Predicting age and clinical risk from the neonatal connectome

AbstractThe development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. With the rise of advanced imaging methods such as diffusion MRI, the study of brain connectivity has emerged as an important tool to understand subtle alterations associated with neurodevelopmental conditions. Brain connectivity derived from diffusion MRI is complex, multi-dimensional and noisy, and hence it can be challenging to interpret on an individual basis. Machine learning methods have proven to be a powerful tool to uncover hidden patterns in such data, thus opening an opportunity for early identification of atypical development and potentially more efficient treatment.In this work, we used Deep Neural Networks and Random Forests to predict neurodevelopmental characteristics from neonatal structural connectomes, in a large sample of neonates (N = 524) derived from the developing Human Connectome Project. We achieved a highly accurate prediction of post menstrual age (PMA) at scan on term-born infants (Mean absolute error (MAE) = 0.72 weeks, r = 0.83, p<<0.001). We also achieved good accuracy when predicting gestational age at birth on a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<<0.001). From our models of PMA at scan for infants born at term, we computed the brain maturation index (i.e. predicted minus actual age) of individual preterm neonates and found significant correlation of this index with motor outcome at 18 months corrected age. Our results suggest that the neural substrate for later neurological functioning is detectable within a few weeks after birth in the structural connectome.

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TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity

10.1101/631952 ◽

2019 ◽

Cited By ~ 6

Author(s):

Guillaume Theaud ◽

Jean-Christophe Houde ◽

Arnaud Boré ◽

François Rheault ◽

Felix Morency ◽

...

Keyword(s):

Diffusion Mri ◽

Diffusion Tensor ◽

Brain Connectivity ◽

Reversed Phase ◽

Fiber Density ◽

Processing Pipeline ◽

Diffusion Weighted Images ◽

Brain Extraction ◽

Human Connectome Project ◽

Over Time

AbstractA diffusion MRI (dMRI) tractography processing pipeline should be: i) reproducible in immediate test-test, ii) reproducible in time, iii) efficient and iv) easy to use. Two runs of the same processing pipeline with the same input data should give the same output today, tomorrow and in 2 years. However, processing dMRI data requires a large number of steps (20+ steps) that, at this time, may not be reproducible between runs or over time. If parameters such as the number of threads or the random number generator are not carefully set in the brain extraction, registration and fiber tracking steps, the end tractography results obtained can be far from reproducible and limit brain connectivity studies. Moreover, processing can take several hours to days of computation for a large database, even more so if the steps are running sequentially.To handle these issues, we present TractoFlow, a fully automated pipeline that processes datasets from the raw diffusion weighted images (DWI) to tractography. It also outputs classical diffusion tensor imaging measures (fractional anisotropy (FA) and diffu-sivities) and several HARDI measures (Number of Fiber Orientation (NuFO), Apparent Fiber Density (AFD)). The pipeline requires a DWI and T1-weighted image as NIfTI files and b-values/b-vectors in FSL format. An optional reversed phase encoded b=0 image can also be used. This pipeline is based on two technologies: Nextflow and Singularity, as well as recommended pre-processing and processing steps from the dMRI community. In this work, the TractoFlow pipeline is evaluated on three databases and shown to be efficient and reproducible from 98% to 100% depending on parameter choices. For example, 105 subjects from the Human Connectome Project (HCP) were fully ran in twenty-five (25) hours to produce, for each subject, a whole-brain tractogram with 4 million streamlines. The contribution of this paper is to introduce the importance of a robust pipeline in terms of runtime and reproducibility over time. In the era of open data and open science, efficiency and reproducibility is critical in neuroimaging projects. Our TractoFlow pipeline is publicly available for academic research and is an important step forward for better structural brain connectivity mapping.

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Structural and functional asymmetry of the neonatal cerebral cortex

10.1101/2021.10.13.464206 ◽

2021 ◽

Author(s):

Logan Zane John Williams ◽

Sean Patrick Fitzgibbon ◽

Jelena Bozek ◽

Anderson M Winkler ◽

Ralica Dimitrova ◽

...

Keyword(s):

New Left ◽

Newborn Infants ◽

Later Life ◽

Perinatal Period ◽

Magnetic Resonance Images ◽

Brain Asymmetry ◽

Preterm Neonates ◽

Term Neonates ◽

Equivalent Age ◽

Human Connectome Project

Features of brain asymmetry have been implicated in a broad range of cognitive processes; however, their origins are still poorly understood. Using a new left-right symmetric, spatiotemporal cortical surface atlas, we investigated cortical asymmetries in 442 healthy newborn infants soon after birth, using structural and functional magnetic resonance images from the Developing Human Connectome Project. We identified previously unrecognised structural and functional asymmetries in auditory, visual and sensorimotor cortices, which closely resemble known asymmetries in adults. These findings show that cortical asymmetries are largely determined prenatally and suggest that they may constrain the development of lateralised functions in later life. In adults, deviations in brain asymmetry have been implicated in a broad range of developmental and psychiatric disorders, some of which have been associated with abnormal perinatal neurodevelopment. To test the hypothesis that normal cortical asymmetry is disrupted in the perinatal period by severe environmental stress, we compared cortical asymmetries between the same group of term neonates and 103 preterm neonates imaged at term-equivalent age. No significant differences were seen between these two cohorts, showing that the development of cortical asymmetries proceeds largely unaffected by preterm birth.

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Functional and diffusion MRI reveal the neurophysiological basis of neonates’ noxious-stimulus evoked brain activity

Nature Communications ◽

10.1038/s41467-021-22960-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Luke Baxter ◽

Fiona Moultrie ◽

Sean Fitzgibbon ◽

Marianne Aspbury ◽

Roshni Mansfield ◽

...

Keyword(s):

White Matter ◽

Prediction Model ◽

Resting State ◽

Early Life ◽

Diffusion Mri ◽

Brain Activity ◽

Noxious Stimulus ◽

Noxious Stimulation ◽

Human Connectome Project ◽

And Diffusion

AbstractUnderstanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-stimulus evoked brain activity. We observe that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n = 18). Applying this prediction model to independent Developing Human Connectome Project data (n = 215), we identify negative associations between predicted noxious-stimulus evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. Here, we observe that noxious-stimulus evoked brain activity in healthy neonates is coupled to resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.

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Development and validation of a brain maturation index using longitudinal neuroanatomical scans

NeuroImage ◽

10.1016/j.neuroimage.2015.05.071 ◽

2015 ◽

Vol 117 ◽

pp. 311-318 ◽

Cited By ~ 21

Author(s):

Bo Cao ◽

Benson Mwangi ◽

Khader M. Hasan ◽

Sudhakar Selvaraj ◽

Cristian P. Zeni ◽

...

Keyword(s):

Brain Maturation ◽

Maturation Index ◽

Development And Validation

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Sex Effects on Brain Maturation in Former Extremely Preterm Neonates – A Quantitative MRI Study

10.1055/s-0041-1732526 ◽

2021 ◽

Author(s):

◽

...

Keyword(s):

Quantitative Mri ◽

Brain Maturation ◽

Preterm Neonates ◽

Sex Effects ◽

Extremely Preterm ◽

Mri Study

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Using diffusion MRI data acquired with ultra-high gradients to improve tractography in routine-quality data

10.1101/2021.06.28.450265 ◽

2021 ◽

Author(s):

Chiara Maffei ◽

Christine Lee ◽

Michael Planich ◽

Manisha Ramprasad ◽

Nivedita Ravi ◽

...

Keyword(s):

Diffusion Mri ◽

Region Of Interest ◽

Training Data ◽

Quality Data ◽

High Quality ◽

Scanning Time ◽

Human Connectome Project ◽

High Gradients ◽

Comprehensive Comparison

The development of scanners with ultra-high gradients, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from today's state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.

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Spatial Patterns for Discriminative Estimation

10.1101/746891 ◽

2019 ◽

Author(s):

Alberto Llera ◽

Roselyne Chauvin ◽

Peter Mulders ◽

Jilly Naaijen ◽

Maarten Mennes ◽

...

Keyword(s):

Spatial Patterns ◽

Brain Connectivity ◽

Covariance Structure ◽

Brain Regions ◽

Linear Filters ◽

Cognitive Changes ◽

Experimental Conditions ◽

Cognitive States ◽

Human Connectome Project ◽

Perfect Discrimination

AbstractFunctional connectivity between brain regions is modulated by cognitive states or experimental conditions. A multivariate methodology that can capture fMRI connectivity maps in light of different experimental conditions would be of primary importance to learn about the specific roles of the different brain areas involved in the observed connectivity variations. Here we detail, adapt, optimize and evaluate a supervised dimensionality reduction model to fMRI timeseries. We demonstrate the strength of such an approach for fMRI data using data from the Human Connectome Project to show that the model provides close to perfect discrimination between different fMRI tasks at low dimensionality. The straightforward interpretability and relevance of the model results is demonstrated by the obtained linear filters relating to anatomical areas well known to be involved on each considered task, and its robustness by testing discriminatory generalization and spatial reproducibility with respect to the number of subjects and fMRI time-points acquired. We additionally suggest how such approach can provide a complementary view to traditional task fMRI analyses by looking at changes in the covariance structure as a substitute to changes in the mean signal. We conclude that the presented methodology provides a robust tool to investigate brain connectivity alterations across induced cognitive changes and has the potential to be used in pathological or pharmacological cohort studies. A publicly available toolbox is provided to facilitate the end use and further development of this methodology to extract Spatial Patterns for Discriminative Estimation (SP♠DE).

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Physiological and head motion signatures in static and time-varying functional connectivity and their subject discriminability

10.1101/2020.02.04.934554 ◽

2020 ◽

Cited By ~ 3

Author(s):

Alba Xifra-Porxas ◽

Michalis Kassinopoulos ◽

Georgios D. Mitsis

Keyword(s):

Functional Connectivity ◽

Brain Connectivity ◽

Brain Activity ◽

Large Data ◽

Head Motion ◽

Time Varying ◽

Resting State Networks ◽

Human Connectome Project ◽

Noninvasive Biomarker ◽

Recurrent Patterns

AbstractHuman brain connectivity yields significant potential as a noninvasive biomarker. Several studies have used fMRI-based connectivity fingerprinting to characterize individual patterns of brain activity. However, it is not clear whether these patterns mainly reflect neural activity or the effect of physiological and motion processes. To answer this question, we capitalize on a large data sample from the Human Connectome Project and rigorously investigate the contribution of the aforementioned processes on functional connectivity (FC) and time-varying FC, as well as their contribution to subject identifiability. We find that head motion, as well as heart rate and breathing fluctuations, induce artifactual connectivity within distinct resting-state networks and that they correlate with recurrent patterns in time-varying FC. Even though the spatiotemporal signatures of these processes yield above-chance levels in subject identifiability, removing their effects at the preprocessing stage improves identifiability, suggesting a neural component underpinning the inter-individual differences in connectivity.

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Sex Classification by Resting State Brain Connectivity

Cerebral Cortex ◽

10.1093/cercor/bhz129 ◽

2019 ◽

Vol 30 (2) ◽

pp. 824-835 ◽

Cited By ~ 12

Author(s):

Susanne Weis ◽

Kaustubh R Patil ◽

Felix Hoffstaedter ◽

Alessandra Nostro ◽

B T Thomas Yeo ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Brain Regions ◽

Brain Organization ◽

Functional Brain ◽

Clear Cut ◽

Human Connectome Project ◽

Machine Learning Approach ◽

Imaging Research ◽

Functional Brain Organization

Abstract A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants’ sex can be classified based on spatially specific resting state (RS) brain connectivity, using 2 samples from the Human Connectome Project (n1 = 434, n2 = 310) and 1 fully independent sample from the 1000BRAINS study (n = 941). The classifier, which was trained on 1 sample and tested on the other 2, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporoparietal regions, insula, and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.

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