Skin conductance response (SCR) is often used as an index of conditioned fear. SCR has been shown to be highly variable, and absence of SC reactivity is sometimes used as criteria for excluding data. It is, however, possible that low or no SC reactivity is the result of a distinct biological signature that underlies individual differences in SCR reactivity. This study examined neural correlates associated with the near absence of SCR conditionability. Archival data from 109 healthy adults aged 18–60 years were pooled. All individuals had participated in a fear conditioning protocol in a fMRI environment, during which two cues were partially reinforced (CS+) with a shock and a third cue was not (CS−). Using SCR to the conditioned stimuli and differential SCR (CS+ minus CS−), we created two groups of 30 individuals: low conditioners (defined as those showing the smallest SCR to the CS+ and smallest differential SCR) and high conditioners (defined as those showing the largest SCR to the CS+ and largest differential SCR). Our analyses showed differences in patterns of brain activations between these two groups during conditioning in the following regions: dorsal anterior cingulate cortex, amygdala, subgenual anterior cingulate cortex, and insular cortex. Our findings suggest that low or absent SCR conditionability is associated with hypoactivation of brain regions involved in fear learning and expression. This highlights the need to be cautious when excluding SCR nonconditioners and to consider the potential implications of such exclusion when interpreting the findings from studies of conditioned fear.

Access to affordable, objective and scalable biomarkers of brain function is needed to transform the healthcare burden of neuropsychiatric and neurodegenerative disease. Electroencephalography (EEG) recordings, both resting and in combination with targeted cognitive tasks, have demonstrated utility in tracking disease state and therapy response in a range of conditions from schizophrenia to Alzheimer's disease. But conventional methods of recording this data involve burdensome clinic visits, and behavioural tasks that are not effective in frequent repeated use. This paper aims to evaluate the technical and human-factors feasibility of gathering large-scale EEG using novel technology in the home environment with healthy adult users. In a large field study, 89 healthy adults aged 40–79 years volunteered to use the system at home for 12 weeks, 5 times/week, for 30 min/session. A 16-channel, dry-sensor, portable wireless headset recorded EEG while users played gamified cognitive and passive tasks through a tablet application, including tests of decision making, executive function and memory. Data was uploaded to cloud servers and remotely monitored via web-based dashboards. Seventy-eight participants completed the study, and high levels of adherence were maintained throughout across all age groups, with mean compliance over the 12-week period of 82% (4.1 sessions per week). Reported ease of use was also high with mean System Usability Scale scores of 78.7. Behavioural response measures (reaction time and accuracy) and EEG components elicited by gamified stimuli (P300, ERN, Pe and changes in power spectral density) were extracted from the data collected in home, across a wide range of ages, including older adult participants. Findings replicated well-known patterns of age-related change and demonstrated the feasibility of using low-burden, large-scale, longitudinal EEG measurement in community-based cohorts. This technology enables clinically relevant data to be recorded outside the lab/clinic, from which metrics underlying cognitive ageing could be extracted, opening the door to potential new ways of developing digital cognitive biomarkers for disorders affecting the brain.

Effective strategies for early detection of cognitive decline, if deployed on a large scale, would have individual and societal benefits. However, current detection methods are invasive or time-consuming and therefore not suitable for longitudinal monitoring of asymptomatic individuals. For example, biological markers of neuropathology associated with cognitive decline are typically collected via cerebral spinal fluid, cognitive functioning is evaluated from face-to-face assessments by experts and brain measures are obtained using expensive, non-portable equipment. Here, we describe scalable, repeatable, relatively non-invasive and comparatively inexpensive strategies for detecting the earliest markers of cognitive decline. These approaches are characterized by simple data collection protocols conducted in locations outside the laboratory: measurements are collected passively, by the participants themselves or by non-experts. The analysis of these data is, in contrast, often performed in a centralized location using sophisticated techniques. Recent developments allow neuropathology associated with potential cognitive decline to be accurately detected from peripheral blood samples. Advances in smartphone technology facilitate unobtrusive passive measurements of speech, fine motor movement and gait, that can be used to predict cognitive decline. Specific cognitive processes can be assayed using ‘gamified’ versions of standard laboratory cognitive tasks, which keep users engaged across multiple test sessions. High quality brain data can be regularly obtained, collected at-home by users themselves, using portable electroencephalography. Although these methods have great potential for addressing an important health challenge, there are barriers to be overcome. Technical obstacles include the need for standardization and interoperability across hardware and software. Societal challenges involve ensuring equity in access to new technologies, the cost of implementation and of any follow-up care, plus ethical issues.

Recent advances have enabled the creation of wireless, “dry” electroencephalography (EEG) recording systems, and easy-to-use engaging tasks, that can be operated repeatedly by naïve users, unsupervised in the home. Here, we evaluated the validity of dry-EEG, cognitive task gamification, and unsupervised home-based recordings used in combination. Two separate cohorts of participants—older and younger adults—collected data at home over several weeks using a wireless dry EEG system interfaced with a tablet for task presentation. Older adults (n = 50; 25 females; mean age = 67.8 years) collected data over a 6-week period. Younger male adults (n = 30; mean age = 25.6 years) collected data over a 4-week period. All participants were asked to complete gamified versions of a visual Oddball task and Flanker task 5–7 days per week. Usability of the EEG system was evaluated via participant adherence, percentage of sessions successfully completed, and quantitative feedback using the System Usability Scale. In total, 1,449 EEG sessions from older adults (mean = 28.9; SD = 6.64) and 684 sessions from younger adults (mean = 22.87; SD = 1.92) were collected. Older adults successfully completed 93% of sessions requested and reported a mean usability score of 84.5. Younger adults successfully completed 96% of sessions and reported a mean usability score of 88.3. Characteristic event-related potential (ERP) components—the P300 and error-related negativity—were observed in the Oddball and Flanker tasks, respectively. Using a conservative threshold for inclusion of artifact-free data, 50% of trials were rejected per at-home session. Aggregation of ERPs across sessions (2–4, depending on task) resulted in grand average signal quality with similar Standard Measurement Error values to those of single-session wet EEG data collected by experts in a laboratory setting from a young adult sample. Our results indicate that easy-to-use task-driven EEG can enable large-scale investigations in cognitive neuroscience. In future, this approach may be useful in clinical applications such as screening and tracking of treatment response.

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Background Adaptation to new demands is a fundamental skill that relies on the neuroplasticity of the brain. The breadth of this adaptation (an aspect of cognitive function) is reduced with ageing and is further perturbed with amnesic mild cognitive impairment. Perturbed adaptation skills could be a potential marker of early cognitive deficits, which is key for early diagnosis. Here we aim to investigate behavioural and neurophysiological adaptation to a gamified tablet‐based version of the visual oddball paradigm in older adults across 12 weeks. Method 89 neurotypical older adults (mean age: 58.7 years, SD: 8.89; mean MoCA score: 27.1, SD: 1.8; 77.5% female) were asked to perform a gamified 2‐stimulus visual oddball paradigm 5 times a week during a 3‐month period, while neurophysiology was recorded with a 16‐channel EEG headset designed for unsupervised use in the home. A session event‐related potential (ERP) was calculated per channel, which was then downsampled into 8‐ms bins. To assess the effect of session on the ERP signal and on the average reaction time (RT), we statistically tested two mixed effects models (per channel‐temporal bin): i) a reference model, with covariates and subject as a random effect; and ii) an across‐sessions model, with Session added as a fixed effect to the reference model. Result Behaviourally, RTs to Target stimulus were significantly faster across sessions. RTs in the first session and the rate of improvement between the 1st and 2nd sessions were modulated by the MoCA score. In response to the Target stimulus, there were amplitude increases with session overlapping the P300 component on frontal channels (FCz, Fpz), and on parietal channels (CPz, Cz, P3, P4). In response to the Non Target stimulus, there was a higher amplitude with Session in FC3 between 209.9‐242 ms, and in Fpz between 482.6‐514.7 ms. Conclusion A gamified visual oddball task is sensitive to MoCA score categorization at baseline and early stages of adaptation in neurotypical subjects. Potential neurophysiological adaptation, accompanying the behavioural adaptation, is suggested by EEG P300 amplitudes that increase across sessions.

Background Neurodegenerative conditions such as AD, PD and MS can develop slowly, making early detection and on‐going characterisation a challenge. Current methods rely primarily on clinical opinion/self‐reports, or on burdensome and expensive clinic‐based methods. Visual and other sensory evoked potential tasks, particularly with steady‐state stimulation, have been used to probe neuronal function in healthy aging (Sridhar & Manian 2019), and neurodegeneration (Jacob et al 2002, Viallate et al 2010), in pre‐clinical and symptomatic stages (Shahmiri et al 2017) – but using conventional lab/clinic based wired wet EEG systems. In this proof‐of‐concept study, we evaluate wireless dry EEG as a less burdensome alternative. If feasible this could provide an easy‐to‐use, scalable and objective measure of neuronal function, for use in larger longitudinal studies of these conditions. Methods We compare signals recorded from a wireless dry‐EEG headset (BrainWaveBank Ltd – Murphy 2018, 2019), and state‐of‐the‐art conventional wet‐EEG hardware (Biosemi ActiveTwo). Informed consent was obtained from 8 healthy adult males for this methodological study. Each attended 2 separate in‐lab sessions, one week apart, in which both dry and wet recordings were made. Static and flickering steady state (14Hz) visual stimulus conditions were presented. Grand average VEPs were calculated for each recording and stimulus condition. Signal stability was quantified using a Monte Carlo process to estimate 95% confidence intervals. From the steady state VEP, scalp topographies of spectral power were also computed. Results After band‐pass and notch filtering, a grand average of a simple visual‐evoked potential (Figure 1) recorded at posterior recording sites with the wireless dry‐EEG headset has similar waveform morphology and noise levels to that from conventional wet‐EEG equipment – as does the steady state VEP (Figure 2). Topographies of spectral power at 14 and 28Hz (Figure 3) show a similar central/posterior scalp distribution. Conclusions In controlled environments, a wireless dry‐sensor EEG headset can yield aggregate data of comparable quality to a state‐of‐the‐art wet‐EEG, making much larger scale studies of sensory processing and broader neuronal function possible for the first time.