A precision functional atlas of personalized network topography and probabilities.

Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.

Heritability estimation of subcortical volumes in a multi-ethnic multi-site cohort study.

Heritability of regional subcortical brain volumes (rSBVs) describes the role of genetics in middle and inner brain development. rSBVs are highly heritable in adults but are not characterized well in adolescents. The Adolescent Brain Cognitive Development study (ABCD), taken over 22 US sites, provides data to characterize the heritability of subcortical structures in adolescence. In ABCD, site-specific effects co-occur with genetic effects which can bias heritability estimates. Existing methods adjusting for site effects require additional steps to adjust for site effects and can lead to inconsistent estimation. We propose a random-effect model-based method of moments approach that is a single step estimator and is a theoretically consistent estimator even when sites are imbalanced and performs well under simulations. We compare methods on rSBVs from ABCD. The proposed approach yielded heritability estimates similar to previous results derived from single-site studies. The cerebellum cortex and hippocampus were the most heritable regions (> 50%).

Cumulative Effects of Resting-State Connectivity Across All Brain Networks Significantly Correlate with Attention-Deficit Hyperactivity Disorder Symptoms.

Identification of replicable neuroimaging correlates of attention-deficit hyperactivity disorder (ADHD) has been hindered by small sample sizes, small effects, and heterogeneity of methods. Given evidence that ADHD is associated with alterations in widely distributed brain networks and the small effects of individual brain features, a whole-brain perspective focusing on cumulative effects is warranted. The use of large, multisite samples is crucial for improving reproducibility and clinical utility of brain-wide MRI association studies. To address this, a polyneuro risk score (PNRS) representing cumulative, brain-wide, ADHD-associated resting-state functional connectivity was constructed and validated using data from the Adolescent Brain Cognitive Development (ABCD, N = 5,543, 51.5% female) study, and was further tested in the independent Oregon-ADHD-1000 case-control cohort (N = 553, 37.4% female). The ADHD PNRS was significantly associated with ADHD symptoms in both cohorts after accounting for relevant covariates (p < 0.001). The most predictive PNRS involved all brain networks, though the strongest effects were concentrated among the default mode and cingulo-opercular networks. In the longitudinal Oregon-ADHD-1000, non-ADHD youth had significantly lower PNRS (Cohen's d = -0.318, robust p = 5.5 × 10-4) than those with persistent ADHD (age 7-19). The PNRS, however, did not mediate polygenic risk for ADHD. Brain-wide connectivity was robustly associated with ADHD symptoms in two independent cohorts, providing further evidence of widespread dysconnectivity in ADHD. Evaluation in enriched samples demonstrates the promise of the PNRS approach for improving reproducibility in neuroimaging studies and unraveling the complex relationships between brain connectivity and behavioral disorders.

Calcium-Sensitive Subthreshold Oscillations and Electrical Coupling in Principal Cells of Mouse Dorsal Cochlear Nucleus.

In higher sensory brain regions, slow oscillations (0.5-5 Hz) associated with quiet wakefulness and attention modulate multisensory integration, predictive coding, and perception. Although often assumed to originate via thalamocortical mechanisms, the extent to which subcortical sensory pathways are independently capable of slow oscillatory activity is unclear. We find that in the first station for auditory processing, the cochlear nucleus, fusiform cells from juvenile mice (of either sex) generate robust 1-2 Hz oscillations in membrane potential and exhibit electrical resonance. Such oscillations were absent prior to the onset of hearing, intrinsically generated by hyperpolarization-activated cyclic nucleotide-gated (HCN) and persistent Na+ conductances (NaP) interacting with passive membrane properties, and reflected the intrinsic resonance properties of fusiform cells. Cx36-containing gap junctions facilitated oscillation strength and promoted pairwise synchrony of oscillations between neighboring neurons. The strength of oscillations were strikingly sensitive to external Ca2+, disappearing at concentrations >1.7 mM, due in part to the shunting effect of small-conductance calcium-activated potassium (SK) channels. This effect explains their apparent absence in previous in vitro studies of cochlear nucleus which routinely employed high-Ca2+ extracellular solution. In contrast, oscillations were amplified in reduced Ca2+ solutions, due to relief of suppression by Ca2+ of Na+ channel gating. Our results thus reveal mechanisms for synchronous oscillatory activity in auditory brainstem, suggesting that slow oscillations, and by extension their perceptual effects, may originate at the earliest stages of sensory processing.

Multi-echo Acquisition and Thermal Denoising Advances Infant Precision Functional Imaging.

The characterization of individual functional brain organization with Precision Functional Mapping has provided important insights in recent years in adults. However, little is known about the ontogeny of inter-individual differences in brain functional organization during human development, but precise characterization of systems organization during periods of high plasticity might be most influential towards discoveries promoting lifelong health. Collecting and analyzing precision fMRI data during early development has unique challenges and emphasizes the importance of novel methods to improve data acquisition, processing, and analysis strategies in infant samples. Here, we investigate the applicability of two such methods from adult MRI research, multi-echo (ME) data acquisition and thermal noise removal with Noise reduction with distribution corrected principal component analysis (NORDIC), in precision fMRI data from three newborn infants. Compared to an adult example subject, T2* relaxation times calculated from ME data in infants were longer and more variable across the brain, pointing towards ME acquisition being a promising tool for optimizing developmental fMRI. The application of thermal denoising via NORDIC increased tSNR and the overall strength of functional connections as well as the split-half reliability of functional connectivity matrices in infant ME data. While our findings related to NORDIC denoising are coherent with the adult literature and ME data acquisition showed high promise, its application in developmental samples needs further investigation. The present work reveals gaps in our understanding of the best techniques for developmental brain imaging and highlights the need for further developmentally-specific methodological advances and optimizations, towards precision functional imaging in infants.

Gestational and postnatal age associations for striatal tissue iron deposition in early infancy.

Striatal development is crucial for later motor, cognitive, and reward behavior, but age-related change in striatal physiology during the neonatal period remains understudied. An MRI-based measure of tissue iron deposition, T2*, is a non-invasive way to probe striatal physiology neonatally, linked to dopaminergic processing and cognition in children and adults. Striatal subregions have distinct functions that may come online at different time periods in early life. To identify if there are critical periods before or after birth, we measured if striatal iron accrued with gestational age at birth [range= 34.57-41.85 weeks] or postnatal age at scan [range= 5-64 days], using MRI to probe the T2* signal in N = 83 neonates in three striatal subregions. We found iron increased with postnatal age in the pallidum and putamen but not the caudate. No significant relationship between iron and gestational age was observed. Using a subset of infants scanned at preschool age (N = 26), we show distributions of iron shift between time points. In infants, the pallidum had the least iron of the three regions but had the most by preschool age. Together, this provides evidence of distinct change for striatal subregions, a possible differentiation between motor and cognitive systems, identifying a mechanism that may impact future trajectories.

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans.

Objectives: Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations. Experimental Design: Included in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance. Principal Observations: Using group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better. Conclusions: BIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.

Polyneuro risk scores capture widely distributed connectivity patterns of cognition.

Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework - the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework's ability to reliably capture brain-behavior relationships across 3 cognitive scores - general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.

Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds.

Resting-state functional connectivity (rsFC) measured with fMRI has been used to characterize functional brain maturation in typically and atypically developing children and adults. However, its reliability and utility for predicting development in infants and toddlers is less well understood. Here, we use fMRI data from the Baby Connectome Project study to measure the reliability and uniqueness of rsFC in infants and toddlers and predict age in this sample (8-to-26 months old; n = 170). We observed medium reliability for within-session infant rsFC in our sample, and found that individual infant and toddler's connectomes were sufficiently distinct for successful functional connectome fingerprinting. Next, we trained and tested support vector regression models to predict age-at-scan with rsFC. Models successfully predicted novel infants' age within ± 3.6 months error and a prediction R2 = .51. To characterize the anatomy of predictive networks, we grouped connections into 11 infant-specific resting-state functional networks defined in a data-driven manner. We found that connections between regions of the same network-i.e. within-network connections-predicted age significantly better than between-network connections. Looking ahead, these findings can help characterize changes in functional brain organization in infancy and toddlerhood and inform work predicting developmental outcome measures in this age range.

Reproducible brain-wide association studies require thousands of individuals.

Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1-3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain-behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available-with a total sample size of around 50,000 individuals-to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain-phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies.

The field of adult neuroimaging relies on well-established principles in research design, imaging sequences, processing pipelines, as well as safety and data collection protocols. The field of infant magnetic resonance imaging, by comparison, is a young field with tremendous scientific potential but continuously evolving standards. The present article aims to initiate a constructive dialog between researchers who grapple with the challenges and inherent limitations of a nascent field and reviewers who evaluate their work. We address 20 questions that researchers commonly receive from research ethics boards, grant, and manuscript reviewers related to infant neuroimaging data collection, safety protocols, study planning, imaging sequences, decisions related to software and hardware, and data processing and sharing, while acknowledging both the accomplishments of the field and areas of much needed future advancements. This article reflects the cumulative knowledge of experts in the FIT'NG community and can act as a resource for both researchers and reviewers alike seeking a deeper understanding of the standards and tradeoffs involved in infant neuroimaging.

Learning Strategies for Contrast-agnostic Segmentation via SynthSeg for Infant MRI data.

Longitudinal studies of infants' brains are essential for research and clinical detection of neurodevelopmental disorders. However, for infant brain MRI scans, effective deep learning-based segmentation frameworks exist only within small age intervals due to the large image intensity and contrast changes that take place in the early postnatal stages of development. However, using different segmentation frameworks or models at different age intervals within the same longitudinal data set would cause segmentation inconsistencies and age-specific biases. Thus, an age-agnostic segmentation model for infants' brains is needed. In this paper, we present "Infant-SynthSeg", an extension of the contrast-agnostic SynthSeg segmentation framework applicable to MRI data of infants at ages within the first year of life. Our work mainly focuses on extending learning strategies related to synthetic data generation and augmentation, with the aim of creating a method that employs training data capturing features unique to infants' brains during this early-stage development. Comparison across different learning strategy settings, as well as a more-traditional contrast-aware deep learning model (nnU-net) are presented. Our experiments show that our trained Infant-SynthSeg models show consistently high segmentation performance on MRI scans of infant brains throughout the first year of life. Furthermore, as the model is trained on ground truth labels at different ages, even labels that are not present at certain ages (such as cerebellar white matter at 1 month) can be appropriately segmented via Infant-SynthSeg across the whole age range. Finally, while Infant-SynthSeg shows consistent segmentation performance across the first year of life, it is outperformed by age-specific deep learning models trained for a specific narrow age range.

Protocol for a mechanistic study of mindfulness based cognitive therapy during pregnancy.

OBJECTIVE: Preventive interventions for postpartum depression (PPD) are critical for women at elevated risk of PPD. Mindfulness based cognitive therapy - perinatal depression (MBCT-PD) is a preventive intervention that has been shown to reduce risk for PPD in women with a prior history of depression. The objective of this clinical trial is to examine two potential mechanisms of action of MBCT-PD, emotion regulation and cognitive control, using behavioral and neuroimaging methods. METHOD: This baseline protocol describes a randomized control trial (RCT) with two arms, MBCT-PD and treatment as usual (TAU). We plan on enrolling 74 females with a prior history of a major depressive episode, with 37 participants randomized to each arm. Participants in the MBCT-PD arm will receive MBCT-PD during pregnancy, and the TAU group will receive standard prenatal care. All participants will complete the Center for Epidemiological Studies Depression Scale - Revised (CESD-R), Emotion Regulation Questionnaire (ERQ), and classic Stroop task at multiple points from pregnancy through six months postpartum. Participants will also complete an fMRI scan at six weeks postpartum. RESULTS: All primary outcomes are collected at six weeks postpartum. Primary behavioral outcomes include: depressive symptoms on the CESD-R, cognitive reappraisal on the ERQ, and Stroop task performance. In parallel, the primary neurobiological outcomes include whole-brain activation during fMRI tasks when participants 1) regulate emotional responding and 2) engage cognitive control. CONCLUSIONS: This results of this innovative RCT will help identify potential behavioral and neurobiological mechanisms of action of preventive interventions for PPD for in-depth examination in larger scale RCTs. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Corelease of Inhibitory Neurotransmitters in the Mouse Auditory Midbrain.

The central nucleus of the inferior colliculus (ICC) of the auditory midbrain, which integrates most ascending auditory information from lower brainstem regions, receives prominent long-range inhibitory input from the ventral nucleus of the lateral lemniscus (VNLL), a region thought to be important for temporal pattern discrimination. Histological evidence suggests that neurons in the VNLL release both glycine and GABA in the ICC, but functional evidence for their corelease is lacking. We took advantage of the GlyT2-Cre mouse line (both male and female) to target expression of ChR2 to glycinergic afferents in the ICC and made whole-cell recordings in vitro while exciting glycinergic fibers with light. Using this approach, it was clear that a significant fraction of glycinergic boutons corelease GABA in the ICC. Viral injections were used to target ChR2 expression specifically to glycinergic fibers ascending from the VNLL, allowing for activation of fibers from a single source of ascending input in a way that has not been previously possible in the ICC. We then investigated aspects of the glycinergic versus GABAergic current components to probe functional consequences of corelease. Surprisingly, the time course and short-term plasticity of synaptic signaling were nearly identical for the two transmitters. We therefore conclude that the two neurotransmitters may be functionally interchangeable and that multiple receptor subtypes subserving inhibition may offer diverse mechanisms for maintaining inhibitory homeostasis.SIGNIFICANCE STATEMENT Corelease of neurotransmitters is a common feature of the brain. GABA and glycine corelease is particularly common in the spinal cord and brainstem, but its presence in the midbrain is unknown. We show corelease of GABA and glycine for the first time in the central nucleus of the inferior colliculus of the auditory midbrain. Glycine and GABA are both inhibitory neurotransmitters involved in fast synaptic transmission, so we explored differences between the currents to establish a physiological foundation for functional differences in vivo In contrast to the auditory brainstem, coreleased GABAergic and glycinergic currents in the midbrain are strikingly similar. This apparent redundancy may ensure homeostasis if one neurotransmitter system is compromised.

Optimization of Thermolytic Response to A1 Adenosine Receptor Agonists in Rats.

Cardiac arrest is a leading cause of death in the United States, and, currently, therapeutic hypothermia, now called targeted temperature management (TTM), is the only recent treatment modality proven to increase survival rates and reduce morbidity for this condition. Shivering and subsequent metabolic stress, however, limit application and benefit of TTM. Stimulating central nervous system A1 adenosine receptors (A1AR) inhibits shivering and nonshivering thermogenesis in rats and induces a hibernation-like response in hibernating species. In this study, we investigated the pharmacodynamics of two A1AR agonists in development as antishivering agents. To optimize body temperature (Tb) control, we evaluated the influence of every-other-day feeding, dose, drug, and ambient temperature (Ta) on the Tb-lowering effects of N6-cyclohexyladenosine (CHA) and the partial A1AR agonist capadenoson in rats. The highest dose of CHA (1.0 mg/kg, i.p.) caused all ad libitum-fed animals tested to reach our target Tb of 32°C, but responses varied and some rats overcooled to a Tb as low as 21°C at 17.0°C Ta Dietary restriction normalized the response to CHA. The partial agonist capadenoson (1.0 or 2.0 mg/kg, i.p.) produced a more consistent response, but the highest dose decreased Tb by only 1.6°C. To prevent overcooling after CHA, we studied continuous i.v. administration in combination with dynamic surface temperature control. Results show that after CHA administration control of surface temperature maintains desired target Tb better than dose or ambient temperature.