfere with emotional dynamics, cognitive functioning, and even physical performance. This is consistent with sleep abnormalities being more common in patients with autonomic dysfunction than in the general population. The autonomic nervous system coordinates various visceral functions ranging from respiration to neuroendocrine secretion in order to maintain homeostasis of the body. Because the cell population and efferent signals involved in autonomic regulation are spatially adjacent to those that regulate the sleep-wake system, sleep architecture and autonomic coordination exert effects on each other, suggesting the presence of a bidirectional relationship in addition to shared pathology. The primary goal of this review is to highlight the bidirectional and shared relationship between sleep and autonomic regulation. It also introduces the effects of autonomic dysfunction on insomnia, breathing disorders, central disorders of hypersomnolence, parasomnias, and movement disorders. This information will assist clinicians in determining how neuromodulation can have the greatest therapeutic effects in patients with sleep disorders.

Intracranial Densitometry-Augmented Machine Learning Enhances the Prognostic Value of Brain CT in Pediatric Patients With Traumatic Brain Injury: A Retrospective Pilot Study.

Background: The inter- and intrarater variability of conventional computed tomography (CT) classification systems for evaluating the extent of ischemic-edematous insult following traumatic brain injury (TBI) may hinder the robustness of TBI prognostic models. Objective: This study aimed to employ fully automated quantitative densitometric CT parameters and a cutting-edge machine learning algorithm to construct a robust prognostic model for pediatric TBI. Methods: Fifty-eight pediatric patients with TBI who underwent brain CT were retrospectively analyzed. Intracranial densitometric information was derived from the supratentorial region as a distribution representing the proportion of Hounsfield units. Furthermore, a machine learning-based prognostic model based on gradient boosting (i.e., CatBoost) was constructed with leave-one-out cross-validation. At discharge, the outcome was assessed dichotomously with the Glasgow Outcome Scale (favorability: 1-3 vs. 4-5). In-hospital mortality, length of stay (>1 week), and need for surgery were further evaluated as alternative TBI outcome measures. Results: Densitometric parameters indicating reduced brain density due to subtle global ischemic changes were significantly different among the TBI outcome groups, except for need for surgery. The skewed intracranial densitometry of the unfavorable outcome became more distinguishable in the follow-up CT within 48 h. The prognostic model augmented by intracranial densitometric information achieved adequate AUCs for various outcome measures [favorability = 0.83 (95% CI: 0.72-0.94), in-hospital mortality = 0.91 (95% CI: 0.82-1.00), length of stay = 0.83 (95% CI: 0.72-0.94), and need for surgery = 0.71 (95% CI: 0.56-0.86)], and this model showed enhanced performance compared to the conventional CRASH-CT model. Conclusion: Densitometric parameters indicative of global ischemic changes during the acute phase of TBI are predictive of a worse outcome in pediatric patients. The robustness and predictive capacity of conventional TBI prognostic models might be significantly enhanced by incorporating densitometric parameters and machine learning techniques.

Prediction of Life-Threatening Intracranial Hypertension During the Acute Phase of Traumatic Brain Injury Using Machine Learning.

Intracranial hypertension (IH) following acute phase traumatic brain injury (TBI) is associated with high mortality. Objective: This study proposes a novel parameter that may identify a potentially life-threatening IH (LTH) event and designs a machine learning model to predict LTH. Continuous recordings of intracranial pressure (ICP) and arterial blood pressure (ABP) from 273 TBI patients were used as the development dataset. The pressure-time dose (PTD) and pressure reactivity index (PRx) were calculated for each IH event, and an IH event with PRx > 0 and PTD > 5 was considered an LTH event. The association between the LTH parameters accumulated over five days and mortality was analyzed. A categorical boosting (CatBoost) model was employed to predict the occurrence of a future LTH event from the onset of IH using the ABP- and ICP-related parameters. Training and validation were performed on a total of 5,938 IH events. External performance evaluation was performed in 307 IH events included in the Cerebral Haemodynamic Autoregulatory Information System (CHARIS) database. The performance of the proposed model was evaluated through the area under the receiver operating characteristic curve (AUROC). The LTH parameters were able to distinguish between the deceased and surviving patients (AUROC > 0.7, p < 0.001). The CatBoost model predicted LTH with an AUROC = 0.7 on the external test dataset. This study demonstrated that the proposed LTH prediction model has a reasonable predictive capacity for mortality. The CatBoost model anticipates whether an IH event will develop into an LTH event. The findings of this study support the usefulness of ICP monitoring.

Cerebrospinal fluid dynamics correlate with neurogenic claudication in lumbar spinal stenosis.

Neurogenic claudication is a typical manifestation of lumbar spinal stenosis (LSS). However, its pathophysiology is still unclear. The severity of clinical symptoms has been shown not to correlate with the degree of structural stenosis. Altered cerebrospinal fluid (CSF) flow has been suggested as one of the causative factors of LSS. The objectives of this study were to compare CSF dynamics at the lumbosacral level between patients with LSS and healthy controls and to investigate whether CSF dynamics parameters explain symptom severity in LSS. Phase-contrast magnetic resonance imaging (PC-MRI) was conducted to measure CSF dynamics in 18 healthy controls and 9 patients with LSS. Cephalic peak, caudal peak, and peak-to-peak CSF velocities were evaluated at the lumbosacral level in the patients and controls. The power of CSF dynamics parameters to predict symptom severity was determined using a linear regression analysis adjusted for demographic and structural variables. Significantly attenuated CSF flow velocity was observed in the patients compared with the controls. The cephalic peak, caudal peak, and peak-to-peak velocities at the lumbar level were greater in the controls than in the patients (p<0.001). The predictive power increased most when the peak-to-peak velocity was added (adjusted R2 = 0.410) to the model with age, body mass index, and the minimum anterior-posterior diameter (adjusted R2 = 0.306), and the peak-to-peak velocity was the only statistically significant variable. CSF dynamics variables showed an association with the severity of LSS symptoms, independent of structural stenosis. PC-MRI can help to further our understanding of the pathophysiology of neurogenic claudication and support the diagnosis of LSS.

Evaluation of ruminal motility using an indwelling 3-axis accelerometer in the reticulum in cattle.

Attempts to increase production and improve farm environments have been made for several years. Rumen motility (RM) is one of the biological parameters that provides essential information of individuals in ruminants, and it is usually evaluated by auscultation. The study was aimed to examine RM using the 3-axis accelerometer (3XA) in cattle. The manufactured 3XA were placed in the reticulum (3XA-R) and implanted in the subcutaneous layer of the brisket (3XA-SC), respectively, and the accelerations were compared following intramuscular injection of xylazine (0.05 mg/kg) or saline in experiment 1 and of xylazine (0.05 mg/kg) or atropine (0.04 mg/kg) in experiment 2. In experiment 3, the dose-dependent decrease of RM was evaluated following xylazine administration (0, 0.05, 0.1 mg/kg) in the 3XA-R equipped cows via a 3 × 3 Latin square method. In experiment 1, saline-treated animals showed a continuous fluctuation while the frequency and amplitude of 3XA-R in xylazine-injected cows were reduced after administration. The acceleration of 3XA-SC was changed after administration, but not abruptly. Among the motion parameters, V2 was calculated only using X- and Z-axis acceleration in consideration of the cylindrical shape, and it showed the apparent difference between pre- and post-xylazine administration. In experiment 2, the V2 of 3XA-R was decreased after atropine administration while that of 3XA-SC was maintained. In experiment 3, a dose-dependent V2 decrement of 3XA-R after xylazine administration was observed and lasted for 40 and 80 min in doses of 0.05 mg/kg and 0.1 mg/kg, respectively. In conclusion, The 3XA detected the decrease in RM efficiently and processed the data wirelessly without interference from body movement. This technology will help detect problems early and prevent a decline in cattle productivity.

Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury.

OBJECTIVE: Monitoring intracranial and arterial blood pressure (ICP and ABP, respectively) provides crucial information regarding the neurological status of patients with traumatic brain injury (TBI). However, these signals are often heavily affected by artifacts, which may significantly reduce the reliability of the clinical determinations derived from the signals. The goal of this work was to eliminate signal artifacts from continuous ICP and ABP monitoring via deep learning techniques and to assess the changes in the prognostic capacities of clinical parameters after artifact elimination. METHODS: The first 24 hours of monitoring ICP and ABP in a total of 309 patients with TBI was retrospectively analyzed. An artifact elimination model for ICP and ABP was constructed via a stacked convolutional autoencoder (SCAE) and convolutional neural network (CNN) with 10-fold cross-validation tests. The prevalence and prognostic capacity of ICP- and ABP-related clinical events were compared before and after artifact elimination. RESULTS: The proposed SCAE-CNN model exhibited reliable accuracy in eliminating ABP and ICP artifacts (net prediction rates of 97% and 94%, respectively). The prevalence of ICP- and ABP-related clinical events (i.e., systemic hypotension, intracranial hypertension, cerebral hypoperfusion, and poor cerebrovascular reactivity) all decreased significantly after artifact removal. CONCLUSIONS: The SCAE-CNN model can be reliably used to eliminate artifacts, which significantly improves the reliability and efficacy of ICP- and ABP-derived clinical parameters for prognostic determinations after TBI.

Novel index for predicting mortality during the first 24 hours after traumatic brain injury.

OBJECTIVE: Failure of cerebral autoregulation and subsequent hypoperfusion is common during the acute phase of traumatic brain injury (TBI). The cerebrovascular pressure-reactivity index (PRx) indirectly reflects cerebral autoregulation and has been used to derive optimal cerebral perfusion pressure (CPP). This study provides a method for the use of a combination of PRx, CPP, and intracranial pressure (ICP) to better evaluate the extent of cerebral hypoperfusion during the first 24 hours after TBI, allowing for a more accurate prediction of mortality risk. METHODS: Continuous ICP and arterial blood pressure (ABP) signals acquired from 295 TBI patients during the first 24 hours after admission were retrospectively analyzed. The CPP at the lowest PRx was determined as the optimal CPP (CPPopt). The duration of a severe hypoperfusion event (dHP) was defined as the cumulative time that the PRx was > 0.2 and the CPP was < 70 mm Hg with the addition of intracranial hypertension (ICP > 20 or > 22 mm Hg). The outcome was determined as 6-month mortality. RESULTS: The cumulative duration of PRx > 0.2 and CPP < 70 mm Hg exhibited a significant association with mortality (p < 0.001). When utilized with basic clinical information available during the first 24 hours after admission (i.e., Glasgow Coma Scale score, age, and mean ICP), a dHP > 25 minutes yielded a significant predictive capacity for mortality (p < 0.05, area under the curve [AUC] = 0.75). The parameter was particularly predictive of mortality for patients with a mean ICP > 20 or > 22 mm Hg (AUC = 0.81 and 0.87, respectively). CONCLUSIONS: A short duration (25 minutes) of severe hypoperfusion, evaluated as lowered CPP during worsened cerebrovascular reactivity during the 1st day after TBI, is highly indicative of mortality.

Changes in the gray and white matter of patients with ischemic-edematous insults after traumatic brain injury.

OBJECTIVE: Gray matter (GM) and white matter (WM) are vulnerable to ischemic-edematous insults after traumatic brain injury (TBI). The extent of secondary insult after brain injury is quantifiable using quantitative CT analysis. One conventional quantitative CT measure, the gray-white matter ratio (GWR), and a more recently proposed densitometric analysis are used to assess the extent of these insults. However, the prognostic capacity of the GWR in patients with TBI has not yet been validated. This study aims to test the prognostic value of the GWR and evaluate the alternative parameters derived from the densitometric analysis acquired during the acute phase of TBI. In addition, the prognostic ability of the conventional TBI prognostic models (i.e., IMPACT [International Mission for Prognosis and Analysis of Clinical Trials in TBI] and CRASH [Corticosteroid Randomisation After Significant Head Injury] models) were compared to that of the quantitative CT measures. METHODS: Three hundred patients with TBI of varying ages (92 pediatric, 94 adult, and 114 geriatric patients) and admitted between 2008 and 2013 were included in this retrospective cohort study. The normality of the density of the deep GM and whole WM was evaluated as the proportion of CT pixels with Hounsfield unit values of 31-35 for GM and 26-30 for WM on CT images of the entire supratentorial brain. The outcome was evaluated using the Glasgow Outcome Scale (GOS) at discharge (GOS score ≤ 3, n = 100). RESULTS: Lower proportions of normal densities in the deep GM and whole WM indicated worse outcomes. The proportion of normal WM exhibited a significant prognostic capacity (area under the curve [AUC] = 0.844). The association between the outcome and the normality of the WM density was significant in adult (AUC = 0.792), pediatric (AUC = 0.814), and geriatric (AUC = 0.885) patients. In pediatric patients, the normality of the overall density and the density of the GM were indicative of the outcome (AUC = 0.751). The average GWR was not associated with the outcome (AUC = 0.511). IMPACT and CRASH models showed adequate and reliable performance in the pediatric and geriatric groups but not in the adult group. The highest overall predictive performance was achieved by the densitometry-augmented IMPACT model (AUC = 0.881). CONCLUSIONS: Both deep GM and WM are susceptible to ischemic-edematous insults during the early phase of TBI. The extent of the secondary injury was better evaluated by analyzing the normality of the deep GM and WM rather than by calculating the GWR.

Abilities of a Densitometric Analysis of Computed Tomography Images and Hemorrhagic Parameters to Predict Outcome Favorability in Patients With Intracerebral Hemorrhage.

BACKGROUND: Intracerebral hemorrhage (ICH) is one of the most devastating subtypes of stroke. A rapid assessment of ICH severity involves the use of computed tomography (CT) and derivation of the hemorrhage volume, which is often estimated using the ABC/2 method. However, these estimates are highly inaccurate and may not be feasible for anticipating outcome favorability. OBJECTIVE: To predict patient outcomes via a quantitative, densitometric analysis of CT images, and to compare the predictive power of these densitometric parameters with the conventional ABC/2 volumetric parameter and segmented hemorrhage volumes. METHODS: Noncontrast CT images of 87 adult patients with ICH (favorable outcomes = 69, unfavorable outcomes = 12, and deceased = 6) were analyzed. In-house software was used to calculate the segmented hemorrhage volumes, ABC/2 and densitometric parameters, including the skewness and kurtosis of the density distribution, interquartile ranges, and proportions of specific pixels in sets of CT images. Nonparametric statistical analyses were conducted. RESULTS: The densitometric parameter interquartile range exhibited greatest accuracy (82.7%) in predicting favorable outcomes. The combination of skewness and the interquartile range effectively predicted mortality (accuracy = 83.3%). The actual volume of the ICH exhibited good coherence with ABC/2 (R = 0.79). Both parameters predicted mortality with moderate accuracy (<78%) but were less effective in predicting unfavorable outcomes. CONCLUSION: Hemorrhage volume was rapidly estimated and effectively predicted mortality in patients with ICH; however, this value may not be useful for predicting favorable outcomes. The densitometric analysis exhibited significantly higher power in predicting mortality and favorable outcomes in patients with ICH.

Hemodynamic Instability and Cardiovascular Events After Traumatic Brain Injury Predict Outcome After Artifact Removal With Deep Belief Network Analysis.

BACKGROUND: Hemodynamic instability and cardiovascular events heavily affect the prognosis of traumatic brain injury. Physiological signals are monitored to detect these events. However, the signals are often riddled with faulty readings, which jeopardize the reliability of the clinical parameters obtained from the signals. A machine-learning model for the elimination of artifactual events shows promising results for improving signal quality. However, the actual impact of the improvements on the performance of the clinical parameters after the elimination of the artifacts is not well studied. MATERIALS AND METHODS: The arterial blood pressure of 99 subjects with traumatic brain injury was continuously measured for 5 consecutive days, beginning on the day of admission. The machine-learning deep belief network was constructed to automatically identify and remove false incidences of hypotension, hypertension, bradycardia, tachycardia, and alterations in cerebral perfusion pressure (CPP). RESULTS: The prevalences of hypotension and tachycardia were significantly reduced by 47.5% and 13.1%, respectively, after suppressing false incidents (P=0.01). Hypotension was particularly effective at predicting outcome favorability and mortality after artifact elimination (P=0.015 and 0.027, respectively). In addition, increased CPP was also statistically significant in predicting outcomes (P=0.02). CONCLUSIONS: The prevalence of false incidents due to signal artifacts can be significantly reduced using machine-learning. Some clinical events, such as hypotension and alterations in CPP, gain particularly high predictive capacity for patient outcomes after artifacts are eliminated from physiological signals.

Finite Element Model for Hydrocephalus and Idiopathic Intracranial Hypertension.

Hydrocephalus and idiopathic intracranial hypertension (IIH) are neuropathies associated with disturbed cerebrospinal fluid dynamics. Several finite element (FE) brain models were suggested to simulate the pathological changes in hydrocephalus, but with overly simplified assumptions regarding the properties of the brain parenchyma. This study proposes a two-dimensional FE brain model, capable of simulating both hydrocephalus and IIH by incorporating poro-hyperelasticity of the brain and detailed structural information (i.e., sulci).

Morphological Feature Extraction From a Continuous Intracranial Pressure Pulse via a Peak Clustering Algorithm.

OBJECTIVE: An increase in intracranial pressure (ICP) is frequently observed in patients with severe traumatic brain injury (TBI). The information derived from the observation of temporal changes in the mean ICP is insufficient for assessment of the compensatory reserve of the injured brain. This assessment can be achieved via continuous morphological analysis of the pulse waveform of the ICP. METHODS: Continuous arterial blood pressure (ABP) and ICP recordings from 292 TBI patients were analyzed. The algorithm extracted morphological landmarks (peaks, troughs, and flats) from the ICP. Among the extracted peaks, P1, P2, and P3 were assigned through peak clustering. The performance of the proposed method was validated through a comparison of the algorithm-defined peaks and those manually identified by experienced observers. RESULTS: The proposed algorithm successfully identified the three distinguishing peaks of the ICP with satisfactory accuracy (95.3%, 87.8%, and 87.5% for P1, P2, and P3, respectively), even from minimally filtered raw signals. CONCLUSION: The algorithm extracted the morphological features from both ABP and ICP recordings with high accuracy. SIGNIFICANCE: The ABP and ICP pulse waveforms can be simultaneously analyzed in real time using the proposed algorithm. The morphological features from these signals may aid the continuous care of patients with TBI.

Spectral analysis of intracranial pressure: Is it helpful in the assessment of shunt functioning in-vivo?

OBJECTIVE: Shunt failure is common in hydrocephalic patients. The cerebrospinal fluid (CSF) infusion test enables the assessment of CSF absorption capacity, which is represented by the resistance to CSF outflow (ROUT) However, shunt failure may not only affect the CSF absorption capacity but also the intracranial compliance or compensatory properties. Spectral analysis of the ICP signal obtained during the infusion test may enable the comprehensive assessment of the overall deterioration caused by shunt failure. MATERIAL AND METHODS: A total of 121 hydrocephalic shunted patients underwent the infusion test with continuous intracranial pressure (ICP) and arterial blood pressure (ABP) recording. The maximum amplitudes of three major frequency bandwidths (0.2-2.6, 2.6-4.0 and 4.0-15 Hz, respectively) were calculated from the ICP. Statistical analyses were conducted to identify factors significantly associated with shunt failure, to construct an index (i.e., the shunt response parameter, SRP) for detecting shunt failure, and to define thresholds for ROUT and SRP. RESULTS: The ROUT threshold for detecting shunt failure was 7.59 mmHg/ml/min, and this threshold showed an accuracy of 82.64%. All spectral parameters were found to be significantly associated with shunt patency (p<0.05). The SRP exhibited significantly better accuracy than ROUT in detecting shunt failure (91.74%). CONCLUSION: The hydrodynamic assessment of shunted patients enhanced by spectral analysis during the infusion test detected shunt failure with high accuracy. Although further validation is needed, the SRP exhibited promising results.

Finite element analysis of periventricular lucency in hydrocephalus: extravasation or transependymal CSF absorption?

OBJECTIVE: Periventricular lucency (PVL) is often observed in the hydrocephalic brain on CT or MRI. Earlier studies have proposed the extravasation of ventricular CSF into the periventricular white matter or transependymal CSF absorption as possible causes of PVL in hydrocephalus. However, there is insufficient evidence for either theory to be conclusive. METHODS: A finite element (FE) model of the hydrocephalic brain with detailed anatomical geometry was constructed to investigate the possible mechanism of PVL in hydrocephalus. The initiation of hydrocephalus was modeled by applying a transmantle pressure gradient (TPG). The model was exposed to varying TPGs to investigate the effects of different geometrical characteristics on the distribution of PVL. The edema map was derived based on the interstitial pore pressure. RESULTS: The model simulated the main radiological features of hydrocephalus, i.e., ventriculomegaly and PVL. The degree of PVL, assessed by the pore pressure, was prominent in mild to moderate ventriculomegaly. As the degree of ventriculomegaly exceeded certain values, the pore pressure across the cerebrum became positive, thus inducing the disappearance of PVL. CONCLUSIONS: The results are in accordance with common clinical findings of PVL. The degree of ventriculomegaly significantly influences the development of PVL, but two factors were not linearly correlated. The results are indicative of the transependymal CSF absorption as a possible cause of PVL, but the extravasation theory cannot be formally rejected.

Finite element analysis for normal pressure hydrocephalus: The effects of the integration of sulci.

Finite element analysis (FEA) is increasingly used to investigate the brain under various pathological changes. Although FEA has been used to study hydrocephalus for decades, previous studies have primarily focused on ventriculomegaly. The present study aimed to investigate the pathologic changes regarding sulcal deformation in normal pressure hydrocephalus (NPH). Two finite element (FE) models-an anatomical brain geometric (ABG) model and the conventional simplified brain geometric (SBG) model-of NPH were constructed. The models were constructed with identical boundary conditions but with different geometries. The ABG model contained details of the sulci geometry, whereas these details were omitted from the SBG model. The resulting pathologic changes were assessed via four biomechanical parameters: pore pressure, von Mises stress, pressure, and void ratio. NPH was induced by increasing the transmantle pressure gradient (TPG) from 0 to a maximum of 2.0 mmHg. Both models successfully simulated the major features of NPH (i.e., ventriculomegaly and periventricular lucency). The changes in the biomechanical parameters with increasing TPG were similar between the models. However, the SBG model underestimated the degree of stress across the cerebral mantle by 150% compared with the ABG model. The SBG model also overestimates the degree of ventriculomegaly (increases of 194.5% and 154.1% at TPG = 2.0 mmHg for the SBG and ABG models, respectively). Including the sulci geometry in a FEA for NPH clearly affects the overall results. The conventional SBG model is inferior to the ABG model, which accurately simulated sulcal deformation and the consequent effects on cortical or subcortical structures. The inclusion of sulci in future FEA for the brain is strongly advised, especially for models used to investigate space-occupying lesions.