Search

Background and Hypothesis: Osteocapsular débridement is a surgical treatment for functionally limiting primary elbow osteoarthritis (PEOA). We hypothesized that postoperative improvement in range of motion (ROM) following elbow osteocapsular débridement could be grouped into predictable patterns. We also hypothesized that significant improvements in ROM frequently take place for up to 6 months after surgery. Methods: A retrospective chart review of patients who underwent open elbow débridement for PEOA was performed. Demographic information and surgical approach were recorded. ROM data were also collected at preoperative, intraoperative, and postoperative intervals of 2 weeks, 6 weeks, 3 months, and 6 months. Growth mixture modeling and latent class growth analysis were performed to identify groups of motion recovery trajectories, while Student’s t-tests were performed to compare ROM data between intervals. Results: Our study included 76 patients who underwent open elbow débridement (9 with a lateral approach, 55 medial, and 12 both) for PEOA. The mean preoperative arc of motion was 95° ± 22°. This improved to a mean final motion arc of 127° ± 11 at final follow-up, which was 92% of the mean intraoperative arc. The mean time to achieve final motion was 3 months, with 79% of patients achieving their final ROM arc by this point. Patients achieved an average of 85% of their final arc of motion by the 2-week postoperative visit (92% of final flexion and 61% of final extension). Growth mixture modeling and latent class growth analysis did not identify any statistically significant groupings for postoperative ROM progression trajectories. Arc of motion preoperatively, intraoperatively, and at 2 weeks postoperatively did not correlate with the final arc of motion. There were no characteristics or thresholds of motion which conferred a higher likelihood of achieving a better result postoperatively. Conclusions: ROM recovery after osteocapsular débridement for PEOA is not dependent on preoperative, intraoperative, or 2-week postoperative arcs of motion. Most of the ROM recovery occurs in the early postoperative period, with flexion restored preferentially faster than extension. The final arc of motion can be expected by 3 months postoperatively. This knowledge has potential benefit in affecting patients’ personal time commitment to rehabilitation and the overall cost for therapy and splinting beyond the 3-month time point.

Background & Hypothesis: Post-traumatic stiffness of the elbow may be treated surgically with open osteocapsular release. This study investigated postoperative range of motion (ROM) improvements after this procedure. We hypothesized that there would be predictable recovery patterns and significant progress up to 6 months after surgery. Methods: A retrospective chart review of patients who underwent open elbow release for post-traumatic stiffness (PTS) was performed. Demographic information and surgical approach were recorded. Patients with ipsilateral primary elbow osteoarthritis were excluded. Range of motion (ROM) data were collected at preoperative, intraoperative, and postoperative intervals of 2 weeks, 6 weeks, 3 months, and 6 months. Growth mixture modeling (GMM) and latent class growth analysis (LCGA) were performed to identify motion recovery trajectory groups, and Student's t-tests were performed to compare ROM data between intervals. Results: One hundred and eighty-seven patients who underwent open elbow release for PTS were included (112 with a medial approach, 50 lateral, and 25 both). The mean preoperative arc was 84° ± 31, and the arc of motion at final follow-up was 119° ± 19 (P

Background: Historically, treatment of partial articular radial head fractures has hinged on radiographic assessment and application of the Mason classification. The inter- and intra-rater reliability of radiographic assessment and classification of radial head fractures may be lower than previously reported. We hypothesized that radiographic assessment leads to an underestimation of the number of fragments, percentage of articular surface involved, and displacement in millimeters. Methods: We performed a retrospective review of all Mason II radial head fractures treated at our institution. Four independent observers performed radiographic assessment of the cohort. An independent observer performed these measurements on high-resolution computed tomography (CT) imaging, the reference standard. Radiographic assessments were then correlated with the CT findings using Pearson's correlation coefficient and Kappa statistic, where indicated. Results: Fifty-nine Mason II radial head fractures were reviewed. These results were not impressive, with all comparisons showing a Kappa statistic less than 0.5 (ie, weak agreement). Intra-rater reliability was similar: displacement (measured by Pearson's correlation coefficient) was 0.58, percent articular involvement was 0.74, and the number of fragments (measured by the Kappa statistic) was 0.28. Fracture displacement was generally underestimated on radiographic measurements when compared to CT scan. Nearly half (45%) of all cases demonstrated inaccurate fragment number assessment when compared to the reference standard. Conclusion: Radiographs show poor inter- and intra-observer reliability for determining radial head fracture morphology. Assessment of the number of fragments was particularly inaccurate. High-resolution CT should be considered for patients with Mason II radial head fractures, especially in cases of poorly visualized fracture characteristics or borderline amounts of displacement, in an effort to appropriately indicate patients for the variety of treatment options available today.

Objective: Transcranial Focused Ultrasound (tFUS) is a promising new potential neuromodulation tool. However, the safety of tFUS neuromodulation has not yet been assessed adequately. Patients with refractory temporal lobe epilepsy electing to undergo an anterior temporal lobe resection present a unique opportunity to evaluate the safety and efficacy of tFUS neuromodulation. Histological changes in tissue after tFUS can be examined after surgical resection, while further potential safety concerns can be assessed using neuropsychological testing. Methods: Neuropsychological functions were assessed in eight patients before and after focused ultrasound sonication of the temporal lobe at intensities up to 5760 mW/cm2. Using the BrainSonix Pulsar 1002, tFUS was delivered under MR guidance, using the Siemens Magnetom 3T Prisma scanner. Neuropsychological changes were assessed using various batteries. Histological changes were assessed using hematoxylin and eosin staining, among others. Results: With respect to safety, the histological analysis did not reveal any detectable damage to the tissue, except for one subject for whom the histology findings were inconclusive. In addition, neuropsychological testing did not show any statistically significant changes in any test, except for a slight decrease in performance on one of the tests after tFUS. Significance: This study supports the hypothesis that low-intensity Transcranial Focused Ultrasound (tFUS) used for neuromodulation of brain circuits at intensities up to 5760 mW/cm2 may be safe for use in human research. However, due to methodological limitations in this study and inconclusive findings, more work is warranted to establish the safety. Future directions include greater number of sonications as well as longer exposure at higher intensity levels to further assess the safety of tFUS for modulation of neuronal circuits.

In patients with triple‐negative breast cancer (TNBC), evidence suggests that tumor‐initiating cells (TIC) have stem cell‐like properties, leading to invasion and metastasis. HSP90 plays a critical role in the conformational maintenance of many client proteins in TIC development. Therefore, we hypothesize that the novel C‐terminal HSP90 inhibitors KU711 and KU758 can target TIC and represent a promising strategy for overcoming metastasis. Human breast cancer cells (MDA‐MB‐468LN, MDA‐MB‐231) treated with the HSP90 inhibitors KU711, KU758, and 17‐AAG showed a 50–80% decrease in TIC markers CD44 and aldehyde dehydrogenase (P

Introduction: Adrenocortical carcinoma (ACC) is a rare endocrine malignancy, with very poor prognosis as a majority of the patients have advanced disease at the time of diagnosis. Currently, adjuvant therapy for most patients consists of either mitotane (M) alone or in combination with multi-drug chemotherapeutics such as etoposide (E), doxorubicin (D), and cisplatin (P), known as the Italian protocol (IP; EDPM). This multi-drug treatment regimen, however, carries significant toxicity potential for patients. One way to improve toxicity profiles with these drugs in combination is to understand where their synergy occurs and over what dosing range so that lower dose regimens could be applied in combination with equal or improved efficacy. We hypothesize that a better understanding of the synergistic effects as well as the regulation of steroidogenic enzymes during combination therapy may provide more optimized combinational options with good potency and lower toxicity profiles. Methods: Two human ACC cell lines, NCI-H295R (hormonally active) and SW13 (hormonally inactive), were grown in 2D culture in appropriate growth medium. The viability of the cells after treatment with varying concentrations of the drugs (E, D, and P) either alone or in combinations with M was determined using the CellTiter Glow assay after 72 h, and the combination index for each was calculated using Compusyn by the Chou–Talalay method. The expression levels of enzymes associated with steroidogenesis were evaluated by RT-PCR in NCI-H295R. Results: When both cell lines were treated with M (ranging 25–50 μM), +E (ranging 18.75–75 μM), and +D (ranging 0.625–2.5 μM) we observed a synergistic effect (CI < 1) with potency equivalent to the full Italian protocol (IP), whereas combining M + P + D had an antagonistic effect (CI > 1) indicating the negative effect of adding cisplatin in the combination. Comparing the hormonally active and inactive cell lines, M + P + E was antagonistic in NCI-H295R and synergistic in SW13. Treatment of NCI-H295R cells with antagonistic combinations (M + P + D, M + P + E) resulted in a significant decrease in the levels of steroidogenic enzymes STAR, CYP11A1, and CYP21A2 compared to IP (p < 0.05) while M + E + D resulted in increased expression or no significant effect compared to IP across all genes tested. Conclusions: The synergistic effect for M + E + D was significant and equivalent in potency to the full IP in both cell lines and resulted in a steroidogenic gene expression profile similar to or better than that of full IP, warranting further evaluation. Future in vivo evaluation of the combination of M + E + D (with removal of P from the IP regimen) may lower toxicity while maintaining anticancer efficacy in ACC.

Introduction: Deficits in visual perception are well-established in schizophrenia and are linked to abnormal activity in the lateral occipital complex (LOC). Related deficits may exist in bipolar disorder. LOC contains neurons tuned to object features. It is unknown whether neural tuning in LOC or other visual areas is abnormal in patients, contributing to abnormal perception during visual tasks. This study used multivariate pattern analysis (MVPA) to investigate perceptual tuning for objects in schizophrenia and bipolar disorder. Methods: Fifty schizophrenia participants, 51 bipolar disorder participants, and 47 matched healthy controls completed five functional magnetic resonance imaging (fMRI) runs of a perceptual task in which they viewed pictures of four different objects and an outdoor scene. We performed classification analyses designed to assess the distinctiveness of activity corresponding to perception of each stimulus in LOC (a functionally localized region of interest). We also performed similar classification analyses throughout the brain using a searchlight technique. We compared classification accuracy and patterns of classification errors across groups. Results: Stimulus classification accuracy was significantly above chance in all groups in LOC and throughout visual cortex. Classification errors were mostly within-category confusions (e.g., misclassifying one chair as another chair). There were no group differences in classification accuracy or patterns of confusion. Conclusions: The results show for the first time MVPA can be used successfully to classify individual perceptual stimuli in schizophrenia and bipolar disorder. However, the results do not provide evidence of abnormal neural tuning in schizophrenia and bipolar disorder. Keywords: Schizophrenia, Bipolar disorder, fMRI, Multivariate pattern analysis, Visual perception

The underlying mechanisms of alpha band (8–12 Hz) neural oscillations are of importance to the functioning of attention control systems as well as to neuropsychiatric conditions that are characterized by deficits of that system, such as attention deficit hyperactivity disorder (ADHD). The objectives of the present study were to test if visual encoding-related alpha event-related desynchronization (ERD) correlates with fronto-parieto-occipital connectivity, and whether this is disrupted in ADHD during spatial working memory (SWM) performance. We acquired EEG concurrently with fMRI in thirty boys (12–16 yrs. old, 15 with ADHD), during SWM encoding. Psychophysiological connectivity analyses indicated that alpha ERD during SWM encoding was associated with both occipital activation and fronto-parieto-occipital functional connectivity, a finding that expands on prior associations between alpha ERD and occipital activation. This finding provides novel support for the interpretation of alpha ERD (and the associated changes in occipital activation) as a phenomenon that involves, and perhaps arises as a result of, top-down network interactions. Alpha ERD was associated less strongly with occipital activity, but associated more strongly with fronto-parieto-occipital connectivity in ADHD, consistent with a compensatory attentional response. Additionally, we illustrate that degradation of EEG data quality by MRI-amplified motion artifacts is robust to existing cleaning algorithms and is significantly correlated with hyperactivity symptoms and the ADHD Combined Type diagnosis. We conclude that persistent motion-related MR artifacts in EEG data can increase variance and introduce bias in interpretation of group differences in populations characterized by hypermobility — a clear limitation of current-state EEG-fMRI methodology.

While geriatric patients have a high likelihood of requiring anesthesia, they carry an increased risk for adverse cognitive outcomes from its use. Previous work suggests this could be mitigated by better intraoperative monitoring using indexes defined by several processed electroencephalogram (EEG) measures. Unfortunately, inconsistencies between patients and anesthetic agents in current analysis techniques have limited the adoption of EEG as standard of care. In attempts to identify new analyses that discriminate clinically-relevant anesthesia timepoints, we tested 1/f frequency scaling as well as measures of complexity from nonlinear dynamics. Specifically, we tested whether analyses that characterize time-delayed embeddings, correlation dimension (CD), phase-space geometric analysis, and multiscale entropy (MSE) capture loss-of-consciousness changes in EEG activity. We performed these analyses on EEG activity collected from a traditionally hard-to-monitor patient population: geriatric patients on beta-adrenergic blockade who were anesthetized using a combination of fentanyl and propofol. We compared these analyses to traditional frequency-derived measures to test how well they discriminated EEG states before and after loss of response to verbal stimuli. We found spectral changes similar to those reported previously during loss of response. We also found significant changes in 1/f frequency scaling. Additionally, we found that our phase-space geometric characterization of time-delayed embeddings showed significant differences before and after loss of response, as did measures of MSE. Our results suggest that our new spectral and complexity measures are capable of capturing subtle differences in EEG activity with anesthesia administration—differences which future work may reveal to improve geriatric patient monitoring.

Watching another person in pain activates brain areas involved in the sensation of our own pain. Importantly, this neural mirroring is not constant; rather, it is modulated by our beliefs about their intentions, circumstances, and group allegiances. We investigated if the neural empathic response is modulated by minimally-differentiating information (e.g., a simple text label indicating another's religious belief), and if neural activity changes predict ingroups and outgroups across independent paradigms. We found that the empathic response was larger when participants viewed a painful event occurring to a hand labeled with their own religion (ingroup) than to a hand labeled with a different religion (outgroup). Counterintuitively, the magnitude of this bias correlated positively with the magnitude of participants' self-reported empathy. A multivariate classifier, using mean activity in empathy-related brain regions as features, discriminated ingroup from outgroup with 72% accuracy; the classifier's confidence correlated with belief certainty. This classifier generalized successfully to validation experiments in which the ingroup condition was based on an arbitrary group assignment. Empathy networks thus allow for the classification of long-held, newly-modified and arbitrarily-formed ingroups and outgroups. This is the first report of a single machine learning model on neural activation that generalizes to multiple representations of ingroup and outgroup. The current findings may prove useful as an objective diagnostic tool to measure the magnitude of one's group affiliations, and the effectiveness of interventions to reduce ingroup biases.

Early visual perception and attention are impaired in schizophrenia, and these deficits can be observed on target detection tasks. These tasks activate distinct ventral and dorsal brain networks which support stimulus-driven and goal-directed attention, respectively. We used single and dual target rapid serial visual presentation (RSVP) tasks during fMRI with an ROI approach to examine regions within these networks associated with target detection and the attentional blink (AB) in 21 schizophrenia outpatients and 25 healthy controls. In both tasks, letters were targets and numbers were distractors. For the dual target task, the second target (T2) was presented at 3 different lags after the first target (T1) (lag1=100ms, lag3=300ms, lag7=700ms). For both single and dual target tasks, patients identified fewer targets than controls. For the dual target task, both groups showed the expected AB effect with poorer performance at lag 3 than at lags 1 or 7, and there was no group by lag interaction. During the single target task, patients showed abnormally increased deactivation of the temporo-parietal junction (TPJ), a key region of the ventral network. When attention demands were increased during the dual target task, patients showed overactivation of the posterior intraparietal cortex, a key dorsal network region, along with failure to deactivate TPJ. Results suggest inefficient and faulty suppression of salience-oriented processing regions, resulting in increased sensitivity to stimuli in general, and difficulty distinguishing targets from non-targets.

The analysis of fMRI data is challenging because they consist generally of a relatively modest signal contained in a high-dimensional space: a single scan can contain over 15 million voxel recordings over space and time. We present a method for classification and discrimination among fMRI that is based on modeling the scans as distance matrices, where each matrix measures the divergence of spatial network signals that fluctuate over time. We used single-subject independent components analysis (ICA), decomposing an fMRI scan into a set of statistically independent spatial networks, to extract spatial networks and time courses from each subject that have unique relationship with the other components within that subject. Mathematical properties of these relationships reveal information about the infrastructure of the brain by measuring the interaction between and strength of the components. Our technique is unique, in that it does not require spatial alignment of the scans across subjects. Instead, the classifications are made solely on the temporal activity taken by the subject's unique ICs. Multiple scans are not required and multivariate classification is implementable, and the algorithm is effectively blind to the subject-uniform underlying task paradigm. Classification accuracy of up to 90% was realized on a resting-scanned schizophrenia/normal dataset and a tasked multivariate Alzheimer's/old/young dataset. We propose that the ICs represent a plausible set of imaging basis functions consistent with network-driven theories of neural activity in which the observed signal is an aggregate of independent spatial networks having possibly dependent temporal activity.