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van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison.

Perceptual decision-making in a dynamic environment requires two integration processes: integration of sensory evidence from multiple modalities to form a coherent representation of the environment, and integration of evidence across time to accurately make a decision. Only recently studies started to unravel how evidence from two modalities is accumulated across time to form a perceptual decision. One important question is whether information from individual senses contributes equally to multisensory decisions. We designed a new psychophysical task that measures how visual and auditory evidence is weighted across time. Participants were asked to discriminate between two visual gratings, and/or two sounds presented to the right and left ear based on respectively contrast and loudness. We varied the evidence, i.e., the contrast of the gratings and amplitude of the sound, over time. Results showed a significant increase in performance accuracy on multisensory trials compared to unisensory trials, indicating that discriminating between two sources is improved when multisensory information is available. Furthermore, we found that early evidence contributed most to sensory decisions. Weighting of unisensory information during audiovisual decision-making dynamically changed over time. A first epoch was characterized by both visual and auditory weighting, during the second epoch vision dominated and the third epoch finalized the weighting profile with auditory dominance. Our results suggest that during our task multisensory improvement is generated by a mechanism that requires cross-modal interactions but also dynamically evokes dominance switching.

The differential impact of rural versus urban residence on mental health remains a controversial topic that requires more in-depth investigations. This calls for a valid and easy measure to assess the degree of urbanisation. The purpose of the present study was to determine the utility of a single-item self-report measure (SIDU) as a tool to classify areas along the rural-urban continuum. The validity of the SIDU was assessed by comparing its scores (1–7) to a commonly used objective surrogate measure of the degree of urbanisation (i.e. surrounding address density, SAD) in two independent older adult samples (A: N = 36, 65+; B: N = 121, 55+). SIDU scores approximated SAD scores, with r = .77 to 0.82, (A), and r = .79 to 0.83 (B). A SIDU threshold score of 6 most accurately distinguished extremely urbanised areas from other areas. Altogether, our findings suggest that SIDU scores could be used as proxy of SAD. Since self-report leaves room for the consideration of additional aspects that confer an urban settlement, this single-item scale may be even more comprehensive, and circumvents the collection and handling of highly sensitive location data when the primary goal is solely to distinguish urbanisation subgroups.

The risk of relapse following successful acute-phase treatment of late-life depression (LLD), including electroconvulsive therapy (ECT), is substantial. In order to improve reliable prediction of individuals' risk of relapse, we assessed the association between individual residual symptoms following a successful acute course of ECT for LLD and relapse at six-month follow-up. This prospective cohort study was part of the MODECT study, which included 110 patients aged 55 years and older with major depressive disorder. Participants who showed response to the index ECT course were monitored for relapse for six months. We used multivariable stepwise logistic regression models to assess the association between the scores on the 10 individual Montgomery-Asberg Depression Rating Scale (MADRS) items at the end of the acute ECT course and relapse at six-month follow-up. Of the 80 responders with available six-month follow-up data (58.75% of which had psychotic features at baseline), 36.25% had relapsed. Higher scores on the MADRS items 'reduced sleep' (odds ratio (OR) = 2.03, 95% confidence interval (CI) = 1.11-3.69, p = 0.0214) and 'lassitude' (OR = 1.62, 95% CI = 1.00-2.62, p = 0.0497) at the end of the acute ECT course were significantly associated with increased risk of relapse at six-month follow-up. In conclusion, some residual depressive symptoms, including sleep disturbance and lassitude, may help better identify patients vulnerable to relapse following a successful acute course of ECT for LLD. If these findings can be replicated, studies assessing interventions that target specific residual symptoms may further reduce post-ECT depressive relapse rates.

The subthalamic nucleus (STN) is a small, subcortical brain structure. It is a target for deep brain stimulation, an invasive treatment that reduces motor symptoms of Parkinson's disease. Side effects of DBS are commonly explained using the tripartite model of STN organization, which proposes three functionally distinct subregions in the STN specialized in cognitive, limbic, and motor processing. However, evidence for the tripartite model exclusively comes from anatomical studies and functional studies using clinical patients. Here, we provide the first experimental tests of the tripartite model in healthy volunteers using ultra-high field 7 Tesla (T) functional magnetic resonance imaging (fMRI). Thirty-four participants performed a random-dot motion decision-making task with a difficulty manipulation and a choice payoff manipulation aimed to differentially affect cognitive and limbic networks. Moreover, participants responded with their left and right index finger, differentially affecting motor networks. We analysed BOLD signal in three subregions of the STN along the dorsolateral–ventromedial axis, identified using manually delineated high resolution anatomical images and based on a previously published atlas. Using these paradigms, all segments responded equally to the experimental manipulations, and the tasks did not provide evidence for the tripartite model.

ObjectiveDespite the established safety of electroconvulsive therapy (ECT), ECT-related anxiety (ERA) remains one of the most distressing complications of ECT. ERA is reported to diminish during an acute course of ECT, but it was never studied during maintenance ECT (M-ECT). Our aim was to study the trajectories of ERA during M-ECT and how they differ from trajectories during the acute course.MethodsThirty-nine patients with unipolar or bipolar depression, retained for M-ECT after an acute ECT course, were included. ERA was assessed the morning before each ECT session using the ECT-related Anxiety Questionnaire (ERAQ).ResultsERA remained stable during M-ECT (RC = −0.05 (SE = 0.06), t(8.35) = −0.86, p = 0.42), while ERA declined significantly during the acute course (RC = −0.85 (SE = 0.30), t(33.6) = −2.81, p = 0.0082). During the acute course, patients with a psychotic depression were more anxious at baseline (t(32)= −2.42, p = 0.02), and showed a significant decline in ERAQ scores (RC = −1.65 (SE = 0.46), t(31.6) = −3.56, p = 0.0012), whereas patients with a non-psychotic depression were less anxious at baseline and retained stable ERAQ scores during the acute course (RC = −0.06 (SE = 0.41), t(32.1) = −0.14, p = 0.89). Whereas a correlation (r = 0.48) was noticed between the decline of depression severity and ERA during the acute course, this was not the case during M-ECT.ConclusionERA runs a stable course during M-ECT, after having decreased during the acute course. During the acute course, ERA trajectories differed significantly between patients with a psychotic and non-psychotic depression. Decline of depression severity and ERA are significantly connected during the acute course of ECT. Both depression severity and ERA remain stable during M-ECT.

For autism spectrum disorder (ASD) in adults there are several diagnostic instruments available with a need for consideration of the psychometric properties. This study aimed to conduct a first psychometric evaluation of a new diagnostic ASD instrument, the NIDA (Dutch Interview for Diagnostic assessment of ASD in adults) in 90 adult males without intellectual disability (age 18-65 years) in the Netherlands: 30 with ASD, 30 with a Personality Disorder and 30 nonpatient controls. The interrater agreement ranged from 0.79 to 1.00, the convergent validity including sensitivity and specificity ranged from 0.76 to 1.00, and we observed an adequate concurrent criterion-related validity. These promising findings can serve as foundation for future psychometric NIDA studies in a more diverse population. TRIAL REGISTRATION: The Netherlands National Trial Register NTR6391. Registered 04 May 2017.

Autism spectrum conditions were once seen as a predominantly male condition. This has caused a paucity of information on common events in the lives of women, such as menstruation and menopause. Some smaller studies indicate that autistic women might suffer from increased difficulties surrounding these events. This study aims to investigate whether autistic women experience more frequent premenstrual dysphoric disorder, and increased complaints surrounding menopause. In partly overlapping samples (premenstrual dysphoric disorder, n = 70, nASC = 28, ncomparisons = 42; menopause, n = 65, nASC = 30, ncomparisons = 35), we investigated premenstrual dysphoric disorder prevalence and menopausal complaints. In 70 individuals, we did not find an increased prevalence of premenstrual dysphoric disorder in autistic women (14.3%) compared with non-autistic women (9.5%). In 65 women aged 40 years and above, we found that autistic women did experience higher levels of menopausal complaints. In autistic women, higher menopausal complaints were associated with higher levels of depression and autistic traits. In non-autistic women, menopausal complaints were associated with increased inattention, hyperactivity/impulsivity (i.e. attention deficit hyperactivity disorder traits), and depression. With this work, we show the important role that major reproductive milestones can have in an autistic woman’s life. Lay abstract Autism spectrum conditions were once seen as a predominantly male condition, but this has caused research to have little focus on women. Therefore, little is known about menstruation and menopause in autism spectrum conditions. Some smaller studies indicate that autistic individuals might suffer from increased difficulties surrounding these events. This study aimed to investigate whether autistic women experience more frequent premenstrual dysphoric disorder, causing extreme physical, emotional, and functional impairment. In a partly overlapping sample, we also examined whether women with autism spectrum condition experience increased complaints surrounding menopause. We did not find an increased prevalence of premenstrual dysphoric disorder in autism spectrum conditions (14.3%) compared with non-autistic women (9.5%). Those with autism spectrum conditions did experience increased menopausal complaints. These menopausal complaints were associated with higher levels of depression and autistic traits. In non-autistic women, menopausal complaints were associated with increased inattention, hyperactivity/impulsivity (i.e. attention deficit hyperactivity disorder traits), and depression. With this work, we show the important role that major reproductive milestones can have in an autistic woman’s life.

Background Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an established treatment for disabling motor symptoms of Parkinson’s disease (PD) that persist despite optimal pharmacological treatment. Currently, DBS is not performed if there is concomitant significant cognitive impairment based on concerns of cognitive deterioration, higher complication rate and less functional improvement. However, this has not been investigated so far. Methods A single center, prospective, randomized, open-label, blinded end-point (PROBE design) pilot clinical trial is being performed. Patients are eligible for the trial if they have PD dementia (PDD), are able to provide informed consent, and experience disabling motor response fluctuations, bradykinesia, dyskinesia, or painful dystonia, despite optimal pharmacological treatment. In total 44 patients will be randomized to either STN-DBS accompanied by best medical treatment (DBS group) or to best medical treatment alone (BMT group). The primary outcome measure is the change from baseline to 30 weeks on the Movement Disorder Society—Unified Parkinson’s Disease Rating Scale part III score in a standardized off-drug phase. The main secondary outcome measures consist of scales assessing cognitive aspects of daily living, neuropsychiatric symptoms and impulsive compulsive disorders. Additional secondary outcome measures include motor signs during on-drug phase, dyskinesia, motor fluctuations, cognitive performance, (severe) adverse events, treatment satisfaction, and caregiver burden. Patients will be followed during 52 weeks after randomization. Discussion The Deep Brain Stimulation for MOtor symptoms in patients with Parkinson’s disease DEmentia (DBS-MODE) trial directly compares the effectiveness and safety of DBS with BMT in patients with PDD. Trial registration The DBS-MODE trial has been registered in the International Clinical Trial Registry Platform (NL9361) on the 24th of March 2021 (https://trialsearch.who.int/Trial2.aspx?TrialID=NL9361).

Spatial attention enhances sensory processing of goal-relevant information and improves perceptual sensitivity. Yet, the specific neural mechanisms underlying the effects of spatial attention on performance are still contested. Here, we examine different attention mechanisms in spiking deep convolutional neural networks. We directly contrast effects of precision (internal noise suppression) and two different gain modulation mechanisms on performance on a visual search task with complex real-world images. Unlike standard artificial neurons, biological neurons have saturating activation functions, permitting implementation of attentional gain as gain on a neuron's input or on its outgoing connection. We show that modulating the connection is most effective in selectively enhancing information processing by redistributing spiking activity and by introducing additional task-relevant information, as shown by representational similarity analyses. Precision only produced minor attentional effects in performance. Our results, which mirror empirical findings, show that it is possible to adjudicate between attention mechanisms using more biologically realistic models and natural stimuli.

OBJECTIVE: Behavioral parent training is an evidence-based intervention for children with attention-deficit/hyperactivity disorder (ADHD), but it is unknown which of its components are most effective. This meta-regression analysis investigated which specific behavioral techniques that parents learn in parent training are associated with effects on parental outcomes. METHOD: A search was performed for randomized controlled trials on parent training for children with ADHD, with positive parenting, negative parenting, parenting sense of competence, parent-child relationship quality, and parental mental health as outcome measures. After screening 23,026 publications, 29 studies contributing 138 effect sizes were included (N = 2,345). For each study, the dosage of 39 behavioral techniques was derived from intervention manuals, and meta-regression determined which techniques were related to outcomes. RESULTS: Parent training had robust small- to medium-sized positive effects on all parental outcomes relative to control conditions, both for unblinded and probably blinded measures. A higher dosage of techniques focusing on the manipulation of antecedents of behavior was associated with better outcomes on parenting sense of competence and parental mental health, and a higher dosage of techniques focusing on reinforcement of desired behaviors was related to larger decreases in negative parenting. Higher dosages of psychoeducation were negatively related to parental outcomes. CONCLUSION: Although techniques were not investigated in isolation, the results suggested that manipulation of antecedents of behavior and reinforcement techniques are key components of parent training for children with ADHD in relation to parental outcomes. These exploratory findings may help to strengthen and tailor parent training interventions for children with ADHD. ispartof: Journal Of The American Academy Of Child And Adolescent Psychiatry vol:61 issue:4 pages:478-494 ispartof: location:United States status: Published online

Learning to predict action outcomes in morally conflicting situations is essential for social decision-making but poorly understood. Here we tested which forms of Reinforcement Learning Theory capture how participants learn to choose between self-money and other-shocks, and how they adapt to changes in contingencies. We find choices were better described by a reinforcement learning model based on the current value of separately expected outcomes than by one based on the combined historical values of past outcomes. Participants track expected values of self-money and other-shocks separately, with the substantial individual difference in preference reflected in a valuation parameter balancing their relative weight. This valuation parameter also predicted choices in an independent costly helping task. The expectations of self-money and other-shocks were biased toward the favored outcome but fMRI revealed this bias to be reflected in the ventromedial prefrontal cortex while the pain-observation network represented pain prediction errors independently of individual preferences.

Almost two decades of research using the Weapon Identification Task (WIT) show that seeing Black face primes facilitates the identification of guns compared to tools. However, it is unknown whether the stereotype-based association between Black faces and guns impacts initial perceptual processing or only later memory or response-related processes. Here, to test for the presence of a perceptual effect while controlling for memory and response-effect we measured the influence of race-priming on visual awareness during continuous flash suppression (CFS), a strong interocular suppression technique. Eighty-four participants were primed with Black or Asian faces, and then indicated the location of a gun or tool target that was temporarily rendered invisible through CFS. Faster localization in this task reflects faster entry into visual awareness. The same participants also completed a standard WIT gun/tool identification task with Black and Asian face primes. We replicated the standard race-priming effect in the WIT: Black faces primed the identification of guns, while Asian faces primed the identification of tools. However, in the CFS task, Black and Asian primes did not affect when guns and tools enter into awareness. This shows that the stereotypical Black-gun association does not alter early visual processing. Instead it suggests that race-priming most likely originates from later memory- or response-related processing stages.

In van Doorn et al. (2021), we outlined a series of open questions concerning Bayes factors for mixed effects model comparison, with an emphasis on the impact of aggregation, the effect of measurement error, the choice of prior distributions, and the detection of interactions. Seven expert commentaries (partially) addressed these initial questions. Surprisingly perhaps, the experts disagreed (often strongly) on what is best practice—a testament to the intricacy of conducting a mixed effect model comparison. Here, we provide our perspective on these comments and highlight topics that warrant further discussion. In general, we agree with many of the commentaries that in order to take full advantage of Bayesian mixed model comparison, it is important to be aware of the specific assumptions that underlie the to-be-compared models.

Models are the hallmark of mature scientific inquiry. In psychology, this maturity has been reached in a pervasive question—what models best represent facial expressions of emotion? Several hypotheses propose different combinations of facial movements [action units (AUs)] as best representing the six basic emotions and four conversational signals across cultures. We developed a new framework to formalize such hypotheses as predictive models, compare their ability to predict human emotion categorizations in Western and East Asian cultures, explain the causal role of individual AUs, and explore updated, culture-accented models that improve performance by reducing a prevalent Western bias. Our predictive models also provide a noise ceiling to inform the explanatory power and limitations of different factors (e.g., AUs and individual differences). Thus, our framework provides a new approach to test models of social signals, explain their predictive power, and explore their optimization, with direct implications for theory development.

The high prevalence and severe consequences of poor sleep following acquired brain injury emphasises the need for an effective treatment. However, treatment studies are scarce. The present study evaluates the efficacy of blended online cognitive behavioural therapy for insomnia (eCBT-I) developed specifically for people with acquired brain injury. In a multicentre prospective, open-label, blinded end-point randomised clinical trial, 52 participants with insomnia and a history of a stroke or traumatic brain injury were randomised to 6 weeks of guided eCBT-I or treatment as usual, with a 6-week follow-up. The primary outcome measure was the change in insomnia severity between baseline and after treatment, measured with the Insomnia Severity Index. Results showed that insomnia severity improved significantly more with eCBT-I than with treatment as usual compared to baseline, both at post-treatment (mean [SEM] 4.0 [1.3] insomnia severity index points stronger decrease, d = 0.96, p d = -0.78, p

Background: Camouflaging behavior is often defined as using strategies to hide autistic characteristics. In this study, we investigated how to measure camouflaging behavior by performing a conceptual replication of the original study of the Camouflaging Autistic Traits Questionnaire (CAT-Q) and testing whether the self-reported camouflaging behavior is measuring the same construct as the second most used manner to measure camouflaging behavior, the discrepancy method. Method: In total, 674 individuals (356 autistic) aged 30–92 years, filled out the Dutch translation of the CAT-Q (CAT-Q-NL) and the Autism Spectrum Quotient (AQ). In 90 autistic adults the Autism Diagnostic Observation Schedule (ADOS-2) was administered. We executed preregistered analyses (AsPredicted #37800) to investigate the factor structure, measurement invariance, internal consistency, convergent validity and group differences. Results: Our analyses showed that the original three-factor structure had an acceptable fit and internal consistency ranged from sufficient to good. However, there was no measurement invariance between autistic and non-autistic individuals and correlations between CAT-Q-NL-scores and the discrepancy between AQ and ADOS-2 varied between low to mediocre (r = 0.04 to.28). Conclusions: The CAT-Q-NL can be used to measure camouflaging between and within autistic adults, but not between autistic and non-autistic adults and its convergent validity is limited. Despite these caveats, the CAT-Q-NL can serve as a useful addition to the clinical assessment toolbox because gaining insight in the level of camouflaging of autistic adults may help provide better mental health care. However, more research is needed into how to optimally measure the camouflaging construct.

Over the last decades, scholars in the psychology of religion have been trying to understand the origin, the function, and the consequences of the fascinating phenomenon that religion is. In the current dissertation, we aimed to contribute to the scientific inquiry of religion, not by proposing new theories or adding hypotheses, but by rigorously testing influential existing ones. Specifically, we tried to add to previous research by reexamining previously reported effects while (1) including large and diverse samples, (2) applying open science practices and Bayesian modeling techniques (3) conducting replications of key effects plus potential alternative explanations or effect (e.g., correlational instead of experimental effects), (4) critically assessing and visualizing patterns in the raw data, and (5) applying new tools to assess robustness (e.g., a many-analysts approach, analysis blinding). Based on the findings in this dissertation, the –perhaps unsurprising– conclusion we can draw is: some effects are and some aren’t replicable. We found that religiosity indeed seems positively related to self-reported well-being; religiosity seems predictive of the tendency to make post-mortem continuity judgments of psychological states (e.g., love); religiosity seems related to credibility ratings for gobbledegook statements, and a reduced relative difference for those from a scientist compared to a spiritual guru. At the same time, we obtained convincing evidence for the absence of other effects: an experimental attenuation of personal control does not seem to activate a compensatory mechanism of belief in a controlling God and neural markers of cognitive conflict and error processing do not seem to be associated with religiosity.

An increasing number of studies show that cancer and cancer treatment is associated with cognitive impairment, underlining the need for further research. For this reason, the Amsterdam Cognition Scan (ACS) was developed earlier —an online cognitive test battery that patients can complete independently. The first aim of this thesis was to further develop the ACS. The second aim was to implement cognitive tests in studies on cognitive effects of endocrine therapy (ET). First, it was investigated how to correct for computer experience when analyzing online cognitive test performance. It was shown that correction was best done using a self-report measure of computer experience. Second, cross-lingual word criteria were developed for new word lists for the ACS verbal memory subtest. The criteria were shown to develop new parallel word lists. Third, a data-driven subgrouping method was applied using ACS data, supporting the existence of two cognitive subgroups in breast cancer (BC) patients who underwent chemotherapy. Subsequently, the literature is described on cognitive effects of ET in patients with hormone-receptor positive (HR+) BC. Increasing evidence for cognitive effects of ET was found, with potentially differential effects between ET agents. Then, cognitive effects of tamoxifen and exemestane were investigated in women with HR+ BC. It was found that tamoxifen alone and after switching to exemestane was associated with cognitive effects, while exemestane was not. Last, the long-term effect of risk-reducing salphingo-oophorectomy (RRSO) was examined on cognition in women at increased risk for ovarian cancer. Approximately 18 years after RRSO, no association was found between premenopausal RRSO and cognition.

Most patients treated for breast cancer encounter adverse effects of cancer and its treatment, such as cognitive problems. However, no behavioral interventions are currently available targeting the underlying causes of these cognitive problems. This dissertation describes the results of the Physical Activity and Memory (PAM) study, which investigated the effects of an exercise intervention on cognitive functioning and the brain. In the PAM study, patients treated for breast cancer, who were inactive and showed cognitive problems, were randomized to an exercise intervention group or control group. The exercise intervention consisted of 4 hours/week of aerobic and resistance training for six months, while the control group maintained habitual activity levels. Results did not show significant exercise effects on tested cognitive functioning. Self-reported cognitive functioning did improve, as did physical fitness, fatigue, depressive symptoms, and quality of life. Additionally, no exercise intervention effects were found on brain measures, including brain volume, white matter integrity, and brain perfusion. In patients who reported clinically relevant levels of fatigue, we reported exercise effects on cognitive functioning. Moreover, changes in the brain were described. Exercise has many beneficial effects, and might have additional effects on cognition and the brain in highly fatigued patients. Fatigue might be a side effect of a common underlying mechanism causing cognitive problems in these patients. These results in highly fatigued should first be replicated but are an interesting topic for future research.

Can deep convolutional neural networks (DCNNs) be used as models of visual cognition to capture the interaction between sensation and cognition during object recognition? In this thesis, I evaluated this question for three different modulations of object recognition: arousal state, spatial attention, and previous visual experience, while building on extensive prior research on object recognition in the visual ventral stream and deep neural networks. For each of these factors, the leading goal was to formulate the interaction between object recognition and cognitive modulation in an image-computable, mechanistic model that reproduces key characteristics of human behaviour. In Chapter 2 of this thesis, I used this approach to investigate how a global gain modulation — previously linked to changes in arousal state — may affect visual processing and recognition performance on a series of tasks with varying difficulty. In Chapter 3, I assessed the efficacy of different attention mechanisms at implementing selective processing during object recognition within naturalistic scenes. In Chapter 4 and 5, I delved into the consequences of dynamic sensory experience for object recognition, examining how different neural mechanisms, such as lateral recurrence and sensory adaptation, may support our ability to recognize objects across a wide range of timescales and during various dynamic tasks. Chapter 6 and 7 summarize and discuss the opportunities and challenges afforded by DCNNs as models of visual cognition. Overall, the mechanistic insights presented in this thesis provide encouraging examples of the usage of DCNNs as a linking framework between sensation and cognitive modulation.

This study is the first to distinguish two possible predictive directions between trauma exposure and executive functioning in children in a community sample. The sample consists of 1006 children from two time points with a seven years’ time interval of a longitudinal Dutch birth cohort study, the ABCD-study (Van Eijsden et al., 2011). We analyzed the longitudinal associations between trauma exposure and executive functioning using structural equation modeling. The results demonstrated that (after controlling for prenatal substance exposure and mothers’ educational level) trauma exposure before age 5 is predictive of poorer executive functioning at age 12 and trauma exposure between age 6 and 12. However, the association between executive functioning at age 5 and trauma exposure between age 6 and 12 was not statistically significant. Our results indicate that early life trauma exposure has a long term impact on later executive functioning and not the other way around. On top of that, trauma exposure seems to accumulate across childhood when children are exposed to a traumatic event before the age of 5. When looking at the potential moderating role of parenting behavior we found no evidence for such a moderating effect of parenting behavior. Our findings showed that children exposed to trauma early in life may experience problems in executive functioning later in life and they seem at higher risk for cumulative trauma exposure. Clinical practice should take this into account in both the way they provide (early) mental health care and in prevention and recognition of early trauma exposure.

Behavioral parent and teacher training and stimulant medication are recommended interventions for children with attention-deficit/hyperactivity disorder (ADHD). However, not all children with ADHD receive this evidence-based care, and the aim of the current study was to find out why. More specifically, we investigated clinicians’ policy, guideline use, and attitudes towards medication and parent training when treating children with ADHD, as well as several factors that could affect this. A total of 219 Dutch clinicians (mainly psychologists, psychiatrists and educationalists) completed a survey. Clinicians were likely to recommend medication more often than parent training, and clinicians’ policy to recommend medication and parent training was positively associated with their attitudes towards these interventions. Less experienced clinicians and those with a non-medical background reported lower rates of guideline use, whereas clinicians with a medical background reported less positive attitudes towards parent training. Furthermore, a substantial portion of the clinicians based their decision to recommend parent training on their clinical judgement (e.g., prior estimations of efficacy, perceived low abilities/motivation of parents), and many clinicians reported barriers for referral to parent training, such as waiting lists or a lack of skilled staff. To achieve better implementation of evidence-based care for children with ADHD, guidelines should be communicated better towards clinicians. Researchers and policy-makers should further focus on barriers that prevent implementation of parent training, which are suggested by the discrepancy between clinicians’ overall positive attitude towards parent training and the relatively low extent to which clinicians actually advise parent training.

Estimating the reliability of cognitive task datasets is commonly done via split-half methods. We review four methods that differ in how the trials are split into parts: a first-second half split, an odd-even trial split, a permutated split, and a Monte Carlo-based split. Additionally, each splitting method could be combined with stratification by task design. These methods are reviewed in terms of the degree to which they are confounded with four effects that may occur in cognitive tasks: effects of time, task design, trial sampling, and non-linear scoring. Based on the theoretical review, we recommend Monte Carlo splitting (possibly in combination with stratification by task design) as being the most robust method with respect to the four confounds considered. Next, we estimated the reliabilities of the main outcome variables from four cognitive task datasets, each (typically) scored with a different non-linear algorithm, by systematically applying each splitting method. Differences between methods were interpreted in terms of confounding effects inflating or attenuating reliability estimates. For three task datasets, our findings were consistent with our model of confounding effects. Evidence for confounding effects was strong for time and task design and weak for non-linear scoring. When confounding effects occurred, they attenuated reliability estimates. For one task dataset, findings were inconsistent with our model but they may offer indicators for assessing whether a split-half reliability estimate is appropriate. Additionally, we make suggestions on further research of reliability estimation, supported by a compendium R package that implements each of the splitting methods reviewed here.

Network approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach in neuroimaging research. In this paper, we introduce a methodology for combining network psychometrics and network neuroscience. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10–21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates.

We report the results of an academic survey into the theoretical and methodological foundations, common assumptions, and the current state of the field of consciousness research. The survey consisted of 22 questions and was distributed on two different occasions of the annual meeting of the Association of the Scientific Study of Consciousness (2018 and 2019). We examined responses from 166 consciousness researchers with different backgrounds (e.g. philosophy, neuroscience, psychology, and computer science) and at various stages of their careers (e.g. junior/senior faculty and graduate/undergraduate students). The results reveal that there remains considerable discussion and debate between the surveyed researchers about the definition of consciousness and the way it should be studied. To highlight a few observations, a majority of respondents believe that machines could have consciousness, that consciousness is a gradual phenomenon in the animal kingdom, and that unconscious processing is extensive, encompassing both low-level and high-level cognitive functions. Further, we show which theories of consciousness are currently considered most promising by respondents and how supposedly different theories cluster together, which dependent measures are considered best to index the presence or absence of consciousness, and which neural measures are thought to be the most likely signatures of consciousness. These findings provide us with a snapshot of the current views of researchers in the field and may therefore help prioritize research and theoretical approaches to foster progress.

Psychotic and autistic symptoms are related to social functioning in individuals with psychotic disorders (PD). The present study used a network approach to (1) evaluate the interactions between autistic symptoms, psychotic symptoms, and social functioning, and (2) investigate whether relations are similar in individuals with and without PD. We estimated an undirected network model in a sample of 504 PD, 572 familial risk for psychosis (FR), and 337 typical comparisons (TC), with a mean age of 34.9 years. Symptoms were assessed with the Autism Spectrum Quotient (AQ; 5 nodes) and the Community Assessment of Psychic Experiences (CAPE; 9 nodes). Social functioning was measured with the Social Functioning Scale (SFS; 7 nodes). We identified statistically significant differences between the FR and PD samples in global strength (P < .001) and network structure (P < .001). Our results show autistic symptoms (social interaction nodes) are negatively and more closely related to social functioning (withdrawal, interpersonal behavior) than psychotic symptoms. More and stronger connections between nodes were observed for the PD network than for FR and TC networks, while the latter 2 were similar in density (P = .11) and network structure (P = .19). The most central items in strength for PD were bizarre experiences, social skills, and paranoia. In conclusion, specific autistic symptoms are negatively associated with social functioning across the psychosis spectrum, but in the PD network symptoms may reinforce each other more easily. These findings emphasize the need for increased clinical awareness of comorbid autistic symptoms in psychotic individuals.

Prior research suggests that autistic adults are at increased risk for developing the core motor components of Parkinson’s disease (i.e. parkinsonism) although knowledge is limited to date. Therefore, we explore: (1) the prevalence of both continuous self-reported motor features typical of parkinsonism and the screen positivity rate for parkinsonism in two samples of older autistic adults without a suspected intellectual disability: the Netherlands-Sample ( N = 296, 50–81 years; 183 males; 113 females) and the United States of America-Sample ( N = 209, 50–83 years; 100 males; 109 females), and (2) whether there are differences between those who Screen+ versus Screen− for parkinsonism. Within the Netherlands-Sample only, the age of onset of the motor features was investigated. Self-reported parkinsonism features were prevalent in both samples (the Netherlands-Sample ~17% screened positive, mean age of onset ranging from 35.9 to 50.4 years; the United States of America-Sample ~33% screened positive). Autistic adults (the Netherlands-Sample) screening positive encountered more health problems than those who screened negative. In both samples, autistic adults who screened positive did not differ on current antipsychotic medication use relative to those who screened negative. The next step is to determine whether a positive screen for parkinsonism on this self-report questionnaire also implies that there is a higher risk for idiopathic Parkinson’s disease. Lay abstract Autistic adults without a suspected intellectual disability reported several motor features such as having tremors, and stiffness in one’s legs which are considered to be part of a complex of motor features called parkinsonism. This so-called parkinsonism was remarkably prevalent in middle-aged and older autistic adults in two independent studies (Dutch study: 50–81 years, 183 males, 113 females, all adulthood diagnoses; the USA study: 50–83 years, 110 females, 109 males, majority adulthood diagnosis). Parkinsonism can be part of the progressive motor disease—Parkinson’s disease. Therefore, it is important that future studies, including in-person neurological assessment, determine if (and if so, why) autistic adults who report these motor features are at increased risk for developing Parkinson’s disease.

Attitudes (or opinions, preferences, biases, stereotypes) can be considered bindings of the perceptual features of the attitudes’ object to affective codes with positive or negative connotations, which effectively renders them “event files” in terms of the Theory of Event Coding. We tested a particularly interesting implication of this theoretical account: that affective codes might “migrate” from one event file to another (i.e., effectively function as a component of one while actually being part of another), if the two files overlap in terms of other features. We tested this feature-migration hypothesis by having participants categorize pictures of fictitious outer space characters as members of two fictitious races by pressing a left or right key, and to categorize positive and negative pictures of the International Affective Picture System (IAPS) as positive and negative by using the same two keys. When the outer space characters were later rated for likability, members of the race that was categorized by means of the same key as positive IAPS pictures were liked significantly more than members of the race that was categorized with the same key as negative IAPS pictures – suggesting that affective feature codes from the event files for the IAPS pictures effectively acted as an ingredient of event files for the outer space characters that shared the same key. These findings were fully replicated in a second experiment in which the two races were replaced by two unfamiliar fonts. These outcomes are consistent with the claim that attitudes, opinions, and preferences are represented in terms of event files and created by feature binding.

Mind wandering (MW) is a highly prevalent phenomenon despite its negative consequences on behavior. Current views about its origin share the idea that MW occurs due to changes in the executive functions system. Here, we argue that not all instances of MW are necessarily related to changes in that system. Combining results from MW and sleep research, we propose that MW could also be related to the depletion of resources in primary task-related networks. To test this hypothesis, participants performed four sessions of the texture discrimination task (TDT) on a day. The TDT is a perceptual learning task in which performance is negatively related to the local build-up of sleep pressure. During the TDT, MW was recorded in both a subjective (i.e., with thought probes) and an objective (i.e., phasic pupillary response) manner. Results showed that accuracy on the TDT was mirrored in the objective measure of MW. For the subjective measure, the pattern was similar to that of task performance but could not be interpreted as reliable. These results demonstrate that not all MW is necessarily related to changes in the executive system and support the hypothesis that MW could be related to the depletion of local, task-related resources. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Most of our knowledge about human emotional memory comes from animal research. Based on this work, the amygdala is often labeled the brain's “fear center”, but it is unclear to what degree neural circuitries underlying fear and extinction learning are conserved across species. Neuroimaging studies in humans yield conflicting findings, with many studies failing to show amygdala activation in response to learned threat. Such null findings are often treated as resulting from MRI-specific problems related to measuring deep brain structures. Here we test this assumption in a mega-analysis of three studies on fear acquisition (n= 98; 68 female) and extinction learning (n= 79; 53 female). The conditioning procedure involved the presentation of two pictures of faces and two pictures of houses: one of each pair was followed by an electric shock [a conditioned stimulus (CS+)], the other one was never followed by a shock (CS–), and participants were instructed to learn these contingencies. Results revealed widespread responses to the CS+compared with the CS–in the fear network, including anterior insula, midcingulate cortex, thalamus, and bed nucleus of the stria terminalis, but not the amygdala, which actually responded stronger to the CS–. Results were independent of spatial smoothing, and of individual differences in trait anxiety and conditioned pupil responses. In contrast, robust amygdala activation distinguished faces from houses, refuting the idea that a poor signal could account for the absence of effects. Moving forward, we suggest that, apart from imaging larger samples at higher resolution, alternative statistical approaches may be used to identify cross-species similarities in fear and extinction learning.SIGNIFICANCE STATEMENTThe science of emotional memory provides the foundation of numerous theories on psychopathology, including stress and anxiety disorders. This field relies heavily on animal research, which suggests a central role of the amygdala in fear learning and memory. However, this finding is not strongly corroborated by neuroimaging evidence in humans, and null findings are too easily explained away by methodological limitations inherent to imaging deep brain structures. In a large nonclinical sample, we find widespread BOLD activation in response to learned fear, but not in the amygdala. A poor signal could not account for the absence of effects. While these findings do not disprove the involvement of the amygdala in human fear learning, they challenge its typical portrayals and illustrate the complexities of translational science.

© 2022 The AuthorsQuantitative MRI (qMRI) acquired at the ultra-high field of 7 Tesla has been used in visualizing and analyzing subcortical structures. qMRI relies on the acquisition of multiple images with different scan settings, leading to extended scanning times. Data redundancy and prior information from the relaxometry model can be exploited by deep learning to accelerate the imaging process. We propose the quantitative Recurrent Inference Machine (qRIM), with a unified forward model for joint reconstruction and R2*-mapping from sparse data, embedded in a Recurrent Inference Machine (RIM), an iterative inverse problem-solving network. To study the dependency of the proposed extension of the unified forward model to network architecture, we implemented and compared a quantitative End-to-End Variational Network (qE2EVN). Experiments were performed with high-resolution multi-echo gradient echo data of the brain at 7T of a cohort study covering the entire adult life span. The error in reconstructed R2* from undersampled data relative to reference data significantly decreased for the unified model compared to sequential image reconstruction and parameter fitting using the RIM. With increasing acceleration factor, an increasing reduction in the reconstruction error was observed, pointing to a larger benefit for sparser data. Qualitatively, this was following an observed reduction of image blurriness in R2*-maps. In contrast, when using the U-Net as network architecture, a negative bias in R2* in selected regions of interest was observed. Compressed Sensing rendered accurate, but less precise estimates of R2*. The qE2EVN showed slightly inferior reconstruction quality compared to the qRIM but better quality than the U-Net and Compressed Sensing. Subcortical maturation over age measured by a linearly increasing interquartile range of R2* in the striatum was preserved up to an acceleration factor of 9. With the integrated prior of the unified forward model, the proposed qRIM can exploit the redundancy among repeated measurements and shared information between tasks, facilitating relaxometry in accelerated MRI.

Objective: Electroconvulsive therapy (ECT) is the most effective treatment for late-life depression (LLD). Research addressing long-term outcome following an acute course of ECT for LLD is limited. We aimed to describe relapse, cognitive impairment and survival 5 years after a treatment with ECT for severe LLD, and assess the association of clinical characteristics with all three outcome measures. Methods: This cohort study was part of the Mood Disorders in Elderly treated with ECT (MODECT) study, which included patients aged 55 years and older with major depressive disorder. Data regarding clinical course, cognitive impairment and mortality were collected 5 years after the index ECT course. We used multivariable Cox proportional hazards models and logistic regression models to assess the association of clinical characteristics with relapse and survival, and cognitive impairment, respectively. Results: We studied 110 patients with a mean age of 72.9 years. 67.1% of patients who showed response at the end of the index ECT course relapsed, and the included clinical characteristics were not significantly associated with the risk of relapse. 38.8% of patients with available data showed cognitive impairment at five-year follow-up. 27.5% were deceased; higher age and a higher number of previous psychiatric admissions were significantly associated with increased risk of mor-tality. Conclusions: Five-year outcome after a course of ECT for severe LLD seems to be in line with long-term outcome following other acute treatments for severe LLD in terms of relapse, cognitive impairment and survival. Additional studies aimed at improving long-term outcome in severe LLD are warranted. (Am J Geriatr Psychiatry 2022; 30:1283-1294)

Recurrent processing is a crucial feature in human visual processing supporting perceptual grouping, figure-ground segmentation, and recognition under challenging conditions. There is a clear need to incorporate recurrent processing in deep convolutional neural networks (DCNNs) but the computations underlying recurrent processing remain unclear. In this paper, we tested a form of recurrence in deep residual networks (ResNets) to capture recurrent processing signals in the human brain. Though ResNets are feedforward networks, they approximate an excitatory additive form of recurrence. Essentially, this form of recurrence consists of repeating excitatory activations in response to a static stimulus. Here, we used ResNets of varying depths (reflecting varying levels of recurrent processing) to explain electroencephalography (EEG) activity within a visual masking paradigm. Sixty-two humans and fifty artificial agents (10 ResNet models of depths - 4, 6, 10, 18 and 34) completed an object categorization task. We show that deeper networks (ResNet-10, 18 and 34) explained more variance in brain activity compared to shallower networks (ResNet-4 and 6). Furthermore, all ResNets captured differences in brain activity between unmasked and masked trials, with differences starting at ∼98ms (from stimulus onset). These early differences indicated that EEG activity reflected ‘pure’ feedforward signals only briefly (up to ∼98ms). After ∼98ms, deeper networks showed a significant increase in explained variance which peaks at ∼200ms, but only within unmasked trials, not masked trials. In summary, we provided clear evidence that excitatory additive recurrent processing in ResNets captures some of the recurrent processing in humans.Significance statementThe challenge of modeling recurrent processes is not trivial and the operationalization of recurrent processing is highly contested. In this paper, we tested the ability of deep residual networks (ResNets) to explain recurrent processes in the human brain. Though ResNets are feedforward networks, they have been shown to equate operations in recurrent neural networks. In this study, we show that deeper networks explained more variance in brain activity than shallower networks. However, all networks still performed far from the noise ceiling. Thus, we conclude that recurrent processing in ResNets captures a form of recurrent processing in humans though other types of recurrent processing (inhibition, multiplicative) that are not present in current regular deep neural networks (alexnet, cornet, resnet) are necessary for building better visual models.

The loss of the radiologic swallow tail sign on MRI scans of the substantia nigra is a promising diagnostic marker of Parkinson disease (1), although its anatom-ic underpinning is unclear. An early influential study showed that the hyperintense inner part of the swallow tail sign on T2*-weighted images (STh) corresponds to iron-poor areas in substantia nigra and suggested it to equal nigrosome 1, the dopaminergic region affected earliest and strongest in Parkinson disease (2). This would render the STh a cellularly specific marker (2). However, recent postmortem tissue studies have chal-lenged this interpretation, reporting that nigrosome 1 is hypointense in T2*-weighted images (3,4). We com-bined three-dimensional histology with 7-T in vivo and postmortem MRI to demonstrate that nigrosome 1 and the radiologic STh are partially overlapping but distinct.

Based on neuroimaging data, the insula is considered important for people to empathize with the pain of others. Here, we present intracranial electroencephalographic (iEEG) recordings and single-cell recordings from the human insula while seven epilepsy patients rated the intensity of a woman’s painful experiences seen in short movie clips. Pain had to be deduced from seeing facial expressions or a hand being slapped by a belt. We found activity in the broadband 20–190 Hz range correlated with the trial-by-trial perceived intensity in the insula for both types of stimuli. Within the insula, some locations had activity correlating with perceived intensity for our facial expressions but not for our hand stimuli, others only for our hand but not our face stimuli, and others for both. The timing of responses to the sight of the hand being hit is best explained by kinematic information; that for our facial expressions, by shape information. Comparing the broadband activity in the iEEG signal with spiking activity from a small number of neurons and an fMRI experiment with similar stimuli revealed a consistent spatial organization, with stronger associations with intensity more anteriorly, while viewing the hand being slapped.

Contains fulltext : 291117.pdf (Publisher’s version ) (Open Access) Apathy is common after stroke and has been associated with cognitive impairment. However, causality between post-stroke apathy and cognitive impairment remains unclear. We assessed the course of apathy in relation to changes in cognitive functioning in stroke survivors. Using the Apathy Scale (AS) and cognitive tests on memory, processing speed and executive functioning at six- and 15 months post-stroke we tested for associations between (1) AS-scores and (change in) cognitive scores; (2) apathy course (persistent/incident/resolved) and cognitive change scores. Of 117 included participants, 29% had persistent apathy, 13% apathy resolving over time and 10% apathy emerging between 6-15 months post-stroke. Higher AS-scores were cross-sectionally and longitudinally associated with lower cognitive scores. Relations between apathy and cognitive change scores were ambiguous. These inconsistent relations between apathy and changes in cognition over time suggest that post-stroke apathy does not directly impact cognitive performance. Both these sequelae of stroke require separate attention. 01 januari 2023 12 p.

In Helmholtz’s illusion, a square with horizontal stripes appears taller than an identical square with vertical stripes. This effect has also been observed in experiments with human stimuli, where a human figure wearing a dress with horizontal stripes appears thinner than a drawing clad in vertical stripes. These findings do not agree with the common belief that clothes with horizontal stripes make someone appear wider, neither do they disentangle whether the horizontal or vertical stripes account for the thinning effect. In the present study, we focused on the effect of horizontal stripes in clothes comparing horizontal stripes against no-stripes (not against vertical; Experiments 1 and 2), using photos of a real-life female model, and controlling for the average luminance of the stripes (Experiment 2). Results showed that horizontal stripes and lower luminance have—independently—a small-to-moderate thinning effect on the perceived size of the body, and the effect is larger when the two variables are combined. In Experiment 3, we further show that the thinning effect due to the luminance of the dress is enhanced when the general background gets darker. © The Author(s) 2021.

Contains fulltext : 238692.pdf (Publisher’s version ) (Open Access) Deficits in facial emotion recognition occur frequently after stroke, with adverse social and behavioural consequences. The aim of this study was to investigate the neural underpinnings of the recognition of emotional expressions, in particular of the distinct basic emotions (anger, disgust, fear, happiness, sadness and surprise). A group of 110 ischaemic stroke patients with lesions in (sub)cortical areas of the cerebrum was included. Emotion recognition was assessed with the Ekman 60 Faces Test of the FEEST. Patient data were compared to data of 162 matched healthy controls (HC's). For the patients, whole brain voxel-based lesion-symptom mapping (VLSM) on 3-Tesla MRI images was performed. Results showed that patients performed significantly worse than HC's on both overall recognition of emotions, and specifically of disgust, fear, sadness and surprise. VLSM showed significant lesion-symptom associations for FEEST total in the right fronto-temporal region. Additionally, VLSM for the distinct emotions showed, apart from overlapping brain regions (insula, putamen and Rolandic operculum), also regions related to specific emotions. These were: middle and superior temporal gyrus (anger); caudate nucleus (disgust); superior corona radiate white matter tract, superior longitudinal fasciculus and middle frontal gyrus (happiness) and inferior frontal gyrus (sadness). Our findings help in understanding how lesions in specific brain regions can selectively affect the recognition of the basic emotions. 17 p.

Working memory and episodic memory are two different processes, although the nature of their interrelationship is debated. As these processes are predominantly studied in isolation, it is unclear whether they crucially rely on different neural substrates. To obtain more insight in this, 81 adults with sub-acute ischemic stroke and 29 elderly controls were assessed on a visual working memory task, followed by a surprise subsequent memory test for the same stimuli. Multivariate, atlas- and track-based lesion-symptom mapping (LSM) analyses were performed to identify anatomical correlates of visual memory. Behavioral results gave moderate evidence for independence between discriminability in working memory and subsequent memory, and strong evidence for a correlation in response bias on the two tasks in stroke patients. LSM analyses suggested there might be independent regions associated with working memory and episodic memory. Lesions in the right arcuate fasciculus were more strongly associated with discriminability in working memory than in subsequent memory, while lesions in the frontal operculum in the right hemisphere were more strongly associated with criterion setting in subsequent memory. These findings support the view that some processes involved in working memory and episodic memory rely on separate mechanisms, while acknowledging that there might also be shared processes. Supplementary Information The online version contains supplementary material available at 10.1007/s00429-021-02281-0.

Efficient neural transmission is crucial for optimal brain function, yet the plastic potential of white matter (WM) has long been overlooked. Growing evidence now shows that modifications to axons and myelin occur not only as a result of long-term learning, but also after short training periods. Motor sequence learning (MSL), a common paradigm used to study neuroplasticity, occurs in overlapping learning stages and different neural circuits are involved in each stage. However, most studies investigating short-term WM plasticity have used a pre-post design, in which the temporal dynamics of changes across learning stages cannot be assessed. In this study, we used multiple magnetic resonance imaging (MRI) scans at 7 T to investigate changes in WM in a group learning a complex visuomotor sequence (LRN) and in a control group (SMP) performing a simple sequence, for five consecutive days. Consistent with behavioral results, where most improvements occurred between the two first days, structural changes in WM were observed only in the early phase of learning (d1-d2), and in overall learning (d1-d5). In LRNs, WM microstructure was altered in the tracts underlying the primary motor and sensorimotor cortices. Moreover, our structural findings in WM were related to changes in functional connectivity, assessed with resting-state functional MRI data in the same cohort, through analyses in regions of interest (ROIs). Significant changes in WM microstructure were found in a ROI underlying the right supplementary motor area. Together, our findings provide evidence for highly dynamic WM plasticity in the sensorimotor network during short-term MSL.

Objective: Since computerized cognitive test performance may be influenced by computer experience, correction for this measure might be needed. This study examined how to correct for computer experience by examining its influence on online and traditional tests. Method: 248 healthy adults completed an online neuropsychological test battery and 70 adults completed traditional equivalents of the tests. Computer experience was assessed by a performance-based and a self-report measure. Regression analyses were applied to examine their influence on the online and traditional tests. Results: After correction for demographics, the performance-based measure was associated with online and traditional, predominantly speed-based, tests. The self-report measure was also associated with speed-based online tests but not with most traditional tests. Conclusions: Correcting computerized neuropsychological tests using a performance-based measure of computer experience would be unwise, because this measure also seems to tap into cognitive functions. A correction using a self-report measure might be better and is appropriate.

Background We provide an overview of Bayesian estimation, hypothesis testing, and model-averaging and illustrate how they benefit parametric survival analysis. We contrast the Bayesian framework to the currently dominant frequentist approach and highlight advantages, such as seamless incorporation of historical data, continuous monitoring of evidence, and incorporating uncertainty about the true data generating process. Methods We illustrate the application of the outlined Bayesian approaches on an example data set, retrospective re-analyzing a colon cancer trial. We assess the performance of Bayesian parametric survival analysis and maximum likelihood survival models with AIC/BIC model selection in fixed-n and sequential designs with a simulation study. Results In the retrospective re-analysis of the example data set, the Bayesian framework provided evidence for the absence of a positive treatment effect of adding Cetuximab to FOLFOX6 regimen on disease-free survival in patients with resected stage III colon cancer. Furthermore, the Bayesian sequential analysis would have terminated the trial 10.3 months earlier than the standard frequentist analysis. In a simulation study with sequential designs, the Bayesian framework on average reached a decision in almost half the time required by the frequentist counterparts, while maintaining the same power, and an appropriate false-positive rate. Under model misspecification, the Bayesian framework resulted in higher false-negative rate compared to the frequentist counterparts, which resulted in a higher proportion of undecided trials. In fixed-n designs, the Bayesian framework showed slightly higher power, slightly elevated error rates, and lower bias and RMSE when estimating treatment effects in small samples. We found no noticeable differences for survival predictions. We have made the analytic approach readily available to other researchers in the RoBSA R package. Conclusions The outlined Bayesian framework provides several benefits when applied to parametric survival analyses. It uses data more efficiently, is capable of considerably shortening the length of clinical trials, and provides a richer set of inferences.

Devastating and persisting traumatic memories are a central symptom of post-traumatic stress disorder (PTSD). Sleep problems are highly co-occurrent with PTSD and intertwined with its etiology. Notably, sleep hosts memory consolidation processes, supported by sleep spindles (11-16 Hz). Here we assess the hypothesis that intrusive memory symptoms in PTSD may arise from excessive memory consolidation, reflected in exaggerated spindling. We use a newly developed spindle detection method, entailing minimal assumptions regarding spindle phenotype, to assess spindle activity in PTSD patients and traumatized controls (n=2×14, matched on gender). Our results show increased spindle activity in PTSD, which positively correlates with daytime intrusive memory symptoms. Together, these findings provide a putative mechanism through which profound sleep disturbance in PTSD may contribute to memory problems. Due to its uniform and unbiased approach, the new, minimal assumption spindle detection method seems a promising tool to detect aberrant spindling in psychiatric disorders.

To facilitate multisensory processing, the brain binds multisensory information when presented within a certain maximum time lag (temporal binding window). In addition, and in audiovisual perception specifically, the brain adapts rapidly to asynchronies within a single trial and shifts the point of subjective simultaneity. Both processes, temporal binding and rapid recalibration, have been found to be altered in individuals with an autism spectrum disorder diagnosis. Here, we used a large adult sample (autism spectrum disorder: n = 75, no autism spectrum disorder: n = 85) to replicate these earlier findings. In this study, audiovisual stimuli were presented in a random order across a range of stimulus onset asynchronies, and participants indicated whether they were perceived simultaneously. Based on the synchrony distribution, their individual temporal binding window and point of subjective simultaneity were calculated. Contrary to previous findings, we found that the temporal binding window was not significantly different between both groups. Rapid recalibration was observed for both groups but did not differ significantly between groups. Evidence of an age effect was found which might explain discrepancies to previous studies. In addition, neither temporal binding window nor rapid recalibration was correlated with self-reported autistic symptoms or sensory sensitivity. Lay abstract It has been known for a long time that individuals diagnosed with autism spectrum disorder perceive the world differently. In this study, we investigated how people with or without autism perceive visual and auditory information. We know that an auditory and a visual stimulus do not have to be perfectly synchronous for us to perceive them as synchronous: first, when the two are within a certain time window (temporal binding window), the brain will tell us that they are synchronous. Second, the brain can also adapt quickly to audiovisual asynchronies (rapid recalibration). Although previous studies have shown that people with autism spectrum disorder have different temporal binding windows, and less rapid recalibration, we did not find these differences in our study. However, we did find that both processes develop over age, and since previous studies tested only young people (children, adolescents, and young adults), and we tested adults from 18 to 55 years, this might explain the different findings. In the end, there might be quite a complex story, where people with and without autism spectrum disorder perceive the world differently, even dependent on how old they are.

The impact of the COVID-19 pandemic on mental health, well-being, and behavior is likely influenced by individual characteristics that determine one's capacity for resilience. In this exploratory study, we examined whether individual differences in working memory (WM) capacity and habit propensity (HP), measured before the outbreak, could predict variation in subsequent psychological coping efficacy (as operationalized by measures of depression, mental well-being, perceived stress, and loneliness) and behavioral adjustment (by evaluating compliance and self-reported automaticity of four COVID-19 guidelines) among Dutch older adults (n = 36) during the pandemic (measured April 25 to May 6, 2020). While we found elevated levels of depression and emotional loneliness, overall mental well-being, and perceived stress were not affected by the pandemic. Contrary to our expectations, we found no robust evidence for a protective role of WM in predicting these outcomes, although our findings hint at a positive relationship with perceived change in mental well-being. Interestingly, WM and HP were found to affect the self-reported automaticity levels of adherence to behavioral COVID-19 guidelines (i.e., washing hands, physical distancing), where a strong HP appeared beneficial when deliberate resources were less available (e.g., low WM capacity). These novel and preliminary findings offer new potential avenues for investigating individual differences in resilience in times of major life events or challenges.Supplementary Information: The online version contains supplementary material available at 10.1007/s10804-022-09404-9.

How can we reconcile the massive fluctuations in neural connections with a stable long-term memory? Two-photon microscopy studies have revealed that large portions of neural connections (spines, synapses) are unexpectedly active, changing unpredictably over time. This appears to invalidate the main assumption underlying the majority of memory models in cognitive neuroscience, which rely on stable connections that retain information over time. Here, we show that such random fluctuations may in fact implement a type of memory consolidation mechanism with a stable very long-term memory that offers novel explanations for several classic memory ‘laws’, namely Jost’s Law (1897: superiority of spaced learning) and Ribot’s Law (1881: loss of recent memories in retrograde amnesia), for which a common neural basis has been postulated but not established, as well as other general ‘laws’ of learning and forgetting. We show how these phenomena emerge naturally from massively fluctuating neural connections.