Short-Term Biological Toxicity Prediction of [177Lu]Lutetium-Oxodotreotide: An Original Retrospective Analysis.

Introduction: [177Lu]Lutetium (Lu)-oxodotreotide is a radiopharmaceutical drug used as peptide receptor radionuclide therapy (PRRT) for somatostatin receptor-expressing neuroendocrine neoplasms. It provides an additional effective alternative treatment for these rare cancers. Although well tolerated, its safety profile must continue to be characterized to support its use as a first-line treatment or for additional cycles. This study aims to evaluate factors associated with the occurrence of [177Lu]Lu-oxodotreotide induced short-term toxicity. Materials and Methods: A retrospective observational monocentric study was carried out from July 2013 to October 2021. Inclusion criteria were defined as follows: patients who received at least four cycles of [177Lu]Lu-oxodotreotide and were followed up for 6 months after the last injection. Graduated toxicity was defined using the National Cancer Institute Common Terminology Criteria for Adverse Events 5.0. Cox regression was used in the analysis. Results: Forty patients were included. The most frequent toxicities occurred during the first cycle and were graded as G1 or G2. As expected, toxicities were predominantly hematological and hepatic, with incomplete reversibility after each cycle. The following factors were significantly related to the occurrence of hematological or hepatic toxicity during PRRT: gastrointestinal primary tumor diagnosis, bone metastases, peritoneal metastases, pancreatic metastases or pulmonary metastases, and high tumor grade. Conclusion: Knowledge and consideration of these factors in adjusting [177Lu]Lu-oxodotreotide treatment regimen could help prevent or reduce the severity of these toxicities. Further studies are still warranted to refine these results and improve treatment management.

Improving multi-pinhole CZT myocardial perfusion imaging specificity without changing sensibility by using adapted filter parameters.

BACKGROUND: Meta-analysis show the diagnostic performance of cardiac dedicated multi-pinhole cadmium-zinc-telluride myocardial perfusion imaging (MPI) with a sensibility around 0.9 and a specificity around 0.7. The aim of the present study is to explore a simple method to generate less artefact on MPI using single photon emission computed tomography (SPECT) and to enhance specificity without changing sensibility. RESULTS: From October 2018 to March 2019, 200 patients who underwent SPECT with [99mTc]Tc-tetrofosmin were prospectively recruited: 100 patients with ischemia or necrosis diagnosis (first arm), and 100 patients with myocardial reversible SPECT artefact (second arm). Each SPECT was explored using two image process based on a Butterworth prefilter and post-filter: the original image processing (reconstruction A) with a cut-off frequency equals to 37% of the Nyquist frequency and order equals to 7, and a second image processing (reconstruction B) with a cut-off frequency equals to 25% of the Nyquist frequency and order equals to 5. For each patient, sum stress or rest score with and without septum (SSRS and SSRSws) were calculated with the two reconstructions. No significant statistical difference between SSRSa and SSRSb was identified for the first arm (P = 0.54) and the relative difference ∆r was - 0.5 ± 11.1% (95% CI - 2.7 to 1.7). We found a significant statistical difference between SSRSa and SSRSb for the second arm (p < 0.0001) and the relative difference ∆r was 69.7 ± 16.2% (95% CI 66.6-72.9). CONCLUSION: In conclusion, using a Butterworth prefilter and post-filter cut-off frequency equal to 25% of the Nyquist frequency before iterative reconstruction generates less artefact and improves myocardial SPECT specificity without affecting sensibility compared with the original reconstruction.

Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients.

We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 participants during movie watching. Recordings consist of single neurons, local field potential, and intracranial EEG activity acquired from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex implanted for monitoring of epileptic seizures. Participants watched an 8-min long excerpt from the video "Bang! You're Dead" and performed a recognition memory test for movie content. 3 T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes spike times, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI. This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.

uB-VisioGeoloc: An image sequences dataset of pedestrian navigation including geolocalised-inertial information and spatial sound rendering of the urban environment's obstacles.

The dataset proposed is a collection of pedestrian navigation data sequences combining visual and spatial information. The pedestrian navigation sequences are situations encountered by a pedestrian walking in an urban outdoor environment, such as moving on the sidewalk, navigating through a crowd, or crossing a street when the pedestrian light traffic is green. The acquired data are timestamped provided RGB-D images and are associated with GPS, and inertial data (acceleration, rotation). These recordings were acquired by separate processes, avoiding delays during their capture to guarantee a synchronisation between the moment of acquisition by the sensor and the moment of recording on the system. The acquisition was made in the city of Dijon, France, including narrow streets, wide avenues, and parks. Annotations of the RGB-D are also provided by bounding boxes indicating the position of relevant static or dynamic objects present in a pedestrian area, such as a tree, bench, or person. This pedestrian navigation dataset aims to support the development of smart mobile systems to assist visually impaired people in their daily movements in an outdoor environment. In this context, the visual data and localisation sequences we provide can be used to elaborate the appropriate visual processing methods to extract relevant information about the obstacles and their current positions on the path. Alongside the dataset, a visual-to-auditory substitution method has been employed to convert each image sequence into corresponding stereophonic sound files, allowing for comparison and evaluation. Synthetic sequences associated with the same information set are also provided based on the recordings of a displacement within the 3D model of a real place in Dijon.

[Evaluation of practices through simulation: Implementation of Horror Week in a cytotoxic preparation unit]. / Évaluation des pratiques par simulation : mise en place de la semaine de l'horreur dans une unité de préparation de cytotoxiques.

OBJECTIVES: The objective of this work is to describe the interest of developing a simulation for the evaluation and improvement of practices through the realization of a horror week in a cytotoxic preparation unit. METHODS: The simulation was divided into five days, each corresponding to a step in the cytotoxic circuit. Five errors per day were produced and presented to the team in a dedicated room. An observation form was used to collect the errors identified by each person. A satisfaction questionnaire was distributed and a collective debriefing took place remotely. RESULTS: The average number of errors identified per day was 3.8. The least common errors were reversal of patient height and weight, overloading of the sterilization basket, failure to follow dressing rules in the controlled area, absence of the temperature-monitoring disk, and absence of an opaque bag for photosensitive chemotherapy. The perceived level of difficulty was 3.33/5 and the organization was satisfactory to all participants. CONCLUSIONS: Horror Week achieved its objectives by raising awareness among professionals of the risks of the chemotherapy circuit. The errors that were found to be the least significant allowed us to develop priority areas for ongoing training for our unit.

Measuring acute effects of subanesthetic ketamine on cerebrovascular hemodynamics in humans using TD-fNIRS.

Quantifying neural activity in natural conditions (i.e. conditions comparable to the standard clinical patient experience) during the administration of psychedelics may further our scientific understanding of the effects and mechanisms of action. This data may facilitate the discovery of novel biomarkers enabling more personalized treatments and improved patient outcomes. In this single-blind, placebo-controlled study with a non-randomized design, we use time-domain functional near-infrared spectroscopy (TD-fNIRS) to measure acute brain dynamics after intramuscular subanesthetic ketamine (0.75 mg/kg) and placebo (saline) administration in healthy participants (n = 15, 8 females, 7 males, age 32.4 ± 7.5 years) in a clinical setting. We found that the ketamine administration caused an altered state of consciousness and changes in systemic physiology (e.g. increase in pulse rate and electrodermal activity). Furthermore, ketamine led to a brain-wide reduction in the fractional amplitude of low frequency fluctuations, and a decrease in the global brain connectivity of the prefrontal region. Lastly, we provide preliminary evidence that a combination of neural and physiological metrics may serve as predictors of subjective mystical experiences and reductions in depressive symptomatology. Overall, our study demonstrated the successful application of fNIRS neuroimaging to study the physiological effects of the psychoactive substance ketamine in humans, and can be regarded as an important step toward larger scale clinical fNIRS studies that can quantify the impact of psychedelics on the brain in standard clinical settings.

Change in brain asymmetry reflects level of acute alcohol intoxication and impacts on inhibitory control.

Alcohol is one of the most commonly used substances and frequently abused, yet little is known about the neural underpinnings driving variability in inhibitory control performance after ingesting alcohol. This study was a single-blind, placebo-controlled, randomized design with participants (N = 48 healthy, social drinkers) completing three study visits. At each visit participants received one of three alcohol doses; namely, a placebo dose [equivalent Blood Alcohol Concentration (BAC) = 0.00%], a low dose of alcohol (target BAC = 0.04%), or a moderate dose of alcohol (target BAC = 0.08%). To measure inhibitory control, participants completed a Go/No-go task paradigm twice during each study visit, once immediately before dosing and once after, while their brain activity was measured with time-domain functional near-infrared spectroscopy (TD-fNIRS). BAC and subjective effects of alcohol were also assessed. We report decreased behavioral performance for the moderate dose of alcohol, but not the low or placebo doses. We observed right lateralized inhibitory prefrontal activity during go-no-go blocks, consistent with prior literature. Using standard and novel metrics of lateralization, we were able to significantly differentiate between all doses. Lastly, we demonstrate that these metrics are not only related to behavioral performance during inhibitory control, but also provide complementary information to the legal gold standard of intoxication (i.e. BAC).

Cross-modal correspondence enhances elevation localization in visual-to-auditory sensory substitution.

Introduction: Visual-to-auditory sensory substitution devices are assistive devices for the blind that convert visual images into auditory images (or soundscapes) by mapping visual features with acoustic cues. To convey spatial information with sounds, several sensory substitution devices use a Virtual Acoustic Space (VAS) using Head Related Transfer Functions (HRTFs) to synthesize natural acoustic cues used for sound localization. However, the perception of the elevation is known to be inaccurate with generic spatialization since it is based on notches in the audio spectrum that are specific to each individual. Another method used to convey elevation information is based on the audiovisual cross-modal correspondence between pitch and visual elevation. The main drawback of this second method is caused by the limitation of the ability to perceive elevation through HRTFs due to the spectral narrowband of the sounds. Method: In this study we compared the early ability to localize objects with a visual-to-auditory sensory substitution device where elevation is either conveyed using a spatialization-based only method (Noise encoding) or using pitch-based methods with different spectral complexities (Monotonic and Harmonic encodings). Thirty eight blindfolded participants had to localize a virtual target using soundscapes before and after having been familiarized with the visual-to-auditory encodings. Results: Participants were more accurate to localize elevation with pitch-based encodings than with the spatialization-based only method. Only slight differences in azimuth localization performance were found between the encodings. Discussion: This study suggests the intuitiveness of a pitch-based encoding with a facilitation effect of the cross-modal correspondence when a non-individualized sound spatialization is used.

Outdoor Navigation Assistive System Based on Robust and Real-Time Visual-Auditory Substitution Approach.

Blindness affects millions of people worldwide, leading to difficulties in daily travel and a loss of independence due to a lack of spatial information. This article proposes a new navigation aid to help people with severe blindness reach their destination. Blind people are guided by a short 3D spatialised sound that indicates the target point to follow. This sound is combined with other sonified information on potential obstacles in the vicinity. The proposed system is based on inertial sensors, GPS data, and the cartographic knowledge of pedestrian paths to define the trajectory. In addition, visual clues are used to refine the trajectory with ground floor information and obstacle information using a camera to provide 3D spatial information. The proposed method is based on a deep learning approach. The different neural networks used in this approach are evaluated on datasets that regroup navigations from pedestrians' point-of-view. This method achieves low latency and real-time processing without relying on remote connections, instead using a low-power embedded GPU target and a multithreaded approach for video processing, sound generation, and acquisition. This system could significantly improve the quality of life and autonomy of blind people, allowing them to reliably and efficiently navigate in their environment.

Kernel Flow: a high channel count scalable time-domain functional near-infrared spectroscopy system.

SIGNIFICANCE: Time-domain functional near-infrared spectroscopy (TD-fNIRS) has been considered as the gold standard of noninvasive optical brain imaging devices. However, due to the high cost, complexity, and large form factor, it has not been as widely adopted as continuous wave NIRS systems. AIM: Kernel Flow is a TD-fNIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-fNIRS system while integrating all of the components into a small modular device. APPROACH: The Kernel Flow modules are built around miniaturized laser drivers, custom integrated circuits, and specialized detectors. The modules can be assembled into a system with dense channel coverage over the entire head. RESULTS: We show performance similar to benchtop systems with our miniaturized device as characterized by standardized tissue and optical phantom protocols for TD-fNIRS and human neuroscience results. CONCLUSIONS: The miniaturized design of the Kernel Flow system allows for broader applications of TD-fNIRS.

A deep learning based approach identifies regions more relevant than resting-state networks to the prediction of general intelligence from resting-state fMRI.

Prediction of cognitive ability latent factors such as general intelligence from neuroimaging has elucidated questions pertaining to their neural origins. However, predicting general intelligence from functional connectivity limit hypotheses to that specific domain, being agnostic to time-distributed features and dynamics. We used an ensemble of recurrent neural networks to circumvent this limitation, bypassing feature extraction, to predict general intelligence from resting-state functional magnetic resonance imaging regional signals of a large sample (n = 873) of Human Connectome Project adult subjects. Ablating common resting-state networks (RSNs) and measuring degradation in performance, we show that model reliance can be mostly explained by network size. Using our approach based on the temporal variance of saliencies, that is, gradients of outputs with regards to inputs, we identify a candidate set of networks that more reliably affect performance in the prediction of general intelligence than similarly sized RSNs. Our approach allows us to further test the effect of local alterations on data and the expected changes in derived metrics such as functional connectivity and instantaneous innovations.

Personality beyond taxonomy.

Human and animal behaviour exhibits complex but regular patterns over time, often referred to as expressions of personality. Yet it remains unclear what personality really is: is it just the behavioural patterns themselves, something in the brain, in the genes or perhaps all of these? Here we offer a set of causal hypotheses about the role of personality, integrating psychological and neuroscientific approaches to personality in a testable framework. These hypotheses clarify the causal and constitutive relations that personality has with genes, environment, brain, mind and behaviour, and we suggest specific experiments that can adjudicate amongst the different hypotheses. We focus on a set of models that propose that personality is instantiated in the brain, distally caused by genes and environment and, in turn, causing the overt behaviours from which it is often inferred. We argue that articulating and testing such models will be essential in a mature science of personality.

Causal mapping of emotion networks in the human brain: Framework and initial findings.

Emotions involve many cortical and subcortical regions, prominently including the amygdala. It remains unknown how these multiple network components interact, and it remains unknown how they cause the behavioral, autonomic, and experiential effects of emotions. Here we describe a framework for combining a novel technique, concurrent electrical stimulation with fMRI (es-fMRI), together with a novel analysis, inferring causal structure from fMRI data (causal discovery). We outline a research program for investigating human emotion with these new tools, and provide initial findings from two large resting-state datasets as well as case studies in neurosurgical patients with electrical stimulation of the amygdala. The overarching goal is to use causal discovery methods on fMRI data to infer causal graphical models of how brain regions interact, and then to further constrain these models with direct stimulation of specific brain regions and concurrent fMRI. We conclude by discussing limitations and future extensions. The approach could yield anatomical hypotheses about brain connectivity, motivate rational strategies for treating mood disorders with deep brain stimulation, and could be extended to animal studies that use combined optogenetic fMRI.

WiseNET: An indoor multi-camera multi-space dataset with contextual information and annotations for people detection and tracking.

Nowadays, camera networks are part of our every-day life environments, consequently, they represent a massive source of information for monitoring human activities and to propose new services to the building users. To perform human activity monitoring, people must be detected and the analysis has to be done according to the information relative to the environment and the context. Available multi-camera datasets furnish videos with few (or none) information of the environment where the network was deployed. The proposed dataset provides multi-camera multi-space video sets along with the complete contextual information of the environment. The dataset regroups 11 video sets (composed of 62 single videos) recorded using 6 indoor cameras deployed on multiple spaces. The video sets represent more than 1 h of video footage, include 77 people tracks and captured different human actions such as walking around, standing/sitting, motionless, entering/leaving a space and group merging/splitting. Moreover, each video has been manually and automatically annotated to include people detection and tracking meta-information. The automatic people detection annotations were obtained by using different complexity and robustness detectors, from machine learning to state-of-art deep Convolutional Neural Network (CNN) models. Concerning the contextual information, the Industry Foundation Classes (IFC) file that represents the environment's Building Information Modeling (BIM) data is also provided. The BIM/IFC file describes the complete structure of the environment, it's topology and the elements contained in it. To our knowledge, the WiseNET dataset is the first to provide a set of videos along with the complete information of the environment. The WiseNET dataset is publicly available at https://doi.org/10.4121/uuid:c1fb5962-e939-4c51-bfd5-eac6f2935d44, as well as at the project's website http://wisenet.checksem.fr/#/dataset.

Intrinsic Functional Connectivity of the Brain in Adults with a Single Cerebral Hemisphere.

A reliable set of functional brain networks is found in healthy people and thought to underlie our cognition, emotion, and behavior. Here, we investigated these networks by quantifying intrinsic functional connectivity in six individuals who had undergone surgical removal of one hemisphere. Hemispherectomy subjects and healthy controls were scanned with identical parameters on the same scanner and compared to a large normative sample (n = 1,482). Surprisingly, hemispherectomy subjects and controls all showed strong and equivalent intrahemispheric connectivity between brain regions typically assigned to the same functional network. Connectivity between parts of different networks, however, was markedly increased for almost all hemispherectomy participants and across all networks. These results support the hypothesis of a shared set of functional networks that underlie cognition and suggest that between-network interactions may characterize functional reorganization in hemispherectomy.

Imaging object-scene relations processing in visible and invisible natural scenes.

Integrating objects with their context is a key step in interpreting complex visual scenes. Here, we used functional Magnetic Resonance Imaging (fMRI) while participants viewed visual scenes depicting a person performing an action with an object that was either congruent or incongruent with the scene. Univariate and multivariate analyses revealed different activity for congruent vs. incongruent scenes in the lateral occipital complex, inferior temporal cortex, parahippocampal cortex, and prefrontal cortex. Importantly, and in contrast to previous studies, these activations could not be explained by task-induced conflict. A secondary goal of this study was to examine whether processing of object-context relations could occur in the absence of awareness. We found no evidence for brain activity differentiating between congruent and incongruent invisible masked scenes, which might reflect a genuine lack of activation, or stem from the limitations of our study. Overall, our results provide novel support for the roles of parahippocampal cortex and frontal areas in conscious processing of object-context relations, which cannot be explained by either low-level differences or task demands. Yet they further suggest that brain activity is decreased by visual masking to the point of becoming undetectable with our fMRI protocol.

Resting-state functional brain connectivity best predicts the personality dimension of openness to experience.

Personality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging data from 884 young healthy adults in the Human Connectome Project (HCP) database. We attempted to predict personality traits from the "Big Five", as assessed with the NEO-FFI test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two inter-subject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 h of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; 3 denoising strategies; 2 alignment schemes; 3 models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=0.24, R2=0.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=0.26, R2=0.044). Other factors (Extraversion, Neuroticism, Agreeableness and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors ("α" and "ß") from a principal components analysis of the NEO-FFI factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the ß superordinate factor (r=0.27, R2=0.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

A distributed brain network predicts general intelligence from resting-state human neuroimaging data.

Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, because it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here, we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N = 884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.This article is part of the theme issue 'Causes and consequences of individual differences in cognitive abilities'.

Building a Science of Individual Differences from fMRI.

To date, fMRI research has been concerned primarily with evincing generic principles of brain function through averaging data from multiple subjects. Given rapid developments in both hardware and analysis tools, the field is now poised to study fMRI-derived measures in individual subjects, and to relate these to psychological traits or genetic variations. We discuss issues of validity, reliability and statistical assessment that arise when the focus shifts to individual subjects and that are applicable also to other imaging modalities. We emphasize that individual assessment of neural function with fMRI presents specific challenges and necessitates careful consideration of anatomical and vascular between-subject variability as well as sources of within-subject variability.