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13 pages, 3866 KiB

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OSBA: An Open Neonatal Neuroimaging Atlas and Template for Spina Bifida Aperta

by Anna Speckert, Hui Ji, Kelly Payette, Patrice Grehten, Raimund Kottke, Samuel Ackermann, Beth Padden, Luca Mazzone, Ueli Moehrlen, Spina Bifida Study Group Zurich and Andras Jakab

Data 2024, 9(9), 107; https://doi.org/10.3390/data9090107 - 17 Sep 2024

Viewed by 1596

Abstract

We present the Open Spina Bifida Aperta (OSBA) atlas, an open atlas and set of neuroimaging templates for spina bifida aperta (SBA). Traditional brain atlases may not adequately capture anatomical variations present in pediatric or disease-specific cohorts. The OSBA atlas fills this gap by representing the computationally averaged anatomy of the neonatal brain with SBA after fetal surgical repair. The OSBA atlas was constructed using structural T2-weighted and diffusion tensor MRIs of 28 newborns with SBA who underwent prenatal surgical correction. The corrected gestational age at MRI was 38.1 ± 1.1 weeks (mean ± SD). The OSBA atlas consists of T2-weighted and fractional anisotropy templates, along with nine tissue prior maps and region of interest (ROI) delineations. The OSBA atlas offers a standardized reference space for spatial normalization and anatomical ROI definition. Our image segmentation and cortical ribbon definition are based on a human-in-the-loop approach, which includes manual segmentation. The precise alignment of the ROIs was achieved by a combination of manual image alignment and automated, non-linear image registration. From the clinical and neuroimaging perspective, the OSBA atlas enables more accurate spatial standardization and ROI-based analyses and supports advanced analyses such as diffusion tractography and connectomic studies in newborns affected by this condition. Full article

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17 pages, 1407 KiB

Open AccessReview

Unusual Mathematical Approaches Untangle Nervous Dynamics

by Arturo Tozzi and Lucio Mariniello

Biomedicines 2022, 10(10), 2581; https://doi.org/10.3390/biomedicines10102581 - 14 Oct 2022

Cited by 2 | Viewed by 2671

Abstract

The massive amount of available neurodata suggests the existence of a mathematical backbone underlying neuronal oscillatory activities. For example, geometric constraints are powerful enough to define cellular distribution and drive the embryonal development of the central nervous system. We aim to elucidate whether underrated notions from geometry, topology, group theory and category theory can assess neuronal issues and provide experimentally testable hypotheses. The Monge’s theorem might contribute to our visual ability of depth perception and the brain connectome can be tackled in terms of tunnelling nanotubes. The multisynaptic ascending fibers connecting the peripheral receptors to the neocortical areas can be assessed in terms of knot theory/braid groups. Presheaves from category theory permit the tackling of nervous phase spaces in terms of the theory of infinity categories, highlighting an approach based on equivalence rather than equality. Further, the physical concepts of soft-matter polymers and nematic colloids might shed new light on neurulation in mammalian embryos. Hidden, unexpected multidisciplinary relationships can be found when mathematics copes with neural phenomena, leading to novel answers for everlasting neuroscientific questions. For instance, our framework leads to the conjecture that the development of the nervous system might be correlated with the occurrence of local thermal changes in embryo–fetal tissues. Full article

(This article belongs to the Section Neurobiology and Clinical Neuroscience)

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17 pages, 1255 KiB

Open AccessReview

Advanced Brain Imaging in Preterm Infants: A Narrative Review of Microstructural and Connectomic Disruption

by Philippe Vo Van, Marianne Alison, Baptiste Morel, Jonathan Beck, Nathalie Bednarek, Lucie Hertz-Pannier and Gauthier Loron

Children 2022, 9(3), 356; https://doi.org/10.3390/children9030356 - 4 Mar 2022

Cited by 11 | Viewed by 5029

Abstract

Preterm birth disrupts the in utero environment, preventing the brain from fully developing, thereby causing later cognitive and behavioral disorders. Such cerebral alteration occurs beneath an anatomical scale, and is therefore undetectable by conventional imagery. Prematurity impairs the microstructure and thus the histological process responsible for the maturation, including the myelination. Cerebral MRI diffusion tensor imaging sequences, based on water’s motion into the brain, allows a representation of this maturation process. Similarly, the brain’s connections become disorganized. The connectome gathers structural and anatomical white matter fibers, as well as functional networks referring to remote brain regions connected one over another. Structural and functional connectivity is illustrated by tractography and functional MRI, respectively. Their organizations consist of core nodes connected by edges. This basic distribution is already established in the fetal brain. It evolves greatly over time but is compromised by prematurity. Finally, cerebral plasticity is nurtured by a lifetime experience at microstructural and macrostructural scales. A preterm birth causes a negative and early disruption, though it can be partly mitigated by positive stimuli based on developmental neonatal care. Full article

(This article belongs to the Special Issue Neurodevelopmental Disabilities in Neonates)

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10 pages, 555 KiB

Open AccessArticle

Functional Connectivity-Derived Optimal Gestational-Age Cut Points for Fetal Brain Network Maturity

by Josepheen De Asis-Cruz, Scott Douglas Barnett, Jung-Hoon Kim and Catherine Limperopoulos

Brain Sci. 2021, 11(7), 921; https://doi.org/10.3390/brainsci11070921 - 12 Jul 2021

Cited by 11 | Viewed by 3579

Abstract

The architecture of the human connectome changes with brain maturation. Pivotal to understanding these changes is defining developmental periods when transitions in network topology occur. Here, using 110 resting-state functional connectivity data sets from healthy fetuses between 19 and 40 gestational weeks, we estimated optimal gestational-age (GA) cut points for dichotomizing fetuses into ‘young’ and ‘old’ groups based on global network features. We computed the small-world index, normalized clustering and path length, global and local efficiency, and modularity from connectivity matrices comprised 200 regions and their corresponding pairwise connectivity. We modeled the effect of GA at scan on each metric using separate repeated-measures generalized estimating equations. Our modeling strategy involved stratifying fetuses into ‘young’ and ‘old’ based on the scan occurring before or after a selected GA (i.e., 28 to 33). We then used the quasi-likelihood independence criterion statistic to compare model fit between ‘old’ and ‘young’ cohorts and determine optimal cut points for each graph metric. Trends for all metrics, except for global efficiency, decreased with increasing gestational age. Optimal cut points fell within 30–31 weeks for all metrics coinciding with developmental events that include a shift from endogenous neuronal activity to sensory-driven cortical patterns. Full article

(This article belongs to the Special Issue Neural Networks and Connectivity among Brain Regions)

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