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Updated: Feb 9, 2026

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks
Published on: December 2, 2015
Locally stable brain states predict suppression of epileptic activity by enhanced cognitive effort
Sarah F Muldoon1, Julia Costantini2, W R S Webber3
1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; US Army Research Laboratory, Aberdeen, MD 21005, USA.
Abstract:
Cognitive effort is known to play a role in healthy brain state organization, but little is known about its effects on pathological brain dynamics. When cortical stimulation is used to map functional brain areas prior to surgery, a common unwanted side effect is the appearance of afterdischarges (ADs), epileptiform and potentially epileptogenic discharges that can progress to a clinical seizure. It is therefore desirable to suppress this activity. Here, we analyze electrocorticography recordings from 15 patients with epilepsy. We show that a cognitive intervention in the form of asking an arithmetic question can be effective in suppressing ADs, but that its effectiveness is dependent upon the brain state at the time of intervention. By applying novel techniques from network analysis to quantify brain states, we find that the spatial organization of ADs with respect to coherent brain regions relates to the success of the cognitive intervention: if ADs are mainly localized within a single stable brain region, a cognitive intervention is likely to suppress the ADs. These findings show that cognitive effort is a useful tactic to modify unstable pathological activity associated with epilepsy, and suggest that the success of therapeutic interventions to alter activity may depend on an individual's brain state at the time of intervention.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

