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BACKGROUND: There is evidence that adherence to Mediterranean-like diet reduces cognitive decline and brain atrophy in Alzheimer's disease (AD). However, lengthy dietary assessments, such as food frequency questionnaires (FFQs), discourage more frequent use. OBJECTIVE: Here we aimed to validate a 6-items short questionnaire for a Mediterranean-like diet (6-QMD) and explore its associations with memory performance and hippocampal atrophy in healthy elders and individuals at risk for AD. METHODS: We analyzed 938 participants (N = 234 healthy controls and N = 704 participants with an increased AD risk) from the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). The 6-QMD was validated against the Mediterranean Diet (MeDi) score and the Mediterranean-DASH Diet Intervention for Neurodegenerative Delay (MIND) score, both derived from a detailed FFQ. Furthermore, associations between the 6-QMD and memory function as well as hippocampal atrophy were evaluated using linear regressions. RESULTS: The 6-QMD was moderately associated with the FFQ-derived MeDi adherence score (ρ = 0.25, p

It has been suggested that visual images are memorized across brief periods of time by vividly imagining them as if they still were there. Such visual imagery has been proposed as a key mechanism underlying working memory. However, the ability to evoke visual imagery varies substantially across individuals, raising the question whether people with weak or even absent visual imagery might use other, non-visual strategies for memorization. Here, we systematically investigated this question in two groups of participants, very strong and very weak imagers, that were asked to remember images across brief delay periods. We were able to reliably reconstruct the memorized stimuli from early visual cortex during the delay period. Importantly, the quality of reconstruction was equally accurate for both strong and weak imagers. The decodable information also closely reflected behavioral precision in both groups, suggesting it could potentially contribute to behavioral performance, even in the extreme case of completely aphantasic individuals. Our data thus suggest that strong and weak imagers do not differ in how early visual areas encode memory-related information and that this information might even be equally used in both groups.

Research on the neural mechanisms of perceptual decision-making has typically focused on simple categorical choices, say between two alternative motion directions. Studies on such discrete alternatives have often suggested that choices are encoded either in a motor-based or in an abstract, categorical format in regions beyond sensory cortex. However, many sensory features are graded rather than discrete, raising the question how choices are encoded when they span the full sensory continuum. Here we assessed this using motion stimuli that could vary anywhere between 0° and 360°. We employed a combination of neuroimaging and encoding models based on Gaussian Process Regression to assess how either stimuli or choices were encoded in brain responses. We found that single-voxel tuning patterns could be used to reconstruct the trial-by-trial physical direction of motion as well as the participants’ continuous choices. Importantly, these continuous choice signals were primarily observed in early visual areas. The tuning properties in this region generalized between choice encoding and stimulus encoding, even for reports that reflected pure guessing. We found only little information related to the decision outcome in regions beyond visual cortex, such as parietal cortex, possibly because our task did not involve differential motor preparation. This could suggest that decisions for continuous stimuli take can place already in sensory brain regions, potentially using similar mechanisms to the sensory recruitment in visual working memory.

We investigated whether the impact of tau-pathology on memory performance and on hippocampal/medial temporal memory function in non-demented individuals depends on the presence of amyloid pathology, irrespective of diagnostic clinical stage. We conducted a cross-sectional analysis of the observational, multicentric DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). Two hundred and thirty-five participants completed task functional MRI and provided CSF (92 cognitively unimpaired, 100 experiencing subjective cognitive decline and 43 with mild cognitive impairment). Presence (A+) and absence (A−) of amyloid pathology was defined by CSF amyloid-β42 (Aβ42) levels. Free recall performance in the Free and Cued Selective Reminding Test, scene recognition memory accuracy and hippocampal/medial temporal functional MRI novelty responses to scene images were related to CSF total-tau and phospho-tau levels separately for A+ and A− individuals. We found that total-tau and phospho-tau levels were negatively associated with memory performance in both tasks and with novelty responses in the hippocampus and amygdala, in interaction with Aβ42 levels. Subgroup analyses showed that these relationships were only present in A+ and remained stable when very high levels of tau (>700 pg/ml) and phospho-tau (>100 pg/ml) were excluded. These relationships were significant with diagnosis, age, education, sex, assessment site and Aβ42 levels as covariates. They also remained significant after propensity score based matching of phospho-tau levels across A+ and A− groups. After classifying this matched sample for phospho-tau pathology (T−/T+), individuals with A+/T+ were significantly more memory-impaired than A−/T+ despite the fact that both groups had the same amount of phospho-tau pathology. ApoE status (presence of the E4 allele), a known genetic risk factor for Alzheimer’s disease, did not mediate the relationship between tau pathology and hippocampal function and memory performance. Thus, our data show that the presence of amyloid pathology is associated with a linear relationship between tau pathology, hippocampal dysfunction and memory impairment, although the actual severity of amyloid pathology is uncorrelated. Our data therefore indicate that the presence of amyloid pathology provides a permissive state for tau-related hippocampal dysfunction and hippocampus-dependent recognition and recall impairment. This raises the possibility that in the predementia stage of Alzheimer’s disease, removing the negative impact of amyloid pathology could improve memory and hippocampal function even if the amount of tau-pathology in CSF is not changed, whereas reducing increased CSF tau-pathology in amyloid-negative individuals may not proportionally improve memory function.

The aim of the project is to better understand how perceptual decisions are represented in cortical brain areas. We employ a novel feature-continuous RDK task in an fMRI experiment to test whether choice-related and sensory-related brain signals are encoded in a similar format.

Several Alzheimer’s disease (AD) atrophy subtypes were identified, but their brain network properties are unclear. We analyzed data from two independent datasets, including 166 participants (103 AD/63 controls) from the DZNE-longitudinal cognitive impairment and dementia study and 151 participants (121 AD/30 controls) from the AD neuroimaging initiative cohorts, aiming to identify differences between AD atrophy subtypes in resting-state functional magnetic resonance imaging intra-network connectivity (INC) and global and nodal network properties. Using a data-driven clustering approach, we identified four AD atrophy subtypes with differences in functional connectivity, accompanied by clinical and biomarker alterations, including a medio-temporal-predominant (S-MT), a limbic-predominant (S-L), a diffuse (S-D), and a mild-atrophy (S-MA) subtype. S-MT and S-D showed INC reduction in the default mode, dorsal attention, visual and limbic network, and a pronounced reduction of “global efficiency” and decrease of the “clustering coefficient” in parietal and temporal lobes. Despite severe atrophy in limbic areas, the S-L exhibited only marginal global network but substantial nodal network failure. S-MA, in contrast, showed limited impairment in clinical and cognitive scores but pronounced global network failure. Our results contribute toward a better understanding of heterogeneity in AD with the detection of distinct differences in functional connectivity networks accompanied by CSF biomarker and cognitive differences in AD subtypes.

ObjectiveTo determine whether following a Mediterranean-like diet (MeDi) relates to cognitive functions and in vivo biomarkers for Alzheimer disease (AD), we analyzed cross-sectional data from the German DZNE-Longitudinal Cognitive Impairment and Dementia Study.MethodThe sample (n = 512, mean age 69.5 ± 5.9 years) included 169 cognitively normal participants and individuals at higher AD risk (53 with relatives with AD, 209 with subjective cognitive decline, and 81 with mild cognitive impairment). We defined MeDi adherence according to the food frequency questionnaire. Brain volume outcomes were generated via voxel-based morphometry on T1-MRI, and cognitive performance was assessed with an extensive neuropsychological battery. AD-related biomarkers (β-amyloid42/40 [Aβ42/40] ratio, phosphorylated tau 181 [pTau181]) in CSF were assessed in n = 226 individuals. We analyzed the associations between MeDi and outcomes with linear regression models controlling for several covariates. In addition, we applied hypothesis-driven mediation and moderation analysis.ResultsHigher MeDi adherence related to larger mediotemporal gray matter volume (p < 0.05 family-wise error corrected), better memory (β ± SE = 0.03 ± 0.02; p = 0.038), and less amyloid (Aβ42/40 ratio, β ± SE = 0.003 ± 0.001; p = 0.008) and pTau181 (β ± SE = −1.96 ± 0.68; p = 0.004) pathology. Mediotemporal volume mediated the association between MeDi and memory (40% indirect mediation). Finally, MeDi favorably moderated the associations among Aβ42/40 ratio, pTau181, and mediotemporal atrophy. Results were consistent correcting for APOE-ε4 status.ConclusionOur findings corroborate the view of MeDi as a protective factor against memory decline and mediotemporal atrophy. They suggest that these associations might be explained by a decrease of amyloidosis and tau pathology. Longitudinal and dietary intervention studies should further examine this conjecture and its treatment implications.

Surgical face masks reduce the spread of airborne pathogens but also disturb the flow of information between individuals. The risk of getting seriously ill after infection with SARS-COV-2 during the present COVID-19 pandemic amplifies with age, suggesting that face masks should be worn especially during face-to-face contact with and between older people. However, the ability to accurately perceive and understand communication signals decreases with age, and it is currently unknown whether face masks impair facial communication more severely in older people. We compared the impact of surgical face masks on dynamic facial emotion recognition in younger (18–30 years) and older (65–85 years) adults (N = 96) in an online study. Participants watched short video clips of young women who facially expressed anger, fear, contempt or sadness. Faces of half of the women were covered by a digitally added surgical face mask. As expected, emotion recognition accuracy declined with age, and face masks reduced emotion recognition accuracy in both younger and older participants. Unexpectedly, the effect of face masks did not differ between age groups. Further analyses showed that masks also reduced the participants’ overall confidence in their emotion judgements, but not their performance awareness (the difference between their confidence ratings for correct and incorrect responses). Again, there were no mask-by-age interactions. Finally, data obtained with a newly developed questionnaire (attitudes towards face masks, atom) suggest that younger and older people do not differ in how much they feel impaired in their understanding of other people’s emotions by face masks or how useful they find face masks in confining the COVID-19 pandemic. In sum, these findings do not provide evidence that the impact of face masks on the decoding of facial signals is disproportionally larger in older people.

Regular musical activity as a complex multimodal lifestyle activity is proposed to be protective against age-related cognitive decline and Alzheimer’s disease. This cross-sectional study investigated the association and interplay between musical instrument playing during life, multi-domain cognitive abilities and brain morphology in older adults (OA) from the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) study. Participants reporting having played a musical instrument across three life periods (n = 70) were compared to controls without a history of musical instrument playing (n = 70), well-matched for reserve proxies of education, intelligence, socioeconomic status and physical activity. Participants with musical activity outperformed controls in global cognition, working memory, executive functions, language, and visuospatial abilities, with no effects seen for learning and memory. The musically active group had greater gray matter volume in the somatosensory area, but did not differ from controls in higher-order frontal, temporal, or hippocampal volumes. However, the association between gray matter volume in distributed frontal-to-temporal regions and cognitive abilities was enhanced in participants with musical activity compared to controls. We show that playing a musical instrument during life relates to better late-life cognitive abilities and greater brain capacities in OA. Musical activity may serve as a multimodal enrichment strategy that could help preserve cognitive and brain health in late life. Longitudinal and interventional studies are needed to support this notion.

The data acquired during a functional magnetic resonance imaging (fMRI) experiment usually comprise experimental conditions, brain signals and behavioral responses. This reflects the underlying causal flow where the experimental conditions evoke brain responses that in turn result in behavior. In multivariate analyses, a common approach is to focus on the second step of that chain and to decode behavioral responses from brain signals. However, a different approach would be to first reconstruct the experimental conditions from brain signals and in turn use these to predict behavior. While this indirect approach would go against the causal chain of events, it might work better under certain circumstances, especially when the experimental conditions evoke much stronger measurable brain signals than the overt motor behavior. Here we tested this question directly by assessing the various mappings between conditions, brain signals and behavior in an open dataset. We found that the path of first decoding experimental conditions works surprisingly well, even though in our example data set, it is still outperformed by directly decoding the behavior from brain responses.

Machine learning studies have shown that various phenotypes can be predicted from structural and functional brain images. However, in most such studies, prediction performance ranged from moderate to disappointing. It is unclear whether prediction performance will substantially improve with larger sample sizes or whether insufficient predictive information in brain images impedes further progress. Here, we systematically assess the effect of sample size on prediction performance using sample sizes far beyond what is possible in common neuroimaging studies. We project 3-9 fold improvements in prediction performance for behavioral and mental health phenotypes when moving from one thousand to one million samples. Moreover, we find that moving from single imaging modalities to multimodal input data can lead to further improvements in prediction performance, often on par with doubling the sample size. Our analyses reveal considerable performance reserves for neuroimaging-based phenotype prediction. Machine learning models may benefit much more from extremely large neuroimaging datasets than currently believed.

Intentions come in several kinds, including motor, cued, self-chosen, proximal, and distal. Each kind of intention requires distinct methods of investigation and recruits different brain activity. This chapter discusses the nature of intentions, neuroscientific work on the different kinds of intentions, the involvement of motivation, and intention-predictive brain signals.

Working memory contents are represented in neural activity patterns across multiple regions of the cortical hierarchy. It has remained unclear to which degree this reflects a specialization for different levels of abstraction. Here, we demonstrate that for color stimuli categorical codes are already present at the level of extrastriate visual cortex (V4 and VO1). Interestingly, this categorical coding was observed during working memory, but not during perception.

1AbstractRegular musical activity as a highly-stimulating lifestyle activity is proposed to be protective against age-related cognitive decline and Alzheimer’s disease (AD). This study investigated associations between lifelong regular musical instrument playing, late-life cognitive abilities and brain morphology in older adults. We show that musical activity over the life course is associated with better global cognition, working memory, executive functions, language, and visuospatial abilities accounting for reserve proxies. Playing music is not significantly associated with gray matter volume in regions most affected by aging and AD. Selectively in the musically active participants, multi-domain cognitive abilities were enhanced with preserved gray matter volume in frontal and temporal regions. Our correlational findings suggest that playing a musical instrument may improve the recruitment of existing brain resources to facilitate late-life cognitive capacities. We propose that engaging in regular musical activity could serve as a low-threshold multimodal enrichment strategy that may promote cognitive resilience in advanced age.

Experiments on choice-predictive brain signals have played an important role in the debate on free will. In a seminal study, Benjamin Libet and colleagues found that a negative-going EEG signal, the readiness potential (RP), can be observed over motor-related brain regions hundreds of ms before the retrospectively reported time of the conscious decision to move. If the onset of the readiness potential is taken as an indicator of the “brain’s decision to move” this could mean that this decision to move is made early, by unconscious brain activity, rather than later, at the time when the subject believes to have decided. However, an alternative kind of interpretation, involving ongoing stochastic fluctuations, has recently been brought to light. One such model, the stochastic decision model (SDM), takes its inspiration from accumulator models of perceptual decision making. It suggests that the RP originates from an accumulation of ongoing stochastic fluctuations. In this view the decision happens only at a much later stage when an accumulated noisy signal (plus imperative) reaches a threshold. Here we clarify a number of confusions regarding both the evidence for the stochastic decision model as well as the interpretation that it offers. We will explore several points that we feel are in need of clarification:(a)that the empirical evidence for the role of stochastic fluctuations is so far only indirect;(b)that the relevance of evidence from animal studies is unclear;(c)that a model that is deterministic during the accumulation stage can explain the data in a similar way;(d)that the primary focus in the literature has been on the role of random fluctuations whereas the deterministic aspects of the model have been largely ignored;(e)that contrary to the original interpretation the deterministic component of the model is the dominant input into the accumulator;(f)that there is confusion regarding the role of “imperative” and “evidence” in the SDM and its link to perceptual decision making; and finally(g)as with other stochastic accumulator processes the question of whether the decision happens early or late depends on the nature of the noise fluctuations, specifically, whether they reflect “absolute” or “epistemic” randomness. Our aim is not to rehabilitate the role of the RP in the free will debate. Rather we aim to address some confusions regarding the evidence for accumulators playing a role in these preparatory brain processes.

Background Decision-making (DM) capabilities are impaired in multiple sclerosis (MS). A variety of researchers hypothesized that this impairment is associated with reduced quality of life (QoL) and neuropsychiatric symptoms. Studies explicitly testing this hypothesis, however, are rare, provided inconclusive results, or evaluated only a limited selection of DM domains. Consequently, we conducted the first MS study on perceptual DM (e.g. deciding whether a car will fit into a parking lot based on a visual percept) to test this assumption. Methods Specifically, we used an fMRI task that measured brain activity in 30 MS patients and 19 healthy controls (HCs) while the participants repeatedly decided whether objects referenced indirectly via their written object names would fit into a shoebox to investigate neural mechanisms of perceptual DM. The objects varied in size and thus decision difficulty. From these data, we determined voxel-wise brain activity parameters reflecting (i) decision difficulty and (ii) decision speed and related them to behavioral DM performance, QoL, mild to moderate depressive symptoms, and fatigue. Results Patients showed reduced DM performance. Activity reflecting decision difficulty in the middle temporal gyrus was negatively related to DM performance across MS patients and HCs; activity reflecting decision speed in MS patients was associated with depressive symptoms and fatigue in areas of the dorsal visual stream. Conclusion The study shows that the perceptual DM capacity is reduced in MS. Moreover, the link between neural mechanisms of perceptual DM and neuropsychiatric symptoms suggests that an impairment in this domain is clinically relevant.

Although dietary decision-making is regulated by multiple interacting neural controllers, their impact on dietary treatment success in obesity has only been investigated individually. Here, we used fMRI to test how well interactions between the Pavlovian system (automatically triggering urges of consumption after food cue exposure) and the goal-directed system (considering long-term consequences of food decisions) predict future dietary success achieved in 39 months. Activity of the Pavlovian system was measured with a cue-reactivity task by comparing perception of food versus control pictures, activity of the goal-directed system with a food-specific delay discounting paradigm. Both tasks were applied in 30 individuals with obesity up to five times: Before a 12-week diet, immediately thereafter, and at three annual follow-up visits. Brain activity was analyzed in two steps. In the first, we searched for areas involved in Pavlovian processes and goal-directed control across the 39-month study period with voxel-wise linear mixed-effects (LME) analyses. In the second, we computed network parameters reflecting the covariation of longitudinal voxel activity (i.e. principal components) in the regions identified in the first step and used them to predict body mass changes across the 39 months with LME models. Network analyses testing the link of dietary success with activity of the individual systems as reference found a moderate negative link to Pavlovian activity primarily in left hippocampus and a moderate positive association to goal-directed activity primarily in right inferior parietal gyrus. A cross-paradigm network analysis that integrated activity measured in both tasks revealed a strong positive link for interactions between visual Pavlovian areas and goal-directed decision-making regions mainly located in right insular cortex. We conclude that adaptation of food cue processing resources to goal-directed control activity is an important prerequisite of sustained dietary weight loss, presumably since the latter activity can modulate Pavlovian urges triggered by frequent cue exposure in everyday life.

Health-related quality of life (HRQoL) is an essential complementary parameter in the assessment of disease burden and treatment outcome in multiple sclerosis (MS) and can be affected by neuropsychiatric symptoms, which in turn are sensitive to psychological stress. However, until now, the impact of neurobiological stress and relaxation on HRQoL in MS has not been investigated. We thus evaluated whether the activity of neural networks triggered by mild psychological stress (elicited in an fMRI task comprising mental arithmetic with feedback) or by stress termination (i.e., relaxation) at baseline (T0) predicts HRQoL variations occurring between T0 and a follow-up visit (T1) in 28 patients using a robust regression and permutation testing. The median delay between T0 and T1 was 902 (range: 363-1,169) days. We assessed HRQoL based on the Hamburg Quality of Life Questionnaire in MS (HAQUAMS) and accounted for the impact of established HRQoL predictors and the cognitive performance of the participants. Relaxation-triggered activity of a widespread neural network predicted future variations in overall HRQoL (t = 3.68, p(family-wise error [FWE])-corrected = 0.008). Complementary analyses showed that relaxation-triggered activity of the same network at baseline was associated with variations in the HAQUAMS mood subscale on an alpha(FWE) = 0.1 level (t = 3.37, p(FWE) = 0.087). Finally, stress-induced activity of a prefronto-limbic network predicted future variations in the HAQUAMS lower limb mobility subscale (t = -3.62, p(FWE) = 0.020). Functional neural network measures of psychological stress and relaxation contain prognostic information for future HRQoL evolution in MS independent of clinical predictors.

Robots are getting increasingly more present in many spheres of human life, making the need for robots that can successfully engage in natural social interactions with humans paramount. Successful human-robot interaction could be achieved more effectively if robots could act predictably and could predict the humans' actions. If robots could represent human partners and generate behaviors that are in line with the partners' expectations based on human's mental models of interdependent action, human agents would be able to apply predictive and adaptive mechanisms acquired in human interactions to interact with robots effectively. How could robots be predictable and be capable of predicting human behavior? We propose that this could be achieved by having an internal representation of both oneself and the other agent, that is by equipping the robot with the ability to co-represent. Here, co-representation refers to the representation of the partner's actions alongside one's own actions. Although co-representation constitutes an essential process for successful human social interaction, as it supports understanding of others' actions, to date co-representation processes have only scarcely been integrated into robotic platforms. We highlight the state-of-the-art findings on co-representation in social robotics, discuss current research limitations and open issues for creating computational models of co-representation in robots, and put forward the idea that predictive learning might constitute a particularly promising framework to build models of co-representing robots. Overall, in this article, we offer an integrated view of the state-of-the-art findings in robotics literature on co-representation and outline directions for future research, with the aim to boost success in building robots equipped with co-representation models fit for smooth social interactions.

Voluntary movements are usually preceded by a slow, negative-going brain signal over motor areas, the so-called readiness potential (RP). To date, the exact nature and causal role of the RP in movement preparation have remained heavily debated. One important open question is whether people can exert conscious control over their RP, for example by learning to suppress it. If people were able to initiate spontaneous movements without eliciting an RP, this would challenge the idea that the RP is a necessary stage of the causal chain leading up to a voluntary movement. We tested the ability of participants to control the magnitude of their RP in a neurofeedback experiment. Participants performed self-initiated movements and after every movement they were provided with immediate feedback about the magnitude of their RP. They were asked to find a mental strategy to perform voluntary movements such that the RPs were as small as possible. We found no evidence that participants were able to to willfully modulate or suppress their RPs while still eliciting voluntary movements. This suggests that the RP might be an involuntary component of voluntary action over which people cannot exert conscious control.

Many philosophers have argued that the subjective character of conscious experience results in a fundamental deficit of third-person (henceforth: extrospective) access to first-person experience. By comparing extrospective measurement techniques with measurement techniques in the natural sciences, we will argue that extrospective methods suffer from no such deficit. After a rejection of some principled objections against extrospective methods, a historical comparison with the development of measurement techniques in the natural sciences will show that extrospective measuring methods are still in an early stage of development. However, they can be significantly improved by way of a bootstrapping strategy, similar to that which has proven successful in the development of physical measurement techniques. One reason to expect such improvement is the availability of multiple sources of evidence, which should allow for substantial advances in extrospective measurement techniques. Finally, we will discuss new developments in pain measurement in order to show that the bootstrapping strategy is already bearing fruit.

How and when motor intentions form has long been controversial. In particular, the extent to which motor preparation and action-related processes produce a conscious experience of intention remains unknown. Here, we used a brain–computer interface (BCI) while participants performed a self-paced movement task to trigger cues upon the detection of a readiness potential (a well-characterized brain signal that precedes movement) or in its absence. The BCI-triggered cues instructed participants either to move or not to move. Following this instruction, participants reported whether they felt they were about to move at the time the cue was presented. Participants were more likely to report an intention (i) when the cue was triggered by the presence of a readiness potential than when the same cue was triggered by its absence, and (ii) when they had just made an action than when they had not. We further describe a time-dependent integration of these two factors: the probability of reporting an intention was maximal when cues were triggered in the presence of a readiness potential, and when participants also executed an action shortly afterwards. Our results provide a first systematic investigation of how prospective and retrospective components are integrated in forming a conscious intention to move.

Background: While cerebral beta-amyloid accumulation was found in many studies to alter precuneus-based functional connectivity (FC) in mild cognitive impairment and demented Alzheimer’s disease (AD) patients, its impact is less well understood in subjective cognitive decline (SCD), which in the presence of underlying AD pathologic change corresponds to a progression to stage 2 of the clinical Alzheimer’s continuum in the 2018 National Institute on Aging and Alzheimer’s Association research framework, and represents the earliest clinical manifestation of AD. Methods: From the DELCODE (DZNE – Longitudinal Cognitive Impairment and Dementia Study) cohort, two groups of 24 age- and gender-matched amyloid-positive SCD (SCDAβ+) and amyloid-negative SCD (SCDAβ-) patients were selected according to visual [18F]-Florbetaben PET readings, and studied with resting-state BOLD fMRI. Local (regional homogeneity (ReHo), amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF)) and global (degree centrality (DC), precuneus seed-based FC maps) measures were calculated and compared between both groups. Furthermore, follow-up correlation analyses probed linear relationships of observed group differences with global as well as precuneal amyloid load measured by Florbetaben standard uptake value ratios (SUVRFBB). Results: For the local measures, ReHo was significantly higher in the bilateral precuneus for the SCDAβ+ group, whereas ALFF and fALFF measures were not altered between groups. For the global measures, relatively higher precuneus-based FC with occipital areas (but no altered DC) was observed in the SCDAβ+ group. Moreover, the FC differences between precuneus and occipital areas were positively correlated with global (SCDAβ+) and local precuneus SUVRFBB (both groups). Conclusions: While confounding influences due to a higher ratio of APOE e4 carriers in the SCDAβ+ group cannot be excluded, results indicate functional alterations in the precuneus hub region that were linearly related to beta-amyloid load, highlighting incipient pathology and possible compensatory mechanisms in stage 2 of the AD continuum.

In diesem Kapitel geht es um ein hochst populares und zugleich heftig umstrittenes psycho-neurobiologisches Thema, namlich Bewusstsein und seine moglichen neurobiologischen Grundlagen. Im Folgenden wollen wir die kaum mehr zu uberschauenden philosophischen Aussagen zu diesem Thema auser Acht lassen, auch wenn wir zumindest einige von ihnen zu schatzen wissen, und uns auf diejenigen Ansatze konzentrieren, die mit empirischen psychologischen und neurobiologischen Untersuchungen verbunden sind. Die Erforschung der neuronalen Korrelate des Bewusstseins versucht zu ermitteln, was im Gehirn anders lauft, wenn ein Reiz bewusst erkannt wird.

This document describes various supplementary analyses and model specifications; This R-script contains code necessary to run all main and supplementary analyses of our study; This file contains the data necessary to run the main analyses; This file contains the data necessary to run the supplementary analyses

This document describes various supplementary analyses and model specifications;This R-script contains code necessary to run all main and supplementary analyses of our study;This file contains the data necessary to run the main analyses;This file contains the data necessary to run the supplementary analyses

ZusammenfassungKann man auf der Basis der Gehirnaktivität einer Person bestimmen, was sie gerade denkt und fühlt? Im Forschungsgebiet des „Brain-Reading” (wörtlich: Gehirnlesen) wird untersucht, inwiefern es möglich ist, aus den Hirnprozessen einer Person auf ihre Gedankeninhalte zu schließen. Die Grundidee ist, dass jeder Gedanke mit einem charakteristischen Aktivierungsmuster im Gehirn einhergeht. Trainiert man einen Computer darauf, solche Muster zu erkennen, wird es möglich, die Gedanken einer Person allein aus der Hirnaktivität auszulesen. Bereits heute sind eine Reihe verschiedener Gedanken ausgelesen worden. Dazu zählen visuelle Wahrnehmungen und Vorstellungen, Erinnerungen, Absichten, Gefühle und Trauminhalte. Es ist bisweilen sogar möglich, aus der Hirnaktivität mehr über die mentalen Prozesse einer Person auszulesen, als ihr selbst bewusst ist. So kann man in bestimmten Situationen Absichten, bereits mehrere Sekunden bevor sie das Bewusstsein erreichen, auslesen. Trotz der erheblichen Erfolge in den letzten Jahren stößt das „Brain-Reading” jedoch auch schnell an Grenzen. So ist es zum Beispiel aus prinzipiellen Gründen schwierig, beliebige Gedanken auszulesen oder Erkenntnisse von einer Person auf andere zu übertragen. Es ist also noch ein langer Weg bis zu einer hypothetischen „universellen Gedankenlesemaschine”, bei der die beliebigen Gedanken einer beliebigen Versuchsperson auf Anhieb ausgelesen werden können. Es zeichnen sich mit heute verfügbaren Ansätzen bereits zwar einige Anwendungsmöglichkeiten ab, wie etwa in der Forensik und Kriminologie, in der Steuerung von Computern und künstlichen Prothesen mittels der Hirnaktivität, oder auch im „Neuromarketing”. Allerdings sind diese Techniken weit von der Anwendungsreife entfernt. Außerdem stellen sich vor ihrem Einsatz erhebliche ethische Fragen.

Alterations in motivated behavior are a hallmark of attention-deficit/hyperactivity disorder (ADHD), one of the most common psychiatric disorders in children and adolescents. The orbitofrontal cortex (OFC) plays a key role in controlling goal-directed behavior, but the link between OFC dysfunction and behavioral deficits in ADHD, particularly in adolescence, remains poorly understood. Here we used advanced high-resolution functional magnetic resonance imaging (fMRI) of the human OFC in adolescents with ADHD and typically developing (TD) controls (N = 39, age 12–16, all male except for one female per group) to study reward-related OFC responses and how they relate to behavioral dysfunction in ADHD. During fMRI data acquisition, participants performed a simple decision-making task, allowing us to image expectation-related responses to small and large monetary outcomes. Across all participants, we observed significant signal increases to large versus small expected rewards in the OFC. These responses were significantly enhanced in ADHD relative to TD participants. Moreover, stronger reward-related activity was correlated with individual differences in hyperactive/impulsive symptoms in the ADHD group, whereas high cognitive ability was associated with normalized OFC responses. These results provide evidence for the importance of OFC dysfunctions in the neuropathology of ADHD, highlighting the role of OFC-dependent goal-directed control mechanisms in this disorder. SIGNIFICANCE STATEMENT Attention-deficit/hyperactivity disorder (ADHD) is characterized by alterations in motivated behavior which can be understood as diminished goal-directed control. The orbitofrontal cortex (OFC) plays a key role in controlling goal-directed behavior, but its potential contribution to ADHD symptomatology remains poorly understood. Using high-resolution fMRI, we show that adolescent ADHD patients display enhanced OFC signaling of future rewards and that these increased reward-related responses are correlated with the severity of hyperactivity/impulsivity. These findings suggest that an inability to adequately evaluate future outcomes may translate into maladaptive behavior in ADHD patients. They also challenge the idea that dysfunctions in dopaminergic brain areas are the sole contributor to reward-related symptoms in ADHD and point to a central contribution of goal-directed control circuits in hyperactivity.

Background: Decision-making (DM) abilities deteriorate with multiple sclerosis (MS) disease progression which impairs everyday life and is thus clinically important. Objective: To investigate the underlying neurocognitive processes and their relation to regional gray matter (GM) loss induced by MS. Methods: We used a functional magnetic resonance imaging (fMRI) Iowa Gambling Task to measure DM-related brain activity in 36 MS patients and 21 healthy controls (HC). From this activity, we determined neural parameters of two cognitive stages, a deliberation (“choice”) period preceding a choice and a post-choice (“feedback”) stage reporting decision outcomes. These measures were related to DM separately in intact and damaged GM areas as determined by a voxel-based morphometry analysis. Results: Severely affected patients (with high lesion burden) showed worse DM-learning than HC ( t = −1.75, p = 0.045), moderately affected (low lesion burden) did not. Activity in the choice stage in intact insular ( t = 4.60, pFamily-Wise Error [FWE] corrected = 0.034), anterior cingulate ( t = 4.50, pFWE = 0.044), and dorsolateral prefrontal areas ( t = 4.43, pFWE = 0.049) and in insular areas with GM loss ( t = 3.78, pFWE = 0.011) was positively linked to DM performance across patients with severe tissue damage and HC. Furthermore, activity in intact orbitofrontal areas was positively linked to DM-learning during the feedback stage across these participants ( t = 4.49, pFWE = 0.032). During none of the stages, moderately affected patients showed higher activity than HC, which might have indicated preserved DM due to compensatory activity. Conclusion: We identified dysregulated activity linked to impairment in specific cognitive stages of reward-related DM. The link of brain activity and impaired DM in areas with MS-induced GM loss suggests that this deficit might be tightly coupled to MS neuropathology.

In dialogue, language processing is adapted to the conversational partner. We hypothesize that the brain facilitates partner-adapted language processing through preparatory neural configurations (task sets) that are tailored to the conversational partner. In this experiment, we measured neural activity with functional magnetic resonance imaging (fMRI) while healthy participants in the scanner (a) engaged in a verbal communication task with a conversational partner outside of the scanner, or (b) spoke outside of a conversational context (to test the microphone). Using multivariate searchlight analysis, we identify cortical regions that represent information on whether speakers plan to speak to a conversational partner or without having a partner. Most notably a region that has been associated with processing social-affective information and perspective taking, the ventromedial prefrontal cortex, as well as regions that have been associated with prospective task representation, the bilateral ventral prefrontal cortex, are involved in encoding the speaking condition. Our results suggest that speakers prepare, in advance of speaking, for the social context in which they will speak.

There is an ongoing debate in philosophy and psychology about when one should consider an action to be free. Several aspects are frequently suggested as relevant: (a) a prior intention, (b) a conscious action-related thought, (c) prior deliberation, (d) a meaningful choice, (e) different consequences of the action, and (f) the duration between intention and action. Here we investigated which criteria laypeople adopt and thus probed their intuitions about free actions in three surveys based on daily life scenarios. First, our results indicate that laypeople consider a conscious intention important for an action to be free. Second, laypeople consider spontaneous actions without consequences to be freer than actions with prior deliberation. Third, laypeople consider proximal rather than distal intentions relevant when it comes to judging actions as free. Taken together, these results suggest that simple laboratory experiments on action choices reflect laypeople’s intuitions of free actions to a considerable degree.

Current theories in cognitive neuroscience assume that internal simulation, i.e., the reproduction of brain activity underlying another person's inner state and behaviour in the perceiver's brain, plays an important role in understanding others. Here we test the prediction that common neural activity during facial communication of affect leads to interpersonal understanding. Six female senders and 30 male observers (six of which were the senders romantic partners and 24 unknown others) underwent pseudo-hyperscanning fMRI (functional magnetic resonance imaging). Senders were asked to submerge themselves into emotional situations and to facially express their emotional feelings as they arose to the observer. Observers were uninformed about the sender's task and were asked to watch and feel with the sender. Using between-brain spatial correlation analysis we found that mere emotion recognition was not closely related to the degree to which an observer reproduced the sender's spatial pattern of neural activity in his own brain. However, in runs in which the observer had correctly identified the communicated emotion, between-brain similarity of spatial patterns of neural activity predicted the degree to which the observer experienced a similar emotional feeling as the sender. This effect remained significant when differences between romantic partners and unknown others and sender effects were removed. These findings are in line with previous studies that suggest that facial emotion recognition, at least at a coarse level, might be supported by neural processes that do not rely on internal simulation. Shared affective experiences, on the other hand, might arise from common neural activity between the sender's and the observer's brain, leading to a "shared space of affect" which might be critical for the flow of more subtle affective information between brains.

Background The cause of cognitive impairment in acutely hospitalized geriatric patients is often unclear. The diagnostic process is challenging but important in order to treat potentially life-threatening etiologies or identify underlying neurodegenerative disease. Objective To evaluate the add-on diagnostic value of structural and metabolic neuroimaging in newly manifested cognitive impairment in elderly geriatric inpatients. Methods Eighty-one inpatients (55 females, 81.6±5.5 y) without history of cognitive complaints prior to hospitalization were recruited in 10 acute geriatrics clinics. Primary inclusion criterion was a clinical hypothesis of Alzheimer's disease (AD), cerebrovascular disease (CVD), or mixed AD+CVD etiology (MD), which remained uncertain after standard diagnostic workup. Additional procedures performed after enrollment included detailed neuropsychological testing and structural MRI and FDG-PET of the brain. An interdisciplinary expert team established the most probable etiologic diagnosis (non-neurodegenerative, AD, CVD, or MD) integrating all available data. Automatic multimodal classification based on Random Undersampling Boosting was used for rater-independent assessment of the complementary contribution of the additional diagnostic procedures to the etiologic diagnosis. Results Automatic 4-class classification based on all diagnostic routine standard procedures combined reproduced the etiologic expert diagnosis in 31% of the patients (p = 0.100, chance level 25%). Highest accuracy by a single modality was achieved by MRI or FDG-PET (both 45%, p≤0.001). Integration of all modalities resulted in 76% accuracy (p≤0.001). Conclusion These results indicate substantial improvement of diagnostic accuracy in uncertain de novo cognitive impairment in acutely hospitalized geriatric patients with the integration of structural MRI and brain FDG-PET into the diagnostic process.

Background: Dysfunction of the cholinergic basal forebrain (cBF) is associated with cognitive decline in Alzheimer's disease (AD). Multimodal MRI allows for the investigation of cBF changes in-vivo. In this study we assessed alterations in cBF functional connectivity (FC), mean diffusivity (MD), and volume across the spectrum of AD. We further assessed effects of amyloid pathology on these changes. Methods: Participants included healthy controls, and subjects with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or AD dementia (ADD) from the multicenter DELCODE study. Resting-state functional MRI (rs-fMRI) and structural MRI data was available for 477 subjects, and a subset of 243 subjects also had DTI data available. Differences between diagnostic groups were investigated using seed-based FC, volumetric, and MD analyses of functionally defined anterior (a-cBF) and posterior (p-cBF) subdivisions of a cytoarchitectonic cBF region-of-interest. In complementary analyses groups were stratified according to amyloid status based on CSF Aβ42/40 biomarker data, which was available in a subset of participants. Results: a-cBF and p-cBF subdivisions showed regional FC profiles that were highly consistent with previously reported patterns, but there were only minimal differences between diagnostic groups. Compared to controls, cBF volumes and MD were significantly different in MCI and ADD but not in SCD. The Aβ42/40 stratified analyses largely matched these results. Conclusions: We reproduced subregion-specific FC profiles of the cBF in a clinical sample spanning the AD spectrum. At least in this multicentric cohort study, cBF-FC did not show marked changes along the AD spectrum, and multimodal MRI did not provide more sensitive measures of AD-related cBF changes compared to volumetry., Michel J. Grothe is supported by the “Miguel Servet” program [CP19/00031] of the Spanish Instituto de Salud Carlos III (ISCIIIFEDER).

Visualizing and interpreting convolutional neural networks (CNNs) is an important task to increase trust in automatic medical decision making systems. In this study, we train a 3D CNN to detect Alzheimer’s disease based on structural MRI scans of the brain. Then, we apply four different gradient-based and occlusion-based visualization methods that explain the network’s classification decisions by highlighting relevant areas in the input image. We compare the methods qualitatively and quantitatively. We find that all four methods focus on brain regions known to be involved in Alzheimer’s disease, such as inferior and middle temporal gyrus. While the occlusion-based methods focus more on specific regions, the gradient-based methods pick up distributed relevance patterns. Additionally, we find that the distribution of relevance varies across patients, with some having a stronger focus on the temporal lobe, whereas for others more cortical areas are relevant. In summary, we show that applying different visualization methods is important to understand the decisions of a CNN, a step that is crucial to increase clinical impact and trust in computer-based decision support systems.

To fully characterize the activity patterns on the cerebral cortex as measured with fMRI, the spatial scale of the patterns must be ascertained. Here we address this problem by constructing steerable bandpass filters on the discrete, irregular cortical mesh, using an improved Gaussian smoothing in combination with differential operators of directional derivatives. We demonstrate the utility of the algorithm in two ways. First, using modelling we show that our algorithm yields superior results in numerical precision and spatial uniformity of filter kernels compared to the most widely adopted approach for cortical smoothing. An important interim insight hereby was that the effective scales of information differ from the nominal filter sizes applied to extract them, and thus need to be calculated separately to compare different algorithms on par. Second, we applied the algorithm to an fMRI dataset to assess the scale and pattern form of cortical encoding of information about visual objects in the ventral visual pathway. We found that filtering by our method improved the detection of discriminant information about experimental conditions over previous methods, that the level of categorization (subordinate versus superordinate) of objects was differentially related to the spatial scale of fMRI patterns, and that the spatial scale at which information was encoded increased along the ventral visual pathway. In sum, our results indicate that the proposed algorithm is particularly suited to assess and detect scale-specific information encoding in cortex, and promises further insight into the topography of cortical encoding in the human brain.

A fundamental issue in visual cognition is whether high-level visual areas code objects in a part-based or a view-based (holistic) format. Previous behavioral and neuroimaging studies that examined the viewpoint invariance of object recognition have yielded ambiguous results, providing evidence for either type of representational format. A critical factor distinguishing the two formats could be the availability of attentional resources, as a number of priming studies have found greater viewpoint invariance for attended compared to unattended objects. It has therefore been suggested that the activation of part-based representations requires attention, whereas the activation of holistic representations occurs automatically irrespective of attention. Using functional magnetic resonance imaging in combination with a novel multivariate pattern analysis approach, the present study probed the format of object representations in human lateral occipital complex and its dependence on attention. We presented human participants with intact and half-split versions of objects that were either attended or unattended. Cross-classifying between intact and split objects, we found that the object-related information coded in activation patterns of intact objects is fully preserved in the patterns of split objects and vice versa. Importantly, the generalization between intact and split objects did not depend on attention. We conclude that lateral occipital complex codes objects in a non-holistic format, both in the presence and absence of attention.

BackgroundPrevious studies have highlighted the role of the brain reward and cognitive control systems in the etiology of anorexia nervosa (AN). In an attempt to disentangle the relative contribution of these systems to the disorder, we used functional magnetic resonance imaging (fMRI) to investigate hemodynamic responses to reward-related stimuli presented both subliminally and supraliminally in acutely underweight AN patients and age-matched healthy controls (HC).MethodsfMRI data were collected from a total of 35 AN patients and 35 HC, while they passively viewed subliminally and supraliminally presented streams of food, positive social, and neutral stimuli. Activation patterns of the group×stimulation condition×stimulus type interaction were interrogated to investigate potential group differences in processing different stimulus types under the two stimulation conditions. Moreover, changes in functional connectivity were investigated using generalized psychophysiological interaction analysis.ResultsAN patients showed a generally increased response to supraliminally presented stimuli in the inferior frontal junction (IFJ), but no alterations within the reward system. Increased activation during supraliminal stimulation with food stimuli was observed in the AN group in visual regions including superior occipital gyrus and the fusiform gyrus/parahippocampal gyrus. No group difference was found with respect to the subliminal stimulation condition and functional connectivity.ConclusionIncreased IFJ activation in AN during supraliminal stimulation may indicate hyperactive cognitive control, which resonates with clinical presentation of excessive self-control in AN patients. Increased activation to food stimuli in visual regions may be interpreted in light of an attentional food bias in AN.

When attention has to be maintained over prolonged periods performance slowly fluctuates and errors can occur. It has been shown that lapses of attention are correlated with BOLD signals in frontal and parietal cortex. This raises the question how attentional fluctuations are linked to the fronto-parietal default network. Because the attentional state fluctuates slowly we expect that potential links between attentional fluctuations and brain activity should be observable on longer time scales and importantly also before the execution of the task. In the present study we used fMRI to identify brain activity that is correlated with vigilance, defined as fluctuations of reaction times (RT) during a sustained attention task. We found that brain activity in visual cortex, parietal lobe (PL), inferior and superior frontal gyrus, and supplementary motor area (SMA) was higher when the subject had a relatively long RT. In contrast to our expectations, activity in the default network (DN) was higher when subjects had a relatively short RT, that means when the performance was improved. This modulation in the DN was present already several seconds before the task execution, thus pointing to activity in the DN as a potential cause of performance increases in simple repetitive tasks.

We present two studies in which brain-computer interfaces (BCIs) used two related EEG signals, the readiness potential (RP) and the lateralized readiness potential (LRP), in order to predict and feed back motor intentions in real-time. In each of the studies, the experimental task was designed as a game that the subjects played against the BCI. In one of the experiments, subjects played a “duel” game against the BCI. They were challenged to perform spontaneous button presses but to withhold any movement when interrupted by a stop signal. This stop signal was controlled by the BCI that had been trained to predict movements by detecting the occurrence of RPs in the ongoing EEG. In the other experiment, participants played a “matching pennies” game. They won a point if they raised a different hand than the opponent at the end of a countdown and lost a point otherwise. The opponent was played by the BCI, who had been trained to predict from the LRP in the ongoing EEG which hand the subject would move at the end of the countdown. Hence, in both experiments a key feature of the BCI was its closed-loop nature, that is the ability to predict the motor intention in real-time and provide an immediate feedback of the prediction to the subject. In both experiments, prediction accuracies of the BCI were substantially higher compared to random predictions, thereby demonstrating the success of this approach. This allows researchers to use BCIs as research tools to address questions from cognitive neuroscience and provide new insights into the coupling of motor preparatory signals and the corresponding actions.

For successful communication, conversational partners need to estimate each other’s current knowledge state. Nonverbal facial and bodily cues can reveal relevant information about how confident a speaker is about what they are saying. Using functional magnetic resonance imaging, we aimed to identify brain regions that encode how confident a speaker is perceived to be. Participants viewed videos of people answering general knowledge questions and judged each respondent’s confidence in their answer. Our results suggest a distinct role of two neural networks known to support social inferences, the so-called mentalizing and the mirroring network. While activation in both networks underlies the processing of nonverbal cues, only activity in the mentalizing network, most notably the medial prefrontal cortex and the bilateral temporoparietal junction, is modulated by how confident the respondent is judged to be. Our results support an integrative account of the mirroring and mentalizing network, in which the two systems support each other in aiding pragmatic processing.

Humans recognize faces and objects with high speed and accuracy regardless of their orientation. Recent studies have proposed that orientation invariance in face recognition involves an intermediate representation where neural responses are similar for mirror-symmetric views. Here, we used fMRI, multivariate pattern analysis, and computational modeling to investigate the neural encoding of faces and vehicles at different rotational angles. Corroborating previous studies, we demonstrate a representation of face orientation in the fusiform face-selective area (FFA). We go beyond these studies by showing that this representation is category-selective and tolerant to retinal translation. Critically, by controlling for low-level confounds, we found the representation of orientation in FFA to be compatible with a linear angle code. Aspects of mirror-symmetric coding cannot be ruled out when FFA mean activity levels are considered as a dimension of coding. Finally, we used a parametric family of computational models, involving a biased sampling of view-tuned neuronal clusters, to compare different face angle encoding models. The best fitting model exhibited a predominance of neuronal clusters tuned to frontal views of faces. In sum, our findings suggest a category-selective and monotonic code of face orientation in the human FFA, in line with primate electrophysiology studies that observed mirror-symmetric tuning of neural responses at higher stages of the visual system, beyond the putative homolog of human FFA.