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Humans’ complex behavior, such as speech, music, or dance, requires us to coordinate our actions with external sounds as well as with social partners. The presence of a partner can influence individuals’ synchronization, and, in turn, social connection with the partner may depend on the degree of synchronization. We manipulated the synchronization quality in intervention conditions to address the causal relationship between observed temporal synchrony and perceived social interaction. Pairs of musician and nonmusician participants first performed a turn-taking task consisting of alternating which partner tapped their melody in synchrony with a metronome (each tap generated the next tone in the melody). In two intervention conditions, participants attempted to synchronize their melodies simultaneously with their partner, either with normal auditory feedback (normal feedback) or randomly placed delayed feedback on 25% of melodic tones (delayed feedback). After each intervention, the turn-taking condition was repeated, and participants completed a questionnaire about connectedness, relationship, and feeling of synchronization with their partner. Results showed that partners’ mean asynchronies were more negative following the delayed feedback intervention. In addition, nonmusician partners’ tapping variability was larger following the delayed feedback intervention when they had the delayed feedback intervention first. Ratings of connectedness, relationship, and feeling of synchronization with their partner were reduced for all participants after the delayed feedback Intervention. We modeled participants’ synchronization performance in the post-intervention turn-taking conditions using delay-coupling oscillator models. Reductions in synchronization performance after delayed feedback intervention were reflected in reduced coupling strength. These findings suggest that turn-taking synchronization performance and social connectedness are altered following short interventions that disrupt synchronization with a partner.

Evidence for the role of procedural memory in second language (L2) acquisition has emerged in our field. However, little is known about the reliability and validity of the procedural memory measures used in this research. The present study (N = 119) examined the reliability and the convergent and discriminant validity of three assessments that have previously been used to examine procedural memory learning ability in L2 acquisition, the dual-task Weather Prediction Task (DT-WPT), the Alternating Serial Reaction Time Task (ASRT), and the Tower of London (TOL). Measures of declarative memory learning ability were also collected. For reliability, the DT-WPT and TOL tasks met acceptable standards. For validity, an exploratory factor analysis did not provide evidence for convergent validity, but the ASRT and the TOL showed reasonable discriminant validity with declarative memory measures. We argue that the ASRT may provide the purest engagement of procedural memory learning ability, although more reliable dependent measures for this task should be considered. The Serial Reaction Time task also appears promising, although we recommend further consideration of this task as the present analyses were post hoc and based on a smaller sample. We discuss these results regarding the assessment of procedural memory learning ability as well as implications for implicit language aptitude.

Interpersonal synchrony refers to the temporal coordination of actions between individuals and is a common feature of social behaviors, from team sport to ensemble music performance. Interpersonal synchrony of many rhythmic (periodic) behaviors displays dynamics of coupled biological oscillators. The current study addresses oscillatory dynamics on the levels of brain and behavior between music duet partners performing at spontaneous (uncued) rates. Wireless EEG was measured from N = 20 pairs of pianists as they performed a melody first in Solo performance (at their spontaneous rate of performance), and then in Duet performances at each partner’s spontaneous rate. Influences of partners’ spontaneous rates on interpersonal synchrony were assessed by correlating differences in partners’ spontaneous rates of Solo performance with Duet tone onset asynchronies. Coupling between partners’ neural oscillations was assessed by correlating amplitude envelope fluctuations of cortical oscillations at the Duet performance frequency between observed partners and between surrogate (re-paired) partners, who performed the same melody but at different times. Duet synchronization was influenced by partners’ spontaneous rates in Solo performance. The size and direction of the difference in partners’ spontaneous rates were mirrored in the size and direction of the Duet asynchronies. Moreover, observed Duet partners showed greater inter-brain correlations of oscillatory amplitude fluctuations than did surrogate partners, suggesting that performing in synchrony with a musical partner is reflected in coupled cortical dynamics at the performance frequency. The current study provides evidence that dynamics of oscillator coupling are reflected in both behavioral and neural measures of temporal coordination during musical joint action.

The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input.

Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke data can aid in clinical decision-making and provide insights into the individual symptoms of mood disorders. This study aims to derive digital phenotypes based on smartphone keyboard backspace use among 128 community adults across 2948 observations using a Bayesian mixture model. Eligible study participants completed a virtual screening visit where all eligible participants were instructed to download the custom-built BiAffect smartphone keyboard (University of Illinois). The BiAffect keyboard unobtrusively captures keystroke dynamics. All eligible and consenting participants were instructed to use this keyboard exclusively for up to 4 weeks of the study in real life, and participants' compliance was checked at the 2 follow-up visits at week 2 and week 4. As part of the research protocol, every study participant underwent evaluations by a study psychiatrist during each visit. We found that derived phenotypes were associated with not only the diagnoses and severity of depression and mania but also specific individual symptoms. Using a linear mixed-effects model with random intercepts accounting for the nested data structure from daily data, the backspace rates on the continuous scale did not differ between participants in the healthy control and in the mood disorders groups (P=.11). The 3-class model had mean backspace rates of 0.112, 0.180, and 0.268, respectively, with a SD of 0.048. In total, 3 classes, respectively, were estimated to comprise 37.5% (n=47), 54.4% (n=72), and 8.1% (n=9) of the sample. We grouped individuals into Low, Medium, and High backspace rate groups. Individuals with unipolar mood disorder were predominantly in the Medium group (n=54), with some in the Low group (n=27) and a few in the High group (n=6). The Medium group, compared with the Low group, had significantly higher ratings of depression (b=2.32, P=.008). The High group was not associated with ratings of depression with (P=.88) or without (P=.27) adjustment for medication and diagnoses. The High group, compared with the Low group, was associated with both nonzero ratings (b=1.91, P=.02) and higher ratings of mania (b=1.46, P<.001). The High group, compared with the Low group, showed significantly higher odds of elevated mood (P=.03), motor activity (P=.04), and irritability (P<.05). This study demonstrates the promise of mobile typing kinematics in mood disorder research and practice. Monitoring a single mobile typing kinematic feature, that is, backspace rates, through passive sensing imposes a low burden on the participants. Based on real-life keystroke data, our derived digital phenotypes from this single feature can be useful for researchers and practitioners to distinguish between individuals with and those without mood disorder symptoms.

Objective The increase in smartphone usage has enabled the possibility of more accessible ways to conduct neuropsychological evaluations. The objective of this study was to determine the feasibility of using smartphone typing dynamics with mood scores to supplement cognitive assessment through trail making tests. Methods Using a custom‐built keyboard, naturalistic keypress dynamics were unobtrusively recorded in individuals with bipolar disorder (n = 11) and nonbipolar controls (n = 8) on an Android smartphone. Keypresses were matched to digital trail making tests part B (dTMT‐B) administered daily in two periods and weekly mood assessments. Following comparison of dTMT‐Bs to the pencil‐and‐paper equivalent, longitudinal mixed‐effects models were used to analyze daily dTMT‐B performance as a function of typing and mood. Results Comparison of the first dTMT‐B to paper TMT‐B showed adequate reliability (intraclass correlations = 0.74). In our model, we observed that participants who typed slower took longer to complete dTMT‐B (b = 0.189, p < .001). This trend was also seen in individual fluctuations in typing speed and dTMT‐B performance (b = 0.032, p = .004). Moreover, participants who were more depressed completed the dTMT‐B slower than less depressed participants (b = 0.189, p < .001). A practice effect was observed for the dTMT‐Bs. Conclusion Typing speed in combination with depression scores has the potential to infer aspects of cognition (visual attention, processing speed, and task switching) in people's natural environment to complement formal in‐person neuropsychological assessments that commonly include the trail making test. This study explored the feasibility of using smartphone typing dynamics with mood scores to supplement cognitive assessment through trail making tests. Naturalistic keypress dynamics were unobtrusively collected from individuals with bipolar disorder and nonbipolar controls using a custom‐built keyboard and compared to serial administrations of the trail making test part B. Typing speed in combination with depression scores significantly predicted trail making test time and may have the potential to be used to assess cognition in real time to complement in‐person assessments.

Objective Examine the associations between smartphone keystroke dynamics and cognitive functioning among persons with multiple sclerosis (MS). Methods Sixteen persons with MS with no self-reported upper extremity or typing difficulties and 10 healthy controls (HCs) completed six weeks of remote monitoring of their keystroke dynamics (i.e., how they typed on their smartphone keyboards). They also completed a comprehensive neuropsychological assessment and symptom ratings about fatigue, depression, and anxiety at baseline. Results A total of 1,335,787 keystrokes were collected, which were part of 30,968 typing sessions. The MS group typed slower (P < .001) and more variably (P = .032) than the HC group. Faster typing speed was associated with better performance on measures of processing speed (P = .016), attention (P = .022), and executive functioning (cognitive flexibility: P = .029; behavioral inhibition: P = .002; verbal fluency: P = .039), as well as less severe impact from fatigue (P < .001) and less severe anxiety symptoms (P = .007). Those with better cognitive functioning and less severe symptoms showed a stronger correlation between the use of backspace and autocorrection events (P < .001). Conclusion Typing speed may be sensitive to cognitive functions subserved by the frontal–subcortical brain circuits. Individuals with better cognitive functioning and less severe symptoms may be better at monitoring their typing errors. Keystroke dynamics have the potential to be used as an unobtrusive remote monitoring method for real-life cognitive functioning among persons with MS, which may improve the detection of relapses, evaluate treatment efficacy, and track disability progression.

Neuronal hyperexcitation affects memory and neural processing across the Alzheimer’s disease (AD) cognitive continuum. Levetiracetam, an antiepileptic, shows promise in improving cognitive impairment by restoring the neural excitation/inhibition balance in AD patients. We previously identified a hyper-excitable phenotype in cognitively unimpaired female APOE -ε4 carriers relative to male counterparts cross-sectionally. This sex difference lacks longitudinal validation; however, clarifying the vulnerability of female ε4-carriers could better inform antiepileptic treatment efficacy. Here, we investigated this sex-by-ε4 interaction using a longitudinal design. We used resting-state fMRI and diffusion tensor imaging collected longitudinally from 106 participants who were cognitively unimpaired for at least one scan event but may have been assessed to have clinical dementia ratings corresponding to early mild cognitive impairment over time. By including scan events where participants transitioned to mild cognitive impairment, we modeled the trajectory of the whole-brain excitation-inhibition ratio throughout the preclinical cognitively healthy continuum and extended to early impairment. A linear mixed model revealed a significant three-way interaction among sex, ε4-status, and time, with female ε4-carriers showing a significant hyper-excitable trajectory. These findings suggest a possible pathway for preventative therapy targeting preclinical hyperexcitation in female ε4-carriers. Longitudinal connectome modeling of excitation-inhibition balance in cognitively unimpaired individuals reveals a hyperexcitable trajectory in female APOE-ε4 carriers. Findings suggest anti-excitatory therapies may mitigate Alzheimer’s disease risk.

A great deal of research in the neuroscience of music suggests that neural oscillations synchronize with musical stimuli. Although neural synchronization is a well-studied mechanism underpinning expectation, it has even more far-reaching implications for music. In this Perspective, we survey the literature on the neuroscience of music, including pitch, harmony, melody, tonality, rhythm, metre, groove and affect. We describe how fundamental dynamical principles based on known neural mechanisms can explain basic aspects of music perception and performance, as summarized in neural resonance theory. Building on principles such as resonance, stability, attunement and strong anticipation, we propose that people anticipate musical events not through predictive neural models, but because brain–body dynamics physically embody musical structure. The interaction of certain kinds of sounds with ongoing pattern-forming dynamics results in patterns of perception, action and coordination that we collectively experience as music. Statistically universal structures may have arisen in music because they correspond to stable states of complex, pattern-forming dynamical systems. This analysis of empirical findings from the perspective of neurodynamic principles sheds new light on the neuroscience of music and what makes music powerful. In this Perspective article, Edward Large and colleagues examine the neuroscience of music, placing their focus on neural resonance theory, which summarizes how the dynamics of fundamental neural mechanisms can explain various aspects of music perception and performance.

Objective Hispanic/Latino/a/x (hereafter Latino) persons living in the U.S. are at increased risk for Alzheimer's disease and related dementias (ADRD) compared to non-Latino Whites. Early detection of preclinical changes is crucial. The SALUD-Tech study aims to identify digital behavioral markers—“digital signatures”—of ADRD risk in diverse middle-aged and older Latinos using passive data from smartphones and smartwatches. Methods Participants include Latino adults aged 50–70 years living in southern California, with varying degrees of ADRD risk as defined by the presence of mild cognitive impairment and cardiovascular disease risk. Data collection began in April 2022 and will continue through 2026. Participants complete comprehensive laboratory assessments (neurobehavioral, medical, sociocultural, and psychiatric assessments). High-frequency data on sensors, keyboard dynamics, and phone use activity are collected for 30 days following the baseline visit. A subset of study participants completes 18- and 36-month longitudinal assessments; these participants are selected based on risk profiles and retention likelihood. All data are securely encrypted, de-identified, and collected respecting participant privacy and consent in accordance with ethical standards. Data analysis involves integrating multimodal data streams using machine learning to identify behavioral patterns associated with early cognitive decline. Results We anticipate 300 participants will be enrolled in the study. Study results will be published in peer-reviewed scientific journals. Discussion Early detection of ADRD risk using smartphone and wearable data could help reduce disparities by providing a low-cost, accessible tool. Ultimately, this approach may be integrated into clinical care to enable earlier interventions and reduce healthcare costs.