The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort

Comparison of different neurite density metrics with brain asymmetry evaluation

The standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, non-linearities in the standard model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reliable diffusion approaches and to avoid computational model degeneracy, additional theoretical assumptions allowing stable numerical implementations are required. Advanced diffusion approaches allow for estimation of intra-axonal water fraction (AWF), describing a key structural characteristic of brain tissue. AWF can be interpreted as an indirect measure or proxy of neurite density and has a potential as useful clinical biomarker. Established diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging (NODDI), and spherical mean technique provide estimates of AWF within their respective theoretical frameworks. In the present study, we estimated AWF metrics using different diffusion approaches and compared measures of brain asymmetry between the different metrics in a sub-sample of 182 subjects from the UK Biobank. Multivariate decomposition by mean of linked independent component analysis revealed that the various AWF proxies derived from the different diffusion approaches reflect partly non-overlapping variance of independent components, with distinct anatomical distributions and sensitivity to age. Further, voxel-wise analysis revealed age-related differences in AWF-based brain asymmetry, indicating less apparent left-right hemisphere difference with higher age. Finally, we demonstrated that NODDI metrics suffer from a quite strong dependence on used numerical algorithms and post-processing pipeline. The analysis based on AWF metrics strongly depends on the used diffusion approach and leads to poorly reproducible results.

Childhood adversity is associated with longitudinal white matter changes after adulthood trauma

Background

Childhood adversity is associated with susceptibility to posttraumatic stress disorder (PTSD) in adulthood. Both PTSD and adverse experiences in childhood are linked to disrupted white matter microstructure, yet the role of white matter as a potential neural mechanism connecting childhood adversity to PTSD remains unclear. The present study investigated the potential moderating role of previous childhood adversity on longitudinal changes in white matter microstructures and posttraumatic stress symptoms following a recent traumatic event in adulthood.

Methods

As part of the AURORA Study, 114 recent trauma survivors completed diffusion weighted imaging at 2-weeks and 6-months after exposure. Participants reported on prior childhood adversity and PTSD symptoms at 2-weeks, 6-months, and 12-months post-trauma. We performed both region-of-interest (ROI) and whole-brain correlational tractography analyses to index associations between white matter microstructure changes and prior adversity.

Results

Whole-brain correlational tractography revealed that greater childhood adversity moderated the changes in quantitative anisotropy (QA) over time across threat and visual processing tracts including the cingulum bundle and inferior fronto-occipital fasciculus (IFOF). Further, QA changes within cingulum bundle, IFOF, and inferior longitudinal fasciculus were associated with changes in PTSD symptoms between 2-weeks and 6-months.

Conclusions

Our findings suggest temporal variability in threat and visual white matter tracts may be a potential neural pathway through which childhood adversity confers risk to PTSD symptoms after adulthood trauma. Future studies should take the temporal properties of white matter into consideration to better understand the neurobiology of childhood adversity and PTSD.

Mapping hippocampal glutamate in healthy aging with in vivo glutamate-weighted CEST (GluCEST) imaging

Introduction

Hippocampal glutamate (Glu) dysfunction is a pertinent indicator of neurodegeneration, yet mapping typical age-related changes in Glu has been challenging. Here, we use a 7T MRI approach, Glutamate Chemical Exchange Saturation Transfer (GluCEST), to measure bilateral hippocampal Glu in healthy old (HOA) and young (HYA) adults.

Methods

Bilateral hippocampal GluCEST data was acquired from 27 HOA and 22 HYA using 7T MRI. GluCEST differences by age and hemisphere were tested with a linear mixed model. GluCEST asymmetry index was also evaluated by age. Exploratory analyses examined associations between hippocampal GluCEST, age group, and scores on the Montreal Cognitive Assessment (MoCA) and Cognitive Complaints Index (CCI).

Results

GluCEST levels showed an age group and hemisphere interaction. In HOA, GluCEST was higher in left than right hippocampus, but in HYA, GluCEST level was equivalent across hemispheres. HOA had lower GluCEST than HYA in the right hippocampus. GluCEST asymmetry index confirmed significant left asymmetry in HOA. Lower GluCEST levels in HOA were associated with subjective cognitive complaints as measured by the CCI.

Discussion

Hippocampal GluCEST provides insight into age-related neural changes, with lower GluCEST in the right hippocampus in older adults. These findings offer a step toward elucidating the asymmetrical trajectory of hippocampal glutamatergic alterations and their relationship to cognitive phenotypes.

White matter correlates of dissociation in a diverse sample of trauma-exposed women

Dissociation is a common response to trauma linked to functional brain disruptions in brain networks subserving emotion regulation and multisensory integration; however, structural neural correlates of dissociation are less known, particularly abnormalities in stress-sensitive white matter (WM) tracts. The present study examined associations between dissociation and WM microstructure, assessed via fractional anisotropy (FA), in a large, diverse sample of women recruited as part of a long-standing trauma study, the Grady Trauma Project (GTP). As part of GTP, 135 trauma-exposed women (18-62 years old, M=34.25, SD=12.96, 84% self-identifying as Black) were recruited, received diffusion tensor imaging, and completed the Multiscale Dissociation Inventory (MDI); FA values were extracted from ten major WM tracts of interest. Partial correlations were conducted to examine associations between dissociation facets (MDI total and subscales) and FA while covarying age and temporal signal-to-noise ratio; false discovery rate corrected p < 0.05 indicated statistical significance. FA in seven tracts showed significant negative associations with overall dissociation (MDI total score; rs<-0.19, pFDR<0.05); the corona radiata, corpus callosum, superior longitudinal fasciculus, thalamic radiation, anterior cingulum, fornix, and uncinate fasciculus. Among facets of dissociation, FA was most consistently associated with dissociative memory disturbance, showing a significant and negative association with all but one of tract of interest, (rs<-0.23, pFDR<0.05). Our findings indicated that dissociation severity was linked to proportionally lesser WM microstructural integrity in tracts involved with sensory integration, emotion regulation, memory, and self-referential processing. Disruptions in these pathways may underlie dissociative phenomena, representing important psychotherapeutic and neuromodulatory targets.

A network control theory pipeline for studying the dynamics of the structural connectome

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.

Cortical lesions and focal white matter injury are associated with attentional performance in chronic traumatic brain injury

Cognitive impairment, often due to attentional deficits, is a primary driver of disability after traumatic brain injury. It remains unclear whether attentional deficits are caused by injury to specific brain structures or the total burden of injury. In this cross-sectional, multicentre cohort study, we tested whether the association between brain injury and attentional performance varies by neuroanatomic location. Participants in the late effects of traumatic brain injury study were at least 18 years old and at least 1 year after a mild, moderate or severe traumatic brain injury. They underwent MRI and neuropsychological assessment at one of two sites. The primary and secondary outcomes, each measuring aspects of attentional performance, were the Trails A t-score and the standardized score on California Verbal Learning Test 2 Immediate Recall Trial 1. Imaging variables included the size and location (seven regions and seven networks) of encephalomalacic brain lesions and regional white matter fractional anisotropy measured with diffusion MRI (14 regions). We used ANOVA to test whether attentional performance differed by lesion location and linear mixed models to test whether attentional performance differed based on regional fractional anisotropy. One hundred eighty-eight participants met inclusion criteria (mean age 57, 69% male, 88% White). Participants with encephalomalacic brain lesions [N = 73 (39%)] had worse Trails A [mean (95% confidence interval) difference: 4.7 (0.3, 9.1); P = 0.036] but not secondary outcome performance [-0.3 (-0.1, 0.7); P = 0.17]. Among participants with lesions, Trails A performance did not differ by lesion size (P = 0.07) or location (P = 0.41 by region; P = 0.78 by network). We identified a significant interaction between regional fractional anisotropy and attentional performance on both primary (P = 0.001) and secondary (P = 0.001) outcome measures. Post hoc testing identified the strongest associations with Trails A performance in the sagittal stratum [1 SD decrement in Trails A: -0.2 (-0.3, -0.1) SD change in fractional anisotropy; P Bonferroni = 0.0057] and external capsule [-0.1 (-0.2, -0.1); P Bonferroni = 0.042] and the strongest association with secondary attentional scores in the corpus callosum [0.2 (0.1, 0.3); P Bonferroni = 0.014]. In a multivariate model, white matter integrity in the sagittal stratum (P = 0.008), but not encephalomalacic lesions (P = 0.3), was independently associated with Trails A performance. Diminished white matter integrity and cortical injury were each associated with attentional test performance, but only white matter injury demonstrated independent and region-specific effects. The peak statistical association with attentional test performance was in the sagittal stratum, a widely connected white matter region. Further investigation into the connections spanning this and nearby regions may reveal therapeutic targets for neuromodulation.

Diffusion MRI with Machine Learning

Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high inter-session and inter-scanner variability in the data, as well as inter-subject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

Causal enhanced drug-target interaction prediction based on graph generation and multi-source information fusion

MOTIVATION

The prediction of drug-target interaction is a vital task in the biomedical field, aiding in the discovery of potential molecular targets of drugs and the development of targeted therapy methods with higher efficacy and fewer side effects. Although there are various methods for drug-target interaction (DTI) prediction based on heterogeneous information networks, these methods face challenges in capturing the fundamental interaction between drugs and targets and ensuring the interpretability of the model. Moreover, they need to construct meta-paths artificially or a lot of feature engineering (prior knowledge), and graph generation can fuse information more flexibly without meta-path selection.

RESULTS

We propose a causal enhanced method for drug-target interaction (CE-DTI) prediction that integrates graph generation and multi-source information fusion. First, we represent drugs and targets by modeling the fusion of their multi-source information through automatic graph generation. Once drugs and targets are combined, a network of drug-target pairs is constructed, transforming the prediction of drug-target interactions into a node classification problem. Specifically, the influence of surrounding nodes on the central node is separated into two groups: causal and non-causal variable nodes. Causal variable nodes significantly impact the central node's classification, while non-causal variable nodes do not. Causal invariance is then used to enhance the contrastive learning of the drug-target pairs network. Our method demonstrates excellent performance compared with other competitive benchmark methods across multiple datasets. At the same time, the experimental results also show that the causal enhancement strategy can explore the potential causal effects between DTPs, and discover new potential targets. Additionally, case studies demonstrate that this method can identify potential drug targets.

AVAILABILITY AND IMPLEMENTATION

The source code of AdaDR is available at: https://github.com/catly/CE-DTI.

The role of functional emotion circuits in distinct dimensions of psychopathology in youth

Several mental disorders emerge during childhood or adolescence and are often characterized by socioemotional difficulties, including alterations in emotion perception. Emotional facial expressions are processed in discrete functional brain modules whose connectivity patterns encode emotion categories, but the involvement of these neural circuits in psychopathology in youth is poorly understood. This study examined the associations between activation and functional connectivity patterns in emotion circuits and psychopathology during development. We used task-based fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC, N = 1221, 8-23 years) and conducted generalized psycho-physiological interaction (gPPI) analyses. Measures of psychopathology were derived from an independent component analysis of questionnaire data. The results showed positive associations between identifying fearful, sad, and angry faces and depressive symptoms, and a negative relationship between sadness recognition and positive psychosis symptoms. We found a positive main effect of depressive symptoms on BOLD activation in regions overlapping with the default mode network, while individuals reporting higher levels of norm-violating behavior exhibited emotion-specific lower functional connectivity within regions of the salience network and between modules that overlapped with the salience and default mode network. Our findings illustrate the relevance of functional connectivity patterns underlying emotion processing for behavioral problems in children and adolescents.

Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis

OBJECT

Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC).

MATERIALS AND METHODS

The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials.

RESULTS

The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps.

DISCUSSION

In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.

A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity

The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.

Head motion in the UK Biobank imaging subsample: longitudinal stability, associations with psychological and physical health, and risk of incomplete data

Participant motion in brain magnetic resonance imaging is associated with processing problems including potentially non-useable/incomplete data. This has implications for representativeness in research. Few large studies have investigated predictors of increased motion in the first instance. We exploratively tested for association between multiple psychological and physical health traits with concurrent motion during T1 structural, diffusion, average resting-state and task functional magnetic resonance imaging in N = 52 951 UK Biobank imaging subsample participants. These traits included history of cardiometabolic, inflammatory, neurological and psychiatric conditions, as well as concurrent cognitive test scores and anthropometric traits. We tested for stability in motion in participants with longitudinal imaging data (n = 5305, average 2.64 years later). All functional and T1 structural motion variables were significantly intercorrelated (Pearson r range 0.3-0.8, all P < 0.001). Diffusion motion variables showed weaker correlations around r = 0.1. Most physical and psychological phenotypes showed significant association with at least one measure of increased motion including specifically in participants with complete useable data (highest β = 0.66 for diabetes versus resting-state functional magnetic resonance imaging motion). Poorer values in most health traits predicted lower odds of complete imaging data, with the largest association for history of traumatic brain injury (odds ratio = 0.720, 95% confidence interval = 0.562 to 0.923, P = 0.009). Worse psychological and physical health are consistent predictors of increased average functional and structural motion during brain imaging and associated with lower odds of complete data. Average motion levels were largely consistent across modalities and longitudinally in participants with repeat data. Together, these findings have implications for representativeness and bias in imaging studies of generally healthy population samples.

Associations of conservatism and jumping to conclusions biases with aberrant salience and default mode network

AIM

While conservatism bias refers to the human need for more evidence for decision-making than rational thinking expects, the jumping to conclusions (JTC) bias refers to the need for less evidence among individuals with schizophrenia/delusion compared to healthy people. Although the hippocampus-midbrain-striatal aberrant salience system and the salience, default mode (DMN), and frontoparietal networks ("triple networks") are implicated in delusion/schizophrenia pathophysiology, the associations between conservatism/JTC and these systems/networks are unclear.

METHODS

Thirty-seven patients with schizophrenia and 33 healthy controls performed the beads task, with large and small numbers of bead draws to decision (DTD) indicating conservatism and JTC, respectively. We performed independent component analysis (ICA) of resting functional magnetic resonance imaging (fMRI) data. For systems/networks above, we investigated interactions between diagnosis and DTD, and main effects of DTD. We similarly applied ICA to structural and diffusion MRI to explore the associations between DTD and gray/white matter.

RESULTS

We identified a significant main effect of DTD with functional connectivity between the striatum and DMN, which was negatively correlated with delusion severity in patients, indicating that the greater the anti-correlation between these networks, the stronger the JTC and delusion. We further observed the main effects of DTD on a gray matter network resembling the DMN, and a white matter network connecting the functional and gray matter networks (all P < 0.05, family-wise error [FWE] correction). Function and gray/white matter showed no significant interactions.

CONCLUSION

Our results support the novel association of conservatism and JTC biases with aberrant salience and default brain mode.

Neuroimaging and plasma evidence of early white matter loss in Parkinson's disease with poor outcomes

Parkinson's disease is a common and debilitating neurodegenerative disorder, with over half of patients progressing to postural instability, dementia or death within 10 years of diagnosis. However, the onset and rate of progression to poor outcomes is highly variable, underpinned by heterogeneity in underlying pathological processes. Quantitative and sensitive measures predicting poor outcomes will be critical for targeted treatment, but most studies to date have been limited to a single modality or assessed patients with established cognitive impairment. Here, we used multimodal neuroimaging and plasma measures in 98 patients with Parkinson's disease and 28 age-matched controls followed up over 3 years. We examined: grey matter (cortical thickness and subcortical volume), white matter (fibre cross-section, a measure of macrostructure; and fibre density, a measure of microstructure) at whole-brain and tract level; structural and functional connectivity; and plasma levels of neurofilament light chain and phosphorylated tau 181. We evaluated relationships with subsequent poor outcomes, defined as development of mild cognitive impairment, dementia, frailty or death at any time during follow-up, in people with Parkinson's disease. We show that extensive white matter macrostructural changes are already evident at baseline assessment in people with Parkinson's disease who progress to poor outcomes (n = 31): with up to 19% reduction in fibre cross-section in multiple tracts, and a subnetwork of reduced structural connectivity strength, particularly involving connections between right frontoparietal and left frontal, right frontoparietal and left parietal and right temporo-occipital and left parietal modules. In contrast, grey matter volumes and functional connectivity were preserved in people with Parkinson's disease with poor outcomes. Neurofilament light chain, but not phosphorylated tau 181 levels were increased in people with Parkinson's disease with poor outcomes, and correlated with white matter loss. These findings suggest that imaging sensitive to white matter macrostructure and plasma neurofilament light chain may be useful early markers of poor outcomes in Parkinson's disease. As new targeted treatments for neurodegenerative disease are emerging, these measures show important potential to aid patient selection for treatment and improve stratification for clinical trials.

Comparative analysis of glymphatic system alterations in multiple sclerosis and neuromyelitis optica spectrum disorder using MRI indices from diffusion tensor imaging

OBJECTIVE

The glymphatic system is a glial-based perivascular network that promotes brain metabolic waste clearance. Glymphatic system dysfunction has been observed in both multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD), indicating the role of neuroinflammation in the glymphatic system. However, little is known about how the two diseases differently affect the human glymphatic system. The present study aims to evaluate the diffusion MRI-based measures of the glymphatic system by contrasting MS and NMOSD.

METHODS

This prospective study included 63 patients with NMOSD (n = 21) and MS (n = 42) who underwent DTI. The fractional volume of extracellular-free water (FW) and an index of diffusion tensor imaging (DTI) along the perivascular space (DTI-ALPS) were used as indirect indicators of water diffusivity in the interstitial extracellular and perivenous spaces of white matter, respectively. Age and EDSS scores were adjusted.

RESULTS

Using Bayesian hypothesis testing, we show that the present data substantially favor the null model of no differences between MS and NMOSD for the diffusion MRI-based measures of the glymphatic system. The inclusion Bayes factor (BF10) of model-averaged probabilities of the group (MS, NMOSD) was 0.280 for FW and 0.236 for the ALPS index.

CONCLUSION

Together, these findings suggest that glymphatic alteration associated with MS and NMOSD might be similar and common as an eventual result, albeit the disease etiologies differ.

PRACTITIONER POINTS

Previous literature indicates important glymphatic system alteration in MS and NMOSD. We explore the difference between MS and NMOSD using diffusion MRI-based measures of the glymphatic system. We show support for the null hypothesis of no difference between MS and NMOSD. This suggests that glymphatic alteration associated with MS and NMOSD might be similar and common etiology.

Neurite density of the hippocampus is associated with trace eyeblink conditioning latency in 4- to 6-year-olds

Limited options exist to evaluate the development of hippocampal function in young children. Research has established that trace eyeblink conditioning (EBC) relies on a functional hippocampus. Hence, we set out to investigate whether trace EBC is linked to hippocampal structure, potentially serving as a valuable indicator of hippocampal development. Our study explored potential associations between individual differences in hippocampal volume and neurite density with trace EBC performance in young children. We used onset latency of conditioned responses (CR) and percentage of conditioned responses (% CR) as measures of hippocampal-dependent associative learning. Using a sample of typically developing children aged 4 to 6 years (N = 30; 14 girls; M = 5.70 years), participants underwent T1- and diffusion-weighted MRI scans and completed a 15-min trace eyeblink conditioning task conducted outside the MRI. % CR and CR onset latency were calculated based on all trials involving tone-puff presentations and tone-alone trials. Findings revealed a connection between greater left hippocampal neurite density and delayed CR onset latency. Children with higher neurite density in the left hippocampus tended to blink closer to the onset of the unconditioned stimulus, indicating that structural variations in the hippocampus were associated with more precise timing of conditioned responses. No other relationships were observed between hippocampal volume, cerebellum volume or neurite density, hippocampal white matter connectivity and any EBC measures. Preliminary results suggest that trace EBC may serve as a straightforward yet innovative approach for studying hippocampal development in young children and populations with atypical development.

Development of white matter fiber covariance networks supports executive function in youth

During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.

Changes in Cortical Microstructure of the Human Brain Resulting from Long-Term Motor Learning

The mechanisms subserving motor skill acquisition and learning in the intact human brain are not fully understood. Previous studies in animals have demonstrated a causal relationship between motor learning and structural rearrangements of synaptic connections, raising the question of whether neurite-specific changes are also observable in humans. Here, we use advanced diffusion magnetic resonance imaging (MRI), sensitive to dendritic and axonal processes, to investigate neuroplasticity in response to long-term motor learning. We recruited healthy male and female human participants (age range 19-29) who learned a challenging dynamic balancing task (DBT) over four consecutive weeks. Diffusion MRI signals were fitted using Neurite Orientation Dispersion and Density Imaging (NODDI), a theory-driven biophysical model of diffusion, yielding measures of tissue volume, neurite density and the organizational complexity of neurites. While NODDI indices were unchanged and reliable during the control period, neurite orientation dispersion increased significantly during the learning period mainly in primary sensorimotor, prefrontal, premotor, supplementary, and cingulate motor areas. Importantly, reorganization of cortical microstructure during the learning phase predicted concurrent behavioral changes, whereas there was no relationship between microstructural changes during the control phase and learning. Changes in neurite complexity were independent of alterations in tissue density, cortical thickness, and intracortical myelin. Our results are in line with the notion that structural modulation of neurites is a key mechanism supporting complex motor learning in humans.SIGNIFICANCE STATEMENT The structural correlates of motor learning in the human brain are not fully understood. Results from animal studies suggest that synaptic remodeling (e.g., reorganization of dendritic spines) in sensorimotor-related brain areas is a crucial mechanism for the formation of motor memory. Using state-of-the-art diffusion magnetic resonance imaging (MRI), we found a behaviorally relevant increase in the organizational complexity of neocortical microstructure, mainly in primary sensorimotor, prefrontal, premotor, supplementary, and cingulate motor regions, following training of a challenging dynamic balancing task (DBT). Follow-up analyses suggested structural modulation of synapses as a plausible mechanism driving this increase, while colocalized changes in cortical thickness, tissue density, and intracortical myelin could not be detected. These results advance our knowledge about the neurobiological basis of motor learning in humans.

Age-associated alterations in thalamocortical structural connectivity in youths with a psychosis-spectrum disorder

Psychotic symptoms typically emerge in adolescence. Age-associated thalamocortical connectivity differences in psychosis remain unclear. We analyzed diffusion-weighted imaging data from 1254 participants 8-23 years old (typically developing (TD):N = 626, psychosis-spectrum (PS): N = 329, other psychopathology (OP): N = 299) from the Philadelphia Neurodevelopmental Cohort. We modeled thalamocortical tracts using deterministic fiber tractography, extracted Q-Space Diffeomorphic Reconstruction (QSDR) and diffusion tensor imaging (DTI) measures, and then used generalized additive models to determine group and age-associated thalamocortical connectivity differences. Compared to other groups, PS exhibited thalamocortical reductions in QSDR global fractional anisotropy (GFA, p-values range = 3.0 × 10-6-0.05) and DTI fractional anisotropy (FA, p-values range = 4.2 × 10-4-0.03). Compared to TD, PS exhibited shallower thalamus-prefrontal age-associated increases in GFA and FA during mid-childhood, but steeper age-associated increases during adolescence. TD and OP exhibited decreases in thalamus-frontal mean and radial diffusivities during adolescence; PS did not. Altered developmental trajectories of thalamocortical connectivity may contribute to the disruptions observed in adults with psychosis.

Brain asymmetry is globally different in males and females: exploring cortical volume, area, thickness, and mean curvature

Brain asymmetry is a cornerstone in the development of higher-level cognition, but it is unclear whether and how it differs in males and females. Asymmetry has been investigated using the laterality index, which compares homologous regions as pairwise weighted differences between the left and the right hemisphere. However, if asymmetry differences between males and females are global instead of pairwise, involving proportions between multiple brain areas, novel methodological tools are needed to evaluate them. Here, we used the Amsterdam Open MRI collection to investigate sexual dimorphism in brain asymmetry by comparing laterality index with the distance index, which is a global measure of differences within and across hemispheres, and with the subtraction index, which compares pairwise raw values in the left and right hemisphere. Machine learning models, robustness tests, and group analyses of cortical volume, area, thickness, and mean curvature revealed that, of the three indices, distance index was the most successful biomarker of sexual dimorphism. These findings suggest that left-right asymmetry in males and females involves global coherence rather than pairwise contrasts. Further studies are needed to investigate the biological basis of local and global asymmetry based on growth patterns under genetic, hormonal, and environmental factors.

Fibre density and fibre-bundle cross-section of the corticospinal tract are distinctly linked to psychosis-specific symptoms in antipsychotic-naïve patients with first-episode schizophrenia

Multiple lines of research support the dysconnectivity hypothesis of schizophrenia. However, findings on white matter (WM) alterations in patients with schizophrenia are widespread and non-specific. Confounding factors from magnetic resonance image (MRI) processing, clinical diversity, antipsychotic exposure, and substance use may underlie some of the variability. By application of refined methodology and careful sampling, we rectified common confounders investigating WM and symptom correlates in a sample of strictly antipsychotic-naïve first-episode patients with schizophrenia. Eighty-six patients and 112 matched controls underwent diffusion MRI. Using fixel-based analysis (FBA), we extracted fibre-specific measures such as fibre density and fibre-bundle cross-section. Group differences on fixel-wise measures were examined with multivariate general linear modelling. Psychopathology was assessed with the Positive and Negative Syndrome Scale. We separately tested multivariate correlations between fixel-wise measures and predefined psychosis-specific versus anxio-depressive symptoms. Results were corrected for multiple comparisons. Patients displayed reduced fibre density in the body of corpus callosum and in the middle cerebellar peduncle. Fibre density and fibre-bundle cross-section of the corticospinal tract were positively correlated with suspiciousness/persecution, and negatively correlated with delusions. Fibre-bundle cross-section of isthmus of corpus callosum and hallucinatory behaviour were negatively correlated. Fibre density and fibre-bundle cross-section of genu and splenium of corpus callosum were negative correlated with anxio-depressive symptoms. FBA revealed fibre-specific properties of WM abnormalities in patients and differentiated associations between WM and psychosis-specific versus anxio-depressive symptoms. Our findings encourage an itemised approach to investigate the relationship between WM microstructure and clinical symptoms in patients with schizophrenia.

Internal capsule microstructure mediates the relationship between childhood maltreatment and PTSD following adulthood trauma exposure

Childhood trauma is a known risk factor for trauma and stress-related disorders in adulthood. However, limited research has investigated the impact of childhood trauma on brain structure linked to later posttraumatic dysfunction. We investigated the effect of childhood trauma on white matter microstructure after recent trauma and its relationship with future posttraumatic dysfunction among trauma-exposed adult participants (n = 202) recruited from emergency departments as part of the AURORA Study. Participants completed self-report scales assessing prior childhood maltreatment within 2-weeks in addition to assessments of PTSD, depression, anxiety, and dissociation symptoms within 6-months of their traumatic event. Fractional anisotropy (FA) obtained from diffusion tensor imaging (DTI) collected at 2-weeks and 6-months was used to index white matter microstructure. Childhood maltreatment load predicted 6-month PTSD symptoms (b = 1.75, SE = 0.78, 95% CI = [0.20, 3.29]) and inversely varied with FA in the bilateral internal capsule (IC) at 2-weeks (p = 0.0294, FDR corrected) and 6-months (p = 0.0238, FDR corrected). We observed a significant indirect effect of childhood maltreatment load on 6-month PTSD symptoms through 2-week IC microstructure (b = 0.37, Boot SE = 0.18, 95% CI = [0.05, 0.76]) that fully mediated the effect of childhood maltreatment load on PCL-5 scores (b = 1.37, SE = 0.79, 95% CI = [-0.18, 2.93]). IC microstructure did not mediate relationships between childhood maltreatment and depressive, anxiety, or dissociative symptomatology. Our findings suggest a unique role for IC microstructure as a stable neural pathway between childhood trauma and future PTSD symptoms following recent trauma. Notably, our work did not support roles of white matter tracts previously found to vary with PTSD symptoms and childhood trauma exposure, including the cingulum bundle, uncinate fasciculus, and corpus callosum. Given the IC contains sensory fibers linked to perception and motor control, childhood maltreatment might impact the neural circuits that relay and process threat-related inputs and responses to trauma.

ADHD-related sex differences in frontal lobe white matter microstructure and associations with response control under conditions of varying cognitive load and motivational contingencies

Children with attention-deficit/hyperactivity disorder (ADHD) demonstrate reduced response inhibition, increased response time variability, and atypical frontal lobe white matter microstructure with emerging evidence of sex differences. This study aims to examine whether frontal lobe white matter microstructure is differentially impacted in ADHD by sex and whether this relates to Go/No-Go (GNG) task performance. Diffusion tensor imaging (DTI) from 187 children (8-12 years), including ADHD (n = 94) and typically developing controls (TD; n = 93). Participants completed three GNG tasks with varying cognitive demands and incentives (standard, cognitive, and motivational). Fractional anisotropy (FA) was examined as an index of white matter microstructure within bilateral frontal lobe regions of interest. Children with ADHD showed reduced FA in primary motor (M1) and supplementary motor area (SMA) regardless of sex. Sex-based dissociation for the effect of diagnosis was observed in medial orbitofrontal cortex (mOFC), with higher FA in girls with ADHD and lower FA in boys with ADHD. Both diagnosis and sex contributed to performance on measures of response inhibition and reaction time (RT) variability, with all children with ADHD demonstrating poorer performance on all GNG tasks, but boys with ADHD demonstrating more impulsivity on standard and motivational behavioral paradigms compared to girls with ADHD. Analyses revealed associations between reduced FA in M1, SMA, and mOFC and increased response inhibition and RT variability with some sex-based differences. These findings provide novel insights regarding the brain basis of ADHD and associated impairments in response inhibition and RT variability, and contribute to our understanding of sexual dimorphic behavioral outcomes.

Spherical means-based free-water volume fraction from diffusion MRI increases non-linearly with age in the white matter of the healthy human brain

The term free-water volume fraction (FWVF) refers to the signal fraction that could be found as the cerebrospinal fluid of the brain, which has been demonstrated as a sensitive measure that correlates with cognitive performance and various neuropathological processes. It can be quantified by properly fitting the isotropic component of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using N=287 healthy subjects (178F/109M) aged 25-94, this study examines in detail the evolution of the FWVF obtained with the spherical means technique from multi-shell acquisitions in the human brain white matter across the adult lifespan, which has been previously reported to exhibit a positive trend when estimated from single-shell data using the bi-tensor signal representation. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the spherical means-based multi-shell FWVF parameter with age, at the same time, a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW corrected diffusion tensor imaging (DTI) leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to the studies conducted over the standard (single-component) DTI. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the conceivable reason for the variations observed in FA and MD parameters across the lifespan with previous studies under the standard diffusion tensor imaging.

Using network control theory to study the dynamics of the structural connectome

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains dynamics. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter dynamics in a desired way. We have extensively developed and validated the application of NCT to the human structural connectome. Through these efforts, we have studied (i) how different aspects of connectome topology affect neural dynamics, (ii) whether NCT outputs cohere with empirical data on brain function and stimulation, and (iii) how NCT outputs vary across development and correlate with behavior and mental health symptoms. In this protocol, we introduce a framework for applying NCT to structural connectomes following two main pathways. Our primary pathway focuses on computing the control energy associated with transitioning between specific neural activity states. Our second pathway focuses on computing average controllability, which indexes nodes' general capacity to control dynamics. We also provide recommendations for comparing NCT outputs against null network models. Finally, we support this protocol with a Python-based software package called network control theory for python (nctpy).

Sex differences in atypical fronto-subcortical structural connectivity among children with attention-deficit/hyperactivity disorder: Associations with delay discounting

PURPOSE

Atypical fronto-subcortical neural circuitry has been implicated in the pathophysiology of attention-deficit/hyperactivity disorder (ADHD), including connections between prefrontal cortical regions involved in top-down cognitive control and subcortical limbic structures (striatum and amygdala) involved in bottom-up reward and emotional processing. The integrity of fronto-subcortical connections may also relate to interindividual variability in delay discounting, or a preference for smaller, immediate over larger, delayed rewards, which is associated with ADHD, with recent evidence of ADHD-related sex differences.

METHODS

We applied diffusion tensor imaging to compare the integrity of the white matter connections within fronto-subcortical tracts among 187 8-12 year-old children either with ADHD ((n = 106; 29 girls) or typically developing (TD) controls ((n = 81; 28 girls). Analyses focused on diagnostic group differences in fractional anisotropy (FA) within fronto-subcortical circuitry implicated in delay discounting, connecting subregions of the striatum (dorsal executive and ventral limbic areas) and amygdala with prefrontal regions of interest (dorsolateral [dlPFC], orbitofrontal [OFC] and anterior cingulate cortex [ACC]), and associations with two behavioral assessments of delay discounting.

RESULTS

Children with ADHD showed reduced FA in tracts connecting OFC with ventral striatum, regardless of sex, whereas reduced FA in the OFC-amygdala and ventral ACC-amygdala tracts were specific to boys with ADHD. Across diagnostic groups and sex, reduced FA in the dorsal ACC-executive striatum tract correlated with greater game time delay discounting.

CONCLUSIONS

These results suggest a potential neurobiological substrate of heightened delay discounting in children with ADHD and support the need for additional studies including larger sample sizes of girls with ADHD to further elucidate ADHD-related sex differences in these relationships.

Association between enhanced carbonyl stress and decreased apparent axonal density in schizophrenia by multimodal white matter imaging

Carbonyl stress is a condition featuring increased rich reactive carbonyl compounds, which facilitate the formation of advanced glycation end products including pentosidine. We previously reported the relationship between enhanced carbonyl stress and disrupted white matter integrity in schizophrenia, although which microstructural component is disrupted remained unclear. In this study, 32 patients with schizophrenia (SCZ) and 45 age- and gender-matched healthy volunteers (HC) were recruited. We obtained blood samples for carbonyl stress markers (plasma pentosidine and serum pyridoxal) and multi-modal magnetic resonance imaging measures of white matter microstructures including apparent axonal density (intra-cellular volume fraction (ICVF)) and orientation (orientation dispersion index (ODI)), and inflammation (free water (FW)). In SCZ, the plasma pentosidine level was significantly increased. Group comparison revealed that mean white matter values were decreased for ICVF, and increased for FW. We found a significant negative correlation between the plasma pentosidine level and mean ICVF values in SCZ, and a significant negative correlation between the serum pyridoxal level and mean ODI value in HC, regardless of age. Our results suggest an association between enhanced carbonyl stress and axonal abnormality in SCZ.

Development of Iron Status Measures during Youth: Associations with Sex, Neighborhood Socioeconomic Status, Cognitive Performance, and Brain Structure

BACKGROUND

Iron is essential to brain function, and iron deficiency during youth may adversely impact neurodevelopment. Understanding the developmental time course of iron status and its association with neurocognitive functioning is important for identifying windows for intervention.

OBJECTIVES

This study aimed to characterize developmental change in iron status and understand its association with cognitive performance and brain structure during adolescence using data from a large pediatric health network.

METHODS

This study included a cross-sectional sample of 4899 participants (2178 males; aged 8-22 y at the time of participation, M [SD] = 14.24 [3.7]) who were recruited from the Children's Hospital of Philadelphia network. Prospectively collected research data were enriched with electronic medical record data that included hematological measures related to iron status, including serum hemoglobin, ferritin, and transferrin (33,015 total samples). At the time of participation, cognitive performance was assessed using the Penn Computerized Neurocognitive Battery, and brain white matter integrity was assessed using diffusion-weighted MRI in a subset of individuals.

RESULTS

Developmental trajectories were characterized for all metrics and revealed that sex differences emerged after menarche such that females had reduced iron status relative to males [all R2partial > 0.008; all false discovery rates (FDRs) < 0.05]. Higher socioeconomic status was associated with higher hemoglobin concentrations throughout development (R2partial = 0.005; FDR < 0.001), and the association was greatest during adolescence. Higher hemoglobin concentrations were associated with better cognitive performance during adolescence (R2partial = 0.02; FDR < 0.001) and mediated the association between sex and cognition (mediation effect = -0.107; 95% CI: -0.191, -0.02). Higher hemoglobin concentration was also associated with greater brain white matter integrity in the neuroimaging subsample (R2partial = 0.06, FDR = 0.028).

CONCLUSIONS

Iron status evolves during youth and is lowest in females and individuals of low socioeconomic status during adolescence. Diminished iron status during adolescence has consequences for neurocognition, suggesting that this critical period of neurodevelopment may be an important window for intervention that has the potential to reduce health disparities in at-risk populations.

Dynamic structural remodeling of the human visual system prompted by bilateral retinal gene therapy

The impact of changes in visual input on neuronal circuitry is complex and much of our knowledge on human brain plasticity of the visual systems comes from animal studies. Reinstating vision in a group of patients with low vision through retinal gene therapy creates a unique opportunity to dynamically study the underlying process responsible for brain plasticity. Historically, increases in the axonal myelination of the visual pathway has been the biomarker for brain plasticity. Here, we demonstrate that to reach the long-term effects of myelination increase, the human brain may undergo demyelination as part of a plasticity process. The maximum change in dendritic arborization of the primary visual cortex and the neurite density along the geniculostriate tracks occurred at three months (3MO) post intervention, in line with timing for the peak changes in postnatal synaptogenesis within the visual cortex reported in animal studies. The maximum change at 3MO for both the gray and white matter significantly correlated with patients' clinical responses to light stimulations called full field sensitivity threshold (FST). Our results shed a new light on the underlying process of brain plasticity by challenging the concept of increase myelination being the hallmark of brain plasticity and instead reinforcing the idea of signal speed optimization as a dynamic process for brain plasticity.

Effectiveness of training before unsedated MRI scans in young children: a randomized control trial

BACKGROUND

Young children requiring clinical magnetic resonance imaging (MRI) may be given general anesthesia. General anesthesia has potential side effects, is costly, and introduces logistical challenges. Thus, methods that allow children to undergo awake MRI scans are desirable.

OBJECTIVES

To compare the effectiveness of mock scanner training with a child life specialist, play-based training with a child life specialist, and home book and video preparation by parents to allow non-sedated clinical MRI scanning in children aged 3-7 years.

MATERIALS AND METHODS

Children (3-7 years, n=122) undergoing clinical MRI scans at the Alberta Children's Hospital were invited to participate and randomized to one of three groups: home-based preparation materials, training with a child life specialist (no mock MRI), or training in a mock MRI with a child life specialist. Training occurred a few days prior to their MRI. Self- and parent-reported functioning (PedsQL VAS) were assessed pre/post-training (for the two training groups) and pre/post-MRI. Scan success was determined by a pediatric radiologist.

RESULTS

Overall, 91% (111/122) of children successfully completed an awake MRI. There were no significant differences between the mock scanner (89%, 32/36), child life (88%, 34/39), and at-home (96%, 45/47) groups (P=0.34). Total functioning scores were similar across groups; however, the mock scanner group had significantly lower self-reported fear (F=3.2, P=0.04), parent-reported sadness (F=3.3, P=0.04), and worry (F=3.5, P=0.03) prior to MRI. Children with unsuccessful scans were younger (4.5 vs. 5.7 years, P<0.001).

CONCLUSIONS

Most young children can tolerate awake MRI scans and do not need to be routinely anesthetized. All preparation methods tested, including at-home materials, were effective.

ModelArray: An R package for statistical analysis of fixel-wise data

Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data.

Development of White Matter Fiber Covariance Networks Supports Executive Function in Youth

The white matter architecture of the human brain undergoes substantial development throughout childhood and adolescence, allowing for more efficient signaling between brain regions that support executive function. Increasingly, the field understands grey matter development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. While white matter development also appears asynchronous, previous studies have largely relied on anatomical atlases to characterize white matter tracts, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Here, we leveraged advances in diffusion modeling and unsupervised machine learning to delineate white matter fiber covariance networks comprised of structurally similar areas of white matter in a cross-sectional sample of 939 youth aged 8-22 years. We then evaluated associations between fiber covariance network structural properties with both age and executive function using generalized additive models. The identified fiber covariance networks aligned with the known architecture of white matter while simultaneously capturing novel spatial patterns of coordinated maturation. Fiber covariance networks showed heterochronous increases in fiber density and cross section that generally followed hierarchically organized temporal patterns of cortical development, with the greatest increases in unimodal sensorimotor networks and the most prolonged increases in superior and anterior transmodal networks. Notably, we found that executive function was associated with structural features of limbic and association networks. Taken together, this study delineates data-driven patterns of white matter network development that support cognition and align with major axes of brain maturation.

Whole-brain white matter abnormalities in human cocaine and heroin use disorders: association with craving, recency, and cumulative use

Neuroimaging studies in substance use disorder have shown widespread impairments in white matter (WM) microstructure suggesting demyelination and axonal damage. However, substantially fewer studies explored the generalized vs. the acute and/or specific drug effects on WM. Our study assessed whole-brain WM integrity in three subgroups of individuals addicted to drugs, encompassing those with cocaine (CUD) or heroin (HUD) use disorder, compared to healthy controls (CTL). Diffusion MRI was acquired in 58 CTL, 28 current cocaine users/CUD+, 32 abstinent cocaine users/CUD-, and 30 individuals with HUD (urine was positive for cocaine in CUD+ and opiates used for treatment in HUD). Tract-Based Spatial Statistics allowed voxelwise analyses of diffusion metrics [fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD)]. Permutation statistics (p-corrected < 0.05) were used for between-group t-tests. Compared to CTL, all individuals with addiction showed widespread decreases in FA, and increases in MD, RD, and AD (19-57% of WM skeleton, p < 0.05). The HUD group showed the most impairments, followed by the CUD+, with only minor FA reductions in CUD- (<0.2% of WM skeleton, p = 0.05). Longer periods of regular use were associated with decreased FA and AD, and higher subjective craving was associated with increased MD, RD, and AD, across all individuals with drug addiction (p < 0.05). These findings demonstrate extensive WM impairments in individuals with drug addiction characterized by decreased anisotropy and increased diffusivity, thought to reflect demyelination and lower axonal packing. Extensive abnormalities in both groups with positive urine status (CUD+ and HUD), and correlations with craving, suggest greater WM impairments with more recent use. Results in CUD-, and correlations with regular use, further imply cumulative and/or persistent WM damage.

Asymmetric signaling across the hierarchy of cytoarchitecture within the human connectome

Cortical variations in cytoarchitecture form a sensory-fugal axis that shapes regional profiles of extrinsic connectivity and is thought to guide signal propagation and integration across the cortical hierarchy. While neuroimaging work has shown that this axis constrains local properties of the human connectome, it remains unclear whether it also shapes the asymmetric signaling that arises from higher-order topology. Here, we used network control theory to examine the amount of energy required to propagate dynamics across the sensory-fugal axis. Our results revealed an asymmetry in this energy, indicating that bottom-up transitions were easier to complete compared to top-down. Supporting analyses demonstrated that asymmetries were underpinned by a connectome topology that is wired to support efficient bottom-up signaling. Lastly, we found that asymmetries correlated with differences in communicability and intrinsic neuronal time scales and lessened throughout youth. Our results show that cortical variation in cytoarchitecture may guide the formation of macroscopic connectome topology.

White matter microstructure of superior longitudinal fasciculus II is associated with intelligence and treatment response of negative symptoms in patients with schizophrenia

Although the potential role of superior longitudinal fasciculus (SLF) in intellectual deficits and treatment response (TR) in patients with schizophrenia (SZ) has been previously described, little is known about the white-matter (WM) integrity of SLF subcomponents (SLF I, II, III, and arcuate fasciculus) and their particular relationships with the clinical presentations of the illness. This study examined the associations between fractional anisotropy (FA) of SLF subcomponents and intelligence level and 6-month treatment response (TR) of negative symptoms (NS) in patients with SZ. At baseline, 101 patients with SZ and 101 healthy controls (HCs) underwent structural magnetic resonance imaging. Voxel-wise group comparison analysis showed significant SLF FA reductions in patients with SZ compared with HCs. Voxel-wise correlation analyses revealed significant positive correlations of FAs of right SLF II with Korean–Wechsler Adult Intelligence Scale at baseline and the percentage reduction of negative syndrome subscale of the Positive and Negative Syndrome Scales at 6 months. These findings suggest that aberrance in WM microstructure in SLF II may be associated with intellectual deficits in patients with SZ and TR of NS, which may support the potential role of SLF II as a novel neuroimaging biomarker for clinical outcomes of the illness.

Attention-deficit/hyperactivity disorder and white matter microstructure: the importance of dimensional analyses and sex differences

Background

Attention-deficit/hyperactive disorder (ADHD) has substantial heterogeneity in clinical presentation. A potentially important clue may be variation in brain microstructure. Using fractional anisotropy (FA), previous studies have produced equivocal results in relation to ADHD. This may be due to insufficient consideration of possible sex differences and ADHD's multi-componential nature.

Methods

Using whole-brain analyses, we investigated the association between FA and both ADHD diagnosis and ADHD symptom domains in a well-characterized, ADHD (n = 234; 32% female youth) and non-ADHD (n = 177; 52% female youth), case-control cohort (ages 7-12). Sex-specific effects were tested.

Results

No ADHD group differences were found using categorical assessment of ADHD without consideration of moderators. However, dimensional analyses found total symptoms were associated with higher FA in the superior corona radiata. Further, inattention symptoms were associated with higher FA in the corpus callosum and ansa lenticularis, and lower FA in the superior longitudinal fasciculus, after control for overlap with hyperactivity-impulsivity. Hyperactivity-impulsivity symptoms were associated with higher FA in the superior longitudinal fasciculus, and lower FA in the superior cerebellar peduncles, after control for overlap with inattention. Meanwhile, both categorical and dimensional analyses revealed ADHD-by-sex interactions (voxel-wise p < 0.01). Girls with ADHD had higher FA, but boys with ADHD had lower FA (or no effect), compared to their same-sex peers, in the bilateral anterior corona radiata. Further, higher ADHD symptom severity was associated with higher FA in girls, but lower FA in boys, in the anterior and posterior corona radiata and cerebral peduncles.

Conclusions

ADHD symptom domains appear to be differentially related to white matter microstructure, highlighting the multi-componential nature of the disorder. Further, sex differences will be crucial to consider in future studies characterizing ADHD-related differences in white matter microstructure.

White matter microstructure and sleep-wake disturbances in individuals at ultra-high risk of psychosis

Aim

White matter changes in individuals at ultra-high risk for psychosis (UHR) may be involved in the transition to psychosis. Sleep-wake disturbances commonly precede the first psychotic episode and predict development of psychosis. We examined associations between white matter microstructure and sleep-wake disturbances in UHR individuals compared to healthy controls (HC), as well as explored the confounding effect of medication, substance use, and level of psychopathology.

Methods

Sixty-four UHR individuals and 35 HC underwent clinical interviews and diffusion weighted imaging. Group differences on global and callosal mean fractional anisotropy (FA) was tested using general linear modeling. Sleep-wake disturbances were evaluated using the subjective measures disturbed sleep index (DSI) and disturbed awakening index (AWI) from the Karolinska Sleep Questionnaire, supported by objective sleep measures from one-night actigraphy. The primary analyses comprised partial correlation analyses between global FA/callosal FA and sleep-wake measures. Secondary analyses investigated multivariate patterns of covariance between measures of sleep-wake disturbances and FA in 48 white matter regions of interest using partial least square correlations.

Results

Ultra-high risk for psychosis individuals displayed lower global FA (F = 14.56, p < 0.001) and lower callosal FA (F = 11.34, p = 0.001) compared to HC. Subjective sleep-wake disturbances were significantly higher among the UHR individuals (DSI: F = 27.59, p < 0.001, AWI: F = 36.42, p < 0.001). Lower callosal FA was correlated with increased wake after sleep onset (r = -0.34, p = 0.011) and increased sleep fragmentation index (r = -0.31, p = 0.019) in UHR individuals. Multivariate analyses identified a pattern of covariance in regional FA which were associated with DSI and AWI in UHR individuals (p = 0.028), but not in HC. Substance use, sleep medication and antipsychotic medication did not significantly confound these associations. The association with objective sleep-wake measures was sustained when controlling for level of depressive and UHR symptoms, but symptom level confounded the covariation between FA and subjective sleep-wake measures in the multivariate analyses.

Conclusion

Compromised callosal microstructure in UHR individuals was related to objectively observed disruptions in sleep-wake functioning. Lower FA in ventrally located regions was associated with subjectively measured sleep-wake disturbances and was partly explained by psychopathology. These findings call for further investigation of sleep disturbances as a potential treatment target.

Reduced basal ganglia tissue-iron concentration in school-age children with attention-deficit/hyperactivity disorder is localized to limbic circuitry

Dopamine-related abnormalities in the basal ganglia have been implicated in attention-deficit/hyperactivity disorder (ADHD). Iron plays a critical role in supporting dopaminergic function, and reduced brain iron and serum ferritin levels have been linked to ADHD symptom severity in children. Furthermore, the basal ganglia is a central brain region implicated in ADHD psychopathology and involved in motor and reward functions as well as emotional responding. The present study repurposed diffusion tensor imaging (DTI) to examine effects of an ADHD diagnosis and sex on iron deposition within the basal ganglia in children ages 8-12 years. We further explored associations between brain iron levels and ADHD symptom severity and affective symptoms. We observed reduced iron levels in children with ADHD in the bilateral limbic region of the striatum, as well as reduced levels of iron-deposition in males in the sensorimotor striatal subregion, regardless of diagnosis. Across the whole sample, iron-deposition increased with age in all regions. Brain-behavior analyses revealed that, across diagnostic groups, lower tissue-iron levels in bilateral limbic striatum correlated with greater ADHD symptom severity, whereas lower tissue-iron levels in the left limbic striatum only correlated with anxious, depressive and affective symptom severity. This study sheds light on the neurobiological underpinnings of ADHD, specifically highlighting the localization of tissue-iron deficiency in limbic regions, and providing support for repurposing DTI for brain iron analyses. Our findings highlight the need for further investigation of iron as a biomarker in the diagnosis and treatment of ADHD and sex differences.

Changes in dynamic transitions between integrated and segregated states underlie visual hallucinations in Parkinson's disease

Hallucinations are a core feature of psychosis and common in Parkinson's. Their transient, unexpected nature suggests a change in dynamic brain states, but underlying causes are unknown. Here, we examine temporal dynamics and underlying structural connectivity in Parkinson's-hallucinations using a combination of functional and structural MRI, network control theory, neurotransmitter density and genetic analyses. We show that Parkinson's-hallucinators spent more time in a predominantly Segregated functional state with fewer between-state transitions. The transition from integrated-to-segregated state had lower energy cost in Parkinson's-hallucinators; and was therefore potentially preferable. The regional energy needed for this transition was correlated with regional neurotransmitter density and gene expression for serotoninergic, GABAergic, noradrenergic and cholinergic, but not dopaminergic, receptors. We show how the combination of neurochemistry and brain structure jointly shape functional brain dynamics leading to hallucinations and highlight potential therapeutic targets by linking these changes to neurotransmitter systems involved in early sensory and complex visual processing.

Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?

The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder in children without an intellectual disability to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.

Structural covariance of the ventral visual stream predicts posttraumatic intrusion and nightmare symptoms: a multivariate data fusion analysis

Visual components of trauma memories are often vividly re-experienced by survivors with deleterious consequences for normal function. Neuroimaging research on trauma has primarily focused on threat-processing circuitry as core to trauma-related dysfunction. Conversely, limited attention has been given to visual circuitry which may be particularly relevant to posttraumatic stress disorder (PTSD). Prior work suggests that the ventral visual stream is directly related to the cognitive and affective disturbances observed in PTSD and may be predictive of later symptom expression. The present study used multimodal magnetic resonance imaging data (n = 278) collected two weeks after trauma exposure from the AURORA study, a longitudinal, multisite investigation of adverse posttraumatic neuropsychiatric sequelae. Indices of gray and white matter were combined using data fusion to identify a structural covariance network (SCN) of the ventral visual stream 2 weeks after trauma. Participant's loadings on the SCN were positively associated with both intrusion symptoms and intensity of nightmares. Further, SCN loadings moderated connectivity between a previously observed amygdala-hippocampal functional covariance network and the inferior temporal gyrus. Follow-up MRI data at 6 months showed an inverse relationship between SCN loadings and negative alterations in cognition in mood. Further, individuals who showed decreased strength of the SCN between 2 weeks and 6 months had generally higher PTSD symptom severity over time. The present findings highlight a role for structural integrity of the ventral visual stream in the development of PTSD. The ventral visual stream may be particularly important for the consolidation or retrieval of trauma memories and may contribute to efficient reactivation of visual components of the trauma memory, thereby exacerbating PTSD symptoms. Potentially chronic engagement of the network may lead to reduced structural integrity which becomes a risk factor for lasting PTSD symptoms.

The Impaired Subcortical Pathway From Superior Colliculus to the Amygdala in Boys With Autism Spectrum Disorder

Objective

Increasing evidence suggests that a subcortical pathway from the superior colliculus (SC) through the pulvinar to the amygdala plays a crucial role in mediating non-conscious processing in response to emotional visual stimuli. Given the atypical eye gaze and response patterns to visual affective stimuli in autism, we examined the functional and white matter structural difference of the pathway in boys with autism spectrum disorder (ASD) and typically developing (TD) boys.

Methods

A total of 38 boys with ASD and 38 TD boys were included. We reconstructed the SC-pulvinar-amygdala pathway in boys with ASD and TD using tractography and analyzed tract-specific measurements to compare the white matter difference between the two groups. A region of interest-based functional analysis was also applied among the key nodes of the pathway to explore the functional connectivity network.

Results

Diffusion tensor imaging analysis showed decreased fractional anisotropy (FA) in pathways for boys with ASD compared to TD. The FA change was significantly associated with the atypical communication pattern in boys with ASD. In addition, compared to TD, we found that the ASD group was associated with increased functional connectivity between the right pulvinar and the left SC.

Conclusion

Our results indicated that the functional and white matter microstructure of the subcortical route to the amygdala might be altered in individuals with autism. This atypical structural change of the SC-pulvinar-amygdala pathway may be related to the abnormal communication patterns in boys with ASD.

Lifespan Volume Trajectories From Non–harmonized T1–Weighted MRI Do Not Differ After Site Correction Based on Traveling Human Phantoms

Multi–site imaging consortiums strive to increase participant numbers by pooling data across sites, but scanner related differences can bias results. This study combines data from three research MRI centers, including three different scanner models from two vendors, to examine non–harmonized T1–weighted brain imaging protocols in two cohorts. First, 23 human traveling phantoms were scanned twice each at all three sites (six scans per person; 138 scans total) to quantify within–participant variability of brain volumes (total brain, white matter, gray matter, lateral ventricles, thalamus, caudate, putamen and globus pallidus), and to calculate site–specific correction factors for each structure. Sample size calculations were used to determine the number of traveling phantoms needed to achieve effect sizes for observed differences to help guide future studies. Next, cross–sectional lifespan volume trajectories were examined in 856 healthy participants (5—91 years of age) scanned at these sites. Cross–sectional trajectories of volume versus age for each structure were then compared before and after application of traveling phantom based site–specific correction factors, as well as correction using the open–source method ComBat. Although small systematic differences between sites were observed in the traveling phantom analysis, correction for site using either method had little impact on the lifespan trajectories. Only white matter had small but significant differences in the intercept parameter after ComBat correction (but not traveling phantom based correction), while no other fits differed. This suggests that age–related changes over the lifespan outweigh systematic differences between scanners for volumetric analysis. This work will help guide pooling of multisite datasets as well as meta–analyses of data from non–harmonized protocols.

Local structure-function relationships in human brain networks across the lifespan

A growing number of studies have used stylized network models of communication to predict brain function from structure. Most have focused on a small set of models applied globally. Here, we compare a large number of models at both global and regional levels. We find that globally most predictors perform poorly. At the regional level, performance improves but heterogeneously, both in terms of variance explained and the optimal model. Next, we expose synergies among predictors by using pairs to jointly predict FC. Finally, we assess age-related differences in global and regional coupling across the human lifespan. We find global decreases in the magnitude of structure-function coupling with age. We find that these decreases are driven by reduced coupling in sensorimotor regions, while higher-order cognitive systems preserve local coupling with age. Our results describe patterns of structure-function coupling across the cortex and how this may change with age.

What's new and what's next in diffusion MRI preprocessing

Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.

Automated Multiclass Artifact Detection in Diffusion MRI Volumes via 3D Residual Squeeze-and-Excitation Convolutional Neural Networks

Diffusion MRI (dMRI) is widely used to investigate neuronal and structural development of brain. dMRI data is often contaminated with various types of artifacts. Hence, artifact type identification in dMRI volumes is an essential pre-processing step prior to carrying out any further analysis. Manual artifact identification amongst a large pool of dMRI data is a highly labor-intensive task. Previous attempts at automating this process are often limited to a binary classification ("poor" vs. "good" quality) of the dMRI volumes or focus on detecting a single type of artifact (e.g., motion, Eddy currents, etc.). In this work, we propose a deep learning-based automated multiclass artifact classifier for dMRI volumes. Our proposed framework operates in 2 steps. In the first step, the model predicts labels associated with 3D mutually exclusive collectively exhaustive (MECE) sub-volumes or "slabs" extracted from whole dMRI volumes. In the second step, through a voting process, the model outputs the artifact class present in the whole volume under investigation. We used two different datasets for training and evaluating our model. Specifically, we utilized 2,494 poor-quality dMRI volumes from the Adolescent Brain Cognitive Development (ABCD) and 4,226 from the Healthy Brain Network (HBN) dataset. Our results demonstrate accurate multiclass volume-level main artifact type prediction with 96.61 and 97.52% average accuracies on the ABCD and HBN test sets, respectively. Finally, in order to demonstrate the effectiveness of the proposed framework in dMRI pre-processing pipelines, we conducted a proof-of-concept dMRI analysis exploring the relationship between whole-brain fractional anisotropy (FA) and participant age, to test whether the use of our model improves the brain-age association.

Mapping Motor Pathways in Parkinson’s Disease Patients with Subthalamic Deep Brain Stimulator: A Diffusion MRI Tractography Study

Introduction

This study assessed the safety of postoperative diffusion tensor imaging (DTI) with on-state deep brain stimulation (DBS) and the feasibility of reconstruction of the white matter tracts in the vicinity of the stimulation site of the subthalamic nucleus (STN). The association between the impact of DBS on the nigrostriatal pathway (NSP) and the treatment effect on motor symptoms in Parkinson’s disease (PD) was then evaluated.

Methods

Thirty-one PD patients implanted with STN-DBS (mean age: 66 years; 25 male) were scanned on a 1.5-T magnetic resonance imaging (MRI) scanner using the DTI sequence with DBS on. Twenty-three of them were scanned a second time with DBS off. The NSP, dentato-rubro-thalamic tract (DRTT), and hyperdirect pathway (HDP) were generated using both deterministic and probabilistic tractography methods. The DBS-on-state and off-state tractography results were validated and compared. Afterward, the relationships between the characteristics of the reconstructed white matter tracts and the clinical assessment of PD symptoms and the DBS effect were further examined.

Results

No adverse events related to DTI were identified in either the DBS-on-state or off-state. Overall, the NSP was best reconstructed, followed by the DRTT and HDP, using the probabilistic tractography method. The connection probability of the left NSP was significantly lower than that of the right side (p < 0.05), and a negative correlation (r = −0.39, p = 0.042) was identified between the preoperative symptom severity in the medication-on state and the connection probability of the left NSP in the DBS-on-state images. Furthermore, the distance from the estimated left-side volume of tissue activated (VTA) by STN-DBS to the ipsilateral NSP was significantly shorter in the DBS-responsive group compared to the DBS-non-responsive group (p = 0.046).

Conclusions

DTI scanning is safe and delineation of white matter pathway is feasible for PD patients implanted with the DBS device. Postoperative DTI is a useful technique to strengthen our current understanding of the therapeutic effect of DBS for PD and has the potential to refine target selection strategies for brain stimulation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40120-022-00331-1.

For some more seriously affected Parkinson’s disease (PD) patients, drugs are no longer effective in treating their symptoms. An alternate treatment is to use deep brain stimulation (DBS), a commonly used neurosurgical therapy for PD patients. For those DBS treatments targeting the subthalamic nucleus (STN), the electrical stimulation used may impact nearby white matter tracts and alter the effectiveness of the DBS treatment. The nigrostriatal pathway (NSP), dentato-rubro-thalamic tract, and hyperdirect pathway are three white matter tracts near the STN. They are all relevant to motor symptoms in PD. This study examined whether imaging these tracts using magnetic resonance imaging (MRI) is safe and feasible in the presence of DBS leads. The relationships between the fiber-tracking characteristics and distance to the DBS leads were then evaluated. For this purpose, 31 PD patients with stimulation-on were scanned on a 1.5 T MRI scanner using a diffusion tensor imaging sequence. A total of 23 subjects underwent another scan using the same sequence with stimulation-off. No adverse events related to diffusion tensor imaging were found. Among the white matter tracts near the STN, the NSP was best delineated, followed by the dentato-rubro-thalamic tract and the hyperdirect pathway. The connection probability of the left NSP was significantly lower than that of the right side as were the subject’s motor symptoms. The closer the distance between the NSP and the stimulation location, the better the DBS outcome. These findings indicate that imaging white matter tracts with DBS on is safe and useful in mapping DBS outcomes.

Network controllability mediates the relationship between rigid structure and flexible dynamics

Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid interareal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brain's structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain region's functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes' boundary controllability, suggesting that a region's strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity.

Cardiometabolic risk factors associated with brain age and accelerate brain ageing

The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross-sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue-specific BAGs. The results showed credible associations between DTI-based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1-based BAG and systolic blood pressure, smoking, pulse, and C-reactive protein (CRP), indicating older-appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low-grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist-to-hip ratio (WHR), and between DTI-based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing.