Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning.

BACKGROUND: Radiotherapy is a core treatment modality for oropharyngeal cancer (OPC), where the primary gross tumor volume (GTVp) is manually segmented with high interobserver variability. This calls for reliable and trustworthy automated tools in clinician workflow. Therefore, accurate uncertainty quantification and its downstream utilization is critical. METHODS: Here we propose uncertainty-aware deep learning for OPC GTVp segmentation, and illustrate the utility of uncertainty in multiple applications. We examine two Bayesian deep learning (BDL) models and eight uncertainty measures, and utilize a large multi-institute dataset of 292 PET/CT scans to systematically analyze our approach. RESULTS: We show that our uncertainty-based approach accurately predicts the quality of the deep learning segmentation in 86.6% of cases, identifies low performance cases for semi-automated correction, and visualizes regions of the scans where the segmentations likely fail. CONCLUSIONS: Our BDL-based analysis provides a first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation.

Radiotherapy is used as a treatment for people with oropharyngeal cancer. It is important to distinguish the areas where cancer is present so the radiotherapy treatment can be targeted at the cancer. Computational methods based on artificial intelligence can automate this task but need to be able to distinguish areas where it is unclear whether cancer is present. In this study we compare these computational methods that are able to highlight areas where it is unclear whether or not cancer is present. Our approach accurately predicts how well these areas are distinguished by the models. Our results could be applied to improve the computational methods used during radiotherapy treatment. This could enable more targeted treatment to be used in the future, which could result in better outcomes for people with oropharyngeal cancer.

Sisterhood predicts similar neural processing of a film.

Relationships between humans are essential for how we see the world. Using fMRI, we explored the neural basis of homophily, a sociological concept that describes the tendency to bond with similar others. Our comparison of brain activity between sisters, friends and acquaintances while they watched a movie, indicate that sisters' brain activity is more similar than that of friends and friends' activity is more similar than that of acquaintances. The increased similarity in brain activity measured as inter-subject correlation (ISC) was found both in higher-order brain areas including the default-mode network (DMN) and sensory areas. Increased ISC could not be explained by genetic relation between sisters neither by similarities in eye-movements, emotional experiences, and physiological activity. Our findings shed light on the neural basis of homophily by revealing that similarity in brain activity in the DMN and sensory areas is the stronger the closer is the relationship between the people.

Bodily Maps of Symptoms and Emotions in Parkinson's Disease.

BACKGROUND: Emotions are reflected in bodily sensations, and these reflections are abnormal in psychiatric conditions. However, emotion-related bodily sensations have not been studied in neurological disorders. OBJECTIVE: The aim of this study was to investigate whether Parkinson's disease (PD) is associated with altered bodily representations of emotions. METHODS: Symptoms and emotion-related sensations were investigated in 380 patients with PD and 79 control subjects, using a topographical self-report method, termed body sensation mapping. The bodily mapping data were analyzed with pixelwise generalized linear models and principal component analyses. RESULTS: Bodily maps of symptoms showed characteristic patterns of PD motor symptom distributions. Compared with control subjects, PD patients showed decreased parasternal sensation of anger, and longer PD symptom duration was associated with increased abdominal sensation of anger (PFWE < 0.05). The PD-related sensation patterns were abnormal across all basic emotions (P < 0.05). CONCLUSIONS: The results demonstrate altered bodily maps of emotions in PD, providing novel insight into the nonmotor effects of PD. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping.

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.

Development and psychometric testing of the perception of nursing profession instrument.

BACKGROUND: Perceptions of the nursing profession influence career choices in nursing. An unrealistic perception might lead students to drop out of nursing education programmes. Objective measurement of the nursing applicants' perceptions at the student selection stage could enhance their career choices in nursing. AIM: To develop and psychometrically evaluate the Perception of Nursing Profession Instrument (PNPI). DESIGN: Mixed method design. METHOD: Two versions of the PNPI were developed during the years 2016-2022. The first version was based on documents describing the nursing profession and the second version was based on an integrative literature review, a focus groups study and a document analysis of descriptions of the nursing profession. The meta-ethnographic approach was used to synthesize the results and form a theoretical framework for developing the PNPI (60 items). Item content validity was evaluated by an expert panel of nurses (n = 7). The psychometric properties of the instrument were analysed using the item response theory approach. RESULTS: The development process resulted in the 40-item PNPI with the following subscales: the content of nursing work, the career in nursing, the nature of nursing work and the characteristics of a nurse. The psychometric analysis revealed unidimensionality and goodness of fit to the partial credit model; however, the item difficulty was not well matched with the participants' abilities. CONCLUSION: The PNPI is a novel instrument for objectively measuring perceptions of the nursing profession. For further development, item difficulty must be enhanced to improve the measurement accuracy of the nursing applicants' perceptions of the nursing profession. IMPACT: Perceptions of the nursing profession influence career choices, but there is a lack of objective assessment instruments that can be used in nursing student selection setting to measure the perception. The results of this study offer an instrument to measure perception, while also suggesting ideas for further development.

Application of simultaneous uncertainty quantification for image segmentation with probabilistic deep learning: Performance benchmarking of oropharyngeal cancer target delineation as a use-case.

Background: Oropharyngeal cancer (OPC) is a widespread disease, with radiotherapy being a core treatment modality. Manual segmentation of the primary gross tumor volume (GTVp) is currently employed for OPC radiotherapy planning, but is subject to significant interobserver variability. Deep learning (DL) approaches have shown promise in automating GTVp segmentation, but comparative (auto)confidence metrics of these models predictions has not been well-explored. Quantifying instance-specific DL model uncertainty is crucial to improving clinician trust and facilitating broad clinical implementation. Therefore, in this study, probabilistic DL models for GTVp auto-segmentation were developed using large-scale PET/CT datasets, and various uncertainty auto-estimation methods were systematically investigated and benchmarked. Methods: We utilized the publicly available 2021 HECKTOR Challenge training dataset with 224 co-registered PET/CT scans of OPC patients with corresponding GTVp segmentations as a development set. A separate set of 67 co-registered PET/CT scans of OPC patients with corresponding GTVp segmentations was used for external validation. Two approximate Bayesian deep learning methods, the MC Dropout Ensemble and Deep Ensemble, both with five submodels, were evaluated for GTVp segmentation and uncertainty performance. The segmentation performance was evaluated using the volumetric Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance at 95% (95HD). The uncertainty was evaluated using four measures from literature: coefficient of variation (CV), structure expected entropy, structure predictive entropy, and structure mutual information, and additionally with our novel Dice-risk measure. The utility of uncertainty information was evaluated with the accuracy of uncertainty-based segmentation performance prediction using the Accuracy vs Uncertainty (AvU) metric, and by examining the linear correlation between uncertainty estimates and DSC. In addition, batch-based and instance-based referral processes were examined, where the patients with high uncertainty were rejected from the set. In the batch referral process, the area under the referral curve with DSC (R-DSC AUC) was used for evaluation, whereas in the instance referral process, the DSC at various uncertainty thresholds were examined. Results: Both models behaved similarly in terms of the segmentation performance and uncertainty estimation. Specifically, the MC Dropout Ensemble had 0.776 DSC, 1.703 mm MSD, and 5.385 mm 95HD. The Deep Ensemble had 0.767 DSC, 1.717 mm MSD, and 5.477 mm 95HD. The uncertainty measure with the highest DSC correlation was structure predictive entropy with correlation coefficients of 0.699 and 0.692 for the MC Dropout Ensemble and the Deep Ensemble, respectively. The highest AvU value was 0.866 for both models. The best performing uncertainty measure for both models was the CV which had R-DSC AUC of 0.783 and 0.782 for the MC Dropout Ensemble and Deep Ensemble, respectively. With referring patients based on uncertainty thresholds from 0.85 validation DSC for all uncertainty measures, on average the DSC improved from the full dataset by 4.7% and 5.0% while referring 21.8% and 22% patients for MC Dropout Ensemble and Deep Ensemble, respectively. Conclusion: We found that many of the investigated methods provide overall similar but distinct utility in terms of predicting segmentation quality and referral performance. These findings are a critical first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation.

Functional organization of social perception networks in the human brain.

Humans rapidly extract diverse and complex information from ongoing social interactions, but the perceptual and neural organization of the different aspects of social perception remains unresolved. We showed short movie clips with rich social content to 97 healthy participants while their haemodynamic brain activity was measured with fMRI. The clips were annotated moment-to-moment for a large set of social features and 45 of the features were evaluated reliably between annotators. Cluster analysis of the social features revealed that 13 dimensions were sufficient for describing the social perceptual space. Three different analysis methods were used to map the social perceptual processes in the human brain. Regression analysis mapped regional neural response profiles for different social dimensions. Multivariate pattern analysis then established the spatial specificity of the responses and intersubject correlation analysis connected social perceptual processing with neural synchronization. The results revealed a gradient in the processing of social information in the brain. Posterior temporal and occipital regions were broadly tuned to most social dimensions and the classifier revealed that these responses showed spatial specificity for social dimensions; in contrast Heschl gyri and parietal areas were also broadly associated with different social signals, yet the spatial patterns of responses did not differentiate social dimensions. Frontal and subcortical regions responded only to a limited number of social dimensions and the spatial response patterns did not differentiate social dimension. Altogether these results highlight the distributed nature of social processing in the brain.

Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases.

Background: Demand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patient identifiers. Defacing, i.e., the removal of facial features from images, is often considered a reasonable compromise between data protection and re-usability for neuroimaging data. While defacing tools have been developed by the neuroimaging community, their acceptability for radiotherapy applications have not been explored. Therefore, this study systematically investigated the impact of available defacing algorithms on HNC organs at risk (OARs). Methods: A publicly available dataset of magnetic resonance imaging scans for 55 HNC patients with eight segmented OARs (bilateral submandibular glands, parotid glands, level II neck lymph nodes, level III neck lymph nodes) was utilized. Eight publicly available defacing algorithms were investigated: afni_refacer, DeepDefacer, defacer, fsl_deface, mask_face, mri_deface, pydeface, and quickshear. Using a subset of scans where defacing succeeded (N=29), a 5-fold cross-validation 3D U-net based OAR auto-segmentation model was utilized to perform two main experiments: 1.) comparing original and defaced data for training when evaluated on original data; 2.) using original data for training and comparing the model evaluation on original and defaced data. Models were primarily assessed using the Dice similarity coefficient (DSC). Results: Most defacing methods were unable to produce any usable images for evaluation, while mask_face, fsl_deface, and pydeface were unable to remove the face for 29%, 18%, and 24% of subjects, respectively. When using the original data for evaluation, the composite OAR DSC was statistically higher (p ≤ 0.05) for the model trained with the original data with a DSC of 0.760 compared to the mask_face, fsl_deface, and pydeface models with DSCs of 0.742, 0.736, and 0.449, respectively. Moreover, the model trained with original data had decreased performance (p ≤ 0.05) when evaluated on the defaced data with DSCs of 0.673, 0.693, and 0.406 for mask_face, fsl_deface, and pydeface, respectively. Conclusion: Defacing algorithms may have a significant impact on HNC OAR auto-segmentation model training and testing. This work highlights the need for further development of HNC-specific image anonymization methods.

Lipreading a naturalistic narrative in a female population: Neural characteristics shared with listening and reading.

INTRODUCTION: Few of us are skilled lipreaders while most struggle with the task. Neural substrates that enable comprehension of connected natural speech via lipreading are not yet well understood. METHODS: We used a data-driven approach to identify brain areas underlying the lipreading of an 8-min narrative with participants whose lipreading skills varied extensively (range 6-100%, mean = 50.7%). The participants also listened to and read the same narrative. The similarity between individual participants' brain activity during the whole narrative, within and between conditions, was estimated by a voxel-wise comparison of the Blood Oxygenation Level Dependent (BOLD) signal time courses. RESULTS: Inter-subject correlation (ISC) of the time courses revealed that lipreading, listening to, and reading the narrative were largely supported by the same brain areas in the temporal, parietal and frontal cortices, precuneus, and cerebellum. Additionally, listening to and reading connected naturalistic speech particularly activated higher-level linguistic processing in the parietal and frontal cortices more consistently than lipreading, probably paralleling the limited understanding obtained via lip-reading. Importantly, higher lipreading test score and subjective estimate of comprehension of the lipread narrative was associated with activity in the superior and middle temporal cortex. CONCLUSIONS: Our new data illustrates that findings from prior studies using well-controlled repetitive speech stimuli and stimulus-driven data analyses are also valid for naturalistic connected speech. Our results might suggest an efficient use of brain areas dealing with phonological processing in skilled lipreaders.

Artificial Intelligence for Radiation Oncology Applications Using Public Datasets.

Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation oncology. However, large curated datasets - often involving imaging data and corresponding annotations - are required to develop radiation oncology AI models. Importantly, the recent establishment of Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management have enabled an increasing number of radiation oncology related datasets to be disseminated through data repositories, thereby acting as a rich source of data for AI model building. This manuscript reviews the current and future state of radiation oncology data dissemination, with a particular emphasis on published imaging datasets, AI data challenges, and associated infrastructure. Moreover, we provide historical context of FAIR data dissemination protocols, difficulties in the current distribution of radiation oncology data, and recommendations regarding data dissemination for eventual utilization in AI models. Through FAIR principles and standardized approaches to data dissemination, radiation oncology AI research has nothing to lose and everything to gain.

Do sparse brain activity patterns underlie human cognition?

Accumulating multivariate pattern analysis (MVPA) results from fMRI studies suggest that information is represented in fingerprint patterns of activations and deactivations during perception, emotions, and cognition. We postulate that these fingerprint patterns might reflect neuronal-population level sparse code documented in two-photon calcium imaging studies in animal models, i.e., information represented in specific and reproducible ensembles of a few percent of active neurons amidst widespread inhibition in neural populations. We suggest that such representations constitute a fundamental organizational principle via interacting across multiple levels of brain hierarchy, thus giving rise to perception, emotions, and cognition.

Classification of emotion categories based on functional connectivity patterns of the human brain.

Neurophysiological and psychological models posit that emotions depend on connections across wide-spread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) to develop a pipeline for applying pattern recognition in functional connectivity data, and (ii) to test whether connectivity patterns differ across emotion categories. Six emotions (anger, fear, disgust, happiness, sadness, and surprise) and a neutral state were induced in 16 participants using one-minute-long emotional narratives with natural prosody while brain activity was measured with functional magnetic resonance imaging (fMRI). We computed emotion-wise connectivity matrices both for whole-brain connections and for 10 previously defined functionally connected brain subnetworks and trained an across-participant classifier to categorize the emotional states based on whole-brain data and for each subnetwork separately. The whole-brain classifier performed above chance level with all emotions except sadness, suggesting that different emotions are characterized by differences in large-scale connectivity patterns. When focusing on the connectivity within the 10 subnetworks, classification was successful within the default mode system and for all emotions. We thus show preliminary evidence for consistently different sustained functional connectivity patterns for instances of emotion categories particularly within the default mode system.

Escitalopram enhances synchrony of brain responses during emotional narratives in patients with major depressive disorder.

One-week treatment with escitalopram decreases amygdala responses to fearful facial expressions in depressed patients, but it remains unknown whether it also modulates processing of complex and freely processed emotional stimuli resembling daily life emotional situations. Inter-subject correlation (ISC) offers a means to track brain activity during complex, dynamic stimuli in a model-free manner. Twenty-nine treatment-seeking patients with major depressive disorder were randomized in a double-blind study design to receive either escitalopram or placebo for one week, after which functional magnetic resonance imaging (fMRI) was performed. During fMRI the participants listened to spoken emotional narratives. Level of ISC between the escitalopram and the placebo group was compared across all the narratives and separately for the episodes with positive and negative valence. Across all the narratives, the escitalopram group had higher ISC in the default mode network of the brain as well as in the fronto-temporal narrative processing regions, whereas lower ISC was seen in the middle temporal cortex, hippocampus and occipital cortex. Escitalopram increased ISC during positive parts of the narratives in the precuneus, medial prefrontal cortex, anterior cingulate and fronto-insular cortex, whereas there was no significant synchronization in brain responses to positive vs negative events in the placebo group. Increased ISC may imply improved emotional synchronization with others, particularly during observation of positive events. Further studies are needed to test whether this contributes to the later therapeutic effect of escitalopram.

The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data.

Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavor is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants' privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU general data protection regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere.

Physical activity and aerobic fitness in relation to local and interhemispheric functional connectivity in adolescents' brains.

INTRODUCTION: Adolescents have experienced decreased aerobic fitness levels and insufficient physical activity levels over the past decades. While both physical activity and aerobic fitness are related to physical and mental health, little is known concerning how they manifest in the brain during this stage of development, characterized by significant physical and psychosocial changes. The aim of the study is to examine the associations between both physical activity and aerobic fitness with brains' functional connectivity. METHODS: Here, we examined how physical activity and aerobic fitness are associated with local and interhemispheric functional connectivity of the adolescent brain (n = 59), as measured with resting-state functional magnetic resonance imaging. Physical activity was measured by hip-worn accelerometers, and aerobic fitness by a maximal 20-m shuttle run test. RESULTS: We found that higher levels of moderate-to-vigorous intensity physical activity, but not aerobic fitness, were linked to increased local functional connectivity as measured by regional homogeneity in 13-16-year-old participants. However, we did not find evidence for significant associations between adolescents' physical activity or aerobic fitness and interhemispheric connectivity, as indicated by homotopic connectivity. CONCLUSIONS: These results suggest that physical activity, but not aerobic fitness, is related to local functional connectivity in adolescents. Moreover, physical activity shows an association with a specific brain area involved in motor functions but did not display any widespread associations with other brain regions. These results can advance our understanding of the behavior-brain associations in adolescents.

Movies and narratives as naturalistic stimuli in neuroimaging.

Using movies and narratives as naturalistic stimuli in human neuroimaging studies has yielded significant advances in understanding of cognitive and emotional functions. The relevant literature was reviewed, with emphasis on how the use of naturalistic stimuli has helped advance scientific understanding of human memory, attention, language, emotions, and social cognition in ways that would have been difficult otherwise. These advances include discovering a cortical hierarchy of temporal receptive windows, which supports processing of dynamic information that accumulates over several time scales, such as immediate reactions vs. slowly emerging patterns in social interactions. Naturalistic stimuli have also helped elucidate how the hippocampus supports segmentation and memorization of events in day-to-day life and have afforded insights into attentional brain mechanisms underlying our ability to adopt specific perspectives during natural viewing. Further, neuroimaging studies with naturalistic stimuli have revealed the role of the default-mode network in narrative-processing and in social cognition. Finally, by robustly eliciting genuine emotions, these stimuli have helped elucidate the brain basis of both basic and social emotions apparently manifested as highly overlapping yet distinguishable patterns of brain activity.

Effects of spatial smoothing on group-level differences in functional brain networks.

Brain connectivity with functional magnetic resonance imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations. Before creating a functional brain network, the fMRI time series must undergo several preprocessing steps to control for artifacts and to improve data quality. However, preprocessing may affect the results in an undesirable way. Spatial smoothing, for example, is known to alter functional network structure. Yet, its effects on group-level network differences remain unknown. Here, we investigate the effects of spatial smoothing on the difference between patients and controls for two clinical conditions: autism spectrum disorder and bipolar disorder, considering fMRI data smoothed with Gaussian kernels (0-32 mm). We find that smoothing affects network differences between groups. For weighted networks, incrementing the smoothing kernel makes networks more different. For thresholded networks, larger smoothing kernels lead to more similar networks, although this depends on the network density. Smoothing also alters the effect sizes of the individual link differences. This is independent of the region of interest (ROI) size, but varies with link length. The effects of spatial smoothing are diverse, nontrivial, and difficult to predict. This has important consequences: The choice of smoothing kernel affects the observed network differences.

Inter-subject Similarity of Brain Activity in Expert Musicians After Multimodal Learning: A Behavioral and Neuroimaging Study on Learning to Play a Piano Sonata.

Human behavior is inherently multimodal and relies on sensorimotor integration. This is evident when pianists exhibit activity in motor and premotor cortices, as part of a dorsal pathway, while listening to a familiar piece of music, or when naïve participants learn to play simple patterns on the piano. Here we investigated the interaction between multimodal learning and dorsal-stream activity over the course of four weeks in ten skilled pianists by adopting a naturalistic data-driven analysis approach. We presented the pianists with audio-only, video-only and audiovisual recordings of a piano sonata during functional magnetic resonance imaging (fMRI) before and after they had learned to play the sonata by heart for a total of four weeks. We followed the learning process and its outcome with questionnaires administered to the pianists, one piano instructor following their training, and seven external expert judges. The similarity of the pianists' brain activity during stimulus presentations was examined before and after learning by means of inter-subject correlation (ISC) analysis. After learning, an increased ISC was found in the pianists while watching the audiovisual performance, particularly in motor and premotor regions of the dorsal stream. While these brain structures have previously been associated with learning simple audio-motor sequences, our findings are the first to suggest their involvement in learning a complex and demanding audiovisual-motor task. Moreover, the most motivated learners and the best performers of the sonata showed ISC in the dorsal stream and in the reward brain network.