Mild traumatic brain injury (mTBI), or concussion, is the most common type of traumatic brain injury. With mTBI comes symptoms that include headaches, fatigue, depression, anxiety and irritability, as well as impaired cognitive function. Symptom resolution is thought to occur within 3 months post-injury, with the exception of a small percentage of individuals who are said to experience persistent post-concussion syndrome. The number of individuals who experience persistent symptoms appears to be low despite clear evidence of longer-term pathophysiological changes resulting from mTBI. In light of the incongruency between these longer-term changes in brain pathology and the number of individuals with longer-term mTBI-related symptoms, particularly impaired cognitive function, we performed a scoping review of the literature that behaviourally assessed short- and long-term cognitive function in individuals with a single mTBI, with the goal of identifying the impact of a single concussion on cognitive function in the chronic stage post-injury. CINAHL, Embase, and Medline/Ovid were searched July 2015 for studies related to concussion and cognitive impairment. Data relating to the presence/absence of cognitive impairment were extracted from 45 studies meeting our inclusion criteria. Results indicate that, in contrast to the prevailing view that most symptoms of concussion are resolved within 3 months post-injury, approximately half of individuals with a single mTBI demonstrate long-term cognitive impairment. Study limitations notwithstanding, these findings highlight the need to carefully examine the long-term implications of a single mTBI.

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Decomposition-enhanced spike-triggered averaging (DE-STA) has been developed as a method for obtaining a motor unit number estimate (MUNE). We describe the method and report control data for the first dorsal interosseous/adductor pollicis and thenar muscles and reliability in the thenar muscles. Seventeen subjects (ages 20-50 years) took part in the study. The maximum M potential was elicited with supramaximal stimulation of the ulnar or median nerve at the wrist. Surface and intramuscularly detected electromyographic signals were then collected simultaneously during mild to moderate contractions. Decomposition algorithms were used to detect and sort the individual motor unit potential (MUP) occurrences of several concurrently active motor units in the needle-detected signals. The MUP occurrences were used as triggering sources to estimate their corresponding surface-detected MUPs (S-MUPs) using STA. The mean S-MUP size was calculated and divided into the maximum M-potential size to derive a MUNE. The MUNE values were consistent with those previously reported with other methods, and thenar MUNEs for the two trials were similar (249 +/- 78 and 246 +/- 90), with high test-retest reliability (r = 0.94, P < 0.05). DE-STA thus appears to be a valid and reliable method to obtain MUNEs.

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Decomposition-based quantitative electromyography (DQEMG) allows for the collection of motor unit potentials (MUPs) over a broad range of force levels. Given the size principle of motor unit recruitment, it may be necessary to control for force when using DQEMG for the purpose of deriving a motor unit number estimate (MUNE). Therefore, this study was performed to examine the effect of force on the physiological characteristics of concentric needle- and surface-detected MUPs and the subsequent impact on MUNEs obtained from the first dorsal interosseous (FDI) muscle sampled using DQEMG. Maximum M waves were elicited in 10 subjects with supramaximal stimulation of the ulnar nerve at the wrist. Intramuscular and surface-detected EMG signals were collected simultaneously during 30-s voluntary isometric contractions performed at specific percentages of maximal voluntary contraction (MVC). Decomposition algorithms were used to identify needle-detected MUPs and their individual MU firing times. These MU firing times were used as triggers to extract their corresponding surface-detected MUPs (S-MUPs) using spike-triggered averaging. A mean S-MUP was then calculated, the size of which was divided into the maximum M-wave size to derive a MUNE. Increased levels of contraction had a significant effect on needle- and surface-detected MUP size, firing rate, and MUNE. These results suggest that force level is an important factor to consider when performing quantitative EMG, including MUNEs with this method.

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Motor learning depends upon plasticity in neural networks involved in the planning and execution of movement. Physical practice (PP) is the primary means of motor learning, but it can be augmented with nonphysical forms of practice including motor imagery (MI)-the mental rehearsal of movement. It is unknown if MI alone, without prior PP of a movement, can produce robust learning. Here the authors used an implicit sequence learning task to explore motor learning via MI alone or PP. Participants underwent implicit sequence learning training via MI (n = 31) or PP (n = 33). Posttraining reaction time was faster for implicit versus random sequences for both the MI group (M = 583 ± 84 ms; 632 ± 86 ms, d = 0.59) and PP group (M = 532 ± 73 ms; 589 ± 70 ms, d = 0.80), demonstrating that MI without PP facilitated skill acquisition. Relative to MI alone, PP led to reduced reaction time for both random (d = 0.65) and implicit sequences (d = 0.55) consistent with a nonspecific motor benefit favoring PP over MI. These results have broad implication for theories of MI and support the use of MI as a form of practice to acquire implicit motor skills. (PsycINFO Database Record

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