Important findings of a technology-assisted in-home music-based intervention for individuals with stroke: a small feasibility study.

PURPOSE: To examine the feasibility of stroke survivors receiving music-based rehabilitation via a mobile app. MATERIALS AND METHODS: We recruited ten chronic stroke survivors who were community-dwelling with mild-moderate upper extremity (UE) paresis. Participants were encouraged to exercise their paretic UE with a commercial instrument training app, Yousician, with a piano keyboard at home for three weeks. The feasibility of the training was measured by: (a) the acceptance of using the app to receive in-home piano training (e.g., daily usage time, exit interview) and (b) the effects of the app functionality as a rehabilitation tool (e.g., participants' motor improvements after training). RESULTS: Our small sample size of participants demonstrated general positive feedback and self-motivation (e.g., interest in extended training time) about using a mobile app to receive in-home, music-based UE training. Participants showed no trend of declined usage and practiced on average ∼33 min per day for 4-5 days per week during the 3-week participation. We also observed positive results in the Fugl-Meyer Assessment, Action Research Arm Test, and Nine Hole Peg Test after training. CONCLUSIONS: This study provided insight into the feasibility of delivering music-based interventions through mobile health (mHealth) technology for stroke populations. Although this was a small sample size, participants' positive and negative comments and feedback provided useful information for future rehab app development. We suggest four ways to further improve and design a patient-oriented app to facilitate the use of a mHealth app to deliver in-home music-based interventions for stroke survivors.

Stroke survivors showed acceptance and positive feedback on receiving in-home music-based training via a mobile app.mHealth technologies may allow positive effects of music-based interventions in motor performance and home practice compliance.Further developments in the co-design process with patients, caregivers, and therapists are needed to design a patient-oriented app to deliver better in-home music-based interventions for stroke survivors.

Surviving cardiac arrest from severe metformin-associated lactic acidosis using extracorporeal membrane oxygenation and double continuous venovenous haemodialysis.

Metformin-associated lactic acidosis (MALA) is a serious condition with high mortality. This case describes a man in the mid-60s with diabetes mellitus type 2 treated with metformin developing MALA 4 days after coronary stenting for non-ST-elevation myocardial infarction. He presented acutely with severe abdominal pain, a lactate of 19 mmol/L and pH 6.74. Despite treatment for MALA, he went into refractory cardiac arrest and was connected to venoarterial extracorporeal membrane oxygenation (VA-ECMO). He suffered a massive haemothorax due to perforation of the right atrial appendage. It was repaired through a sternotomy while being given massive blood transfusions. The following days, he was on VA-ECMO and double continuous venovenous haemodialysis (CVVHD). He survived with only mild paresis of the left hand. VA-ECMO should be considered a rescue therapy alongside treatment with CVVHD in case of cardiac arrest due to severe MALA.

Representing melodic relationships using network science.

Music is a complex system consisting of many dimensions and hierarchically organized information-the organization of which, to date, we do not fully understand. Network science provides a powerful approach to representing such complex systems, from the social networks of people to modelling the underlying network structures of different cognitive mechanisms. In the present research, we explored whether network science methodology can be extended to model the melodic patterns underlying expert improvised music. Using a large corpus of transcribed improvisations, we constructed a network model in which 5-pitch sequences were linked depending on consecutive occurrences, constituting 116,403 nodes (sequences) and 157,429 edges connecting them. We then investigated whether mathematical graph modelling relates to musical characteristics in real-world listening situations via a behavioral experiment paralleling those used to examine language. We found that as melodic distance within the network increased, participants judged melodic sequences as less related. Moreover, the relationship between distance and reaction time (RT) judgements was quadratic: participants slowed in RT up to distance four, then accelerated; a parallel finding to research in language networks. This study offers insights into the hidden network structure of improvised tonal music and suggests that humans are sensitive to the property of melodic distance in this network. More generally, our work demonstrates the similarity between music and language as complex systems, and how network science methods can be used to quantify different aspects of its complexity.

Linked auditory and motor patterns in the improvisation vocabulary of an artist-level jazz pianist.

Improvising musicians possess a stored library of musical patterns forming the basis for their improvisations. According to a prominent theoretical framework by Pressing (1988), this library includes linked auditory and motor information. Though examples of libraries of melodic patterns have been shown in extant recordings by some improvising musicians, the underlying motor component has not been experimentally investigated nor related to its auditory counterparts. Here we analyzed a large corpus of ∼100,000 notes from improvisations by one artist-level jazz pianist recorded during 11 live performances with audience. We compared the library identified from these recordings to a control corpus consisting of improvisations by 24 different advanced jazz pianists. In addition to pitch, our recordings included accurate micro-timing and key velocity (i.e., force) data. Following a previously validated procedure, this information was used to identify the underlying motor patterns through correlations between relative timing and velocity between notes in different iterations of the same pitch pattern. A computational model was, furthermore, used to estimate the information content and generated entropy exhibited by recurring pitch patterns with high and low timing and velocity correlations as perceived by a stylistically enculturated expert listener. Though both corpora contained a large number of recurring patterns, the single-player corpus showed stronger evidence that pitch patterns were linked to motor programs in that within-pattern timing and velocity correlations were significantly higher compared to the control corpus. Even when controlling for potentially greater baseline levels of motor self-consistency in the single-player corpus, this effect remained significant for velocity correlations. Amongst recurring 5-tone pitch patterns, those exhibiting more consistent motor schema also used less idiomatic pitch transitions that were both more unexpected and generated more uncertain expectations in enculturated experts than less consistently repeated patterns. Interestingly, we only found partial evidence for fixed pattern boundaries as predicted by the Pressing model and therefore suggest an expanded view in which the beginning and ends of idiomatic audio-motor patterns are not always clear-cut. Our results indicate that the library of melodic patterns may be idiosyncratic to the individual improviser and relies both on motor programming and predictive processing to promote stylistic distinctiveness.

Correspondence between gene expression and neurotransmitter receptor and transporter density in the human brain.

Neurotransmitter receptors modulate signaling between neurons. Thus, neurotransmitter receptors and transporters play a key role in shaping brain function. Due to the lack of comprehensive neurotransmitter receptor/transporter density datasets, microarray gene expression measuring mRNA transcripts is often used as a proxy for receptor densities. In the present report, we comprehensively test the spatial correlation between gene expression and protein density for a total of 27 neurotransmitter receptors, receptor binding-sites, and transporters across 9 different neurotransmitter systems, using both PET and autoradiography radioligand-based imaging modalities. We find poor spatial correspondences between gene expression and density for all neurotransmitter receptors and transporters except four single-protein metabotropic receptors (5-HT1A, CB1, D2, and MOR). These expression-density associations are related to gene differential stability and can vary between cortical and subcortical structures. Altogether, we recommend using direct measures of receptor and transporter density when relating neurotransmitter systems to brain structure and function.

Mapping neurotransmitter systems to the structural and functional organization of the human neocortex.

Neurotransmitter receptors support the propagation of signals in the human brain. How receptor systems are situated within macro-scale neuroanatomy and how they shape emergent function remain poorly understood, and there exists no comprehensive atlas of receptors. Here we collate positron emission tomography data from more than 1,200 healthy individuals to construct a whole-brain three-dimensional normative atlas of 19 receptors and transporters across nine different neurotransmitter systems. We found that receptor profiles align with structural connectivity and mediate function, including neurophysiological oscillatory dynamics and resting-state hemodynamic functional connectivity. Using the Neurosynth cognitive atlas, we uncovered a topographic gradient of overlapping receptor distributions that separates extrinsic and intrinsic psychological processes. Finally, we found both expected and novel associations between receptor distributions and cortical abnormality patterns across 13 disorders. We replicated all findings in an independently collected autoradiography dataset. This work demonstrates how chemoarchitecture shapes brain structure and function, providing a new direction for studying multi-scale brain organization.

Expert musical improvisations contain sequencing biases seen in language production.

Language production involves action sequencing to produce fluent speech in real time, placing a computational burden on working memory that leads to sequencing biases in production. Here we examine whether these biases extend beyond language to constrain one of the most complex human behaviors: music improvisation. Using a large corpus of improvised solos from eminent jazz musicians, we test for a production bias observed in language termed easy first-a tendency for more accessible sequences to occur at the beginning of a phrase, allowing incremental planning later in the same phrase. Our analysis shows consistent evidence of easy first in improvised music, with the beginning of musical phrases containing both more frequent and less complex sequences. The findings indicate that expert jazz musicians, known for spontaneous creative performance, reliably retrieve easily accessed melodic sequences before creating more complex sequences, suggesting that a domain-general sequencing system may support multiple forms of complex human behavior, from language production to music improvisation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Functional network connectivity during Jazz improvisation.

One of the most complex forms of creativity is musical improvisation where new music is produced in real time. Brain behavior during music production has several dimensions depending on the conditions of the performance. The expression of creativity is suspected to be different whether novel ideas must be externalized using a musical instrument or can be imagined internally. This study explores whole brain functional network connectivity from fMRI data during jazz music improvisation compared against a baseline of prelearned score performance. Given that creativity might be affected by external execution, another dimension where musicians imagine or vocalize the music was also tested. We found improvisation was associated with a state of weak connectivity necessary for attenuated executive control network recruitment associated with a feeling of "flow" allowing unhindered musical creation. In addition, elicited connectivity for sensorimotor and executive control networks is not different whether musicians imagine or externalize (through vocalization) musical performance.

Automated segmentation of deep brain nuclei using convolutional neural networks and susceptibility weighted imaging.

The advent of susceptibility-sensitive MRI techniques, such as susceptibility weighted imaging (SWI), has enabled accurate in vivo visualization and quantification of iron deposition within the human brain. Although previous approaches have been introduced to segment iron-rich brain regions, such as the substantia nigra, subthalamic nucleus, red nucleus, and dentate nucleus, these methods are largely unavailable and manual annotation remains the most used approach to label these regions. Furthermore, given their recent success in outperforming other segmentation approaches, convolutional neural networks (CNN) promise better performances. The aim of this study was thus to evaluate state-of-the-art CNN architectures for the labeling of deep brain nuclei from SW images. We implemented five CNN architectures and considered ensembles of these models. Furthermore, a multi-atlas segmentation model was included to provide a comparison not based on CNN. We evaluated two prediction strategies: individual prediction, where a model is trained independently for each region, and combined prediction, which simultaneously predicts multiple closely located regions. In the training dataset, all models performed with high accuracy with Dice coefficients ranging from 0.80 to 0.95. The regional SWI intensities and volumes from the models' labels were strongly correlated with those obtained from manual labels. Performances were reduced on the external dataset, but were higher or comparable to the intrarater reliability and most models achieved significantly better results compared to multi-atlas segmentation. CNNs can accurately capture the individual variability of deep brain nuclei and represent a highly useful tool for their segmentation from SW images.

Online Involvement for Georgia Student Teachers During Covid-19.

As concerns about Covid-19 rapidly escalated in March 2020 in the United States, all levels of education were impacted. A unique population (student teachers) faced challenges from two perspectives: as students and as teachers forced to teach and learn from a distance. Student Teachers, or preservice teachers (PST), are university students finishing a degree and/or teacher certification program by serving as an intern in a school setting. As schools were closed, these PSTs may not have been given access to the online learning platforms of their cooperating teachers (CT) and were no longer included in classroom instruction. The purpose of this study was to examine how the sudden shift away from traditional face-to-face instruction, co-teaching, and mentorship affected the involvement of music PSTs and their CT mentors in one region of the United States. Specifically, the research questions were: (1) How and in what ways were PSTs involved in planning, instruction, and/or assessment synchronously and asynchronously after school closures? (2) In what subdomains (performance, music theory/ear-training, etc.) were PSTs engaged in instruction and learning activities? (3) What challenges and solutions did PSTs report related to Covid-19 closures? A survey was sent, via email, to PSTs attending teacher preparation programs at universities in the state of Georgia at the end of the spring semester. Thirty-seven participants responded to the survey questions representing about 32% of all PSTs in Georgia in Spring 2020. Twenty-one were not given access to the online teaching platform of their placement school. A thematic analysis of the open-ended questions identified common themes including whether experiences were perceived as negative or positive. Of the PSTs given access, the majority of their responsibilities and experiences were creating assignments, additional help videos, participating in Zoom meetings, and assessing student assignment submissions. Of these experiences, interestingly, most were classified as positive by the PSTs. However, the importance of face-to-face interactions for both PST and the P-12 students was mentioned throughout survey responses. Approximately 10 PSTs mentioned their CT relationship/interaction and four of the respondents noted that their CT never reached out for help; however, six noted collaborative meetings or teaching with their CT. Importantly, some PSTs reported a lack of knowledge related to the planning and implementation of music instruction in the online modality. Therefore, teacher preparation programs should consider incorporating technology including online solutions into the music curriculum so that future music educators may more flexibly incorporate both in-person and distance learning.

Enhanced White Matter Fiber Tracts in Advanced Jazz Improvisers.

Human cognition and behavior arise from neuronal interactions over brain structural networks. These neuronal interactions cause changes in structural networks over time. How a creative activity such as musical improvisation performance changes the brain structure is largely unknown. In this diffusion magnetic resonance imaging study, we examined the brain's white matter fiber properties in previously identified functional networks and compared the findings between advanced jazz improvisers and non-musicians. We found that, for advanced improvisers compared with non-musicians, the normalized quantitative anisotropy (NQA) is elevated in the lateral prefrontal areas and supplementary motor area, and the underlying white matter fiber tracts connecting these areas. This enhancement of the diffusion anisotropy along the fiber pathway connecting the lateral prefrontal and supplementary motor is consistent with the functional networks during musical improvisation tasks performed by expert jazz improvisers. These findings together suggest that experts' creative skill is associated with the task-relevant, long-timescale brain structural network changes, in support of related cognitive underpinnings.

A high-resolution in vivo atlas of the human brain's benzodiazepine binding site of GABAA receptors.

Gamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the human brain and plays a key role in several brain functions and neuropsychiatric disorders such as anxiety, epilepsy, and depression. For decades, several in vivo and ex vivo techniques have been used to highlight the mechanisms of the GABA system, however, no studies have currently combined the techniques to create a high-resolution multimodal view of the GABA system. Here, we present a quantitative high-resolution in vivo atlas of the human brain benzodiazepine receptor sites (BZR) located on postsynaptic ionotropic GABAA receptors (GABAARs), generated on the basis of in vivo [11C]flumazenil Positron Emission Tomography (PET) data. Next, based on ex vivo autoradiography data, we transform the PET-generated atlas from binding values into BZR protein density. Finally, we examine the brain regional association between BZR protein density and ex vivo mRNA expression for the 19 subunits in the GABAAR, including an estimation of the minimally required expression of mRNA levels for each subunit to translate into BZR protein. This represents the first publicly available quantitative high-resolution in vivo atlas of the spatial distribution of BZR densities in the healthy human brain. The atlas provides a unique neuroscientific tool as well as novel insights into the association between mRNA expression for individual subunits in the GABAAR and the BZR density at each location in the brain.

Characterisation of Children's Head Motion for Magnetic Resonance Imaging With and Without General Anaesthesia.

Head motion is one of the major reasons for artefacts in Magnetic Resonance Imaging (MRI), which is especially challenging for children who are often intimidated by the dimensions of the MR scanner. In order to optimise the MRI acquisition for children in the clinical setting, insights into children's motion patterns are essential. In this work, we analyse motion data from 61 paediatric patients. We compare structural MRI data of children imaged with and without general anaesthesia (GA), all scanned using the same hybrid PET/MR scanner. We analyse several metrics of motion based on the displacement relative to a reference, decompose the transformation matrix into translation and rotation, as well as investigate whether different regions in the brain are affected differently by the children's motion. Head motion for children without GA was significantly higher, with a median of the mean displacements of 2.19 ± 0.93 mm (median ± standard deviation) during 41.7±7.5 min scans; however, even anaesthetised children showed residual head motion (mean displacement of 1.12±0.35 mm). For both patient groups translation along the z-axis (along the scanner bore) was significantly larger in absolute terms (GA / no GA: 0.87±0.29/0.92 ± 0.49 mm) compared to the other directions. Considering directionality, both patient groups were moving in negative z-direction and thus, out of the scanner. The awake children additionally showed significantly more nodding rotation (0.33±0.20°). In future studies as well as in the clinical setting, these predominant types of motion need to be taken into consideration to limit artefacts and reduce re-scans due to poor image quality.

False positive rates in positron emission tomography (PET) voxelwise analyses.

Issues with inflated false positive rates (FPRs) in brain imaging have recently received significant attention. However, to what extent FPRs present a problem for voxelwise analyses of Positron Emission Tomography (PET) data remains unknown. In this work, we evaluate the FPR using real PET data under group assignments that should yield no significant results after correcting for multiple comparisons. We used data from 159 healthy participants, imaged with the serotonin transporter ([11C]DASB; N = 100) or the 5-HT4 receptor ([11C]SB207145; N = 59). Using this null data, we estimated the FPR by performing 1,000 group analyses with randomly assigned groups of either 10 or 20, for each tracer, and corrected for multiple comparisons using parametric Monte Carlo simulations (MCZ) or non-parametric permutation testing. Our analyses show that for group sizes of 10 or 20, the FPR for both tracers was 5-99% using MCZ, much higher than the expected 5%. This was caused by a heavier-than-Gaussian spatial autocorrelation, violating the parametric assumptions. Permutation correctly controlled the FPR in all cases. In conclusion, either a conservative cluster forming threshold and high smoothing levels, or a non-parametric correction for multiple comparisons should be performed in voxelwise analyses of brain PET data.

Guidelines for the content and format of PET brain data in publications and archives: A consensus paper.

It is a growing concern that outcomes of neuroimaging studies often cannot be replicated. To counteract this, the magnetic resonance (MR) neuroimaging community has promoted acquisition standards and created data sharing platforms, based on a consensus on how to organize and share MR neuroimaging data. Here, we take a similar approach to positron emission tomography (PET) data. To facilitate comparison of findings across studies, we first recommend publication standards for tracer characteristics, image acquisition, image preprocessing, and outcome estimation for PET neuroimaging data. The co-authors of this paper, representing more than 25 PET centers worldwide, voted to classify information as mandatory, recommended, or optional. Second, we describe a framework to facilitate data archiving and data sharing within and across centers. Because of the high cost of PET neuroimaging studies, sample sizes tend to be small and relatively few sites worldwide have the required multidisciplinary expertise to properly conduct and analyze PET studies. Data sharing will make it easier to combine datasets from different centers to achieve larger sample sizes and stronger statistical power to test hypotheses. The combining of datasets from different centers may be enhanced by adoption of a common set of best practices in data acquisition and analysis.

Different preprocessing strategies lead to different conclusions: A [11C]DASB-PET reproducibility study.

Positron emission tomography (PET) neuroimaging provides unique possibilities to study biological processes in vivo under basal and interventional conditions. For quantification of PET data, researchers commonly apply different arrays of sequential data analytic methods ("preprocessing pipeline"), but it is often unknown how the choice of preprocessing affects the final outcome. Here, we use an available data set from a double-blind, randomized, placebo-controlled [11C]DASB-PET study as a case to evaluate how the choice of preprocessing affects the outcome of the study. We tested the impact of 384 commonly used preprocessing strategies on a previously reported positive association between the change from baseline in neocortical serotonin transporter binding determined with [11C]DASB-PET, and change in depressive symptoms, following a pharmacological sex hormone manipulation intervention in 30 women. The two preprocessing steps that were most critical for the outcome were motion correction and kinetic modeling of the dynamic PET data. We found that 36% of the applied preprocessing strategies replicated the originally reported finding (p < 0.05). For preprocessing strategies with motion correction, the replication percentage was 72%, whereas it was 0% for strategies without motion correction. In conclusion, the choice of preprocessing strategy can have a major impact on a study outcome.

Optimization of preprocessing strategies in Positron Emission Tomography (PET) neuroimaging: A [11C]DASB PET study.

Positron Emission Tomography (PET) is an important neuroimaging tool to quantify the distribution of specific molecules in the brain. The quantification is based on a series of individually designed data preprocessing steps (pipeline) and an optimal preprocessing strategy is per definition associated with less noise and improved statistical power, potentially allowing for more valid neurobiological interpretations. In spite of this, it is currently unclear how to design the best preprocessing pipeline and to what extent the choice of each preprocessing step in the pipeline minimizes subject-specific errors. To evaluate the impact of various preprocessing strategies, we systematically examined 384 different pipeline strategies in data from 30 healthy participants scanned twice with the serotonin transporter (5-HTT) radioligand [11C]DASB. Five commonly used preprocessing steps with two to four options were investigated: (1) motion correction (MC) (2) co-registration (3) delineation of volumes of interest (VOI's) (4) partial volume correction (PVC), and (5) kinetic modeling. To quantitatively compare and evaluate the impact of various preprocessing strategies, we used the performance metrics: test-retest bias, within- and between-subject variability, the intraclass-correlation coefficient, and global signal-to-noise ratio. We also performed a power analysis to estimate the required sample size to detect either a 5% or 10% difference in 5-HTT binding as a function of preprocessing pipeline. The results showed a complex downstream dependency between the various preprocessing steps on the performance metrics. The choice of MC had the most profound effect on 5-HTT binding, prior to the effects caused by PVC and kinetic modeling, and the effects differed across VOI's. Notably, we observed a negative bias in 5-HTT binding across test and retest in 98% of pipelines, ranging from 0 to 6% depending on the pipeline. Optimization of the performance metrics revealed a trade-off in within- and between-subject variability at the group-level with opposite effects (i.e. minimization of within-subject variability increased between-subject variability and vice versa). The sample size required to detect a given effect size was also compromised by the preprocessing strategy, resulting in up to 80% increases in sample size needed to detect a 5% difference in 5-HTT binding. This is the first study to systematically investigate and demonstrate the effect of choosing different preprocessing strategies on the outcome of dynamic PET studies. We provide a framework to show how optimal and maximally powered neuroimaging results can be obtained by choosing appropriate preprocessing strategies and we provide recommendations depending on the study design. In addition, the results contribute to a better understanding of methodological uncertainty and variability in preprocessing decisions for future group- and/or longitudinal PET studies.

Higher Node Activity with Less Functional Connectivity During Musical Improvisation.

Musical improvisation is one of the most complex forms of creative behavior, which offers a realistic task paradigm for the investigation of real-time creativity where revision is not possible. Despite some previous studies on musical improvisation and brain activity, what and how brain areas are involved during musical improvisation are not clearly understood. In this article, we designed a new functional magnetic resonance imaging (fMRI) study, in which, while being in the MRI scanner, advanced jazz improvisers performed improvisatory vocalization and imagery as main tasks and performed a prelearned melody as a control task. We incorporated a musical imagery task to avoid possible confounds of mixed motor and perceptual variables in previous studies. We found that musical improvisation compared with prelearned melody is characterized by higher node activity in the Broca's area, dorsolateral prefrontal cortex, lateral premotor cortex, supplementary motor area and cerebellum, and lower functional connectivity in number and strength among these regions. We discuss various explanations for the divergent activation and connectivity results. These results point to the notion that a human creative behavior performed under real-time constraints is an internally directed behavior controlled primarily by a smaller brain network in the frontal cortex.

Cerebral serotonin transporter measurements with [11C]DASB: A review on acquisition and preprocessing across 21 PET centres.

Positron Emission Tomography (PET) imaging has become a prominent tool to capture the spatiotemporal distribution of neurotransmitters and receptors in the brain. The outcome of a PET study can, however, potentially be obscured by suboptimal and/or inconsistent choices made in complex processing pipelines required to reach a quantitative estimate of radioligand binding. Variations in subject selection, experimental design, data acquisition, preprocessing, and statistical analysis may lead to different outcomes and neurobiological interpretations. We here review the approaches used in 105 original research articles published by 21 different PET centres, using the tracer [11C]DASB for quantification of cerebral serotonin transporter binding, as an exemplary case. We highlight and quantify the impact of the remarkable variety of ways in which researchers are currently conducting their studies, while implicitly expecting generalizable results across research groups. Our review provides evidence that the foundation for a given choice of a preprocessing pipeline seems to be an overlooked aspect in modern PET neuroscience. Furthermore, we believe that a thorough testing of pipeline performance is necessary to produce reproducible research outcomes, avoiding biased results and allowing for better understanding of human brain function.