Real-time motion analytics during brain MRI improve data quality and reduce costs

Potential correlations between asymmetric disruption of functional connectivity and metabolism in major depressive disorder

OBJECTIVE

Previous research has suggested a connection between major depressive disorder (MDD) and certain comorbidities, including gastrointestinal issues, thyroid dysfunctions, and glycolipid metabolism abnormalities. However, the relationships between these factors and asymmetrical alterations in functional connectivity (FC) in adults with MDD remain unclear.

METHOD

We conducted a study on a cohort of 42 MDD patients and 42 healthy controls (HCs). Participants underwent comprehensive clinical assessments, including evaluations of blood lipids and thyroid hormone levels, as well as resting-state functional magnetic resonance imaging (Rs-fMRI) scans. Data analysis involved correlation analysis to compute the parameter of asymmetry (PAS) for the entire brain's functional connectome. We then examined the interrelationships between abnormal PAS regions in the brain, thyroid hormone levels, and blood lipid levels.

RESULTS

The third-generation ultra-sensitive thyroid stimulating hormone (TSH3UL) level was found to be significantly lower in MDD patients compared to HCs. The PAS score of the left inferior frontal gyrus (IFG) decreased, while the bilateral posterior cingulate cortex (Bi-PCC) PAS increased in MDD patients relative to HCs. Notably, the PAS score of the left IFG negatively correlated with both TSH and total cholesterol (CHOL) levels. However, these correlations lose significance after the Bonferroni correction.

CONCLUSION

MDD patients demonstrated abnormal asymmetry in resting-state FC (Rs-FC) within the fronto-limbic system, which may be associated with CHOL and thyroid hormone levels.

Neighborhood Crime and Externalizing Behavior in Toddlers: A Longitudinal Study With Neonatal fMRI and Parenting

OBJECTIVE

Prenatal exposure to neighborhood crime has been associated with weaker neonatal frontolimbic connectivity; however, associations with early childhood behavior remain unclear. We hypothesized that living in a high-crime neighborhood would be related to higher externalizing symptoms at age 1 and 2 years, over and above other adversities, and that neonatal frontolimbic connectivity and observed parenting behaviors at 1 year would mediate this relationship.

METHOD

Participants included 399 pregnant women, recruited as part of the Early Life Adversity, Biological Embedding, and Risk for Developmental Precursors of Mental Disorders (eLABE) study. Geocoded neighborhood crime data was obtained from Applied Geographic Solution. A total of 319 healthy, non-sedated neonates underwent scanning using resting-state functional magnetic resonance imaging (fMRI) on a Prisma 3T scanner and had ≥10 minutes of high-quality data. Infant-Toddler Socioemotional Assessment Externalizing T scores were available for 274 mothers of 1-year-olds and 257 mothers of 2-year-olds. Observed parenting behaviors were available for 202 parent-infant dyads at 1 year. Multilevel and mediation models tested longitudinal associations.

RESULTS

Living in a neighborhood with high violent (β = 0.15, CI = 0.05-0.27, p = .004) and property (β = 0.10, CI = 0.01-0.20, p = .039) crime was related to more externalizing symptoms at 1 and 2 years, controlling for other adversities. Weaker frontolimbic connectivity was also associated with higher externalizing symptoms at 1 and 2 years. After controlling for other adversities, parenting behaviors mediated the specific association between crime and externalizing symptoms, but frontolimbic connectivity did not.

CONCLUSION

These findings provide evidence that early exposure to neighborhood crime and weaker neonatal frontolimbic connectivity may influence later externalizing symptoms, and suggest that parenting may be an early intervention target for families in high-crime areas.

PLAIN LANGUAGE SUMMARY

This longitudinal study of 399 women and their children found that toddlers who lived in a high crime area during the first 2 years of their lives displayed more externalizing symptoms. Toddlers with weaker frontolimbic brain function at birth also had higher externalizing symptoms at 1 and 2 years. Interestingly, parenting behaviors, but not neonatal brain function, mediated the relationship between neighborhood crime exposure and externalizing symptoms in toddlerhood.

DIVERSITY & INCLUSION STATEMENT

We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

Increasing the representation of minoritized youth for inclusive and reproducible brain-behavior associations

Population neuroscience datasets allow researchers to estimate reliable effect sizes for brain-behavior associations because of their large sample sizes. However, these datasets undergo strict quality control to mitigate sources of noise, such as head motion. This practice often excludes a disproportionate number of minoritized individuals. We employ motion-ordering and motion-ordering+resampling (bagging) to test if these methods preserve functional MRI (fMRI) data in the Adolescent Brain Cognitive Development Study ( N =5,733). Black and Hispanic youth exhibited excess head motion relative to data collected from White youth, and were discarded disproportionately when using conventional approaches. Both methods retained more than 99% of Black and Hispanic youth. They produced reproducible brain-behavior associations across low-/high-motion racial/ethnic groups based on motion-limited fMRI data. The motion-ordering and bagging methods are two feasible approaches that can enhance sample representation for testing brain-behavior associations and fulfill the promise of consortia datasets to produce generalizable effect sizes across diverse populations.

Organization of the human cerebral cortex estimated within individuals: networks, global topography, and function

The cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks from functional MRI (fMRI) data in intensively sampled participants. The procedure was developed in two participants (scanned 31 times) and then prospectively applied to 15 participants (scanned 8-11 times). Analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that linked to distant regions. Third-order networks possessed regions distributed widely throughout association cortex. Regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated across multiple cortical zones. We refer to these as supra-areal association megaclusters (SAAMs). Within each SAAM, two candidate control regions were adjacent to three separate domain-specialized regions. Response properties were explored with task data. The somatomotor and visual networks responded to body movements and visual stimulation, respectively. Second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions dissociated across language, social, and spatial/episodic processing domains. These results suggest that progressively higher-order networks nest outward from primary sensory and motor cortices. Within the apex zones of association cortex, there is specialization that repeatedly divides domain-flexible from domain-specialized regions. We discuss implications of these findings, including how repeating organizational motifs may emerge during development.NEW & NOTEWORTHY The organization of cerebral networks was estimated within individuals with intensive, repeat sampling of fMRI data. A hierarchical organization emerged in each individual that delineated first-, second-, and third-order cortical networks. Regions of distinct third-order association networks consistently exhibited side-by-side juxtapositions that repeated across multiple cortical zones, with clear and robust functional specialization among the embedded regions.

Resting state functional brain connectivity in child and adolescent psychiatry: where are we now?

Approaching the 30th anniversary of the discovery of resting state functional magnetic resonance imaging (rsfMRI) functional connectivity, we reflect on the impact of this neuroimaging breakthrough on the field of child and adolescent psychiatry. The study of intrinsic functional brain architecture that rsfMRI affords across a wide range of ages and abilities has yielded numerous key insights. For example, we now know that many neurodevelopmental conditions are associated with more widespread circuit alterations across multiple large-scale brain networks than previously suspected. The emergence of population neuroscience and effective data-sharing initiatives have made large rsfMRI datasets publicly available, providing sufficient power to begin to identify brain-based subtypes within heterogeneous clinical conditions. Nevertheless, several methodological and theoretical challenges must still be addressed to fulfill the promises of personalized child and adolescent psychiatry. In particular, incomplete understanding of the physiological mechanisms driving developmental changes in intrinsic functional connectivity remains an obstacle to further progress. Future directions include cross-species and multimodal neuroimaging investigations to illuminate such mechanisms. Data collection and harmonization efforts that span multiple countries and diverse cohorts are urgently needed. Finally, incorporating naturalistic fMRI paradigms such as movie watching should be a priority for future research efforts.

Neural Reward Anticipation Moderates Longitudinal Relation between Parents' Familism Values and Latinx American Youth's School Disengagement

Parents' familism values predict a variety of Latinx American youth's academic adjustment. However, it is unclear how cultural values such as familism interact with youth's brain development, which is sensitive to sociocultural input, to shape their academic adjustment. Using a sample of 1916 Latinx American youth (mean age = 9.90 years, SD = .63 years; 50% girls) and their primary caregivers (mean age = 38.43 years, SD = 6.81 years; 90% mothers) from the Adolescent Brain Cognitive Development Study, this study examined the longitudinal relation between parents' familism values and youth's school disengagement, as well as the moderating role of youth's neural sensitivity to personal reward. Parents' familism values predicted youth's decreased school disengagement 1 year later, adjusting for their baseline school disengagement and demographic covariates. Notably, this association was more salient among youth who showed lower (vs. higher) neural activation in the ventral striatum and the lateral OFC during the anticipation of a personal reward. These findings underscore the protective role of familism for Latinx American youth, highlighting the necessity of developing culturally informed interventions that take into consideration a youth's brain development.

Brexanolone Treatment in a Real-World Patient Population: A Case Series and Pilot Feasibility Study of Precision Neuroimaging

PURPOSE/BACKGROUND

Brexanolone is approved for postpartum depression (PPD) by the United States Food and Drug Administration. Brexanolone has outperformed placebo in clinical trials, but less is known about the efficacy in real-world patients with complex social and medical histories. Furthermore, the impact of brexanolone on large-scale brain systems such as changes in functional connectivity (FC) is unknown.

METHODS/PROCEDURES

We tracked changes in depressive symptoms across a diverse group of patients who received brexanolone at a large medical center. Edinburgh Postnatal Depression Scale (EPDS) scores were collected through chart review for 17 patients immediately prior to infusion through approximately 1 year postinfusion. In 2 participants, we performed precision functional neuroimaging (pfMRI), including before and after treatment in 1 patient. pfMRI collects many hours of data in individuals for precision medicine applications and was performed to assess the feasibility of investigating changes in FC with brexanolone.

FINDINGS/RESULTS

The mean EPDS score immediately postinfusion was significantly lower than the mean preinfusion score (mean change [95% CI]: 10.76 [7.11-14.40], t (15) = 6.29, P < 0.0001). The mean EPDS score stayed significantly lower at 1 week (mean difference [95% CI]: 9.50 [5.23-13.76], t (11) = 4.90, P = 0.0005) and 3 months (mean difference [95% CI]: 9.99 [4.71-15.27], t (6) = 4.63, P = 0.0036) postinfusion. Widespread changes in FC followed infusion, which correlated with EPDS scores.

IMPLICATIONS/CONCLUSIONS

Brexanolone is a successful treatment for PPD in the clinical setting. In conjunction with routine clinical care, brexanolone was linked to a reduction in symptoms lasting at least 3 months. pfMRI is feasible in postpartum patients receiving brexanolone and has the potential to elucidate individual-specific mechanisms of action.

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.

Neurodevelopmental Subtypes of Functional Brain Organization in the ABCD Study Using a Rigorous Analytic Framework

The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9-11, we identified four distinct RSFC subtypes We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.

Concussion-Related Disruptions to Hub Connectivity in the Default Mode Network Are Related to Symptoms and Cognition

Concussions present with a myriad of symptomatic and cognitive concerns; however, the relationship between these functional disruptions and the underlying changes in the brain are not yet well understood. Hubs, or brain regions that are connected to many different functional networks, may be specifically disrupted after concussion. Given the implications in concussion research, we quantified hub disruption within the default mode network (DMN) and between the DMN and other brain networks. We collected resting-state functional magnetic resonance imaging data from collegiate student-athletes (n = 44) at three time points: baseline (before beginning their athletic season), acute post-injury (approximately 48h after a diagnosed concussion), and recovery (after starting return-to-play progression, but before returning to contact). We used self-reported symptoms and computerized cognitive assessments collected across similar time points to link these functional connectivity changes to clinical outcomes. Concussion resulted in increased connectivity between regions within the DMN compared with baseline and recovery, and this post-injury connectivity was more positively related to symptoms and more negatively related to visual memory performance compared with baseline and recovery. Further, concussion led to decreased connectivity between DMN hubs and visual network non-hubs relative to baseline and recovery, and this post-injury connectivity was more negatively related to somatic symptoms and more positively related to visual memory performance compared with baseline and recovery. Relationships between functional connectivity, symptoms, and cognition were not significantly different at baseline versus recovery. These results highlight a unique relationship between self-reported symptoms, visual memory performance, and acute functional connectivity changes involving DMN hubs after concussion in athletes. This may provide evidence for a disrupted balance of within- and between-network communication highlighting possible network inefficiencies after concussion. These results aid in our understanding of the pathophysiological disruptions after concussion and inform our understanding of the associations between disruptions in brain connectivity and specific clinical presentations acutely post-injury.

High-angular resolution diffusion imaging generation using 3d u-net

PURPOSE

To investigate the effects on tractography of artificial intelligence-based prediction of motion-probing gradients (MPGs) in diffusion-weighted imaging (DWI).

METHODS

The 251 participants in this study were patients with brain tumors or epileptic seizures who underwent MRI to depict tractography. DWI was performed with 64 MPG directions and b = 0 s/mm2 images. The dataset was divided into a training set of 191 (mean age 45.7 [± 19.1] years), a validation set of 30 (mean age 41.6 [± 19.1] years), and a test set of 30 (mean age 49.6 [± 18.3] years) patients. Supervised training of a convolutional neural network was performed using b = 0 images and the first 32 axes of MPG images as the input data and the second 32 axes as the reference data. The trained model was applied to the test data, and tractography was performed using (a) input data only; (b) input plus prediction data; and (c) b = 0 images and the 64 MPG data (as a reference).

RESULTS

In Q-ball imaging tractography, the average dice similarity coefficient (DSC) of the input plus prediction data was 0.715 (± 0.064), which was significantly higher than that of the input data alone (0.697 [± 0.070]) (p < 0.05). In generalized q-sampling imaging tractography, the average DSC of the input plus prediction data was 0.769 (± 0.091), which was also significantly higher than that of the input data alone (0.738 [± 0.118]) (p < 0.01).

CONCLUSION

Diffusion tractography is improved by adding predicted MPG images generated by an artificial intelligence model.

Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development

The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9-10 years; n = 1,115 individuals; both sexes; 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition.

Functional parcellation of the neonatal cortical surface

The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that existing parcellations, including surface-based parcels derived from older samples as well as volume-based neonatal parcels, are a poor fit for neonatal surface data. We next derive a set of 283 cortical surface parcels from a sample of n = 261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.

Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization

Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.

Precision Brain Morphometry Using Cluster Scanning

Measurement error limits the statistical power to detect group differences and longitudinal change in structural MRI morphometric measures (e.g., hippocampal volume, prefrontal thickness). Recent advances in scan acceleration enable extremely fast T1-weighted scans (~1 minute) to achieve morphometric errors that are close to the errors in longer traditional scans. As acceleration allows multiple scans to be acquired in rapid succession, it becomes possible to pool estimates to increase measurement precision, a strategy known as "cluster scanning." Here we explored brain morphometry using cluster scanning in a test-retest study of 40 individuals (12 younger adults, 18 cognitively unimpaired older adults, and 10 adults diagnosed with mild cognitive impairment or Alzheimer's Dementia). Morphometric errors from a single compressed sensing (CS) 1.0mm scan with 6x acceleration (CSx6) were, on average, 12% larger than a traditional scan using the Alzheimer's Disease Neuroimaging Initiative (ADNI) protocol. Pooled estimates from four clustered CSx6 acquisitions led to errors that were 34% smaller than ADNI despite having a shorter total acquisition time. Given a fixed amount of time, a gain in measurement precision can thus be achieved by acquiring multiple rapid scans instead of a single traditional scan. Errors were further reduced when estimates were pooled from eight CSx6 scans (51% smaller than ADNI). Neither pooling across a break nor pooling across multiple scan resolutions boosted this benefit. We discuss the potential of cluster scanning to improve morphometric precision, boost statistical power, and produce more sensitive disease progression biomarkers.

Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks

The two hemispheres of the human brain are functionally asymmetric. At the network level, the language network exhibits left-hemisphere lateralization. While this asymmetry is widely replicated, the extent to which other functional networks demonstrate lateralization remains a subject of Investigation. Additionally, it is unknown how the lateralization of one functional network may affect the lateralization of other networks within individuals. We quantified lateralization for each of 17 networks by computing the relative surface area on the left and right cerebral hemispheres. After examining the ecological, convergent, and external validity and test-retest reliability of this surface area-based measure of lateralization, we addressed two hypotheses across multiple datasets (Human Connectome Project = 553, Human Connectome Project-Development = 343, Natural Scenes Dataset = 8). First, we hypothesized that networks associated with language, visuospatial attention, and executive control would show the greatest lateralization. Second, we hypothesized that relationships between lateralized networks would follow a dependent relationship such that greater left-lateralization of a network would be associated with greater right-lateralization of a different network within individuals, and that this pattern would be systematic across individuals. A language network was among the three networks identified as being significantly left-lateralized, and attention and executive control networks were among the five networks identified as being significantly right-lateralized. Next, correlation matrices, an exploratory factor analysis, and confirmatory factor analyses were used to test the second hypothesis and examine the organization of lateralized networks. We found general support for a dependent relationship between highly left- and right-lateralized networks, meaning that across subjects, greater left lateralization of a given network (such as a language network) was linked to greater right lateralization of another network (such as a ventral attention/salience network) and vice versa. These results further our understanding of brain organization at the macro-scale network level in individuals, carrying specific relevance for neurodevelopmental conditions characterized by disruptions in lateralization such as autism and schizophrenia.

Framewise multi-echo distortion correction for superior functional MRI

Functional MRI (fMRI) data are severely distorted by magnetic field (B0) inhomogeneities which currently must be corrected using separately acquired field map data. However, changes in the head position of a scanning participant across fMRI frames can cause changes in the B0 field, preventing accurate correction of geometric distortions. Additionally, field maps can be corrupted by movement during their acquisition, preventing distortion correction altogether. In this study, we use phase information from multi-echo (ME) fMRI data to dynamically sample distortion due to fluctuating B0 field inhomogeneity across frames by acquiring multiple echoes during a single EPI readout. Our distortion correction approach, MEDIC (Multi-Echo DIstortion Correction), accurately estimates B0 related distortions for each frame of multi-echo fMRI data. Here, we demonstrate that MEDIC's framewise distortion correction produces improved alignment to anatomy and decreases the impact of head motion on resting-state functional connectivity (RSFC) maps, in higher motion data, when compared to the prior gold standard approach (i.e., TOPUP). Enhanced framewise distortion correction with MEDIC, without the requirement for field map collection, furthers the advantage of multi-echo over single-echo fMRI.

Functional parcellation of the neonatal brain

The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that adult- and older infant-derived parcels are a poor fit with neonatal data, emphasizing the need for neonatal-specific parcels. We next derive a set of 283 cortical surface parcels from a sample of n=261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.

Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning

OBJECTIVE

Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data.

METHODS

Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme.

RESULTS

The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations.

CONCLUSIONS

Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.

Beyond sense-specific processing: decoding texture in the brain from touch and sonified movement

Texture, a fundamental object attribute, is perceived through multisensory information including touch and auditory cues. Coherent perceptions may rely on shared texture representations across different senses in the brain. To test this hypothesis, we delivered haptic textures coupled with a sound synthesizer to generate real-time textural sounds. Participants completed roughness estimation tasks with haptic, auditory, or bimodal cues in an MRI scanner. Somatosensory, auditory, and visual cortices were all activated during haptic and auditory exploration, challenging the traditional view that primary sensory cortices are sense-specific. Furthermore, audio-tactile integration was found in secondary somatosensory (S2) and primary auditory cortices. Multivariate analyses revealed shared spatial activity patterns in primary motor and somatosensory cortices, for discriminating texture across both modalities. This study indicates that primary areas and S2 have a versatile representation of multisensory textures, which has significant implications for how the brain processes multisensory cues to interact more efficiently with our environment.

Prenatal cannabis exposure is associated with localized brain differences that partially mediate associations with increased adolescent psychopathology

Prenatal cannabis exposure (PCE) is associated with mental health problems, but the neurobiological mechanisms remain unknown. We find that PCE is associated with localized differences across neuroimaging metrics that longitudinally mediate associations with mental health in adolescence (n=9,322-10,186). Differences in brain development may contribute to PCE-related variability in adolescent mental health.

Substructure of the brain's Cingulo-Opercular network

The Cingulo-Opercular network (CON) is an executive network of the human brain that regulates actions. CON is composed of many widely distributed cortical regions that are involved in top-down control over both lower-level (i.e., motor) and higher-level (i.e., cognitive) functions, as well as in processing of painful stimuli. Given the topographical and functional heterogeneity of the CON, we investigated whether subnetworks within the CON support separable aspects of action control. Using precision functional mapping (PFM) in 15 participants with > 5 hours of resting state functional connectivity (RSFC) and task data, we identified three anatomically and functionally distinct CON subnetworks within each individual. These three distinct subnetworks were linked to Decisions, Actions, and Feedback (including pain processing), respectively, in convergence with a meta-analytic task database. These Decision, Action and Feedback subnetworks represent pathways by which the brain establishes top-down goals, transforms those goals into actions, implemented as movements, and processes critical action feedback such as pain.

Changes in the superior longitudinal fasciculus and anterior thalamic radiation in the left brain are associated with developmental dyscalculia

Developmental dyscalculia is a neurodevelopmental disorder specific to arithmetic learning even with normal intelligence and age-appropriate education. Difficulties often persist from childhood through adulthood lowering the individual's quality of life. However, the neural correlates of developmental dyscalculia are poorly understood. This study aimed to identify brain structural connectivity alterations in developmental dyscalculia. All participants were recruited from a large scale, non-referred population sample in a longitudinal design. We studied 10 children with developmental dyscalculia (11.3 ± 0.7 years) and 16 typically developing peers (11.2 ± 0.6 years) using diffusion-weighted magnetic resonance imaging. We assessed white matter microstructure with tract-based spatial statistics in regions-of-interest tracts that had previously been related to math ability in children. Then we used global probabilistic tractography for the first time to measure and compare tract length between developmental dyscalculia and typically developing groups. The high angular resolution diffusion-weighted magnetic resonance imaging and crossing-fiber probabilistic tractography allowed us to evaluate the length of the pathways compared to previous studies. The major findings of our study were reduced white matter coherence and shorter tract length of the left superior longitudinal/arcuate fasciculus and left anterior thalamic radiation in the developmental dyscalculia group. Furthermore, the lower white matter coherence and shorter pathways tended to be associated with the lower math performance. These results from the regional analyses indicate that learning, memory and language-related pathways in the left hemisphere might be related to developmental dyscalculia in children.

Real-time feedback reduces participant motion during task-based fMRI

The potential negative impact of head movement during fMRI has long been appreciated. Although a variety of prospective and retrospective approaches have been developed to help mitigate these effects, reducing head movement in the first place remains the most appealing strategy for optimizing data quality. Real-time interventions, in which participants are provided feedback regarding their scan-to-scan motion, have recently shown promise in reducing motion during resting state fMRI. However, whether feedback might similarly reduce motion during task-based fMRI is an open question. In particular, it is unclear whether participants can effectively monitor motion feedback while attending to task-related demands. Here we assessed whether a combination of real-time and between-run feedback could reduce head motion during task-based fMRI. During an auditory word repetition task, 78 adult participants (aged 19-81) were pseudorandomly assigned to receive feedback or not. Feedback was provided FIRMM software that used real-time calculation of realignment parameters to estimate participant motion. We quantified movement using framewise displacement (FD). We found that motion feedback resulted in a statistically significant reduction in participant head motion, with a small-to-moderate effect size (reducing average FD from 0.347 to 0.282). Reductions were most apparent in high-motion events. We conclude that under some circumstances real-time feedback may reduce head motion during task-based fMRI, although its effectiveness may depend on the specific participant population and task demands of a given study.

Psilocybin desynchronizes brain networks

1The relationship between the acute effects of psychedelics and their persisting neurobiological and psychological effects is poorly understood. Here, we tracked brain changes with longitudinal precision functional mapping in healthy adults before, during, and for up to 3 weeks after oral psilocybin and methylphenidate (17 MRI visits per participant) and again 6+ months later. Psilocybin disrupted connectivity across cortical networks and subcortical structures, producing more than 3-fold greater acute changes in functional networks than methylphenidate. These changes were driven by desynchronization of brain activity across spatial scales (area, network, whole brain). Psilocybin-driven desynchronization was observed across association cortex but strongest in the default mode network (DMN), which is connected to the anterior hippocampus and thought to create our sense of self. Performing a perceptual task reduced psilocybin-induced network changes, suggesting a neurobiological basis for grounding, connecting with physical reality during psychedelic therapy. The acute brain effects of psilocybin are consistent with distortions of space-time and the self. Psilocybin induced persistent decrease in functional connectivity between the anterior hippocampus and cortex (and DMN in particular), lasting for weeks but normalizing after 6 months. Persistent suppression of hippocampal-DMN connectivity represents a candidate neuroanatomical and mechanistic correlate for psilocybin's pro-plasticity and anti-depressant effects.

Prenatal environment is associated with the pace of cortical network development over the first three years of life

Environmental influences on brain structure and function during early development have been well-characterized. In pre-registered analyses, we test the theory that socioeconomic status (SES) is associated with differences in trajectories of intrinsic brain network development from birth to three years (n = 261). Prenatal SES is associated with developmental increases in cortical network segregation, with neonates and toddlers from lower-SES backgrounds showing a steeper increase in cortical network segregation with age, consistent with accelerated network development. Associations between SES and cortical network segregation occur at the local scale and conform to a sensorimotor-association hierarchy of cortical organization. SES-associated differences in cortical network segregation are associated with language abilities at two years, such that lower segregation is associated with improved language abilities. These results yield key insight into the timing and directionality of associations between the early environment and trajectories of cortical development.

Circadian, Reward, and Emotion Systems in Teens prospective longitudinal study: protocol overview of an integrative reward-circadian rhythm model of first onset of bipolar spectrum disorder in adolescence

BACKGROUND

Bipolar spectrum disorders (BSDs) are associated with a heightened sensitivity to rewards and elevated reward-related brain function in cortico-striatal circuitry. A separate literature documents social and circadian rhythm disruption in BSDs. Recently, integrated reward-circadian models of BSDs have been proposed. These models draw on work indicating that the two systems influence each other and interact to affect mood functioning. When dysregulated, reward and circadian system signaling may combine to form a positive feedback loop, whereby dysregulation in one system exacerbates dysregulation in the other. Project CREST (Circadian, Reward, and Emotion Systems in Teens) provides a first systematic test of reward-circadian dysregulation as a synergistic and dynamic vulnerability for first onset of BSD and increases in bipolar symptoms during adolescence.

METHODS

This NIMH-funded R01 study is a 3-year prospective, longitudinal investigation of approximately 320 community adolescents from the broader Philadelphia area, United States of America. Eligible participants must be 13-16 years old, fluent in English, and without a prior BSD or hypomanic episode. They are being selected along the entire dimension of self-reported reward responsiveness, with oversampling at the high tail of the dimension in order to increase the likelihood of BSD onsets. At Times 1-6, every 6 months, participants will complete assessments of reward-relevant and social rhythm disruption life events and self-report and diagnostic assessments of bipolar symptoms and episodes. Yearly, at Times 1, 3, and 5, participants also will complete self-report measures of circadian chronotype (morningness-eveningness) and social rhythm regularity, a salivary dim light melatonin onset (DLMO) procedure to assess circadian phase, self-report, behavioral, and neural (fMRI) assessments of monetary and social reward responsiveness, and a 7-day ecological momentary assessment (EMA) period. During each EMA period, participants will complete continuous measures of sleep/wake and activity (actigraphy), a daily sleep diary, and three within-day (morning, afternoon, evening) measures of life events coded for reward-relevance and social rhythm disruption, monetary and social reward responsiveness, positive and negative affect, and hypo/manic and depressive symptoms. The fMRI scan will occur on the day before and the DLMO procedure will occur on the first evening of the 7-day EMA period.

DISCUSSION

This study is an innovative integration of research on multi-organ systems involved in reward and circadian signaling in understanding first onset of BSD in adolescence. It has the potential to facilitate novel pharmacological, neural, and behavioral interventions to treat, and ideally prevent, bipolar conditions.

Within-Individual Organization of the Human Cerebral Cortex: Networks, Global Topography, and Function

The human cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks using a Multi-Session Hierarchical Bayesian Model (MS-HBM) applied to intensively sampled within-individual functional MRI (fMRI) data. The network estimation procedure was initially developed and tested in two participants (each scanned 31 times) and then prospectively applied to 15 new participants (each scanned 8 to 11 times). Detailed analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that also linked to distant regions. Third-order networks each possessed regions distributed widely throughout association cortex. Moreover, regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated similarly across multiple cortical zones. We refer to these as Supra-Areal Association Megaclusters (SAAMs). Within each SAAM, two candidate control regions were typically adjacent to three separate domain-specialized regions. Independent task data were analyzed to explore functional response properties. The somatomotor and visual first-order networks responded to body movements and visual stimulation, respectively. A subset of the second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient or novel events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions within each SAAM did not track working memory load but rather dissociated across language, social, and spatial / episodic processing domains. These results support a model of the cerebral cortex in which progressively higher-order networks nest outwards from primary sensory and motor cortices. Within the apex zones of association cortex there is specialization of large-scale networks that divides domain-flexible from domain-specialized regions repeatedly across parietal, temporal, and prefrontal cortices. We discuss implications of these findings including how repeating organizational motifs may emerge during development.

Reproducibility in Neuroimaging Analysis: Challenges and Solutions

Recent years have marked a renaissance in efforts to increase research reproducibility in psychology, neuroscience, and related fields. Reproducibility is the cornerstone of a solid foundation of fundamental research-one that will support new theories built on valid findings and technological innovation that works. The increased focus on reproducibility has made the barriers to it increasingly apparent, along with the development of new tools and practices to overcome these barriers. Here, we review challenges, solutions, and emerging best practices with a particular emphasis on neuroimaging studies. We distinguish 3 main types of reproducibility, discussing each in turn. Analytical reproducibility is the ability to reproduce findings using the same data and methods. Replicability is the ability to find an effect in new datasets, using the same or similar methods. Finally, robustness to analytical variability refers to the ability to identify a finding consistently across variation in methods. The incorporation of these tools and practices will result in more reproducible, replicable, and robust psychological and brain research and a stronger scientific foundation across fields of inquiry.

Writing letters in two graphic systems: Behavioral and neural correlates in Latin-Arabic biscripters

Biscriptuality is the ability to read and write using two scripts. Despite the increasing number of biscripters, this phenomenon remains poorly understood. Here, we focused on investigating graphomotor processing in French-Arabic biscripters. We chose the French and Arabic alphabets because they have comparable visuospatial complexity and linguistic features, but differ dramatically in their graphomotor characteristics. In a first experiment we describe the graphomotor features of the two alphabets and showed that while Arabic and Latin letters are produced with the same velocity and fluency, Arabic letters require more pen lifts, contain more right-to-left strokes and clockwise curves, and take longer to write than Latin letters. These results suggest that Arabic and Latin letters are produced via different motor patterns. In a second experiment we used functional magnetic resonance imaging to ask whether writing the two scripts relies upon partially distinct or fully overlapping neural networks, and whether the elements of the previously described handwriting network are recruited to the same extent by the two scripts. We found that both scripts engaged the so-called "writing network", but that within the network, Arabic letters recruited the left superior parietal lobule (SPL) and the left primary motor cortex (M1) more strongly than Latin letters. Both regions have previously been identified as holding scale-invariant representations of letter trajectories. Arabic and Latin letters also activated distinct regions that do not belong to the writing network. Complementary analyses indicate that the differences observed between scripts at the neural level could be driven by the specific graphomotor features of each script. Overall, our results indicate that particular features of the practiced scripts can lead to different motor organization at both the behavioral and brain levels in biscripters.

Reward and Immune Systems in Emotion (RISE) prospective longitudinal study: Protocol overview of an integrative reward-inflammation model of first onset of major depression in adolescence

Background

Depression is associated with a reduced sensitivity to rewards and low reward-related brain function in cortico-striatal circuitry. A separate literature documents elevated peripheral inflammation in depression. Recently, integrated reward-inflammation models of depression have been proposed. These models draw on work indicating that peripheral inflammatory proteins access the brain, where they lower reward responsiveness. This blunted reward responsiveness is proposed to initiate unhealthy behaviors (substance use, poor diet), as well as sleep disruption and stress generation, which further heighten inflammation. Over time, dysregulation in reward responsiveness and immune signaling may synergize in a positive feedback loop, whereby dysregulation in each system exacerbates dysregulation in the other. Project RISE (Reward and Immune Systems in Emotion) provides a first systematic test of reward-immune dysregulation as a synergistic and dynamic vulnerability for first onset of major depressive disorder and increases in depressive symptoms during adolescence.

Methods

This NIMH-funded R01 study is a 3-year prospective, longitudinal investigation of approximately 300 community adolescents from the broader Philadelphia area, United States of America. Eligible participants must be 13-16 years old, fluent in English, and without a prior major depressive disorder. They are being selected along the entire dimension of self-reported reward responsiveness, with oversampling at the low tail of the dimension in order to increase the likelihood of major depression onsets. At Time 1 (T1), T3, and T5, each a year apart, participants complete blood draws to quantify biomarkers of low-grade inflammation, self-report and behavioral measures of reward responsiveness, and fMRI scans of reward neural activity and functional connectivity. At T1-T5 (with T2 and T4 six months between the yearly sessions), participants also complete diagnostic interviews and measures of depressive symptoms, reward-relevant life events, and behaviors that increase inflammation. Adversity history is assessed at T1 only.

Discussion

This study is an innovative integration of research on multi-organ systems involved in reward and inflammatory signaling in understanding first onset of major depression in adolescence. It has the potential to facilitate novel neuroimmune and behavioral interventions to treat, and ideally prevent, depression.

Functional MRI responses to naturalistic stimuli are increasingly typical across early childhood

While findings show that throughout development, there are child- and age-specific patterns of brain functioning, there is also evidence for significantly greater inter-individual response variability in young children relative to adults. It is currently unclear whether this increase in functional "typicality" (i.e., inter-individual similarity) is a developmental process that occurs across early childhood, and what changes in BOLD response may be driving changes in typicality. We collected fMRI data from 81 typically developing 4-8-year-old children during passive viewing of age-appropriate television clips and asked whether there is increasing typicality of brain response across this age range. We found that the "increasing typicality" hypothesis was supported across many regions engaged by passive viewing. Post hoc analyses showed that in a priori ROIs related to language and face processing, the strength of the group-average shared component of activity increased with age, with no concomitant decline in residual signal or change in spatial extent or variability. Together, this suggests that increasing inter-individual similarity of functional responses to audiovisual stimuli is an important feature of early childhood functional brain development.

Comparison of fMRI language laterality with and without sedation in pediatric epilepsy

Functional MRI is an essential component of presurgical language mapping. In clinical settings, young children may be sedated for the MRI with the functional stimuli presented passively. Research has found that sedation changes language activation in healthy adults and children. However, there is limited research comparing sedated and unsedated functional MRI in pediatric epilepsy patients. We compared language activation patterns in children with epilepsy who received sedation for functional MRI to the ones who did not. We retrospectively identified the patients with focal epilepsy who underwent presurgical functional MRI including Auditory Descriptive Decision Task at Boston Children's Hospital from 2014 to 2022. Patients were divided into sedated and awake groups, based on their sedation status during functional MRI. Auditory Descriptive Decision Task stimuli were presented passively to the sedated group per clinical protocol. We extracted language activation maps contrasted against a control task (reverse speech) in the Frontal and Temporal language regions and calculated separate language laterality indexes for each region. We considered positive laterality indexes as left dominant, negative laterality indexes as right dominant, and absolute laterality indexes <0.2 as bilateral. We defined 2 language patterns: typical (i.e., primarily left-sided) and atypical. Typical pattern required at least one left dominant region (either frontal or temporal) and no right dominant region. We then compared the language patterns between the sedated and awake groups. Seventy patients met the inclusion criteria, 25 sedated, and 45 awake. Using the Auditory Descriptive Decision Task paradigm, when adjusted for age, handedness, gender, and laterality of lesion in a weighted logistic regression model, the odds of the atypical pattern were 13.2 times higher in the sedated group compared to the awake group (Confidence Interval: 2.55-68.41, p-value < 0.01). Sedation may alter language activation patterns in pediatric epilepsy patients. Language patterns on sedated functional MRI with passive tasks may not represent language networks during wakefulness, sedation may differentially suppress some networks, or require a different task or method of analysis to capture the awake language network. Given the critical surgical implication of these findings, additional studies are needed to better understand how sedation impacts the functional MRI blood oxygenation level-dependent signal. Consistent with current practice, sedated functional MRI should be interpreted with greater caution and requires additional validation as well as research on post-surgical language outcomes.

Brief mock-scan training reduces head motion during real scanning for children: A growth curve study

Pediatric neuroimaging datasets are rapidly increasing in scales. Despite strict protocols in data collection and preprocessing focused on improving data quality, the presence of head motion still impedes our understanding of neurodevelopmental mechanisms. Large head motion can lead to severe noise and artifacts in magnetic resonance imaging (MRI) studies, inflating correlations between adjacent brain areas and decreasing correlations between spatial distant territories, especially in children and adolescents. Here, by leveraging mock-scans of 123 Chinese children and adolescents, we demonstrated the presence of increased head motion in younger participants. Critically, a 5.5-minute training session in an MRI mock scanner was found to effectively suppress the head motion in the children and adolescents. Therefore, we suggest that mock scanner training should be part of the quality assurance routine prior to formal MRI data collection, particularly in large-scale population-level neuroimaging initiatives for pediatrics.

Quantifying MR Head Motion in the Rhineland Study - A Robust Method for Population Cohorts

Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.

Functional connectivity analysis of childhood depressive symptoms

BACKGROUND

Childhood depression is a highly distinct and prevalent condition with an unknown neurobiological basis. We wish to explore the resting state fMRI data in children for potential associations between neural connectivity and childhood depressive symptoms.

METHODS

A longitudinal birth cohort study with neuroimaging data obtained at 4.5, 6.0 and 7.5 years of age and the Children Depression Inventory 2 (CDI) administered between 8.5 and 10.5 years was used. The CDI score was used as the dependent variable and tested for correlation, both simple Pearson and network based statistic, with the functional connectivity values obtained from the resting state fMRI. Cross-validated permutation testing with a general linear model was used to validate that the identified functional connections were indeed implicated in childhood depression.

RESULTS

Ten functional connections and four brain regions (Somatomotor Area B, Temporoparietal Junction, Orbitofrontal Cortex and Insula) were identified as significantly associated with childhood depressive symptoms for girls at 6.0 and 7.5 years. No significant functional connections were found in girls at 4.5 years or for boys at any timepoint. Network based statistic and permutation testing confirmed these findings.

CONCLUSIONS

This study revealed significant sex-dependent associations of neural connectivity and childhood depressive symptoms. The regions identified are implicated in speech/language, social cognition and information integration and suggest unique pathways to childhood depressive symptoms.

The art and science of using quality control to understand and improve fMRI data

Designing and executing a good quality control (QC) process is vital to robust and reproducible science and is often taught through hands on training. As FMRI research trends toward studies with larger sample sizes and highly automated processing pipelines, the people who analyze data are often distinct from those who collect and preprocess the data. While there are good reasons for this trend, it also means that important information about how data were acquired, and their quality, may be missed by those working at later stages of these workflows. Similarly, an abundance of publicly available datasets, where people (not always correctly) assume others already validated data quality, makes it easier for trainees to advance in the field without learning how to identify problematic data. This manuscript is designed as an introduction for researchers who are already familiar with fMRI, but who did not get hands on QC training or who want to think more deeply about QC. This could be someone who has analyzed fMRI data but is planning to personally acquire data for the first time, or someone who regularly uses openly shared data and wants to learn how to better assess data quality. We describe why good QC processes are important, explain key priorities and steps for fMRI QC, and as part of the FMRI Open QC Project, we demonstrate some of these steps by using AFNI software and AFNI’s QC reports on an openly shared dataset. A good QC process is context dependent and should address whether data have the potential to answer a scientific question, whether any variation in the data has the potential to skew or hide key results, and whether any problems can potentially be addressed through changes in acquisition or data processing. Automated metrics are essential and can often highlight a possible problem, but human interpretation at every stage of a study is vital for understanding causes and potential solutions.

Transcranial focused ultrasound selectively increases perfusion and modulates functional connectivity of deep brain regions in humans

Background

Low intensity, transcranial focused ultrasound (tFUS) is a re-emerging brain stimulation technique with the unique capability of reaching deep brain structures non-invasively.

Objective/Hypothesis

We sought to demonstrate that tFUS can selectively and accurately target and modulate deep brain structures in humans important for emotional functioning as well as learning and memory. We hypothesized that tFUS would result in significant longitudinal changes in perfusion in the targeted brain region as well as selective modulation of BOLD activity and BOLD-based functional connectivity of the target region.

Methods

In this study, we collected MRI before, simultaneously during, and after tFUS of two deep brain structures on different days in sixteen healthy adults each serving as their own control. Using longitudinal arterial spin labeling (ASL) MRI and simultaneous blood oxygen level dependent (BOLD) functional MRI, we found changes in cerebral perfusion, regional brain activity and functional connectivity specific to the targeted regions of the amygdala and entorhinal cortex (ErC).

Results

tFUS selectively increased perfusion in the targeted brain region and not in the contralateral homolog or either bilateral control region. Additionally, tFUS directly affected BOLD activity in a target specific fashion without engaging auditory cortex in any analysis. Finally, tFUS resulted in selective modulation of the targeted functional network connectivity.

Conclusion

We demonstrate that tFUS can selectively modulate perfusion, neural activity and connectivity in deep brain structures and connected networks. Lack of auditory cortex findings suggests that the mechanism of tFUS action is not due to auditory or acoustic startle response but rather a direct neuromodulatory process. Our findings suggest that tFUS has the potential for future application as a novel therapy in a wide range of neurological and psychiatric disorders associated with subcortical pathology.

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.

Network-specific selectivity of functional connections in the neonatal brain

The adult human brain is organized into functional brain networks, groups of functionally connected segregated brain regions. A key feature of adult functional networks is long-range selectivity, the property that spatially distant regions from the same network have higher functional connectivity than spatially distant regions from different networks. Although it is critical to establish the status of functional networks and long-range selectivity during the neonatal period as a foundation for typical and atypical brain development, prior work in this area has been mixed. Although some studies report distributed adult-like networks, other studies suggest that neonatal networks are immature and consist primarily of spatially isolated regions. Using a large sample of neonates (n = 262), we demonstrate that neonates have long-range selective functional connections for the default mode, fronto-parietal, and dorsal attention networks. An adult-like pattern of functional brain networks is evident in neonates when network-detection algorithms are tuned to these long-range connections, when using surface-based registration (versus volume-based registration), and as per-subject data quantity increases. These results help clarify factors that have led to prior mixed results, establish that key adult-like functional network features are evident in neonates, and provide a foundation for studies of typical and atypical brain development.

Does prefrontal connectivity during task switching help or hinder children's performance?

The ability to flexibly switch between tasks is key for goal-directed behavior and continues to improve across childhood. Children's task switching difficulties are thought to reflect less efficient engagement of sustained and transient control processes, resulting in lower performance on blocks that intermix tasks (sustained demand) and trials that require a task switch (transient demand). Sustained and transient control processes are associated with frontoparietal regions, which develop throughout childhood and may contribute to task switching development. We examined age differences in the modulation of frontoparietal regions by sustained and transient control demands in children (8-11 years) and adults. Children showed greater performance costs than adults, especially under sustained demand, along with less upregulation of sustained and transient control activation in frontoparietal regions. Compared to adults, children showed increased connectivity between the inferior frontal junction (IFJ) and lateral prefrontal cortex (lPFC) from single to mixed blocks. For children whose sustained activation was less adult-like, increased IFJ-lPFC connectivity was associated with better performance. Children with more adult-like sustained activation showed the inverse effect. These results suggest that individual differences in task switching in later childhood at least partly depend on the recruitment of frontoparietal regions in an adult-like manner.

Characterizing different cognitive and neurobiological profiles in a community sample of children using a non-parametric approach: An fMRI study

Executive Functions (EF) is an umbrella term for a set of mental processes geared towards goal-directed behavior supporting academic skills such as reading abilities. One of the brain's functional networks implicated in EF is the Default Mode Network (DMN). The current study uses measures of inhibitory control, a main sub-function of EF, to create cognitive and neurobiological "inhibitory control profiles" and relate them to reading abilities in a large sample (N = 5055) of adolescents aged 9-10 from the Adolescent Brain Cognitive Development (ABCD) study. Using a Latent Profile Analysis (LPA) approach, data related to inhibitory control was divided into four inhibition classes. For each class, functional connectivity within the DMN was calculated from resting-state data, using a non-parametric algorithm for detecting group similarities. These inhibitory control profiles were then related to reading abilities. The four inhibitory control groups showed significantly different reading abilities, with neurobiologically different DMN segregation profiles for each class versus controls. The current study demonstrates that a community sample of children is not entirely homogeneous and is composed of different subgroups that can be differentiated both behaviorally/cognitively and neurobiologically, by focusing on inhibitory control and the DMN. Educational implications relating these results to reading abilities are noted.

Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy

OBJECTIVE

Accurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE.

METHODS

EPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n = 52). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were used for comparison: (a) least squares support-vector machine and (b) convolutional neural networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. The analysis was stratified across age and sex.

RESULTS

EPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those < 5 years of age. It helped achieve a ~5-fold reduction in the number of ICs to be potentially analyzed during pre-surgical screening.

SIGNIFICANCE

Automated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.

A pilot randomized sham controlled trial of bilateral iTBS for depression and executive function in older adults

INTRODUCTION

Executive function deficits (EFD) in late life depression (LLD) are associated with poor outcomes. Dysfunction of the cognitive control network (CCN) has been posited in the pathophysiology of LLD with EFD.

METHODS

Seventeen older adults with depression and EFD were randomized to iTBS or sham for 6 weeks. Intervention was delivered bilaterally using a recognized connectivity target.

RESULTS

A total of 89% (17/19) participants completed all study procedures. No serious adverse events occurred. Pre to post-intervention change in mean Montgomery-Asberg-depression scores was not different between iTBS or sham, p = 0.33. No significant group-by-time interaction for Montgomery-Asberg Depression rating scale scores (F _{3, 44}  = 0.51; p = 0.67) was found. No significant differences were seen in the effects of time between the two groups on executive measures: Flanker scores (F _{1, 14}  = 0.02, p = 0.88), Dimensional-change-card-sort scores F _{1, 14}  = 0.25, p = 0.63, and working memory scores (F _{1, 14}  = 0.98, p = 0.34). The Group-by-time interaction effect for functional connectivity (FC) within the Fronto-parietal-network was not significant (F _{1, 14}  = 0.36, p = 0.56). No significant difference in the effect-of-time between the two groups was found on FC within the Cingulo-opercular-network (F _{1, 14}  = 0, p = 0.98).

CONCLUSION

Bilateral iTBS is feasible in LLD. Preliminary results are unsupportive of efficacy on depression, executive function or target engagement of the CCN. A future Randomized clinical trial requires a larger sample size with stratification of cognitive and executive variables and refinement in the target engagement.

The Age of Reason: Functional Brain Network Development during Childhood

Human childhood is characterized by dramatic changes in the mind and brain. However, little is known about the large-scale intrinsic cortical network changes that occur during childhood because of methodological challenges in scanning young children. Here, we overcome this barrier by using sophisticated acquisition and analysis tools to investigate functional network development in children between the ages of 4 and 10 years ([Formula: see text]; 50 female, 42 male). At multiple spatial scales, age is positively associated with brain network segregation. At the system level, age was associated with segregation of systems involved in attention from those involved in abstract cognition, and with integration among attentional and perceptual systems. Associations between age and functional connectivity are most pronounced in visual and medial prefrontal cortex, the two ends of a gradient from perceptual, externally oriented cortex to abstract, internally oriented cortex. These findings suggest that both ends of the sensory-association gradient may develop early, in contrast to the classical theories that cortical maturation proceeds from back to front, with sensory areas developing first and association areas developing last. More mature patterns of brain network architecture, controlling for age, were associated with better visuospatial reasoning abilities. Our results suggest that as cortical architecture becomes more specialized, children become more able to reason about the world and their place in it.SIGNIFICANCE STATEMENT Anthropologists have called the transition from early to middle childhood the "age of reason", when children across cultures become more independent. We employ cutting-edge neuroimaging acquisition and analysis approaches to investigate associations between age and functional brain architecture in childhood. Age was positively associated with segregation between cortical systems that process the external world and those that process abstract phenomena like the past, future, and minds of others. Surprisingly, we observed pronounced development at both ends of the sensory-association gradient, challenging the theory that sensory areas develop first and association areas develop last. Our results open new directions for research into how brains reorganize to support rapid gains in cognitive and socioemotional skills as children reach the age of reason.

Increasing diversity in developmental cognitive neuroscience: A roadmap for increasing representation in pediatric neuroimaging research

Understanding of human brain development has advanced rapidly as the field of developmental cognitive neuroscience (DCN) has matured into an established scientific discipline. Despite substantial progress, DCN lags behind other related disciplines in terms of diverse representation, standardized reporting requirements for socio-demographic characteristics of participants in pediatric neuroimaging studies, and use of intentional sampling strategies to more accurately represent the socio-demographic, ethnic, and racial composition of the populations from which participants are sampled. Additional efforts are needed to shift DCN towards a more inclusive field that facilitates the study of individual differences across a variety of cultural and contextual experiences. In this commentary, we outline and discuss barriers within our current scientific practice (e.g., research methods) and beliefs (i.e., what constitutes good science, good scientists, and good research questions) that contribute to under-representation and limited diversity within pediatric neuroimaging studies and propose strategies to overcome those barriers. We discuss strategies to address barriers at intrapersonal, interpersonal, community, systemic, and structural levels. Highlighting strength-based models of inclusion and recognition of the value of diversity in DCN research, along with acknowledgement of the support needed to diversify the field is critical for advancing understanding of neurodevelopment and reducing health inequities.

Functional Connectome-Based Predictive Modeling in Autism

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

Real-time and Recursive Estimators for Functional MRI Quality Assessment

Real-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well as time-series of target brain areas and resting-state networks. We estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR), and real-time head motion parameters by a framewise rigid-body transformation (translations and rotations) using the conventional current to template volume registration. In addition, we derived real-time framewise (FD) and micro (MD) displacements based on head motion parameters and evaluated the temporal derivative of root mean squared variance over voxels (DVARS). For monitoring time-series of target regions and networks, we estimated the number of spikes and amount of filtered noise by means of a modified Kalman filter. Finally, we applied the incremental general linear modeling (GLM) to evaluate real-time contributions of nuisance regressors (linear trend and head motion). Proposed rtQA was demonstrated in real-time fMRI neurofeedback runs without and with excessive head motion and real-time simulations of neurofeedback and resting-state fMRI data. The rtQA was implemented as an extension of the open-source OpenNFT software written in Python, MATLAB and C++ for neurofeedback, task-based, and resting-state paradigms. We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications. Flexible estimation and visualization of rtQA facilitates efficient rtQA of fMRI data and helps the robustness of fMRI acquisitions by means of substantiating decisions about the necessity of the interruption and re-start of the experiment and increasing the confidence in neural estimates.

Key considerations for child and adolescent MRI data collection

Cognitive neuroimaging researchers' ability to infer accurate statistical conclusions from neuroimaging depends greatly on the quality of the data analyzed. This need for quality control is never more evident than when conducting neuroimaging studies with children and adolescents. Developmental neuroimaging requires patience, flexibility, adaptability, extra time, and effort. It also provides us a unique, non-invasive way to understand the development of cognitive processes, individual differences, and the changing relations between brain and behavior over the lifespan. In this discussion, we focus on collecting magnetic resonance imaging (MRI) data, as it is one of the more complex protocols used with children and youth. Through our extensive experience collecting MRI datasets with children and families, as well as a review of current best practices, we will cover three main topics to help neuroimaging researchers collect high-quality datasets. First, we review key recruitment and retention techniques, and note the importance for consistency and inclusion across groups. Second, we discuss ways to reduce scan anxiety for families and ways to increase scan success by describing the pre-screening process, use of a scanner simulator, and the need to focus on participant and family comfort. Finally, we outline several important design considerations in developmental neuroimaging such as asking a developmentally appropriate question, minimizing data loss, and the applicability of public datasets. Altogether, we hope this article serves as a useful tool for those wishing to enter or learn more about developmental cognitive neuroscience.

Visual system assessment for predicting a transition to psychosis

The field of psychiatry is far from perfect in predicting which individuals will transition to a psychotic disorder. Here, we argue that visual system assessment can help in this regard. Such assessments have generated medium-to-large group differences with individuals prior to or near the first psychotic episode or have shown little influence of illness duration in larger samples of more chronic patients. For example, self-reported visual perceptual distortions-so-called visual basic symptoms-occur in up to 2/3rds of those with non-affective psychosis and have already longitudinally predicted an impending onset of schizophrenia. Possibly predictive psychophysical markers include enhanced contrast sensitivity, prolonged backward masking, muted collinear facilitation, reduced stereoscopic depth perception, impaired contour and shape integration, and spatially restricted exploratory eye movements. Promising brain-based markers include visual thalamo-cortical hyperconnectivity, decreased occipital gamma band power during visual detection (MEG), and reduced visually evoked occipital P1 amplitudes (EEG). Potentially predictive retinal markers include diminished cone a- and b-wave amplitudes and an attenuated photopic flicker response during electroretinography. The foregoing assessments are often well-described mechanistically, implying that their findings could readily shed light on the underlying pathophysiological changes that precede or accompany a transition to psychosis. The retinal and psychophysical assessments in particular are inexpensive, well-tolerated, easy to administer, and brief, with few inclusion/exclusion criteria. Therefore, across all major levels of analysis-from phenomenology to behavior to brain and retinal functioning-visual system assessment could complement and improve upon existing methods for predicting which individuals go on to develop a psychotic disorder.