Desikan-Killiany-Tourville Atlas Compatible Version of M-CRIB Neonatal Parcellated Whole Brain Atlas: The M-CRIB 2.0

Right thalamic volume mediates impact of the dopamine beta-hydroxylase gene on the endowment effect

The endowment effect is a tendency that individuals overvalue items belonging to them relative to those items that do not. Previous studies showed a strong relation between the dopamine beta-hydroxylase (DBH) gene and the endowment effect (EE), and a link between EE and task-based functional MRI activation in multiple brain regions. However, the role of brain structure on EE remains unclear. In this study, we have explored whether regional brain volume mediate the effect of the DBH gene on EE. Results showed that rs1611115, single-nucleotide polymorphisms (SNPs) at DBH loci, were significantly associated with right thalamus volume and the endowment effect in males but not in female participants. Specifically, male DBH rs1611115 T-carriers had larger right thalamus volume compared to carriers of CC genotype and exhibited a greater endowment effect. Importantly, we found that right thalamus volume mediated the effect of rs1611115 on the endowment effect in male participants. This study demonstrated how thalamic volume plays an important mediating role between genetics and decision-making in humans.

Low-Quality Video Target Detection Based on EEG Signal Using Eye Movement Alignment

The target detection based on electroencephalogram (EEG) signals is a new target detection method. This method recognizes the target by decoding the specific neural response when an operator observes the target, which has important theoretical and application values. This paper focuses on the EEG detection of low-quality video targets, which breaks through the limitation of previous target detection based on EEG signals only for high-quality video targets. We first design an experimental paradigm for EEG-based low-quality video target detection and propose an epoch extraction method based on eye movement signals to solve the asynchronous problem faced by low-quality video target detection. Then, the neural representation in the process of operator recognition is analyzed based on the time domain, frequency domain, and source space domain, respectively. We design the time-frequency features based on continuous wavelet transform according to the neural representation and obtain an average decoding test accuracy of 84.56%. The research results of this paper lay the foundation for the development of a video target detection system based on EEG signals in the future.

Population Transformer: Learning Population-level Representations of Intracranial Activity

We present a self-supervised framework that learns population-level codes for intracranial neural recordings at scale, unlocking the benefits of representation learning for a key neuroscience recording modality. The Population Transformer (PopT) lowers the amount of data required for decoding experiments, while increasing accuracy, even on never-before-seen subjects and tasks. We address two key challenges in developing PopT: sparse electrode distribution and varying electrode location across patients. PopT stacks on top of pretrained representations and enhances downstream tasks by enabling learned aggregation of multiple spatially-sparse data channels. Beyond decoding, we interpret the pretrained PopT and fine-tuned models to show how it can be used to provide neuroscience insights learned from massive amounts of data. We release a pretrained PopT to enable off-the-shelf improvements in multi-channel intracranial data decoding and interpretability, and code is available at https://github.com/czlwang/PopulationTransformer.

An epidemiological study of season of birth, mental health, and neuroimaging in the UK Biobank

Environmental exposures during the perinatal period are known to have a long-term effect on adult physical and mental health. One such influential environmental exposure is the time of year of birth which affects the amount of daylight, nutrients, and viral load that an individual is exposed to within this key developmental period. Here, we investigate associations between season of birth (seasonality), four mental health traits (n = 137,588) and multi-modal neuroimaging measures (n = 33,212) within the UK Biobank. Summer births were associated with probable recurrent Major Depressive Disorder (β = 0.026, pcorr = 0.028) and greater mean cortical thickness in temporal and occipital lobes (β = 0.013 to 0.014, pcorr<0.05). Winter births were associated with greater white matter integrity globally, in the association fibers, thalamic radiations, and six individual tracts (β = -0.013 to -0.022, pcorr<0.05). Results of sensitivity analyses adjusting for birth weight were similar, with an additional association between winter birth and white matter microstructure in the forceps minor and between summer births, greater cingulate thickness and amygdala volume. Further analyses revealed associations between probable depressive phenotypes and a range of neuroimaging measures but a paucity of interactions with seasonality. Our results suggest that seasonality of birth may affect later-life brain structure and play a role in lifetime recurrent Major Depressive Disorder. Due to the small effect sizes observed, and the lack of associations with other mental health traits, further research is required to validate birth season effects in the context of different latitudes, and by co-examining genetic and epigenetic measures to reveal informative biological pathways.

A precision functional atlas of personalized network topography and probabilities

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

Updates to the Melbourne Children's Regional Infant Brain Software Package (M-CRIB-S)

The delineation of cortical areas on magnetic resonance images (MRI) is important for understanding the complexities of the developing human brain. The previous version of the Melbourne Children's Regional Infant Brain (M-CRIB-S) (Adamson et al. Scientific Reports, 10(1), 10, 2020) is a software package that performs whole-brain segmentation, cortical surface extraction and parcellation of the neonatal brain. Available cortical parcellation schemes in the M-CRIB-S are the adult-compatible 34- and 31-region per hemisphere Desikan-Killiany (DK) and Desikan-Killiany-Tourville (DKT), respectively. We present a major update to the software package which achieves two aims: 1) to make the voxel-based segmentation outputs derived from the Freesurfer-compatible M-CRIB scheme, and 2) to improve the accuracy of whole-brain segmentation and cortical surface extraction. Cortical surface extraction has been improved with additional steps to improve penetration of the inner surface into thin gyri. The improved cortical surface extraction is shown to increase the robustness of measures such as surface area, cortical thickness, and cortical volume.

Cortical surface analysis for focal cortical dysplasia diagnosis by using PET images

Focal cortical dysplasia (FCD) is a neurological disorder distinguished by faulty brain cell structure and development. Repetitive and uncontrollable seizures may be linked to FCD's aberrant cortical thickness, gyrification, and sulcal depth. Quantitative cortical surface analysis is a crucial alternative to ineffective visual inspection. This study recruited 42 subjects including 22 FCD patients who underwent surgery and 20 healthy controls (HC). For the FCD patients, T1-weighted and PET images were obtained by a PET-MRI scanner, and the confirmed epileptogenic zone (EZ) was collected from postsurgical follow-up. For the HCs, CT and PET images were obtained by a PET-CT scanner. Cortical thickness, gyrification index, and sulcal depth were calculated using a computational anatomical toolbox (CAT12). A cluster-based analysis is carried out to determine each FCD patient's aberrant cortical surface. After parcellating the cerebral cortex into 68 regions by the Desikan-Killiany atlas, a region of interest (ROI) analysis was conducted to know whether the feature in the FCD group is significantly different from that in the HC group. Finally, the features of all ROIs were utilised to train a support vector machine classifier (SVM). The classification performance is evaluated by the leave-one-out cross-validation. The cluster-based analysis can localize the EZ cluster with the highest accuracy of 54.5 % (12/22) for cortical thickness, 40.9 % (9/22) and 13.6 % (3/22) for sulcal depth and gyrification, respectively. Moderate concordance (Kappa, 0.6) is observed between the confirmed EZs and identified clusters by using the cortical thickness. Fair concordance (Kappa, 0.3) and no concordance (Kappa, 0.1) is found by using sulcal depth and gyrification. Significant differences are found in 46 of 68 regions (67.7 %) for the three measures. The trained SVM classifier achieved a prediction accuracy of 95.5 % for the cortical thickness, while the sulcal depth and the gyrification obtained 86.0 % and 81.5 %. Cortical thickness, as determined by quantitative cortical surface analysis of PET data, has a greater ability than sulcal depth and gyrification to locate aberrant EZ clusters in FCD. Surface measures might be different in many regions for FCD and HC. By integrating machine learning and cortical morphologies features, individual prediction of FCD seems to be feasible.

Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features

Introduction

The electroencephalographic (EEG) based on the motor imagery task is derived from the physiological electrical signal caused by the autonomous activity of the brain. Its weak potential difference changes make it easy to be overwhelmed by noise, and the EEG acquisition method has a natural limitation of low spatial resolution. These have brought significant obstacles to high-precision recognition, especially the recognition of the motion intention of the same upper limb.

Methods

This research proposes a method that combines signal traceability and Riemannian geometric features to identify six motor intentions of the same upper limb, including grasping/holding of the palm, flexion/extension of the elbow, and abduction/adduction of the shoulder. First, the EEG data of electrodes irrelevant to the task were screened out by low-resolution brain electromagnetic tomography. Subsequently, tangential spatial features are extracted by the Riemannian geometry framework in the covariance matrix estimated from the reconstructed EEG signals. The learned Riemannian geometric features are used for pattern recognition by a support vector machine with a linear kernel function.

Results

The average accuracy of the six classifications on the data set of 15 participants is 22.47%, the accuracy is 19.34% without signal traceability, the accuracy is 18.07% when the features are the filter bank common spatial pattern (FBCSP), and the accuracy is 16.7% without signal traceability and characterized by FBCSP.

Discussion

The results show that the proposed method can significantly improve the accuracy of intent recognition. In addressing the issue of temporal variability in EEG data for active Brain-Machine Interfaces, our method achieved an average standard deviation of 2.98 through model transfer on different days' data.

Pathway-Based Polygenic Risk Scores for Schizophrenia and Associations With Reported Psychotic-like Experiences and Neuroimaging Phenotypes in the UK Biobank

Background

Schizophrenia is a heritable psychiatric disorder with a polygenic architecture. Genome-wide association studies have reported that an increasing number of risk-associated variants and polygenic risk scores (PRSs) explain 17% of the variance in the disorder. Substantial heterogeneity exists in the effect of these variants, and aggregating them based on biologically relevant functions may provide mechanistic insight into the disorder.

Methods

Using the largest schizophrenia genome-wide association study conducted to date, we associated PRSs based on 5 gene sets previously found to contribute to schizophrenia pathophysiology-postsynaptic density of excitatory synapses, postsynaptic membrane, dendritic spine, axon, and histone H3-K4 methylation-along with respective whole-genome PRSs, with neuroimaging (n > 29,000) and reported psychotic-like experiences (n > 119,000) variables in healthy UK Biobank subjects.

Results

Several variables were significantly associated with the axon gene-set (psychotic-like communications, parahippocampal gyrus volume, fractional anisotropy thalamic radiations, and fractional anisotropy posterior thalamic radiations (β range -0.016 to 0.0916, false discovery rate-corrected p [pFDR] ≤ .05), postsynaptic density gene-set (psychotic-like experiences distress, global surface area, and cingulate lobe surface area [β range -0.014 to 0.0588, pFDR ≤ .05]), and histone gene set (entorhinal surface area: β = -0.016, pFDR = .035). From these, whole-genome PRSs were significantly associated with psychotic-like communications (β = 0.2218, pFDR = 1.34 × 10-7), distress (β = 0.1943, pFDR = 7.28 × 10-16), and fractional anisotropy thalamic radiations (β = -0.0143, pFDR = .036). Permutation analysis revealed that these associations were not due to chance.

Conclusions

Our results indicate that genetic variation in 3 gene sets relevant to schizophrenia may confer risk for the disorder through effects on previously implicated neuroimaging variables. Because associations were stronger overall for whole-genome PRSs, findings here highlight that selection of biologically relevant variants is not yet sufficient to address the heterogeneity of the disorder.

Prediction of early improvement of major depressive disorder to antidepressant medication in adolescents with radiomics analysis after ComBat harmonization based on multiscale structural MRI

BACKGROUND

Due to individual differences and lack of objective biomarkers, only 30-40% patients with major depressive disorder (MDD) achieve remission after initial antidepressant medication (ADM). We aimed to employ radiomics analysis after ComBat harmonization to predict early improvement to ADM in adolescents with MDD by using brain multiscale structural MRI (sMRI) and identify the radiomics features with high prediction power for selection of selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs).

METHODS

121 MDD patients were recruited for brain sMRI, including three-dimensional T1 weighted imaging (3D-T₁WI)and diffusion tensor imaging (DTI). After receiving SSRIs or SNRIs for 2 weeks, the subjects were divided into ADM improvers (SSRIs improvers and SNRIs improvers) and non-improvers according to reduction rate of the Hamilton Depression Rating Scale, 17 item (HAM-D₁₇) score. Then, sMRI data were preprocessed, and conventional imaging indicators and radiomics features of gray matter (GM) based on surface-based morphology (SBM) and voxel-based morphology (VBM) and diffusion properties of white matter (WM) were extracted and harmonized with ComBat harmonization. Two-level reduction strategy with analysis of variance (ANOVA) and recursive feature elimination (RFE) was utilized sequentially to decrease high-dimensional features. Support vector machine with radial basis function kernel (RBF-SVM) was used to integrate multiscale sMRI features to construct models for early improvement prediction. Area under the curve (AUC), accuracy, sensitivity, and specificity based on the leave-one-out cross-validation (LOO-CV) and receiver operating characteristic (ROC) curve analysis were calculated to evaluate the model performance. Permutation tests were used for assessing the generalization rate.

RESULTS

After 2-week ADM, 121 patients were divided into 67 ADM improvers (31 SSRIs improvers and 36 SNRIs improvers) and 54 ADM non-improvers. After two-level dimensionality reduction, 8 conventional indicators (2 VBM-based features and 6 diffusion features) and 49 radiomics features (16 VBM-based features and 33 diffusion features) were selected. The overall accuracy of RBF-SVM models based on conventional indicators and radiomics features was 74.80% and 88.19%. The radiomics model achieved the AUC, sensitivity, specificity, and accuracy of 0.889, 91.2%, 80.1% and 85.1%, 0.954, 89.2%, 87.4% and 88.5%, 0.942, 91.9%, 82.5% and 86.8% for predicting ADM improvers, SSRIs improvers and SNRIs improvers, respectively. P value of permutation tests were less than 0.001. The radiomics features predicting ADM improver were mainly located in the hippocampus, medial orbitofrontal gyrus, anterior cingulate gyrus, cerebellum (lobule vii-b), body of corpus callosum, etc. The radiomics features predicting SSRIs improver were primarily distributed in hippocampus, amygdala, inferior temporal gyrus, thalamus, cerebellum (lobule vi), fornix, cerebellar peduncle, etc. The radiomics features predicting SNRIs improver were primarily located in the medial orbitofrontal cortex, anterior cingulate gyrus, ventral striatum, corpus callosum, etc. CONCLUSIONS: These findings suggest the radiomics analysis based on brain multiscale sMRI after ComBat harmonization could effectively predict the early improvement of ADM in adolescent MDD patients with a high accuracy, which was superior to the model based on the conventional indicators. The radiomics features with high prediction power may help for the individual selection of SSRIs and SNRIs.

COVID-19 vaccine-hesitancy is associated with lower cortical volume in elderly individuals

Background

According to a large number of scientific reports, the main problem is COVID-19 vaccine hesitancy which slowed down the vaccination program. Previous studies revealed that COVID-19 vaccine hesitancy is associated with lower cognitive performance. However, the neurobiology of such behavior is less known, and investigating the brain structural patterns in this regard can extend our knowledge on the basis of this behavior. This study aimed to investigate the link between brain structural features including cortical and subcortical volume with COVID-19 vaccine hesitancy in elderly individuals.

Methods

A total of 221 healthy subjects without any cognitive impairment with a mean age of 63.7 ± 6.1 were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Overall, 87 vaccine-hesitant (VH) and 134 vaccine-accepted (VA) were entered into this study. The difference in the volume of cortical and subcortical regions was investigated between VH and VA groups.

Results

There was no significant difference in cognitive status measured by MMSE, MoCA, ADAS-cog, and RAVLT between VA and VH groups (P>0.05). The analysis showed that VA subjects had significantly higher left pars orbitalis (P: 0.013), left precentral (P: 0.042), right caudal anterior cingulate (P: 0.044), and right isthmus cingulate (P: 0.013) volume compared to the VH group. There was no significant difference in other cortical and subcortical regions.

Conclusion

In conclusion, this finding demonstrated that in the era of complicated decision-making due to social media reports, elderly adults with smaller frontal and cingulate regions are more likely to be vaccine-hesitant. These findings can highlight the link between cortical regions and health-protective behaviors such as taking up the offer of vaccination.

Cot-side imaging of functional connectivity in the developing brain during sleep using wearable high-density diffuse optical tomography

Studies of cortical function in newborn infants in clinical settings are extremely challenging to undertake with traditional neuroimaging approaches. Partly in response to this challenge, functional near-infrared spectroscopy (fNIRS) has become an increasingly common clinical research tool but has significant limitations including a low spatial resolution and poor depth specificity. Moreover, the bulky optical fibres required in traditional fNIRS approaches present significant mechanical challenges, particularly for the study of vulnerable newborn infants. A new generation of wearable, modular, high-density diffuse optical tomography (HD-DOT) technologies has recently emerged that overcomes many of the limitations of traditional, fibre-based and low-density fNIRS measurements. Driven by the development of this new technology, we have undertaken the first cot-side study of newborn infants using wearable HD-DOT in a clinical setting. We use this technology to study functional brain connectivity (FC) in newborn infants during sleep and assess the effect of neonatal sleep states, active sleep (AS) and quiet sleep (QS), on resting state FC. Our results demonstrate that it is now possible to obtain high-quality functional images of the neonatal brain in the clinical setting with few constraints. Our results also suggest that sleep states differentially affect FC in the neonatal brain, consistent with prior reports.

Dynamical interactions reconfigure the gradient of cortical timescales

The functional organization of the brain is usually presented with a back-to-front gradient of timescales, reflecting regional specialization with sensory areas (back) processing information faster than associative areas (front), which perform information integration. However, cognitive processes require not only local information processing but also coordinated activity across regions. Using magnetoencephalography recordings, we find that the functional connectivity at the edge level (between two regions) is also characterized by a back-to-front gradient of timescales following that of the regional gradient. Unexpectedly, we demonstrate a reverse front-to-back gradient when nonlocal interactions are prominent. Thus, the timescales are dynamic and can switch between back-to-front and front-to-back patterns.

Concurrent mapping of brain ontogeny and phylogeny within a common space: Standardized tractography and applications

Developmental and evolutionary effects on brain organization are complex, yet linked, as evidenced by the correspondence in cortical area expansion across these vastly different time scales. However, it is still not possible to study concurrently the ontogeny and phylogeny of cortical areal connections, which is arguably more relevant to brain function than allometric measurements. Here, we propose a novel framework that allows the integration of structural connectivity maps from humans (adults and neonates) and nonhuman primates (macaques) onto a common space. We use white matter bundles to anchor the common space and use the uniqueness of cortical connection patterns to these bundles to probe area specialization. This enabled us to quantitatively study divergences and similarities in connectivity over evolutionary and developmental scales, to reveal brain maturation trajectories, including the effect of premature birth, and to translate cortical atlases between diverse brains. Our findings open new avenues for an integrative approach to imaging neuroanatomy.

Lithium increases cortical and subcortical volumes in subjects with bipolar disorder

Bipolar disorder (BD) is a highly variable and burdensome disease for patients and caregivers. A BD diagnosis almost triples the likelihood of developing dementia as the disease progresses. Neurocognitive reserve appears to be one of the most important influences on lifelong functional outcomes and quality of life in BD. Though several prior studies have assessed the effects of lithium on regional gray and white matter volumes in this population, representative cohorts are typically middle-aged, have a more severe pathology, and are not as commonly assessed in the depressive phase (which represents the majority of most patients' lifespans outside of remission). Here we have shown that positive adaptations with lithium can be observed throughout the brain after only six weeks of monotherapy at low-therapeutic serum levels. Importantly, these results remove some confounders seen in prior studies (patients were treatment free at time of enrollment and mostly treatment naïve). This cohort also includes underrepresented demographics in the literature (young adult patients, mostly bipolar II, and exclusively in the depressed phase). These findings bolster the extensive body of evidence in support of long-term lithium therapy in BD, furthering the possibility of its expanded use to wider demographics.

Alcohol use is associated with mental health problems and brain structural alterations in adolescents with perinatally acquired HIV infection on ART

Alcohol use, presents unique challenges for HIV-1 treatment in adolescents with perinatally acquired infection. The effects of alcohol on host-virus interaction in the brain and the immune system remains understudied in this population. Adolescents with perinatally acquired HIV infection (PHIV) well established on ART, from the Cape Town Adolescent Antiretroviral Cohort who self-reported alcohol use (PHIV + alcohol) (n = 26) were compared to age matched 26 PHIV (PHIV-alcohol) and 26 healthy controls (HC) who reported no use of alcohol. Participants completed clinical investigations including highly-sensitive CRP (hs-CRP), a comprehensive neurocognitive test battery and mental health measures. In addition, we investigated the relationship between alcohol use in PHIV and diffusion tensor imaging (DTI) and structural brain magnetic resonance imaging (MRI) to determine fractional anisotropy (FA), mean diffusivity (MD), grey and white matter volumes and cortical thickness. PHIV (mean age 12,5 years; mean age of ART initiation 3.15 years) reported an occasional weekend drinking pattern of alcohol use. hs-CRP was significantly different between groups, with PHIV + alcohol higher than PHIV-alcohol and HC. General intelligence, attention, working memory, processing speed and executive function were more impaired in the PHIV + alcohol than PHIV alone, with HC having the highest scores. In addition, self-concept was significantly lower in PHIV + alcohol. The Child Behavior Checklist (CBCL) Externalizing behaviour, internalising behaviour and CBCL Total problems were significantly higher in PHIV + alcohol. FA of the superior corona radiata, superior fronto-occipital fasciculus and corpus callosum was significantly lower in PHIV + alcohol compared to PHIV-alcohol and MD of the corona radiata was significantly increased in PHIV + alcohol. The cortical thickness of the lateral orbitofrontal, middle frontal and precentral gyri were significantly lower in PHIV + alcohol compared to PHIV-alcohol and HC. In conclusion PHIV associated impairments in systemic inflammation, cognitive function, mental health and changes in brain structure may be exacerbated by alcohol use, even if only occasional use. However, the study is cross-sectional, which is not able to distinguish between cause and effect.

A generative network model of neurodevelopmental diversity in structural brain organization

The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis-from genes to cognition.

REGION SPECIFIC AUTOMATIC QUALITY ASSURANCE FOR MRI-DERIVED CORTICAL SEGMENTATIONS

Quality control (QC) is a vital step for all scientific data analyses and is critically important in the biomedical sciences. Image segmentation is a common task in medical image analysis, and automated tools to segment many regions from human brain MRIs are now well established. However, these methods do not always give anatomically correct labels. Traditional methods for QC tend to reject statistical outliers, which may not necessarily be inaccurate. Here, we make use of a large database of over 12,000 brain images that contain 68 parcellations of the human cortex, each of which was assessed for anatomical accuracy by a human rater. We trained three machine learning models to determine if a region was anatomically accurate (as 'pass', or 'fail') and tested the performance on an independent dataset. We found good performance for the majority of labeled regions. This work will facilitate more anatomically accurate large-scale multi-site research.

Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years

The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.

Non-negative data-driven mapping of structural connections with application to the neonatal brain

Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation.

Parcellation of the neonatal cortex using Surface-based Melbourne Children's Regional Infant Brain atlases (M-CRIB-S)

Longitudinal studies measuring changes in cortical morphology over time are best facilitated by parcellation schemes compatible across all life stages. The Melbourne Children’s Regional Infant Brain (M-CRIB) and M-CRIB 2.0 atlases provide voxel-based parcellations of the cerebral cortex compatible with the Desikan-Killiany (DK) and the Desikan-Killiany-Tourville (DKT) cortical labelling schemes. This study introduces surface-based versions of the M-CRIB and M-CRIB 2.0 atlases, termed M-CRIB-S(DK) and M-CRIB-S(DKT), with a pipeline for automated parcellation utilizing FreeSurfer and developing Human Connectome Project (dHCP) tools. Using T₂-weighted magnetic resonance images of healthy neonates (n = 58), we created average spherical templates of cortical curvature and sulcal depth. Manually labelled regions in a subset (n = 10) were encoded into the spherical template space to construct M-CRIB-S(DK) and M-CRIB-S(DKT) atlases. Labelling accuracy was assessed using Dice overlap and boundary discrepancy measures with leave-one-out cross-validation. Cross-validated labelling accuracy was high for both atlases (average regional Dice = 0.79–0.83). Worst-case boundary discrepancy instances ranged from 9.96–10.22 mm, which appeared to be driven by variability in anatomy for some cases. The M-CRIB-S atlas data and automatic pipeline allow extraction of neonatal cortical surfaces labelled according to the DK or DKT parcellation schemes.

A Novel Approach for Manual Segmentation of the Amygdala and Hippocampus in Neonate MRI

The gross anatomy of the infant brain at term is fairly similar to that of the adult brain, but structures are immature, and the brain undergoes rapid growth during the first 2 years of life. Neonate magnetic resonance (MR) images have different contrasts compared to adult images, and automated segmentation of brain magnetic resonance imaging (MRI) can thus be considered challenging as less software options are available. Despite this, most anatomical regions are identifiable and thus amenable to manual segmentation. In the current study, we developed a protocol for segmenting the amygdala and hippocampus in T2-weighted neonatal MR images. The participants were 31 healthy infants between 2 and 5 weeks of age. Intra-rater reliability was measured in 12 randomly selected MR images, where 6 MR images were segmented at 1-month intervals between the delineations, and another 6 MR images at 6-month intervals. The protocol was also tested by two independent raters in 20 randomly selected T2-weighted images, and finally with T1 images. Intraclass correlation coefficient (ICC) and Dice similarity coefficient (DSC) for intra-rater, inter-rater, and T1 vs. T2 comparisons were computed. Moreover, manual segmentations were compared to automated segmentations performed by iBEAT toolbox in 10 T2-weighted MR images. The intra-rater reliability was high ICC ≥ 0.91, DSC ≥ 0.89, the inter-rater reliabilities were satisfactory ICC ≥ 0.90, DSC ≥ 0.75 for hippocampus and DSC ≥ 0.52 for amygdalae. Segmentations for T1 vs. T2-weighted images showed high consistency ICC ≥ 0.90, DSC ≥ 0.74. The manual and iBEAT segmentations showed no agreement, DSC ≥ 0.39. In conclusion, there is a clear need to improve and develop the procedures for automated segmentation of infant brain MR images.