Biology subjects

Windt, J.

Publications and source records attributed to Windt, J..

6 recordsLinked to original sources

bioRxiv2026

Diminished Stimulus-Evoked Activity During Sustained Attention Without Behavioural Cost in ADHD

Attention-Deficit/Hyperactivity Disorder (ADHD) is often accompanied by attentional challenges, sleep difficulties and increased reports of daytime sleepiness, indicating potential links between attention and arousal mechanisms. Electroencephalographic (EEG) studies have shown that sleep-like wake slow wave (SW) activity during wakefulness, characterised by reduced cortical activity, corresponds with periods of inattention in both ADHD and neurotypical populations. However, it remains unclear whether group differences in waking SWs are consistent across different types of sustained attention tasks or may be context-dependent. The present study compared sustained attention performance and wake SW activity between adults with and without ADHD during a continuous visual target detection task. EEG data were collected while adults with (n = 52) and without ADHD (n = 49) completed a sustained attention paradigm. The task presented a continuous stream of rotating (anti-clockwise or clockwise) black-and-white checkerboard stimuli, and participants were required to detect infrequent targets that were marginally longer in duration than non-targets. Behavioural and neural measures (power spectra, steady-state visually evoked potentials (SSVEP), event-related potentials and SW density during wake) were analysed. No significant group differences were observed for task performance and wake SW activity. However, electrophysiological analyses revealed the ADHD group showed reduced P300, elevated beta-band power, and lower SSVEPs to target stimuli compared with the neurotypical group. These neural differences in the context of comparable performance suggest compensatory cortical recruitment in ADHD. The absence of SW differences further supports this, suggesting comparable performance and maintained arousal may reflect successful neural compensation in ADHD in this cognitive task. Significance StatementIndividuals with ADHD commonly experience attention differences, but emerging evidence suggests these difficulties may depend on task context. Using clinical diagnostic interviews, medication washout, and Bayesian statistics, we found strong evidence for comparable performance between ADHD and neurotypical adults on a sustained attention task. Notably, slow wave activity during wakefulness did not differ between groups, consistent with maintained arousal. Nevertheless, differences in brain activity, including altered target processing, increased cortical activation and reduced visual entrainment, suggested compensatory neural recruitment in ADHD. This context-dependent pattern has important theoretical and clinical implications: identifying task features that challenge or support performance in ADHD can reveal how individuals compensate neurally, and guide interventions that leverage task features to support attention in ADHD.

neuroscience↗

bioRxiv2025

Sleep-like Slow Waves During Wakefulness Mediate Attention and Vigilance Difficulties in Adult Attention-Deficit/Hyperactivity Disorder

BackgroundAttention-Deficit/Hyperactivity Disorder (ADHD) is characterised by behavioural variability and heightened inattention associated with increased mind wandering (MW) and mind blanking (MB). Individuals with ADHD frequently experience sleep disorders and excessive daytime sleepiness, suggesting interactions between attention and arousal systems. Research examining brain activity using electroencephalography (EEG) has demonstrated that sleep-like slow waves (SW) during wakefulness are linked to inattention in neurotypical individuals, particularly following sleep deprivation, yet their role in ADHD remains unclear. This study investigated whether individuals with ADHD present with altered waking SW distribution compared to neurotypical controls and whether SW explain attentional difficulties in ADHD. MethodsAdults with (n = 32) and without ADHD (n = 31) completed a sustained attention task while EEG recorded brain activity. Mental state probes (on-task, MW, MB) were embedded within the task. Sleep-like SW reflect a slowing of cortical activity and was detected from EEG activity. Omission/commission errors, reaction time (RT), RT variability, mental state reports and subjective sleepiness were analysed. Mediation analysis examined whether SW density explained ADHD-related performance differences. ResultsIndividuals with ADHD exhibited more commission errors, MW and MB, and higher SW density (SW/min), particularly over parieto-temporal electrodes. Increased SW density correlated with higher omission errors, slower RTs, greater RT variability, and elevated sleepiness ratings. On-task reports were negatively correlated with SW density. Mediation analysis revealed that SW density significantly accounted for ADHD-related attentional difficulties. ConclusionsWake SW may explain attentional difficulties in ADHD, providing a potential mechanistic link between sleep disturbances and attentional fluctuations.

neuroscience↗

bioRxiv2023

Accounting for observation biases associated with counts of young when estimating fecundity: case study on the arboreal-nesting red kite (Milvus milvus)

AO_SCPLOWBSTRACTC_SCPLOWCounting the number of young in a brood from a distance is common practice, for example in tree-nesting birds. These counts can, however, suffer from over and undercounting, which can lead to biased estimates of fecundity (average number of nestlings per brood). Statistical model development to account for observation bias has focused on false negatives (undercounts), yet it has been shown that these models are sensitive to the presence of false positives (overcounts) when they are not accounted for. Here, we develop a model that estimates fecundity while accounting for both false positives and false negatives in brood counts. Its parameters can be estimated using a calibration approach that combines uncertain counts with certain ones, which can be obtained by accessing the brood, for example during ringing. The model uses multinomial distributions to estimate the probabilities of observing y young conditional on the true state of a brood z (i.e., true number of young) from paired uncertain and certain counts. These classification probabilities are then used to estimate the true state of broods for which only uncertain counts are available. We use a simulation study to investigate bias and precision of the model and parameterize the simulation with empirical data from 26 red kite nests visited with ground and nest-based counts during 2021 and 2022 in central Europe. In these data, bias in counts was at most 1 in either direction, more common in larger broods, and undercounting was more common than overcounting. This led to an overall 5% negative bias in fecundity in uncertain counts. The model produced essentially unbiased estimates (relative bias < 2%) of fecundity across a range of sample sizes. This held true whether or not fecundity was the same for nests with paired counts and those with uncertain-only counts. But the model could not estimate parameters when true states were missing from the paired data, which happened frequently in small sample sizes (n = 10 or 25). Further, we projected populations 50 years into the future using fecundity estimates corrected for observation biases from the multinomial model, and based on "raw" uncertain observations. We found that ignoring observation bias led to strong negative bias in projected population size for growing populations, but only minor negative bias in declining populations. Accounting for apparently minor biases associated with ground counts is important for ensuring accurate estimates of abundance and population dynamics especially for increasing populations. This could be particularly important for informing conservation decisions in projects aimed at recovering depleted populations.

ecology↗

bioRxiv2021

Gliding in the Amazonian canopy: adaptive evolution of flight in Morpho butterflies

The diversity of flying animals suggests that countless combinations of morphologies and behaviors have evolved with specific lifestyles, thereby exploiting diverse aerodynamic mechanisms. Elucidating how morphology, flight behavior and aerodynamic properties together diversify with contrasted ecologies remains however seldom accomplished. Here, we studied the adaptive co-divergence in wing shape, flight behavior and aerodynamic efficiency among Morpho butterflies living in different forest strata, by combining high-speed videography in the field with morphometric analyses and aerodynamic modelling. By comparing canopy and understory species, we show that adaptation to an open canopy environment resulted in increased glide efficiency. Moreover, this enhanced glide efficiency was achieved by different canopy species through strikingly distinct combinations of flight behavior, wing shape and aerodynamic mechanisms, highlighting the multiple pathways of adaptive evolution. One Sentence SummaryBy combining high-speed videography, geometric morphometrics and computational aerodynamic modelling, our study of wild Amazonian Morpho butterflies reveals a strong contrast between the efficient gliding flight of canopy species and the powerful flapping flight of understory species, pointing at a combined adaptive divergence of wing shape and flight behavior among sympatric species flying in different forest strata.

evolutionary biology↗

bioRxiv2020

Wandering minds, sleepy brains: lapses of attention and local sleep in wakefulness.

Attentional lapses are ubiquitous and can negatively impact performance. They correlate with mind wandering, or thoughts that are unrelated to ongoing tasks and environmental demands. In other cases, the stream of consciousness itself comes to a halt and the mind goes blank. What is happening in the brain that leads to these mental states? To understand the neural mechanisms underlying attentional lapses, we cross-analysed the behaviour, subjective experience and neural activity of healthy participants performing a task. Random interruptions prompted participants to indicate their mental states as task-focused, mind-wandering or mind-blanking. High-density electroencephalography revealed the occurrence of spatially and temporally localized slow waves, a pattern of neural activity characteristic of the transition toward sleep. These slow waves accompanied behavioural markers of lapses and preceded reports of mind wandering and mind blanking. Furthermore, the location of slow waves distinguished sluggish versus impulsive behaviours, mind wandering versus mind blanking. Our results suggest attentional lapses share a common physiological origin: the emergence of local sleep-like activity within the awake brain.

neuroscience↗

bioRxiv2019

The Dream Catcher experiment: Blinded analyses disconfirm markers of dreaming consciousness in EEG spectral power

The Dream Catcher test defines the criteria for a genuine discovery of the neural constituents of phenomenal consciousness. Passing the test implies that some patterns of purely brain-based data directly correspond to the subjective features of phenomenal experience, which would help to bridge the explanatory gap between consciousness and brain. Here, we conducted the Dream Catcher test for the first time in a graded and simplified form, capturing its core idea. The experiment involved a Data Team, who measured participants brain activity during sleep and collected dream reports, and a blinded Analysis Team, who was challenged to predict better than chance, based solely on brain measurements, whether or not a participant had a dream experience. Using a serial-awakening paradigm, the Data Team prepared 54 one-minute polysomnograms of NREM sleep--27 of dreamful sleep (3 from each of the 9 participants) and 27 of dreamless sleep--redacting from them all associated participant and dream information. The Analysis Team attempted to classify each recording as either dreamless or dreamful using an unsupervised machine learning classifier, based on hypothesis-driven, extracted features of EEG spectral power and electrode location. The procedure was repeated over five iterations with a gradual removal of blindness. At no level of blindness did the Analysis Team perform significantly better than chance, suggesting that EEG spectral power does not carry any signatures of phenomenal consciousness. Furthermore, we demonstrate an outright failure to replicate key findings of recently reported correlates of dreaming consciousness.\n\nHighlightsThe first reported attempt of the Dream Catcher test.\nThe correlates of conscious experience may not lie in EEG spectral power.\nReported markers of NREM dreaming consciousness misperformed in a blinded setting.\nThose markers also could not be confirmed in an unblinded setting.

neuroscience↗