Mild Traumatic Brain Injury Affects Cognitive Processing and Modifies Oscillatory Brain Activity during Attentional Tasks.

Despite the high prevalence of mild traumatic brain injury (mTBI), current diagnostic tools to objectively assess cognitive complaints after mTBI continue to be inadequate. Our aim was to identify neuronal correlates for cognitive difficulties in mTBI patients by evaluating the possible alterations in oscillatory brain activity during a behavioral task known to be sensitive to cognitive impairment after mTBI. We compared oscillatory brain activity during rest and cognitive tasks (Paced Auditory Serial Addition Test [PASAT] and a vigilance test [VT]) with magnetoencephalography between 25 mTBI patients and 20 healthy controls. Whereas VT induced no significant differences compared with resting state in either group, patients exhibited stronger attenuation of 8- to 14-Hz oscillatory activity during PASAT than healthy controls in the left parietotemporal cortex (p ≤ 0.05). Further, significant task-related modulation in the left superior frontal gyrus and right prefrontal cortex was detected only in patients. The ∼10-Hz (alpha) peak frequency declined in frontal, temporal, and parietal regions during PASAT compared with rest (p < 0.016) in patients, whereas in controls it remained the same or showed a tendency to increase. In patients, the ∼10-Hz peak amplitude was negatively correlated with behavioral performance in the Trail Making Test. The observed alterations in the cortical oscillatory activity during cognitive load may provide measurable neurophysiological correlates of cognitive difficulties in mTBI patients, even at the individual level.

Overnight features of transcutaneous carbon dioxide measurement as predictors of metabolic status.

OBJECTIVE: To systematically investigate whether overnight features in transcutaneous carbon dioxide (P(TcCO(2)) measurements can predict metabolic variables in subject with suspected sleep-disordered breathing. METHODS: The features extracted from the P(TcCO(2)) signal included the number of abrupt descents per hour and attributes that characterize the recovery after such an event. For each outcome variable, the subgroup of the 108 study subjects with the particular variable present was divided into two representative classes, and the optimal features that can predict the classes were learned. Overfitting was avoided by evaluating the classification algorithms using 10-fold cross-validation. RESULTS: (P(TcCO(2)) signal has a key role in determining the classes of high-density lipoprotein cholesterol and thyroid-stimulating hormone concentrations, and it improves the classification accuracy of glycosylated hemoglobin A1c and fasting plasma glucose values. CONCLUSIONS: The features learned from the (P(TcCO(2)) signal reflected the state of the selected metabolic variables in a subtle, but systematic, way. These findings provide a step towards understanding how metabolic disturbances are connected to carbon dioxide exchange during sleep.