BACKGROUND AND OBJECTIVES:Brain atrophy is relevant for understanding disease progression and treatment response in people with multiple sclerosis (pwMS). Automatic brain volume-reporting tools often rely on healthy control (HC) reference curves to interpret brain volumes, whereas brain volume loss is different in pwMS. This observational study aimed to develop an MS-specific reference model for brain volumes and evaluate its performance compared with HC-based curves, as a proof-of-concept. METHODS:Participants, pwMS and HCs, from the Amsterdam MS cohort were included based on the availability of T1-weighted MR scans. Normalized brain volumes (NBVs) were obtained using commercially available software. The software program also provides NBV percentiles, based on age-specific and sex-specific HC curves, grouped into NBV quartiles, describing deviation from expected NBVs. Disease severity was determined with the MS severity score (MSSS), Symbol Digit Modalities Test (SDMT), and 9-Hole Peg Test (9HPT). An MS-specific model was developed by regressing NBVs against age, sex, disease duration, and MS phenotype. The resulting MS model was also used to classify pwMS into quartiles describing deviation from expected NBV, given the modeled patient characteristics, with leave-one-out predictions. Quartile classification from HC-based and MS-based reference curves was compared with MSSS using analysis of variance (ANOVA). RESULTS:Regressions for NBVs from 713 pwMS and 259 HCs (mean age: 49.1 ± 9.7 and 48.3 ± 10.1, %female: 70.4% and 67.2%, respectively) were significant for age, sex, disease duration, and phenotype, which were included in the MS-specific model. MS-specific model quartile designations significantly improved associations with MSSS values (p = 2.2*10-9, η2 = 0.06) compared with HC-based quartiles. MSSS values worsened with lower NBV quartiles in the MS-specific model (difference between quartiles 1-4 = -0.84, p = 6.1*10-3, 95% CI [-1.5 to -0.18])), which was not observed for HC-based quartiles (p = 0.98). Quartile group differences were observed for 9HPT (MS: p = 3.5*10-3, η2 = 0.02, HC: p = 6.6*10-3, η2 = 0.02) and SDMT (MS: p = 3.1*10-4, η2 = 0.05, HC: p = 5.4*10-4, η2 = 0.04) values, but MS-specific quartiles again improved quartile associations (p = 0.036, η2 = 0.01 and p = 0.02, η2 = 0.01, respectively). DISCUSSION:NBV values derived from an MS-specific reference model offer improved relevance for assessing disease severity compared with curves derived from age-specific and sex-specific HC reference models. Improving the model toward application in individual people could enhance clinical implementation.

Background: Delirium in the ICU is a major and potentially modifiable risk factor for subsequent neurocognitive decline. However, the cerebral dysfunctions underlying its behavioural manifestations remain poorly understood, limiting targeted clinical management. These dysfunctions may reflect impairments in low-level perceptual processes (stimulus detection), higher-order integrative processes (stimulus discrimination), or both. The P3 event-related potential (ERP), a positive deflection in the electroencephalogram occurring approximately 300 ms after stimulus presentation, reflects neural processes involved in attention and cognitive integration and provides an objective measure of auditory detection (P3a) and discrimination (P3b). Methods: In this prospective single-centre observational study conducted between March and October 2021 in the Critical Care Unit of Purpan University Hospital, Toulouse, France, we used a multidimensional P3 ERP battery incorporating auditory paradigms designed to assess stimulus detection (P3a) and discrimination (P3b). The battery included both non-verbal stimuli (local–global paradigm) and verbal stimuli (subject’s own name and arithmetic paradigms). Critically ill COVID-19 patients with delirium (n=18) and without delirium (n=20) were prospectively recruited. The primary outcome was the presence of P3 subcomponents across paradigms. Group differences were assessed using spatial–temporal cluster-based permutation testing, with statistical significance defined at cluster-level P≤0.05. Results: In patients without delirium, both detection-related (P3a) and discrimination-related (P3b) responses were present across verbal and non-verbal paradigms. In contrast, patients with delirium demonstrated preserved P3a responses to non-verbal auditory stimuli but absence of P3b responses, indicating impaired auditory discrimination (cluster-level P≤0.05). Source modelling suggested reduced activation of frontal cortical generators in patients with delirium compared with those without delirium. Conclusions: In critically ill patients with COVID-19, delirium was associated with preserved automatic auditory detection but impaired higher-order auditory discrimination. This dissociation provides neurophysiological evidence of selective impairment in integrative cognitive processing during ICU delirium and contributes to improved mechanistic understanding of delirium-related brain dysfunction.

When reproducing sounds over headphones, the simulated source can be externalized (i.e., perceived outside the head) or internalized (i.e., perceived within the head). Is it because it is perceived as more or less distant? To investigate this question, 18 participants evaluated distance and externalization for three types of sound (speech, piano, helicopter) in 27 conditions using nonindividualized stimuli. Distance and externalization ratings were significantly correlated across conditions and listeners, and when averaged across listeners or conditions. However, they were also decoupled in some circumstances: (1) Sound type had different effects on distance and externalization: the helicopter was evaluated as more distant, while speech was judged as less externalized. (2) Distance estimations increased with simulated distances even for stimuli judged as internalized. (3) Diotic reverberation influenced distance but not externalization. Overall, a source was not rated as externalized as soon as and only if its perceived distance exceeded a threshold (e.g., the head radius). These results suggest that distance and externalization are correlated but might not be aspects of a single perceptual continuum. In particular, a virtual source might be judged as both internalized and with a distance. Hence, it could be important to avoid using a scale related to distance when evaluating externalization.

Accumulating evidence indicates that coronavirus disease 2019 is a major cause of delirium. Given the global dimension of the current pandemic and the fact that delirium is a strong predictor of cognitive decline for critically ill patients, this raises concerns regarding the neurological cost of coronavirus disease 2019. Currently, there is a major knowledge gap related to the covert yet potentially incapacitating higher-order cognitive impairment underpinning coronavirus disease 2019 related delirium. The aim of the current study was to analyse the electrophysiological signatures of language processing in coronavirus disease 2019 patients with delirium by using a specifically designed multidimensional auditory event-related potential battery to probe hierarchical cognitive processes, including self-processing (P300) and semantic/lexical priming (N400). Clinical variables and electrophysiological data were prospectively collected in controls subjects (n = 14) and in critically ill coronavirus disease 2019 patients with (n = 19) and without (n = 22) delirium. The time from intensive care unit admission to first clinical sign of delirium was of 8 (3.5-20) days, and the delirium lasted for 7 (4.5-9.5) days. Overall, we have specifically identified in coronavirus disease 2019 patients with delirium, both a preservation of low-level central auditory processing (N100 and P200) and a coherent ensemble of covert higher-order cognitive dysfunctions encompassing self-related processing (P300) and sematic/lexical language priming (N400) (spatial-temporal clustering, P-cluster ≤ 0.05). We suggest that our results shed new light on the neuropsychological underpinnings of coronavirus disease 2019 related delirium, and may constitute a valuable method for patient's bedside diagnosis and monitoring in this clinically challenging setting.

Introduction:Behavioral and cerebral dissociation has been now clearly established in some patients with acquired disorders of consciousness (DoC). Altogether, these studies mainly focused on the preservation of high-level cognitive markers in prolonged DoC, but did not specifically investigate lower but key-cognitive functions to consciousness emergence, such as the ability to take a first-person perspective, notably at the acute stage of coma. We made the hypothesis that the preservation of self-recognition (i) is independent of the behavioral impairment of consciousness, and (ii) can reflect the ability to recover consciousness.Methods:Hence, using bedside Electroencephalography (EEG) recordings, we acquired, in a large cohort of 129 severely brain damaged patients, the brain response to the passive listening of the subject's own name (SON) and unfamiliar other first names (OFN). One hundred and twelve of them (mean age ± SD = 46 ± 18.3 years, sex ratio M/F: 71/41) could be analyzed for the detection of an individual and significant discriminative P3 event-related brain response to the SON as compared to OFN ('SON effect', primary endpoint assessed by temporal clustering permutation tests).Results:Patients were either coma (n = 38), unresponsive wakefulness syndrome (UWS, n = 30) or minimally conscious state (MCS, n = 44), according to the revised version of the Coma Recovery Scale (CRS-R). Overall, 33 DoC patients (29%) evoked a 'SON effect'. This electrophysiological index was similar between coma (29%), MCS (23%) and UWS (34%) patients (p = 0.61). MCS patients at the time of enrolment were more likely to emerged from MCS (EMCS) at 6 months than coma and UWS patients (p = 0.013 for comparison between groups). Among the 72 survivors' patients with event-related responses recorded within 3 months after brain injury, 75% of the 16 patients with a SON effect were EMCS at 6 months, while 59% of the 56 patients without a SON effect evolved to this favorable behavioral outcome.Discussion:About 30% of severely brain-damaged patients suffering from DoC are capable to process salient self-referential auditory stimuli, even in case of absence of behavioral detection of self-conscious processing. We suggest that self-recognition covert brain ability could be an index of consciousness recovery, and thus could help to predict good outcome.

Adequate detection of the histopathological extraprostatic extension (EPE) of prostate cancer (PCa) remains a challenge using conventional radiomics on 3 Tesla multiparametric magnetic resonance imaging (3T mpMRI). This study focuses on the assessment of artificial intelligence (AI)-driven models with innovative MRI radiomics in predicting EPE of prostate cancer (PCa) at a lesion-specific level. With a dataset encompassing 994 lesions from 794 PCa patients who underwent robot-assisted radical prostatectomy (RARP) at two Dutch hospitals, the study establishes and validates three classification models. The models were validated on an internal validation cohort of 162 lesions and an external validation cohort of 189 lesions in terms of discrimination, calibration, net benefit, and comparison to radiology reporting. Notably, the achieved AUCs ranged from 0.86 to 0.91 at the lesion-specific level, demonstrating the superior accuracy of the random forest model over conventional radiological reporting. At the external test cohort, the random forest model was the best-calibrated model and demonstrated a significantly higher accuracy compared to radiological reporting (83% vs. 67%, p = 0.02). In conclusion, an AI-powered model that includes both existing and novel MRI radiomics improves the detection of lesion-specific EPE in prostate cancer.

Neuroscientific and clinical studies on auditory perception often use headphones to limit sound interference. In these conditions, sounds are perceived as internalized because they lack the sound-attributes that normally occur with a sound produced from a point in space around the listener. Without the spatial attention mechanisms that occur with localized sounds, auditory functional assessments could thus be underestimated. We hypothesize that adding virtually externalization and localization cues to sounds through headphones enhance sound discrimination in both healthy participants and patients with a disorder of consciousness (DOC). Hd-EEG was analyzed in 14 healthy participants and 18 patients while they listened to self-relevant and irrelevant stimuli in two forms: diotic (classic sound presentation with an "internalized" feeling) and convolved with a binaural room impulse response (to create an "externalized" feeling). Convolution enhanced the brains' discriminative response as well as the processing of irrelevant sounds itself, in both healthy participants and DOC patients. For the healthy participants, these effects could be associated with enhanced activation of both the dorsal (where/how) and ventral (what) auditory streams, suggesting that spatial attributes support speech discrimination. Thus, virtually spatialized sounds might "call attention to the outside world" and improve the sensitivity of assessment of brain function in DOC patients.

In auditory behavioral and EEG experiments, the variability of stimulation solutions, for both software and hardware, adds unnecessary technical constraints. Currently, there is no easy to use, inexpensive, and shareable solution that could improve collaborations and data comparisons across different sites and contexts. This article outlines a system composed by a Raspberry Pi coupled with Python programming and associated with a HifiBerry sound card. We compare its sound performances with those of a wide variety of materials and configurations. This solution achieves the high timing accuracy and sound quality important in auditory cognition experiments, while being simple to use and open source. The present system shows high performances and results along with excellent feedback from users. It is inexpensive, easy to build, share, and improve on. Working with such low-cost, powerful, and collaborative hardware and software tools allows people to create their own specific, adapted, and shareable system that can be standardized across different collaborative sites, while being extremely simple and robust in use.

Music is often intimately linked to identity, as evidenced by the high value many people place on musical activities and the way in which music can become seemingly effortlessly coupled to important memories from throughout one’s lifespan. Previous research has revealed a consistent reminiscence bump in autobiographical memory—the disproportionate recall of memories from between ages 10 to 30 years in comparison with other lifetime periods—which also appears to extend to music-related memories. The present study represents one of the largest explorations of the musical reminiscence bump across adulthood to date. Participants ( N = 470; ages 18 to 82 years) were shown the titles and artists of 111 popular songs that had featured in the charts between 1950 and 2015 and rated the degree to which they had autobiographical memories associated with each song, as well as the degree to which they were familiar with and liked the song. We found a reminiscence bump in adolescence (peaking around age 14) for both ratings of the autobiographical salience of songs featured in the charts during that period and the familiarity of these songs. Liking ratings showed more divergent results depending on a participant’s current age, including evidence for a cascading reminiscence bump, in which liking ratings from young adults increased for music from their parents’ adolescent years. We also revealed new evidence that music-related autobiographical memories appear to invoke similar retrieval processes to the common methodology of eliciting autobiographical memories via word cues. We contextualize these results in relation to general theoretical accounts of the reminiscence bump, and age-related differences in the bump are discussed in relation to various sociocultural and technological changes in music listening habits.

Background Given its emotional and autobiographical properties, music appears as a potential aid in diagnostic assessment and therapeutics in patients with disorders of consciousness (DOC). Several studies have shown boosting effects on behavior and brain functioning when patients were exposed to (their preferred) music. Objective To investigate if these effects on the brain's spontaneous activity are dependent on the level of consciousness. Methods Nine post-comatose patients and eight healthy control subjects were assessed using fMRI in two conditions: rest (without stimulation) and music. All patients presented at least an auditory startle, thereby suggesting the presence of residual auditory function. A gradient of consciousness (from unresponsive wakefulness syndrome to healthy subjects) was formulated to evaluate consciousness-level-dependent increases in brain activity and connectivity. Network-based functional connectivity assessed auditory, default-mode, frontoparietal and music-evoked emotions networks. Furthermore, exploratory voxel-to-voxel analyses were performed at the whole brain level using intrinsic connectivity contrast and (fractional) amplitude of low frequency fluctuations. Results Stronger consciousness-level-dependent increases within network-to-voxel analysis of connectivity were found in the frontoparietal network with the precuneus during music stimulation compared to rest. Voxel-to-voxel analyses showed stronger increases of intrinsic connectivity in the music condition compared to rest in regions previously related to music processing. There were consistently more regions with increased connectivity during the main effect of music compared to rest. These increases of connectivity during music were observed in brain regions involved in consciousness, language, emotion and memory processing. Conclusions Our results show that music seems to trigger more substantial connectivity changes compared to rest, pointing towards the potential ability of music to stimulate patients' brain function. Further research should focus on effects of music in general, its specific acoustical features and the effects of simple auditory stimuli, as well as the possible therapeutic and diagnostic effects of music in well controlled clinical trials.

Background The accurate assessment of patients with disorders of consciousness (DOC) is a challenge to most experienced clinicians. As a potential clinical tool, functional magnetic resonance imaging (fMRI) could detect residual awareness without the need for the patients’ actual motor responses. Methods We adopted a simple active fMRI motor paradigm (hand raising) to detect residual awareness in these patients. Twenty-nine patients were recruited. They met the diagnosis of minimally conscious state (MCS) (male = 6, female = 2; n = 8), vegetative state/unresponsive wakefulness syndrome (VS/UWS) (male = 17, female = 4; n = 21). Results We analyzed the command-following responses for robust evidence of statistically reliable markers of motor execution, similar to those found in 15 healthy controls. Of the 29 patients, four (two MCS, two VS/UWS) could adjust their brain activity to the “hand-raising” command, and they showed activation in motor-related regions (which could not be discovered in the own-name task). Conclusion Longitudinal behavioral assessments showed that, of these four patients, two in a VS/UWS recovered to MCS and one from MCS recovered to MCS+ (i.e., showed command following). In patients with no response to hand raising task, six VS/UWS and three MCS ones showed recovery in follow-up procedure. The simple active fMRI “hand-raising” task can elicit brain activation in patients with DOC, similar to those observed in healthy volunteers. Activity of the motor-related network may be taken as an indicator of high-level cognition that cannot be discerned through conventional behavioral assessment.

Background: Disorders of consciousness are challenging to diagnose, with inconsistent behavioral responses, motor and cognitive disabilities, leading to approximately 40% misdiagnoses. Heart rate variability (HRV) reflects the complexity of the heart-brain two-way dynamic interactions. HRV entropy analysis quantifies the unpredictability and complexity of the heart rate beats intervals. We here investigate the complexity index (CI), a score of HRV complexity by aggregating the non-linear multi-scale entropies over a range of time scales, and its discriminative power in chronic patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), and its relation to brain functional connectivity. Methods: We investigated the CI in short (CIs) and long (CIl) time scales in 14 UWS and 16 MCS sedated. CI for MCS and UWS groups were compared using a Mann-Whitney exact test. Spearman's correlation tests were conducted between the Coma Recovery Scale-revised (CRS-R) and both CI. Discriminative power of both CI was assessed with One-R machine learning model. Correlation between CI and brain connectivity (detected with functional magnetic resonance imagery using seed-based and hypothesis-free intrinsic connectivity) was investigated using a linear regression in a subgroup of 10 UWS and 11 MCS patients with sufficient image quality. Results: Higher CIs and CIl values were observed in MCS compared to UWS. Positive correlations were found between CRS-R and both CI. The One-R classifier selected CIl as the best discriminator between UWS and MCS with 90% accuracy, 7% false positive and 13% false negative rates after a 10-fold cross-validation test. Positive correlations were observed between both CI and the recovery of functional connectivity of brain areas belonging to the central autonomic networks (CAN). Conclusion: CI of MCS compared to UWS patients has high discriminative power and low false negative rate at one third of the estimated human assessors' misdiagnosis, providing an easy, inexpensive and non-invasive diagnostic tool. CI reflects functional connectivity changes in the CAN, suggesting that CI can provide an indirect way to screen and monitor connectivity changes in this neural system. Future studies should assess the extent of CI's predictive power in a larger cohort of patients and prognostic power in acute patients.