Associations Between Individual Differences in Mathematical Competencies and Surface Anatomy of the Adult Brain

Separating neurocognitive mechanisms of maintenance and compensation to support financial ability in middle-aged and older adults: The role of language and the inferior frontal gyrus

This study investigated the role of brain regions involved in arithmetic processing in explaining individual differences in financial ability in 67 50-74-year-old cognitively normal adults. Structural integrity and resting-state functional connectivity measures were collected in the MRI scanner. Outside the scanner, participants performed financial ability and other cognitive tasks, and answered questionnaires to determine dementia risk, and financial risk and protective factors. Regions of interest involved in arithmetic processing were defined, focusing on language- and quantity-processing areas in temporo-frontal and parieto-frontal cortices, respectively. Our results showed that structural integrity and functional connectivity in brain regions associated with arithmetic retrieval were positively associated with financial ability, with language skill mediating left IFG structural integrity and financial ability. Connectivity patterns suggested that reliance on quantity mechanisms (i.e. calculation) was associated with poorer financial ability. Analyses revealed that reliance on these brain mechanisms did not depend on participants' age or risk of dementia and that protective factors such as household income or financial literacy supported the maintenance of connectivity related to financial abilities.

Associations between brain imaging-derived phenotypes and cognitive functions

We aimed to evaluate the potential causal relationship between brain imaging-derived phenotypes and cognitive functions via Mendelian randomization analyses. Genetic instruments for 470 brain imaging-derived phenotypes were selected from a genome-wide association study based on the UK Biobank (n = 33,224). Statistics for cognitive functions were obtained from the genome-wide association study based on the UK Biobank. We used the inverse variance weighted Mendelian randomization method to investigate the associations between brain imaging-derived phenotypes and cognitive functions, and reverse Mendelian randomization analyses were performed for significant brain imaging-derived phenotypes to examine the reverse causation for the identified associations. We identified three brain imaging-derived phenotypes to be associated with verbal-numerical reasoning, including cortical surface area of the left fusiform gyrus (beta, 0.18 [95% confidence interval, 0.11 to 0.25], P = 4.74 × 10-7), cortical surface area of the right superior temporal gyrus (beta, 0.25 [95% confidence interval, 0.15 to 0.35], P = 6.30 × 10-7), and orientation dispersion in the left superior longitudinal fasciculus (beta, 0.14 [95% confidence interval, 0.09 to 0.20], P = 8.37 × 10-7). The reverse Mendelian randomization analysis indicated that verbal-numerical reasoning had no effect on these three brain imaging-derived phenotypes. This Mendelian randomization study identified cortical surface area of the left fusiform gyrus, cortical surface area of the right superior temporal gyrus, and orientation dispersion in the left superior longitudinal fasciculus as predictors of verbal-numerical reasoning.

Identifying the Neural Bases of Math Competence Based on Structural and Functional Properties of the Human Brain

Human populations show large individual differences in math performance and math learning abilities. Early math skill acquisition is critical for providing the foundation for higher quantitative skill acquisition and succeeding in modern society. However, the neural bases underlying individual differences in math competence remain unclear. Modern neuroimaging techniques allow us to not only identify distinct local cortical regions but also investigate large-scale neural networks underlying math competence both structurally and functionally. To gain insights into the neural bases of math competence, this review provides an overview of the structural and functional neural markers for math competence in both typical and atypical populations of children and adults. Although including discussion of arithmetic skills in children, this review primarily focuses on the neural markers associated with complex math skills. Basic number comprehension and number comparison skills are outside the scope of this review. By synthesizing current research findings, we conclude that neural markers related to math competence are not confined to one particular region; rather, they are characterized by a distributed and interconnected network of regions across the brain, primarily focused on frontal and parietal cortices. Given that human brain is a complex network organized to minimize the cost of information processing, an efficient brain is capable of integrating information from different regions and coordinating the activity of various brain regions in a manner that maximizes the overall efficiency of the network to achieve the goal. We end by proposing that frontoparietal network efficiency is critical for math competence, which enables the recruitment of task-relevant neural resources and the engagement of distributed neural circuits in a goal-oriented manner. Thus, it will be important for future studies to not only examine brain activation patterns of discrete regions but also examine distributed network patterns across the brain, both structurally and functionally.

Sexual dimorphism in the relationship between brain complexity, volume and general intelligence (g): a cross-cohort study

Changes in brain morphology have been reported during development, ageing and in relation to different pathologies. Brain morphology described by the shape complexity of gyri and sulci can be captured and quantified using fractal dimension (FD). This measure of brain structural complexity, as well as brain volume, are associated with intelligence, but less is known about the sexual dimorphism of these relationships. In this paper, sex differences in the relationship between brain structural complexity and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian) are investigated. 3D T1-weighted magnetic resonance imaging (MRI) data and a battery of cognitive tests were acquired from participants belonging to three different cohorts: Mysore Parthenon Cohort (MPC); Aberdeen Children of the 1950s (ACONF) and UK Biobank. We computed MRI derived structural brain complexity and g estimated from a battery of cognitive tests for each group. Brain complexity and volume were both positively corelated with intelligence, with the correlations being significant in women but not always in men. This relationship is seen across populations of differing ages and geographical locations and improves understanding of neurobiological sex-differences.

A simple nomogram prediction model to identify relatively young patients with mild cognitive impairment who may progress to Alzheimer's disease

AIM

Construct a clinical predictive model based on easily accessible clinical features and imaging data to identify patients 65 years of age and younger with mild cognitive impairment(MCI) who may progress to Alzheimer's disease(AD).

METHODS

From the ADNI database, patients with MCI who were less than or equal to 65 years of age and who had been followed for 6-60 months were selected.We collected demographic data, neuropsychological test scale scores, and structural magnetic images of these patients. Clinical characteristics were then screened, and VBM and SBM analyses were performed using structural nuclear magnetic images to obtain imaging histology characteristics. Finally, predictive models were constructed combining the clinical and imaging histology characteristics.

RESULTS

The constructed nomogram has a cross-validated AUC of 0.872 in the training set and 0.867 in the verification set, and the calibration curve fits well.We also provide an online model-based forecasting tool.

CONCLUSION

The model has good performance and uses convenience,it should be able to provide assistance in clinical work to screen relatively young MCI patients who may progress to AD.

The relationship between brain structure and proficiency in reading and mathematics in children, adolescents, and emerging adults

Behavioral and brain imaging studies speak to commonalities between reading and math. Here, we investigated relationships between individual differences in reading and math ability (single word reading and calculation) with brain anatomy (cortical thickness and surface area) in 342 participants between 6-22 years of age from the NIH Pediatric MRI Database. We found no brain-behavioral correlations in the full sample. When dividing the dataset into three age-specific subgroups, cortical thickness of the left supramarginal gyrus (SMG) and fusiform gyrus (FG) correlated with reading ability in the oldest subgroup (15-22 years) only. Next, we tested unique contributions of these educational measures to neuroanatomy. Single word reading ability, age, and their interaction all contributed unique variance to cortical thickness in the left SMG and intraparietal sulcus (IPS). Age, and the interaction between age and reading, predicted cortical thickness in the left FG. However, regression analyses for math ability showed no relationships with cortical thickness; nor for math or reading ability with surface area. Overall, our results demonstrate relationships between cortical thickness and reading ability in emerging adults, but not in younger age groups. Surprisingly, there were no such relationships with math, and hence no convergence between the reading and math results.