Choir singing is associated with enhanced structural connectivity across the adult lifespan.

The global ageing of populations calls for effective, ecologically valid methods to support brain health across adult life. Previous evidence suggests that music can promote white matter (WM) microstructure and grey matter (GM) volume while supporting auditory and cognitive functioning and emotional well-being as well as counteracting age-related cognitive decline. Adding a social component to music training, choir singing is a popular leisure activity among older adults, but a systematic account of its potential to support healthy brain structure, especially with regard to ageing, is currently missing. The present study used quantitative anisotropy (QA)-based diffusion MRI connectometry and voxel-based morphometry to explore the relationship of lifetime choir singing experience and brain structure at the whole-brain level. Cross-sectional multiple regression analyses were carried out in a large, balanced sample (N = 95; age range 21-88) of healthy adults with varying levels of choir singing experience across the whole age range and within subgroups defined by age (young, middle-aged, and older adults). Independent of age, choir singing experience was associated with extensive increases in WM QA in commissural, association, and projection tracts across the brain. Corroborating previous work, these overlapped with language and limbic networks. Enhanced corpus callosum microstructure was associated with choir singing experience across all subgroups. In addition, choir singing experience was selectively associated with enhanced QA in the fornix in older participants. No associations between GM volume and choir singing were found. The present study offers the first systematic account of amateur-level choir singing on brain structure. While no evidence for counteracting GM atrophy was found, the present evidence of enhanced structural connectivity coheres well with age-typical structural changes. Corroborating previous behavioural studies, the present results suggest that regular choir singing holds great promise for supporting brain health across the adult life span.

Outliers in diffusion-weighted MRI: Exploring detection models and mitigation strategies.

Diffusion-weighted MRI (dMRI) is a medical imaging method that can be used to investigate the brain microstructure and structural connections between different brain regions. The method, however, requires relatively complex data processing frameworks and analysis pipelines. Many of these approaches are vulnerable to signal dropout artefacts that can originate from subjects moving their head during the scan. To combat these artefacts and eliminate such outliers, researchers have proposed two approaches: to replace outliers or to downweight outliers during modelling and analysis. With the rising interest in dMRI for clinical research, these types of corrections are increasingly important. Therefore, we set out to investigate the differences between outlier replacement and weighting approaches to help the dMRI community to select the best tool for their data processing pipelines. We evaluated dMRI motion correction registration and single tensor model fit pipelines using Gaussian Process and Spherical Harmonic based replacement approaches and outlier downweighting using highly realistic whole-brain simulations. As a proof of concept, we applied these approaches to dMRI infant data sets that contained varying numbers of dropout artefacts. Based on our results, we concluded that the Gaussian Process based outlier replacement provided similar tensor fit results to Gaussian Process based outlier detection and downweighting. Therefore, if only the least-squares estimate of the single tensor model is of interest, our recommendation is to use outlier replacement. However, outlier downweighting can potentially provide a more accurate estimate of the model precision which could be relevant for applications such as probabilistic tractoraphy.

Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses.

The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been proposed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical studies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.

Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination.

The segmentation of brain structures is a key component of many neuroimaging studies. Consistent anatomical definitions are crucial to ensure consensus on the position and shape of brain structures, but segmentations are prone to variation in their interpretation and execution. White-matter (WM) pathways are global structures of the brain defined by local landmarks, which leads to anatomical definitions being difficult to convey, learn, or teach. Moreover, the complex shape of WM pathways and their representation using tractography (streamlines) make the design and evaluation of dissection protocols difficult and time-consuming. The first iteration of Tractostorm quantified the variability of a pyramidal tract dissection protocol and compared results between experts in neuroanatomy and nonexperts. Despite virtual dissection being used for decades, in-depth investigations of how learning or practicing such protocols impact dissection results are nonexistent. To begin to fill the gap, we evaluate an online educational tractography course and investigate the impact learning and practicing a dissection protocol has on interrater (groupwise) reproducibility. To generate the required data to quantify reproducibility across raters and time, 20 independent raters performed dissections of three bundles of interest on five Human Connectome Project subjects, each with four timepoints. Our investigation shows that the dissection protocol in conjunction with an online course achieves a high level of reproducibility (between 0.85 and 0.90 for the voxel-based Dice score) for the three bundles of interest and remains stable over time (repetition of the protocol). Suggesting that once raters are familiar with the software and tasks at hand, their interpretation and execution at the group level do not drastically vary. When compared to previous work that used a different method of communication for the protocol, our results show that incorporating a virtual educational session increased reproducibility. Insights from this work may be used to improve the future design of WM pathway dissection protocols and to further inform neuroanatomical definitions.

Fast and accurate Slicewise OutLIer Detection (SOLID) with informed model estimation for diffusion MRI data.

The accurate characterization of the diffusion process in tissue using diffusion MRI is greatly challenged by the presence of artefacts. Subject motion causes not only spatial misalignments between diffusion weighted images, but often also slicewise signal intensity errors. Voxelwise robust model estimation is commonly used to exclude intensity errors as outliers. Slicewise outliers, however, become distributed over multiple adjacent slices after image registration and transformation. This challenges outlier detection with voxelwise procedures due to partial volume effects. Detecting the outlier slices before any transformations are applied to diffusion weighted images is therefore required. In this work, we present i) an automated tool coined SOLID for slicewise outlier detection prior to geometrical image transformation, and ii) a framework to naturally interpret data uncertainty information from SOLID and include it as such in model estimators. SOLID uses a straightforward intensity metric, is independent of the choice of the diffusion MRI model, and can handle datasets with a few or irregularly distributed gradient directions. The SOLID-informed estimation framework prevents the need to completely reject diffusion weighted images or individual voxel measurements by downweighting measurements with their degree of uncertainty, thereby supporting convergence and well-conditioning of iterative estimation algorithms. In comprehensive simulation experiments, SOLID detects outliers with a high sensitivity and specificity, and can achieve higher or at least similar sensitivity and specificity compared to other tools that are based on more complex and time-consuming procedures for the scenarios investigated. SOLID was further validated on data from 54 neonatal subjects which were visually inspected for outlier slices with the interactive tool developed as part of this study, showing its potential to quickly highlight problematic volumes and slices in large population studies. The informed model estimation framework was evaluated both in simulations and in vivo human data.

TMS uncovers details about sub-regional language-specific processing networks in early bilinguals.

Despite numerous functional neuroimaging and intraoperative electrical cortical mapping studies aimed at investigating the cortical organisation of native (L1) and second (L2) language processing, the neural underpinnings of bilingualism remain elusive. We investigated whether the neural network engaged in speech production over the bilateral posterior inferior frontal gyrus (pIFG) is the same (i.e., shared) or different (i.e., language-specific) for the two languages of bilingual speakers. Navigated transcranial magnetic stimulation (TMS) was applied over the left and right posterior inferior gyrus (pIFG), while early simultaneous bilinguals performed a picture naming task with their native languages. An ex-Gaussian distribution was fitted to the naming latencies and the resulting parameters were compared between languages and across stimulation conditions. The results showed that although the naming performance in general was highly comparable between the languages, TMS produced a language-specific effect when the pulses were delivered to the left pIFG at 200 ms poststimulus. We argue that this result causally demonstrates, for the first time, that even within common language-processing areas, there are distinct language-specific neural populations for the different languages in early simultaneous bilinguals.

A novel measure of reliability in Diffusion Tensor Imaging after data rejections due to subject motion.

Diffusion Tensor Imaging (DTI) is commonly challenged by subject motion during data acquisition, which often leads to corrupted image data. Currently used procedure in DTI analysis is to correct or completely reject such data before tensor estimations, however assessing the reliability and accuracy of the estimated tensor in such situations has evaded previous studies. This work aims to define the loss of data accuracy with increasing image rejections, and to define a robust method for assessing reliability of the result at voxel level. We carried out simulations of every possible sub-scheme (N=1,073,567,387) of Jones30 gradient scheme, followed by confirming the idea with MRI data from four newborn and three adult subjects. We assessed the relative error of the most commonly used tensor estimates for DTI and tractography studies, fractional anisotropy (FA) and the major orientation vector (V1), respectively. The error was estimated using two measures, the widely used electric potential (EP) criteria as well as the rotationally variant condition number (CN). Our results show that CN and EP are comparable in situations with very few rejections, but CN becomes clearly more sensitive to depicting errors when more gradient vectors and images were rejected. The error in FA and V1 was also found depend on the actual FA level in the given voxel; low actual FA levels were related to high relative errors in the FA and V1 estimates. Finally, the results were confirmed with clinical MRI data. This showed that the errors after rejections are, indeed, inhomogeneous across brain regions. The FA and V1 errors become progressively larger when moving from the thick white matter bundles towards more superficial subcortical structures. Our findings suggest that i) CN is a useful estimator of data reliability at voxel level, and ii) DTI preprocessing with data rejections leads to major challenges when assessing brain tissue with lower FA levels, such as all newborn brain, as well as the adult superficial, subcortical areas commonly traced in precise connectivity analyses between cortical regions.

Bilingualism modulates the white matter structure of language-related pathways.

Learning and speaking a second language (L2) may result in profound changes in the human brain. Here, we investigated local structural differences along two language-related white matter trajectories, the arcuate fasciculus and the inferior fronto-occipital fasciculus (IFOF), between early simultaneous bilinguals and late sequential bilinguals. We also examined whether early exposure to two languages might lead to a more bilateral structural organization of the arcuate fasciculus. Fractional anisotropy, mean and radial diffusivities (FA, MD, and RD respectively) were extracted to analyse tract-specific changes. Additionally, global voxel-wise effects were investigated with Tract-Based Spatial Statistics (TBSS). We found that relative to late exposure, early exposure to L2 leads to increased FA along a phonology-related segment of the arcuate fasciculus, but induces no modulations along the IFOF, associated to semantic processing. Late sequential bilingualism, however, was associated with decreased MD along the bilateral IFOF. Our results suggest that early vs. late bilingualism may lead to qualitatively different kind of changes in the structural language-related network. Furthermore, we show that early bilingualism contributes to the structural laterality of the arcuate fasciculus, leading to a more bilateral organization of these perisylvian language-related tracts.

Visual fixation in human newborns correlates with extensive white matter networks and predicts long-term neurocognitive development.

Infants are well known to seek eye contact, and they prefer to fixate on developmentally meaningful objects, such as the human face. It is also known, that visual abilities are important for the developmental cascades of cognition from later infancy to childhood. It is less understood, however, whether newborn visual abilities relate to later cognitive development, and whether newborn ability for visual fixation can be assigned to early microstructural maturation. Here, we investigate relationship between newborn visual fixation (VF) and gaze behavior (GB) to performance in visuomotor and visual reasoning tasks in two cohorts with cognitive follow-up at 2 (n = 57) and 5 (n = 1410) years of age. We also analyzed brain microstructural correlates to VF (n = 45) by voxel-based analysis of fractional anisotropy (FA) in newborn diffusion tensor imaging. Our results show that newborn VF is significantly related to visual-motor performance at both 2 and 5 years, as well as to visual reasoning at 5 years of age. Moreover, good newborn VF relates to widely increased FA levels across the white matter. Comparison to motor performance indicated that early VF is preferentially related to visuocognitive development, and that early motor performance relates neither to white matter integrity nor to visuocognitive development. The present findings suggest that newborn VF is supported by brainwide subcortical networks and it represents an early building block for the developmental cascades of cognition.

Structural Neuroplasticity Effects of Singing in Chronic Aphasia.

Singing-based treatments of aphasia can improve language outcomes, but the neural benefits of group-based singing in aphasia are unknown. Here, we set out to determine the structural neuroplasticity changes underpinning group-based singing-induced treatment effects in chronic aphasia. Twenty-eight patients with at least mild nonfluent poststroke aphasia were randomized into two groups that received a 4-month multicomponent singing intervention (singing group) or standard care (control group). High-resolution T1 images and multishell diffusion-weighted MRI data were collected in two time points (baseline/5 months). Structural gray matter (GM) and white matter (WM) neuroplasticity changes were assessed using language network region of interest-based voxel-based morphometry (VBM) and quantitative anisotropy-based connectometry, and their associations to improved language outcomes (Western Aphasia Battery Naming and Repetition) were evaluated. Connectometry analyses showed that the singing group enhanced structural WM connectivity in the left arcuate fasciculus (AF) and corpus callosum as well as in the frontal aslant tract (FAT), superior longitudinal fasciculus, and corticostriatal tract bilaterally compared with the control group. Moreover, in VBM, the singing group showed GM volume increase in the left inferior frontal cortex (Brodmann area 44) compared with the control group. The neuroplasticity effects in the left BA44, AF, and FAT correlated with improved naming abilities after the intervention. These findings suggest that in the poststroke aphasia group, singing can bring about structural neuroplasticity changes in left frontal language areas and in bilateral language pathways, which underpin treatment-induced improvement in speech production.

Altered corpus callosum structure in adolescents with cerebral palsy: connection to gait and balance.

Cerebral palsy (CP) is the most common motor disorder in childhood. Recent studies in children with CP have associated weakened sensorimotor performance with impairments in the major brain white-matter (WM) structure, corpus callosum (CC). However, the relationship between CC structure and lower extremity performance, specifically gait and balance, remains unknown. This study investigated the transcallosal WM structure and lower limb motor stability performance in adolescents aged 10-18 years with spastic hemiplegic (n = 18) or diplegic (n = 13) CP and in their age-matched controls (n = 34). The modern diffusion-weighted MRI analysis included the diffusivity properties of seven CC subparts and the transcallosal lower limb sensorimotor tract of the dominant hemisphere. Children with CP had comprehensive impairments in the cross-sectional area, fractional anisotropy, and mean diffusivity of the CC and sensorimotor tract. Additionally, the extent of WM alterations varied between hemiplegic and diplegic subgroups, which was seen especially in the fractional anisotropy values along the sensorimotor tract. The diffusion properties of transcallosal WM were further associated with static stability in all groups, and with dynamic stability in healthy controls. Our novel results clarify the mechanistic role of the corpus callosum in adolescents with and without CP offering valuable insight into the complex interplay between the brain's WM organization and motor performance. A better understanding of the brain basis of weakened stability performance could, in addition, improve the specificity of clinical diagnosis and targeted rehabilitation in CP.

Visual alertness and brain diffusion tensor imaging at term age predict neurocognitive development at preschool age in extremely preterm-born children.

INTRODUCTION: Cognitive development is characterized by the structural and functional maturation of the brain. Diffusion-weighted magnetic resonance imaging (dMRI) provides methods of investigating the brain structure and connectivity and their correlations with the neurocognitive outcome. Our aim was to examine the relationship between early visual abilities, brain white matter structures, and the later neurocognitive outcome. METHODS: This study included 20 infants who were born before 28 gestational weeks and followed until the age of 6.5 years. At term age, visual alertness was evaluated and dMRI was used to investigate the brain white matter structure using fractional anisotropy (FA) in tract-based spatial statistics analysis. The JHU DTI white matter atlas was used to locate the findings. The neuropsychological assessment was used to assess neurocognitive performance at 6.5 years. RESULTS: Optimal visual alertness at term age was significantly associated with better visuospatial processing (p < .05), sensorimotor functioning (p < .05), and social perception (p < .05) at 6.5 years of age. Optimal visual alertness related to higher FA values, and further, the FA values positively correlated with the neurocognitive outcome. The tract-based spatial differences in FA values were detected between children with optimal and nonoptimal visual alertness according to performance at 6.5 years. CONCLUSION: We provide neurobiological evidence for the global and tract-based spatial differences in the white matter maturation between extremely preterm children with optimal and nonoptimal visual alertness at term age and a link between white matter maturation, visual alertness and the neurocognitive outcome at 6.5 years proposing that early visual function is a building block for the later neurocognitive development.

Hodological organization of spoken language production and singing in the human brain.

Theories expounding the neural relationship between speech and singing range from sharing neural circuitry, to relying on opposite hemispheres. Yet, hodological studies exploring their shared and distinct neural networks remain scarce. In this study, we combine a white matter connectometry approach together with comprehensive and naturalistic appraisal of verbal expression during spoken language production and singing in a sample of individuals with post-stroke aphasia. Our results reveal that both spoken language production and singing are mainly supported by the left hemisphere language network and projection pathways. However, while spoken language production mostly engaged dorsal and ventral streams of speech processing, singing was associated primarily with the left ventral stream. These findings provide evidence that speech and singing share core neuronal circuitry within the left hemisphere, while distinct ventral stream contributions explain frequently observed dissociations in aphasia. Moreover, the results suggest prerequisite biomarkers for successful singing-based therapeutic interventions.

Statistical Permutation Test Reveals Progressive and Region-Specific Iron Accumulation in the Thalami of Children with Aspartylglucosaminuria.

Aspartylglucosaminuria (AGU) is a rare lysosomal storage disorder causing developmental delay, intellectual disability, and eventual death. A distinct feature in AGU is iron accumulation within the thalamus. Our aim is to demonstrate that susceptibility-weighted images (SWI) could be used as an MRI biomarker to evaluate the response within the AGU population to newly evolving treatments. SWI from 16 patients with AGU and 16 age-matched controls were used in the analysis. Thalamic volume with an iron accumulation was identified using a permutation test. Group differences were investigated for both the complete thalamus and the iron accumulation regions. Group-wise age correlation within these volumes were assessed with analysis of variance and multivariate regression. We found a statistically significant and large difference (p-value = 0.01, Cohen's D = 0.97) for the whole thalamus comparison and an even greater difference in the iron accumulation regions (p-value < 0.01, Cohen's D = 3.52). Furthermore, we found strong evidence for iron accumulation as a linear function of age with R2 = 0.65 only for AGU. The statistical analysis of SWI provides tools for assessing the degree of iron accumulation. This method could be used to study the response to treatments, in that a successful treatment would be expected to result in a decline in iron accumulation.