Genoarchitectural Definition of the Adult Mouse Mesocortical Ring: A Contribution to Cortical Ring Theory.

Data mining was performed at the databases of the Allen Institute for Brain Science (RRID:SCR_017001) searching for genes expressed selectively throughout the adult mouse mesocortex (transitional cortex ring predicted within the concentric ring theory of mammalian cortical structure, in contrast with central isocortex [ICx] and peripheral allocortex). We aimed to explore a shared molecular profile selective of all or most mesocortex areas. This approach checks and corroborates the precision of other previous definitory criteria, such as poor myelination and high kainate receptor level. Another aim was to examine which cortical areas properly belong to mesocortex. A total of 34 positive adult selective marker genes of mesocortex were identified, jointly with 12 negative selective markers, making a total of 46 markers. All of them identify the same set of cortical areas surrounding the molecularly different ICx as well as excluding adjacent allocortex. Four representative mesocortex markers-Crym, Lypd1, Cdh13, and Smoc2-are amply illustrated, jointly with complementary material including myelin basic protein, to check myelination, and Rorb, to check layer 4 presence. The retrosplenial (ReSp) area, long held to be mesocortical, does not share any of the 46 markers of mesocortex and instead expresses Nr4a2 and Tshz2, selective parahippocampal allocortex markers. Moreover, it is not hypomyelinic and lacks a Rorb-positive layer 4, aspects generally present in mesocortex. Exclusion of the ReSp area from the mesocortex ring reveals the latter to be closed at this locus instead by two adjacent areas previously thought to be associative visual ICx (reidentified here molecularly as postsplenial and parasplenial mesocortex areas). The concepts of ICx, mesocortex, and parahippocampal allocortex are thus subtly modified by substantial molecular evidence.

Partitioning variance in cortical morphometry into genetic, environmental, and subject-specific components.

The relative contributions of genetic variation and experience in shaping the morphology of the adolescent brain are not fully understood. Using longitudinal data from 11,665 subjects in the ABCD Study, we fit vertex-wise variance components including family effects, genetic effects, and subject-level effects using a computationally efficient framework. Variance in cortical thickness and surface area is largely attributable to genetic influence, whereas sulcal depth is primarily explained by subject-level effects. Our results identify areas with heterogeneous distributions of heritability estimates that have not been seen in previous work using data from cortical regions. We discuss the biological importance of subject-specific variance and its implications for environmental influences on cortical development and maturation.

Population-level normative models reveal race- and socioeconomic-related variability in cortical thickness of threat neurocircuitry.

The inequitable distribution of economic resources and exposure to adversity between racial groups contributes to mental health disparities within the United States. Consideration of the potential neurodevelopmental consequences, however, has been limited particularly for neurocircuitry known to regulate the emotional response to threat. Characterizing the consequences of inequity on threat neurocircuitry is critical for robust and generalizable neurobiological models of psychiatric illness. Here we use data from the Adolescent Brain and Cognitive Development Study 4.0 release to investigate the contributions of individual and neighborhood-level economic resources and exposure to discrimination. We investigate the potential appearance of race-related differences using both standard methods and through population-level normative modeling. We show that, in a sample of white and Black adolescents, racial inequities in socioeconomic factors largely contribute to the appearance of race-related differences in cortical thickness of threat neurocircuitry. The race-related differences are preserved through the use of population-level models and such models also preserve associations between cortical thickness and specific socioeconomic factors. The present findings highlight that such socioeconomic inequities largely underlie race-related differences in brain morphology. The present findings provide important new insight for the generation of generalizable neurobiological models of psychiatric illness.

Mapping morphological cortical networks with joint probability distributions from multiple morphological features.

Morphological features sourced from structural magnetic resonance imaging can be used to infer human brain connectivity. Although integrating different morphological features may theoretically be beneficial for obtaining more precise morphological connectivity networks (MCNs), the empirical evidence to support this supposition is scarce. Moreover, the incorporation of different morphological features remains an open question. In this study, we proposed a method to construct cortical MCNs based on multiple morphological features. Specifically, we adopted a multi-dimensional kernel density estimation algorithm to fit regional joint probability distributions (PDs) from different combinations of four morphological features, and estimated inter-regional similarity in the joint PDs via Jensen-Shannon divergence. We evaluated the method by comparing the resultant MCNs with those built based on different single morphological features in terms of topological organization, test-retest reliability, biological plausibility, and behavioral and cognitive relevance. We found that, compared to MCNs built based on different single morphological features, MCNs derived from multiple morphological features displayed less segregated, but more integrated network architecture and different hubs, had higher test-retest reliability, encompassed larger proportions of inter-hemispheric edges and edges between brain regions within the same cytoarchitectonic class, and explained more inter-individual variance in behavior and cognition. These findings were largely reproducible when different brain atlases were used for cortical parcellation. Further analysis of macaque MCNs revealed weak, but significant correlations with axonal connectivity from tract-tracing, independent of the number of morphological features. Altogether, this paper proposes a new method for integrating different morphological features, which will be beneficial for constructing MCNs.

Longitudinally consistent registration and parcellation of cortical surfaces using semi-supervised learning.

Temporally consistent and accurate registration and parcellation of longitudinal cortical surfaces is of great importance in studying longitudinal morphological and functional changes of human brains. However, most existing methods are developed for registration or parcellation of a single cortical surface. When applying to longitudinal studies, these methods independently register/parcellate each surface from longitudinal scans, thus often generating longitudinally inconsistent and inaccurate results, especially in small or ambiguous cortical regions. Essentially, longitudinal cortical surface registration and parcellation are highly correlated tasks with inherently shared constraints on both spatial and temporal feature representations, which are unfortunately ignored in existing methods. To this end, we unprecedentedly propose a novel semi-supervised learning framework to exploit these inherent relationships from limited labeled data and extensive unlabeled data for more robust and consistent registration and parcellation of longitudinal cortical surfaces. Our method utilizes the spherical topology characteristic of cortical surfaces. It employs a spherical network to function as an encoder, which extracts high-level cortical features. Subsequently, we build two specialized decoders dedicated to the tasks of registration and parcellation, respectively. To extract more meaningful spatial features, we design a novel parcellation map similarity loss to utilize the relationship between registration and parcellation tasks, i.e., the parcellation map warped by the deformation field in registration should match the atlas parcellation map, thereby providing extra supervision for the registration task and augmented data for parcellation task by warping the atlas parcellation map to unlabeled surfaces. To enable temporally more consistent feature representation, we additionally enforce longitudinal consistency among longitudinal surfaces after registering them together using their concatenated features. Experiments on two longitudinal datasets of infants and adults have shown that our method achieves significant improvements on both registration/parcellation accuracy and longitudinal consistency compared to existing methods, especially in small and challenging cortical regions.

A Reconsideration of the Core and Matrix Classification of Thalamocortical Projections.

In 1998, Jones suggested a classification of thalamocortical projections into core and matrix divisions (Jones, 1998). In this classification, core projections are specific, topographical, innervate middle cortical layers, and serve to transmit specific information to the cortex for further analysis; matrix projections, in contrast, are diffuse, much less topographic, innervate upper layers, especially Layer 1, and serve a more global, modulatory function, such as affecting levels of arousal. This classification has proven especially influential in studies of thalamocortical relationships. Whereas it may be the case that a clear subset of thalamocortical connections fit the core motif, since they are specific, topographic, and innervate middle layers, we argue that there is no clear evidence for any single class that encompasses the remainder of thalamocortical connections as is claimed for matrix. Instead, there is great morphological variation in connections made by thalamocortical projections fitting neither a core nor matrix classification. We thus conclude that the core/matrix classification should be abandoned, because its application is not helpful in providing insights into thalamocortical interactions and can even be misleading. As one example of the latter, recent suggestions indicate that core projections are equivalent to first-order thalamic relays (i.e., those that relay subcortical information to the cortex) and matrix to higher-order relays (i.e., those that relay information from one cortical area to another), but available evidence does not support this relationship. All of this points to a need to replace the core/matrix grouping with a more complete classification of thalamocortical projections.

Cronología de la investigación sobre la dinámica cerebral de Justo Gonzalo / Chronology of Justo Gonzalo’s research on brain dynamics

Introducción: El neurocientífico español Justo Gonzalo y Rodríguez-Leal (1910-1986) investiga la organización funcional de la corteza cerebral durante más de cuatro décadas. Sus hallazgos le llevan a formular una teoría neurofisiológica basada en las leyes de la excitabilidad nerviosa, que denomina dinámica cerebral. En el presente trabajo se expone de forma cronológica cómo surgen las principales ideas sobre las que se articula.Desarrollo: En 1939 Gonzalo observa los denominados fenómenos de acción dinámica: desfasamiento, facilitación y repercusión cerebral. Le siguen dos principios: efecto cerebral de la lesión según la magnitud y posición (1941), y organización sensorial, según un desarrollo espiral (1947). Paralelamente, caracteriza lo que llama el síndrome central de la corteza cerebral. En la década de los cincuenta desarrolla los conceptos de gradiente cortical, similitud y alometría. En contraposición a las concepciones modulares de la corteza cerebral, en las que una región es responsable de una función, Gonzalo expresa que ‘los gradientes corticales dan la localización de los sistemas mientras la similitud y alometría revelan su trama funcional’.Conclusiones: La teoría de dinámica cerebral se articula en dos etapas. La primera (de 1938 a 1950) se caracteriza por una importante base clínica con observación de nuevos fenómenos y formulación de nuevos conceptos. La segunda (de 1950 a 1960) incluye la introducción de conceptos de mayor alcance, como el gradiente funcional cortical, y leyes de alometría que se basan en un cambio de escala. Actualmente, varios autores consideran que el concepto de gradiente es clave para entender la organización cerebral.(AU)

Introduction: The Spanish neuroscientist Justo Gonzalo y Rodríguez-Leal (1910-1986) investigated the functional organisation of the cerebral cortex over more than four decades. His findings led him to formulate a neurophysiological theory based on the laws of nervous excitability, which he called brain dynamics. This paper presents in chronological order how the main ideas on which it is based arose.Development: In 1939, Gonzalo observed the phenomena of dynamic action: asynchrony or disaggregation, facilitation and cerebral repercussion. This was followed by two principles: the cerebral effect of lesions according to their magnitude and position (1941), and spiral development of the sensory field (1947). At the same time, he characterised what he called the central syndrome of the cerebral cortex. In the 1950s he developed the concepts of the cortical gradient, similarity and allometry. In contrast to modular conceptions of the cerebral cortex, in which one region is responsible for one function, Gonzalo argued that ‘cortical gradients provide the location of systems, while similarity and allometry reveal their functional mechanism.’Conclusions: The theory of brain dynamics was established in two stages. The first (between 1938 and 1950) had an important clinical foundation, involving the observation of new phenomena and the formulation of new concepts. The second (between 1950 and 1960) included the introduction of more far-reaching concepts, such as the functional cortical gradient, and allometry laws based on a change of scale. Today, various authors believe that the concept of the gradient is crucial for understanding how the brain is organised.(AU)

Intelligence and cortical morphometry: caveats in brain-behavior associations.

It is well-established that brain size is associated with intelligence. But the relationship between cortical morphometric measures and intelligence is unclear. Studies have produced conflicting results or no significant relations between intelligence and cortical morphometric measures such as cortical thickness and peri-cortical contrast. This discrepancy may be due to multicollinearity amongst the independent variables in a multivariate regression analysis, or a failure to fully account for the relationship between brain size and intelligence in some other way. Our study shows that neither cortical thickness nor peri-cortical contrast reliably improves IQ prediction accuracy beyond what is achieved with brain volume alone. We show this in multiple datasets, with child data, developmental data, and with adult data; we show this with data acquired either at multiple sites, or at a single site; we show this with data acquired with different MRI scanner manufacturers, or with all data acquired on a single scanner; and we show this with fluid intelligence, full-scale IQ, performance IQ, and verbal IQ. But our point is not really even about IQ; rather we proffer a methodological caveat and potential explanation of the discrepancies in previous results, and which applies broadly.

Profiling cortical morphometric similarity in perinatal brains: Insights from development, sex difference, and inter-individual variation.

The topological organization of the macroscopic cortical networks important for the development of complex brain functions. However, how the cortical morphometric organization develops during the third trimester and whether it demonstrates sexual and individual differences at this particular stage remain unclear. Here, we constructed the morphometric similarity network (MSN) based on morphological and microstructural features derived from multimodal MRI of two independent cohorts (cross-sectional and longitudinal) scanned at 30-44 postmenstrual weeks (PMW). Sex difference and inter-individual variations of the MSN were also examined on these cohorts. The cross-sectional analysis revealed that both network integration and segregation changed in a nonlinear biphasic trajectory, which was supported by the results obtained from longitudinal analysis. The community structure showed remarkable consistency between bilateral hemispheres and maintained stability across PMWs. Connectivity within the primary cortex strengthened faster than that within high-order communities. Compared to females, male neonates showed a significant reduction in the participation coefficient within prefrontal and parietal cortices, while their overall network organization and community architecture remained comparable. Furthermore, by using the morphometric similarity as features, we achieved over 65 % accuracy in identifying an individual at term-equivalent age from images acquired after birth, and vice versa. These findings provide comprehensive insights into the development of morphometric similarity throughout the perinatal cortex, enhancing our understanding of the establishment of neuroanatomical organization during early life.

SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites.

In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter-scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter-scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex-level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called spatial autocorrelation normalization (SAN) that preserves homogeneous covariance vertex-level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner-invariant and scanner-specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.

Automated individual cortical parcellation via consensus graph representation learning.

Cortical parcellation plays a pivotal role in elucidating the brain organization. Despite the growing efforts to develop parcellation algorithms using functional magnetic resonance imaging, achieving a balance between intra-individual specificity and inter-individual consistency proves challenging, making the generation of high-quality, subject-consistent cortical parcellations particularly elusive. To solve this problem, our paper proposes a fully automated individual cortical parcellation method based on consensus graph representation learning. The method integrates spectral embedding with low-rank tensor learning into a unified optimization model, which uses group-common connectivity patterns captured by low-rank tensor learning to optimize subjects' functional networks. This not only ensures consistency in brain representations across different subjects but also enhances the quality of each subject's representation matrix by eliminating spurious connections. More importantly, it achieves an adaptive balance between intra-individual specificity and inter-individual consistency during this process. Experiments conducted on a test-retest dataset from the Human Connectome Project (HCP) demonstrate that our method outperforms existing methods in terms of reproducibility, functional homogeneity, and alignment with task activation. Extensive network-based comparisons on the HCP S900 dataset reveal that the functional network derived from our cortical parcellation method exhibits greater capabilities in gender identification and behavior prediction than other approaches.

Trends in Intracranial and Cerebral Volumes of Framingham Heart Study Participants Born 1930 to 1970.

Importance: Human brain development and maintenance is under both genetic and environmental influences that likely affect later-life dementia risk. Objective: To examine environmental influences by testing whether time-dependent secular differences occurred in cranial and brain volumes and cortical thickness over birth decades spanning 1930 to 1970. Design, Setting, and Participants: This cross-sectional study used data from the community-based Framingham Heart Study cohort for participants born in the decades 1930 to 1970. Participants did not have dementia or history of stroke and had magnetic resonance imaging (MRI) obtained from March 18, 1999, to November 15, 2019. The final analysis dataset was created in October 2023. Exposure: Years of birth ranging from 1925 to 1968. Main Measures: Cross-sectional analysis of intracranial, cortical gray matter, white matter, and hippocampal volumes as well as cortical surface area and cortical thickness. The secular measure was the decade in which the participant was born. Covariates included age at MRI and sex. Results: The main study cohort consisted of 3226 participants with a mean (SD) age of 57.7 (7.8) years at the time of their MRI. A total of 1706 participants were female (53%) and 1520 (47%) were male. The birth decades ranged from the 1930s to 1970s. Significant trends for larger intracranial, hippocampal, and white matter volumes and cortical surface area were associated with progressive birth decades. Comparing the 1930s birth decade to the 1970s accounted for a 6.6% greater volume (1234 mL; 95% CI, 1220-1248, vs 1321 mL; 95% CI, 1301-1341) for ICV, 7.7% greater volume (441.9 mL; 95% CI, 435.2-448.5, vs 476.3 mL; 95% CI, 467.0-485.7) for white matter, 5.7% greater value (6.51 mL; 95% CI, 6.42-6.60, vs 6.89 mL; 95% CI, 6.77-7.02) for hippocampal volume, and a 14.9% greater value (1933 cm2; 95% CI, 1908-1959, vs 2222 cm2; 95% CI, 2186-2259) for cortical surface area. Repeat analysis applied to a subgroup of 1145 individuals of similar age range born in the 1940s (mean [SD] age, 60.0 [2.8] years) and 1950s (mean [SD] age, 59.0 [2.8] years) resulted in similar findings. Conclusion and Relevance: In this study, secular trends for larger brain volumes suggested improved brain development among individuals born between 1930 and 1970. Early life environmental influences may explain these results and contribute to the declining dementia incidence previously reported in the Framingham Heart Study cohort.

Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models.

This work illustrates the use of normative models in a longitudinal neuroimaging study of children aged 6-17 years and demonstrates how such models can be used to make meaningful comparisons in longitudinal studies, even when individuals are scanned with different scanners across successive study waves. More specifically, we first estimated a large-scale reference normative model using Hierarchical Bayesian Regression from N = 42,993 individuals across the lifespan and from dozens of sites. We then transfer these models to a longitudinal developmental cohort (N = 6285) with three measurement waves acquired on two different scanners that were unseen during estimation of the reference models. We show that the use of normative models provides individual deviation scores that are independent of scanner effects and efficiently accommodate inter-site variations. Moreover, we provide empirical evidence to guide the optimization of sample size for the transfer of prior knowledge about the distribution of regional cortical thicknesses. We show that a transfer set containing as few as 25 samples per site can lead to good performance metrics on the test set. Finally, we demonstrate the clinical utility of this approach by showing that deviation scores obtained from the transferred normative models are able to detect and chart morphological heterogeneity in individuals born preterm.

Comprehensive mapping of the exterior architecture of the dromedary camel brain.

The morphological perspective of the camel brain remains largely unexplored. Therefore, studying the topography of the camel brain is of crucial importance. This study aimed to provide a detailed color-coded topographic representation of the camel brain's gross anatomy and nomenclature, showing its various gyri and sulci and their borders. We compared them to previously known information to develop a detailed description of camel brain exterior architecture. Our research identified distinctive gyri and sulci with discrete positions and surrounding structures, allowing us to define sulci boundaries and establish logical gyri nomenclature. This study uncovered previously overlooked gyri and sulci and improved descriptions of specific sulci. The ectomarginal sulcus, splenial sulcus, splenial gyrus, and ectogenual gyrus are a few examples. These findings highlight several unique anatomical features of the dromedary brain, which can guide future research. By providing a comprehensive examination of the distinctive exterior anatomical features of the camel brain, this study may serve as a point of convergence for all researchers, providing more accurate identification of the gyri and sulci.

Ventral temporal and posteromedial sulcal morphology in autism spectrum disorder.

Two parallel research tracks link the morphology of small and shallow indentations, or sulci, of the cerebral cortex with functional features of the cortex and human cognition, respectively. The first track identified a relationship between the mid-fusiform sulcus (MFS) in ventral temporal cortex (VTC) and cognition in individuals with Autism Spectrum Disorder (ASD). The second track identified a new sulcus, the inframarginal sulcus (IFRMS), that serves as a tripartite landmark within the posteromedial cortex (PMC). As VTC and PMC are structurally and functionally different in ASD, here, we integrated these two tracks and tested if there are morphological differences in VTC and PMC sulci in a sample of young (5-17 years old) male participants (50 participants with ASD and 50 neurotypical controls). Our approach replicates and extends recent findings in four ways. First, regarding replication, the standard deviation (STD) of MFS cortical thickness (CT) was increased in ASD. Second, MFS length was shorter in ASD. Third, the CT STD effect extended to other VTC and to PMC sulci. Fourth, additional morphological features of VTC sulci (depth, surface area, gray matter volume) and PMC sulci (mean CT) were decreased in ASD, including putative tertiary sulci, which emerge last in gestation and continue to develop after birth. To our knowledge, this study is the most extensive comparison of the sulcal landscape (including putative tertiary sulci) in multiple cortical expanses between individuals with ASD and NTs based on manually defined sulci at the level of individual hemispheres, providing novel targets for future studies of neurodevelopmental disorders more broadly.

Population-wise labeling of sulcal graphs using multi-graph matching.

Population-wise matching of the cortical folds is necessary to compute statistics, a required step for e.g. identifying biomarkers of neurological or psychiatric disorders. The difficulty arises from the massive inter-individual variations in the morphology and spatial organization of the folds. The task is challenging both methodologically and conceptually. In the widely used registration-based techniques, these variations are considered as noise and the matching of folds is only implicit. Alternative approaches are based on the extraction and explicit identification of the cortical folds. In particular, representing cortical folding patterns as graphs of sulcal basins-termed sulcal graphs-enables to formalize the task as a graph-matching problem. In this paper, we propose to address the problem of sulcal graph matching directly at the population level using multi-graph matching techniques. First, we motivate the relevance of the multi-graph matching framework in this context. We then present a procedure for generating populations of artificial sulcal graphs, which allows us to benchmark several state-of-the-art multi-graph matching methods. Our results on both artificial and real data demonstrate the effectiveness of multi-graph matching techniques in obtaining a population-wise consistent labeling of cortical folds at the sulcal basin level.

Single-subject cortical morphological brain networks: Phenotypic associations and neurobiological substrates.

Although single-subject morphological brain networks provide an important way for human connectome studies, their roles and origins are poorly understood. Combining cross-sectional and repeated structural magnetic resonance imaging scans from adults, children and twins with behavioral and cognitive measures and brain-wide transcriptomic, cytoarchitectonic and chemoarchitectonic data, this study examined phenotypic associations and neurobiological substrates of single-subject morphological brain networks. We found that single-subject morphological brain networks explained inter-individual variance and predicted individual outcomes in Motor and Cognition domains, and distinguished individuals from each other. The performance can be further improved by integrating different morphological indices for network construction. Low-moderate heritability was observed for single-subject morphological brain networks with the highest heritability for sulcal depth-derived networks and higher heritability for inter-module connections. Furthermore, differential roles of genetic, cytoarchitectonic and chemoarchitectonic factors were observed for single-subject morphological brain networks. Cortical thickness-derived networks were related to the three factors with contributions from genes enriched in membrane and transport related functions, genes preferentially located in supragranular and granular layers, overall thickness in the molecular layer and thickness of wall in the infragranular layers, and metabotropic glutamate receptor 5 and dopamine transporter; fractal dimension-, gyrification index- and sulcal depth-derived networks were only associated with the chemoarchitectonic factor with contributions from different sets of neurotransmitter receptors. Most results were reproducible across different parcellation schemes and datasets. Altogether, this study demonstrates phenotypic associations and neurobiological substrates of single-subject morphological brain networks, which provide intermediate endophenotypes to link molecular and cellular architecture and behavior and cognition.

The effect of processing pipelines, input images and age on automatic cortical morphology estimates.

BACKGROUND AND OBJECTIVE: Magnetic resonance imaging of the brain allows to enrich the study of the relationship between cortical morphology, healthy ageing, diseases and cognition. Since manual segmentation of the cerebral cortex is time consuming and subjective, many software packages have been developed. FreeSurfer (FS) and Advanced Normalization Tools (ANTs) are the most used and allow as inputs a T1-weighted (T1w) image or its combination with a T2-weighted (T2w) image. In this study we evaluated the impact of different software and input images on cortical estimates. Additionally, we investigated whether the variation of the results depending on software and inputs is also influenced by age. METHODS: For 240 healthy subjects, cortical thickness was computed with ANTs and FreeSurfer. Estimates were derived using both the T1w image and adding the T2w image. Significant effects due to software, input images and age range were investigated with ANOVA statistical analysis. Moreover, the accuracy of the cortical thickness estimates was assessed based on their age-prediction precision. RESULTS: Using FreeSurfer and ANTs with T1w or T1w-T2w images resulted in significant differences in the cortical thickness estimates. These differences change with the age range of the subjects. Regardless of the images used, the more recent FS version tested exhibited the best performances in terms of age prediction. CONCLUSIONS: Our study points out the importance of i) consistently processing data using the same tool; ii) considering the software, input images and the age range of the subjects when comparing multiple studies.

Changed cortical thickness and sulcal depth in pediatric acute lymphoblastic leukemia survivors treated with chemotherapy only.

The purpose of this study is to observe the changes of cortical morphological characteristics and their potential contribution to cognitive function in ALL survivors by using surface-based morphometry (SBM). Using SBM analysis, we calculated and compared group differences in cortical thickness, sulcal depth, gyrification, and fractal dimension of the cerebral cortex between 18 pediatric ALL survivors treated on chemotherapy-only protocols and off treatment within 2 years, and 18 healthy controls (HCs) with two-sample t-tests [P < 0.05, family-wise error (FWE) corrected]. Relationships between abnormal cortical characteristic values and cognitive function parameters were investigated with partial correlation analysis, taking age as a covariate. We found decreased cortical thickness mainly located in the prefrontal and temporal region, and increased sulcal depth in left rostral middle frontal cortex and left pars orbitalis in the ALL survivors compared to HCs. There were no statistically significant differences in the gyrification and fractal dimension between the two groups. In ALL survivors, cortical thickness and sulcal depth of above areas values revealed no significant correlation with the cognitive function parameters. In conclusion, pediatric ALL survivors show decreased cortical thickness in prefrontal and temporal regions, and increased sulcal depth in prefrontal region. These results suggest that SBM-based approach can be used to assess changes of cortical morphological characteristics in pediatric ALL survivors.

Volumetric Assessment of the Insula in a Normally Functioning Human Brain Using Magnetic Resonance Imaging / Evaluación Volumétrica de la Ínsula en un Cerebro Humano que Funciona Normalmente Utilizando Imágenes de Resonancia Magnética

SUMMARY: Volumetric assessment of brain structures is an important tool in neuroscience research and clinical practice. The volumetric measurement of normally functioning human brain helps detect age-related changes in some regions, which can be observed at varying degrees. This study aims to estimate the insular volume in the normally functioning human brain in both genders, different age groups, and side variations. A cross-sectional retrospective study was conducted on 42 adult Sudanese participants in Al-Amal Hospital, Sudan, between May to August 2022, using magnetic resonance imaging (MRI) and automatic brain segmentation through a software program (BrainSuite). The statistical difference in total insular volume on both sides of the cerebral hemisphere was small. The insular volume on the right side was greater in males, while the left side showed no difference between both genders. A statistically significant difference between males and females was found (p > 0.05), and no statistical difference in different age groups was found according to the one-way ANOVA test (p>0.05). Adult Sudanese males showed a larger insular volume than females. MRI can be used to morphometrically assess the insula to detect any pathological variations based on volume changes.

La evaluación volumétrica de las estructuras cerebrales es una herramienta importante en la investigación y la práctica clínica de la neurociencia. La medición volumétrica del cerebro humano, que funciona normalmente, ayuda a detectar cambios relacionados con la edad en algunas regiones, las cuales se pueden observar en diversos grados. Este estudio tuvo como objetivo estimar el volumen insular en el cerebro humano que funciona normalmente, en ambos sexos, de diferentes grupos de edad y sus variaciones laterales. Se realizó un estudio retrospectivo transversal en 42 participantes sudaneses adultos en el Hospital Al-Amal, Sudán, entre mayo y agosto de 2022, utilizando imágenes de resonancia magnética y segmentación automática del cerebro a través de un software (BrainSuite). Fue pequeña la diferencia estadística en el volumen insular total, en los hemisferios cerebrales. El volumen insular del lado derecho fue mayor en los hombres, mientras que el lado izquierdo no mostró diferencia entre ambos sexos. Se encontró una diferencia estadísticamente significativa entre hombres y mujeres (p > 0,05), y no se encontró diferencia estadística en los diferentes grupos de edad, según la prueba de ANOVA de una vía (p> 0,05). Los hombres sudaneses adultos mostraron un mayor volumen insular que las mujeres. La resonancia magnética se puede utilizar para evaluar morfométricamente la ínsula y para detectar cualquier variación patológica basada en cambios de volumen.