Vestibular rehabilitation should be considered in the management of individuals post concussion who have dizziness and gait and balance dysfunction that do not resolve with rest.

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The diagnosis of cervicogenic dizziness is characterized by dizziness and dysequilibrium that is associated with neck pain in patients with cervical pathology. The diagnosis and treatment of an individual presenting with cervical spine dysfunction and associated dizziness complaints can be a challenging experience to orthopaedic and vestibular rehabilitation specialists. The purpose of this article is to review the incidence and prevalence, historical background, and proposed pathophysiology underlying cervicogenic dizziness. In addition, we have outlined the diagnostic criteria, evaluation, and treatment of dizziness attributed to disorders of the cervical spine. The diagnosis of cervicogenic dizziness is dependent upon correlating symptoms of imbalance and dizziness with neck pain and excluding other vestibular disorders based on history, examination, and vestibular function tests. When diagnosed correctly, cervicogenic dizziness can be successfully treated using a combination of manual therapy and vestibular rehabilitation. We present 2 cases, of patients diagnosed with cervicogenic dizziness, as an illustration of the clinical decisionmaking process in regard to this diagnosis. ) Orthop Sports Phys Ther 2000;30:755-766.

Significance: Isolating task-evoked brain signals from background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations) poses a major challenge for the analysis of functional near-infrared spectroscopy (fNIRS) data. Aim: The performance of several analytic methods to separate background physiological noise from brain activity including spatial and temporal filtering, regression, component analysis, and the use of short-separation (SS) measurements were quantitatively compared. Approach: Using experimentally recorded background signals (breath-hold task), receiver operating characteristics simulations were performed by adding various levels of additive synthetic "brain" responses in order to examine the sensitivity and specificity of several previously proposed analytic approaches. Results: We found that the use of SS fNIRS channels as regressors of no-interest within a linear regression model was the best performing approach examined. Furthermore, we found that the addition of all available SS data, including all recorded channels and both hemoglobin species, improved the method performance despite the additional degrees-of-freedom of the models. When SS data were not available, we found that principal component filtering using a separate baseline scan was the best alternative. Conclusions: The use of multiple SS measurements as regressors of no interest implemented in a robust, iteratively prewhitened, general linear model has the best performance of the tested existing methods.

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