Middle Meningeal Artery Embolization Versus Conventional Treatment of Chronic Subdural Hematomas.

BACKGROUND: Middle meningeal artery (MMA) embolization is an emerging minimally invasive endovascular technique for chronic subdural hematoma (cSDH). Currently, limited literature exists on its safety and efficacy compared with conventional treatment (open-surgical-evacuation-only). OBJECTIVE: To compare MMA embolization to conventional treatment. METHODS: Retrospective analysis of patients with cSDHs treated with MMA embolization in a single center from 2018 to 2019 was performed. Comparisons were made with a historical conventional treatment cohort from 2006 to 2016. Propensity score matching analysis was used to assemble a balanced group of subjects. RESULTS: A total of 357 conventionally treated cSDH and 45 with MMA embolization were included. After balancing with propensity score matching, a total of 25 pairs of cSDH were analyzed. Comparing the embolization with the conventional treatment group yielded no significant differences in complications (4% vs 4%; P > .99), clinical improvement (82.6% vs 83.3%; P = .95), cSDH recurrence (4.3% vs 21.7%; P = .08), overall re-intervention rates (12% vs 24%; P = .26), modified Rankin scale >2 on last follow-up (17.4% vs 32%; P = .24), as well as mortality (0% vs 12%; P = .09). Radiographic improvement at last follow-up was significantly higher in the open surgery cohort (73.9% vs 95.6%; P = .04). However, there was a trend for lengthier last follow-up for the historical cohort (72 vs 104 d; P = .07). CONCLUSION: There was a trend for lower recurrence and mortality rates in the embolization era cohort. There were significantly higher radiological improvement rates on last follow-up in the surgical only cohort era. There were no significant differences in complications and clinical improvement.

Rhythmic movement in Parkinson's disease: effects of visual feedback and medication state.

Previous studies examining discrete movements of Parkinson's disease (PD) patients have found that in addition to performing movements that were slower than those of control participants, they exhibit specific deficits in movement coordination and in sensorimotor integration required to accurately guide movements. With medication, movement speed was normalized, but the coordinative aspects of movement were not. This led to the hypothesis that dopaminergic medication more readily compensates for intensive aspects of movement (such as speed), than for coordinative aspects (such as coordination of different limb segments) (Schettino et al., Exp Brain Res 168:186-202, 2006). We tested this hypothesis on rhythmic, continuous movements of the forearm. In our task, target peak speed and amplitude, availability of visual feedback, and medication state (on/off) were varied. We found, consistent with the discrete-movement results, that peak speed (intensive aspect) was normalized by medication, while accuracy, which required coordination of speed and amplitude modulation (coordinative aspect), was not normalized by dopaminergic treatment. However, our findings that amplitude, an intensive aspect of movement, was also not normalized by medication, suggests that a simple pathway gain increase does not act to remediate all intensive aspects of movement to the same extent. While it normalized movement peak speed, it did not normalize movement amplitude. Furthermore, we found that when visual feedback was not available, all participants (PD and controls) made faster movements. The effects of dopaminergic medication and availability of visual feedback on movement speed were additive. The finding that movement speed uniformly increased both in the PD and the control groups suggests that visual feedback may be necessary for calibration of peak speed, otherwise underestimated by the motor control system.

Modeling parkinsonian circuitry and the DBS electrode. I. Biophysical background and software.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson's disease (PD) has become routine over the past decade, utilizing microelectrode recordings to ensure accurate placement of the stimulating electrodes. The clinical benefits of STN DBS for PD are well documented, but the mechanisms by which DBS achieves these results remain elusive. We have created a closed-form mathematical function of the potential field generated by a typical 4-contact DBS electrode and inserted this function into a computational model designed to simulate individual neurons and neural circuitry of significant portions of the basal ganglia. We present the mathematical function representing the potential field itself and the basis for the neural circuitry modeling in this paper.

Modeling parkinsonian circuitry and the DBS electrode. II. Evaluation of a computer simulation model of the basal ganglia with and without subthalamic nucleus stimulation.

Treatment with deep brain stimulation (DBS) for Parkinson's disease (PD) has become routine over the past decade, particularly using the subthalamic nucleus (STN) as a target and utilizing microelectrode recordings to ensure accurate placement of the stimulating electrodes. The clinical changes seen with DBS in the STN for PD are consistently beneficial, but there continues to be only marginal understanding of the mechanisms by which DBS achieves these results. Using an analytical model of the typical DBS 4-contact electrode and software developed to simulate individual neurons and neural circuitry of the basal ganglia we compare the results of the model to those of data obtained during DBS surgery of the STN. Firing rate, interspike intervals and regularity analyses were performed on the simulated data and compared to results in the literature.

Extracranial aneurysms of the posterior inferior cerebellar artery.

OBJECTIVE: Extracranial aneurysms of the distal posterior inferior cerebellar artery (PICA) are extremely rare and sometimes difficult to diagnose without an adequate angiogram. We present the first series of 3 patients who were evaluated by the senior author and treated surgically. METHODS AND RESULTS: All 3 patients presented with subarachnoid hemorrhage (SAH). Clincial symptoms, included occipital headache, nuchal rigidity, abducens nerve palsy and rapid neurologic deterioration. A unilateral injection of the vertebral artery failed to show the distal contralateral PICA and the aneurysm in 1 patient. All patients underwent aneurysm clipping through a posterior fossa craniectomy and C-1 laminectomy. The aneurysms were located on the tonsillomedullary segment of the PICA, 10-12 mm below the level of the foramen magnum. CONCLUSIONS: It is important to adequately visualize the distal extent of both PICAs or these aneurysms may not be seen. Patients who present with SAH must have the entirety of both vertebral arteries evaluated to avoid missing these aneurysms. The aneurysms were located adjacent to the atlas necessitating an upper cervical laminectomy for adequate surgical exposure. In general, the patients did well postoperatively and none of the patients developed cerebral vasospasm.

Surgical treatment of common entrapment neuropathies in the upper limbs.

Entrapment neuropathies of the upper extremity are common, debilitating conditions. Most patients with these neuropathies are readily diagnosed on purely clinical grounds and may be effectively managed with nonoperative measures. However, the broad differential diagnosis often necessitates electrodiagnostic testing and radiographic imaging to clarify the situation. This review focuses on three of the most common entrapment neuropathies in the upper limbs: carpal tunnel syndrome (median nerve entrapment at the wrist), cubital tunnel syndrome (ulnar nerve entrapment at the elbow), and radial tunnel syndrome (posterior interosseous nerve entrapment). Anatomical considerations, patient evaluation, indications for surgical intervention, options for surgical approaches, outcomes, and complications are discussed.

Neural network analysis of preoperative variables and outcome in epilepsy surgery.

OBJECT: Because appropriate patient selection is essential for achieving successful outcomes after epilepsy surgery, the need for more robust methods of predicting postoperative seizure control has been created. Standard multivariate techniques have been only 75 to 80% accurate in this regard. Recent use of artificial intelligence techniques, including neural networks, for analyzing multivariate clinical data has been successful in predicting medical outcome. METHODS: The authors applied neural network techniques to 80 consecutive patients undergoing epilepsy surgery in whom data on demographic, seizure, operative, and clinical variables to predict postoperative seizures were collected. Neural networks could be used to predict postoperative seizures in up to 98% of cases. Student's t-tests or chi-square analysis performed on individual variables revealed that only the preoperative medication index was significantly different (p = 0.02) between the two outcome groups. Six different combinations of input variables were used to train the networks. Neural network accuracies differed in their ability to predict seizures: using all data (96%); all data minus electroencephalography concordance and operative side (93%); all data except intra- or postoperative variables such as tissue pathological category (98%); all data excluding pathological category, intelligence quotient (IQ) data, and Wada results (84%); only demographics and tissue pathological category (65%); and only IQ data (63%). CONCLUSIONS: Analysis of the results reveals that several networks that are trained with the usual accepted variables characterizing the typical evaluation of epilepsy patients can predict postoperative seizures with greater than 95% accuracy.

Surgical options in Parkinson's disease.

Surgical treatments for Parkinson's disease (PD) have again become important adjuncts of care in these patients. We have learned much from the thousands of lesions performed historically, and are now advancing the entire field of movement disorder surgery to new levels of sophistication and understanding. The last 5 years have seen more precise and reliable lesioning and the arrival of multiple sites of intervention afforded by recent developments in deep brain stimulators. Because patients typically derive significant benefit in their quality of life from these procedures, while undergoing little risk, the surgical options should be carefully considered for selected PD patients.

Targeting for thalamic deep brain stimulator implantation without computer guidance: assessment of targeting accuracy.

The authors assess the accuracy of targeting nucleus ventralis intermedius (Vim) with fast spin echo inversion recovery (FSE/IR) magnetic resonance imaging (MRI) in 18 successful deep brain stimulator (DBS) implants for medically refractory tremor. FSE/IR-MRI-derived coordinates are compared to the final coordinates employed for DBS lead placement, selected with intraoperative neurophysiology. The authors conclude that FSE/IR MRI is sufficiently reliable to serve as the sole means of anatomically targeting Vim for DBS lead placement. An independent computer workstation is not required for accurate targeting; however, intraoperative neurophysiology remains essential.

Neurosarcoidosis presenting in the pituitary gland with normal endocrine studies.

Two cases of neurosarcoidosis in the pituitary gland are presented with a review of past cases from the literature. Previous reported cases have always shown changes on the ondocrine exis clinically. These two cases, however, were endocrinologically normal prior to surgery. The evaluation of neurosarcoid in the pituitary, clinically and radiographically, is discussed.

Prediction of posterior fossa tumor type in children by means of magnetic resonance image properties, spectroscopy, and neural networks.

Recent studies have explored characteristics of brain tumors by means of magnetic resonance spectroscopy (MRS) to increase diagnostic accuracy and improve understanding of tumor biology. In this study, a computer-based neural network was developed to combine MRS data (ratios of N-acetyl-aspartate, choline, and creatine) with 10 characteristics of tumor tissue obtained from magnetic resonance (MR) studies, as well as tumor size and the patient's age and sex, in hopes of further improving diagnostic accuracy. Data were obtained in 33 children presenting with posterior fossa tumors. The cases were analyzed by a neuroradiologist, who then predicted the tumor type from among three categories (primitive neuroectodermal tumor, astrocytoma, or ependymoma/other) based only on the data obtained via MR imaging. These predictions were compared with those made by neural networks that had analyzed different combinations of the data. The neuroradiologist correctly predicted the tumor type in 73% of the cases, whereas four neural networks using different datasets as inputs were 58 to 95% correct. The neural network that used only the three spectroscopy ratios had the least predictive ability. With the addition of data including MR imaging characteristics, age, sex, and tumor size, the network's accuracy improved to 72%, consistent with the predictions of the neuroradiologist who was using the same information. Use of only the analog data (leaving out information obtained from MR imaging), resulted in 88% accuracy. A network that used all of the data was able to identify 95% of the tumors correctly. It is concluded that a neural network provided with imaging data, spectroscopic data, and a limited amount of clinical information can predict pediatric posterior fossa tumor type with remarkable accuracy.

Simulations of cochlear nucleus neural circuitry: excitatory-inhibitory response-area types I-IV.

Several circuitry schemes have been explored among model stellate and fusiform cochlear nucleus neurons in an effort to reproduce excitatory-inhibitory response-area (EIRA scheme) types I-IV. Single cell models incorporated known nonlinear membrane properties and spike-discharge characteristics, as described in previous modeling and intracellular recording. In addition, a unique method of implementing dendritic electrotonic distance processing was developed that provides greater computational efficiency, but with results similar to compartmental models. As an initial simple case, results were examined for a kHz pure tone. Auditory nerve (AN) population responses across characteristic frequencies from 200 Hz to 50 kHz based on actual single unit recordings were incorporated into the model as input. The findings and conclusions are (1) relatively simple inhibitory connections among stellate and fusiform cells, all of which receive AN excitatory inputs, can account for the salient features of EIRA-scheme types I-IV; (2) both types III and IV may be obtained using fusiform cells with small adjustments in the anatomical connections; (3) if stellate cells laterally inhibit their own neighbors, they can create inhibitory sidebands, but may have difficulty avoiding multiple sidebands; (4) in the model, type II cells are not responsive to broadband noise but rather to pure tones, and the reason for this was partly because the type II cells were inhibited by other CN units, and partly because the simulated AN fiber response to broadband noise was near their threshold; and (5) the type IV complex response areas may actually arise not necessarily because of elaborate circuitry, but as a result of a complex AN fiber population profile at high stimulus levels in conjunction with the type II inhibitory input to the type IV cells.

Neural modeling of intrinsic and spike-discharge properties of cochlear nucleus neurons.

The purpose of this study was to develop neurobiologically plausible models to account for the response properties of several types of cochlear nucleus neurons. Three cell types--the bushy cells, stellate cells, and fusiform cells--were selected because useful data from intracellular recordings were available for these cell types, and because these three cell types exhibit distinct contrasts in their neuronal signal coding strategies. Stellate cells have primarily linear current-voltage (I-V) characteristics, but both bushy and fusiform cells have highly non-linear I-V characteristics. In light of this, we hypothesize that some of these cells have non-linear voltage-dependent conductances which alter their response properties. We modeled the bushy cell membrane conductance as an exponentially increasing function of membrane voltage, that of the fusiform cell as an exponentially decreasing function of the voltage, and that of the stellate cell as being voltage-independent. We have combined the voltage-dependent non-linear conductances of the cell membrane with a simple R-C circuit type of neuron model. These models reproduced the salient features of the experimentally observed I-V characteristics of the cells. In addition, we found that the models reproduced the spike discharge behavior to intracellularly injected current steps. Moreover, a more detailed study of stellate cell 'chopper'-type response patterns yielded hypotheses regarding the nature of the current that must exist at the soma during a pure-tone stimulus in order for the cells to exhibit various chopper subtype patterns, such as chop-S, chop-T, and Oc. The chop-S pattern requires a steady average current level with a relatively small variability during the tone-burst stimulus. The chop-T pattern, in contrast, requires that the current become more irregular during the tone-burst stimulus. The Oc pattern arises, however, when the input is similar to the chop-T case but the intrinsic properties of the cell model have been changed to increase the accommodation of the threshold. The implications of these findings for circuitry in the cochlear nucleus are discussed. Our analysis of these models revealed that this approach can be used to simulate neuronal cell types where I-V characteristics are known but more detailed ion channel data are not known.

An application of fractal dimension to the detection of transients in the electroencephalogram.

It is sometimes desirable to identify a brief seizure, occasional spike, single evoked potential, or other transient in the EEG. Transient detection in an EEG can be a difficult task, often requiring prior knowledge of the characteristics of the transient. A fractal is a shape which retains structural detail despite magnification (scaling). The complexity of the structure of such a set, invariant under this scaling, can be characterized by a single number: the fractal dimension. Regarding the EEG as a fractal, we have shown that transient deterministic data in the EEG have a fractal dimension different from the quasirandom background. An extensive introduction to fractals is presented with the assumption that the reader is unfamiliar with the theory. In the preliminary results presented here, analysis by fractal dimension is shown to be a promising method of transient detection, requiring no prior knowledge of the characteristics of the transient. Possible applications of the technique to evoked potential technology and epilepsy surgery are discussed. Other applications to biology, neuroscience and medicine are reviewed.