Biology subjects

Fernandez-Miranda, J.

Publications and source records attributed to Fernandez-Miranda, J..

3 recordsLinked to original sources

bioRxiv2023

Probabilistic Coverage of the Frontal Aslant Tract in Young Adults: Insights into Individual Variability, Lateralization, and Language Functions

The frontal aslant tract (FAT) is a crucial neural pathway of language and speech, but little is known about its connectivity and segmentation differences across populations. In this study, we utilized diffusion MRI automatic tractography to investigate the probabilistic coverage of the FAT in a large sample of 1065 young adults. Our primary goal was to reveal individual variability and lateralization of FAT and its structure-function correlations in language processing. Our results showed that the left anterior FAT exhibited the most substantial individual differences, particularly in the superior and middle frontal gyrus, with greater variability in the superior than the inferior region. Furthermore, we found significant left lateralization in FAT, with a greater difference in innervation coverage in the inferior and posterior portions. Additionally, our analysis revealed a significant correlation between the size of left FAT inferior innervation areas and Picture Vocabulary function, highlighting the structural and functional importance of the left FAT in language processing. In comparison, the anisotropy of FAT did not show significant correlation. Overall, our study provides valuable insights into individual and population differences in FAT connectivity and segmentation and sheds light on its critical role in language functions.

neuroscience↗

bioRxiv2018

Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

BackgroundMachine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathological features. Recently the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly-dimensional database of glioma patients.\n\nMethodsWe applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients of all grades. We compared the effect of applying the algorithms to the raw database, versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).\n\nResultsRaw input consisted of 21 variables, and achieved performance of (accuracy/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 for ANN, 73.3%/0.74 for SVM, 69.3%/0.73 for LR and 65.2%/0.63 for DT.\n\nConclusionsWe demonstrate that these techniques can also be applied to small, yet highly-dimensional datasets. Our ML techniques achieved reasonable performance compared to similar studies in the literature. Though local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis, though traditional statistical methods are of similar benefit.

neuroscience↗

bioRxiv2018

A Quantitative Tractography Study into the Connectivity, Segmentation and Laterality of the Human Inferior Longitudinal Fasciculus

The human inferior longitudinal fasciculus (ILF) is a ventral, temporo-occipital association tract. Though described in early neuroanatomical works, its existence was later questioned. Application of in vivo tractography to the neuroanatomical study of the ILF has generally confirmed its existence, however consensus is lacking regarding its subdivision, laterality and connectivity. Further, there is a paucity of detailed neuroanatomic data pertaining to the exact anatomy of the ILF. Generalized Q-Sampling imaging (GQI) is a non-tensor tractographic modality permitting high resolution imaging of white-matter structures. As it is a non-tensor modality, it permits visualization of crossing fibers and accurate delineation of close-proximity fiber-systems. We applied deterministic GQI tractography to data from 30 healthy subjects and a large-volume diffusion atlas, to delineate ILF anatomy. Post-mortem white matter dissection was also carried out in a cadaveric specimen for further validation. The ILF was found in all 60 hemispheres. At its occipital extremity, it demonstrated a trifurcated termination pattern which was used to separate the ILF into 3 distinct sub-fascicles: Dorsolateral, ventrolateral and ventromedial. These divisions were consistent across the subject set and within the atlas. We applied quantitative techniques to study connectivity strength of the ILF at its anterior and posterior extremities. Overall, the 3 sub-fascicles, and the whole ILF, demonstrated strong leftward-lateralized connectivity patterns. Leftward-lateralization was also found for ILF volumes across the subject set. Due to connective and volumetric leftward-dominance and ventral location, we postulate the ILFs role in the semantic system. Further, our results are in agreement with functional and lesion-based postulations pertaining to the ILFs role in facial recognition.

neuroscience↗