Cluneal neuropathy: Background, diagnosis, and treatment.

BACKGROUND AND AIMS: Cluneal neuropathy is encompassed by three distinct clinical entities. Superior, middle, and inferior cluneal neuralgia make up the constellation of symptoms associated with cluneal neuropathy. Each has its own variable anatomy. MATERIALS AND METHODS: We compiled a narrative review including a review of available literature. We conducted searches on PubMed/MEDLINE, Embase and Google Scholar on the topics of cluneal neuropathy and treatment therein. RESULTS: We collected source articles regarding original descriptions of the disease entities in addition to articles focused on treatment. DISCUSSION: Adjusted incidence rates of superior cluneal neuropathy are 1.6%-11.7%. Accurate diagnosis remains challenging due to the lack of standardized criteria and the aforementioned variability. Treatment may include therapeutic nerve blocks, ablative techniques, neuromodulation, and surgical decompression. Gaps including those related to true incidence and work up exist. Outcomes from interventional studies are limited and mixed due to significant population heterogeneity and non-standardized treatment approaches coupled with very small sample sizes.

Outcome Measures in Cancer Rehabilitation: Pain, Function, and Symptom Assessment.

Assessment of cancer rehabilitation outcome measures is integral for patient assessment, symptom screening, and advancing scientific research. In the broad field of cancer rehabilitation, outcome measures can cross-cut across many different branches of oncologic care including clinician-reported, patient-reported, and objective measures. Specific outcome measures that apply to cancer rehabilitation include those pertinent to pain, function, quality of life, fatigue, and cognition. These outcome measures, when used in cancer rehabilitation, can be utilized to evaluate the effectiveness of an intervention and to triage to the appropriate supportive care service. This review article summarizes some of the commonly used outcome measures that can be applied in the cancer rehabilitation setting to support scholarly work and patient care.

Neural decoding of expressive human movement from scalp electroencephalography (EEG).

Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities ("Neutral"), non-expressive movements while thinking about specific expressive qualities ("Think"), and enacted expressive movements ("Do"). The expressive movement qualities that were used in the "Think" and "Do" actions consisted of a sequence of eight Laban Effort qualities as defined by LMA-a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2-4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.