Mathematical learning deficits originate in early childhood from atypical development of a frontoparietal brain network

Mathematical learning deficits are defined as a neurodevelopmental disorder (dyscalculia) in the International Classification of Diseases. It is not known, however, how such deficits emerge in the course of early brain development. Here, we conducted functional and structural magnetic resonance imaging (MRI) experiments in 3- to 6-year-old children without formal mathematical learning experience. We followed this sample until the age of 7 to 9 years, identified individuals who developed deficits, and matched them to a typically developing control group using comprehensive behavioral assessments. Multivariate pattern classification distinguished future cases from controls with up to 87% accuracy based on the regional functional activity of the right posterior parietal cortex (PPC), the network-level functional activity of the right dorsolateral prefrontal cortex (DLPFC), and the effective functional and structural connectivity of these regions. Our results indicate that mathematical learning deficits originate from atypical development of a frontoparietal network that is already detectable in early childhood

A comprehensive analysis of the correlations between resting-state oscillations in multiple-frequency bands and big five traits

Recently, the association between human personality traits and resting-state brain activity has gained interest in neuroimaging studies. However, it remains unclear if Big Five personality traits are represented in frequency bands (~0.25 Hz) of resting-state functional magnetic resonance imaging (fMRI) activity. Based on earlier neurophysiological studies, we investigated the correlation between the five personality traits assessed by the NEO Five-Factor Inventory (NEO-FFI), and the fractional amplitude of low-frequency fluctuation (fALFF) at four distinct frequency bands (slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz) and slow-2 (0.198–0.25 Hz)). We enrolled 835 young subjects and calculated the correlations of resting-state fMRI signals using a multiple regression analysis. We found a significant and consistent correlation between fALFF and the personality trait of extraversion at all frequency bands. Furthermore, significant correlations were detected in distinct brain regions for each frequency band. This finding supports the frequency-specific spatial representations of personality traits as previously suggested. In conclusion, our data highlight an association between human personality traits and fALFF at four distinct frequency bands

Univariate and multivariate pattern analysis of preterm subjects: a multimodal neuroimaging study

Background: Widespread lasting functional connectivity (FC) and brain volume changes in cortices and subcortices after premature birth have been researched in recent studies. However, the relationship remains unclear between spontaneously slow blood oxygen dependent level (BOLD) fluctuations and gray matter volume (GMV) changes in specific brain areas, such as temporal insular cortices, and whether classification methods based on MRI could be successfully applied to the identification of preterm individuals. In this thesis I hypothesized that in prematurely born adults 1. Ongoing neural excitability and brain activity, as estimated by regional functional connectivity of resting state functional MRI (rs-fMRI) is accompanied with altered low-frequency fluctuations and neonatal complications; 2. Altered regional functional connectivity is connected with superimposed cerebral structural reductions; and 3. multivariate neuroanatomical and functional brain patterns could be treated as features to identify preterm subjects from term subjects individually. Methods: To investigate these hypotheses, the principal results of structural alterations were measured with voxel-based morphometry (VBM), while rs-fMRI outcomes were estimated with amplitude of low-frequency fluctuations (ALFF) in analysis with ninety-four very preterm/very low birth weight (VP/VLBW) and ninety-two full-term (FT) born young adults. Results: The results of the thesis support the hypotheses by showing that, in univariate results, first in VP/VLBW grownups, ALFF was decreased in the left lateral temporal cortices no matter with global signal regression, and this reduction was closely associated with neonatal complications and cognitive variables. Second overlapped brain regions were found between reduced ALFF and reduced brain volumes in the left temporal cortices, and positively associated with each other, demonstrating a potential relationship between VBM and ALFF in this brain area. In multimodal multivariate pattern recognition analysis (MVPA), the gray matter volume (GMV) classifier displayed a higher accuracy (80.7%) contrast with the ALFF classifier (77.4%). The late fusion of GMV and ALFF did not outperform single GMV modality classification by reaching 80.4% accuracy. Moderator analysis from both rs-fMRI and structural MRI (sMRI) uncovered that the neuro-prematurity performance was predominantly determined by neonatal complications. Conclusions: In conclusion, these outcomes exhibit the long term effects of premature labour on lateral temporal cortices, which changed in both ongoing BOLD fluctuations and decreased cerebral structural volumes. This thesis further provided evidence that multivariate pattern analysis such as support vector machine (SVM) may identify imaging-based biomarkers and reliably detect signatures of preterm birth

Univariate and multivariate pattern analysis of preterm subjects: a multimodal neuroimaging study

Background: Widespread lasting functional connectivity (FC) and brain volume changes in cortices and subcortices after premature birth have been researched in recent studies. However, the relationship remains unclear between spontaneously slow blood oxygen dependent level (BOLD) fluctuations and gray matter volume (GMV) changes in specific brain areas, such as temporal insular cortices, and whether classification methods based on MRI could be successfully applied to the identification of preterm individuals. In this thesis I hypothesized that in prematurely born adults 1. Ongoing neural excitability and brain activity, as estimated by regional functional connectivity of resting state functional MRI (rs-fMRI) is accompanied with altered low-frequency fluctuations and neonatal complications; 2. Altered regional functional connectivity is connected with superimposed cerebral structural reductions; and 3. multivariate neuroanatomical and functional brain patterns could be treated as features to identify preterm subjects from term subjects individually. Methods: To investigate these hypotheses, the principal results of structural alterations were measured with voxel-based morphometry (VBM), while rs-fMRI outcomes were estimated with amplitude of low-frequency fluctuations (ALFF) in analysis with ninety-four very preterm/very low birth weight (VP/VLBW) and ninety-two full-term (FT) born young adults. Results: The results of the thesis support the hypotheses by showing that, in univariate results, first in VP/VLBW grownups, ALFF was decreased in the left lateral temporal cortices no matter with global signal regression, and this reduction was closely associated with neonatal complications and cognitive variables. Second overlapped brain regions were found between reduced ALFF and reduced brain volumes in the left temporal cortices, and positively associated with each other, demonstrating a potential relationship between VBM and ALFF in this brain area. In multimodal multivariate pattern recognition analysis (MVPA), the gray matter volume (GMV) classifier displayed a higher accuracy (80.7%) contrast with the ALFF classifier (77.4%). The late fusion of GMV and ALFF did not outperform single GMV modality classification by reaching 80.4% accuracy. Moderator analysis from both rs-fMRI and structural MRI (sMRI) uncovered that the neuro-prematurity performance was predominantly determined by neonatal complications. Conclusions: In conclusion, these outcomes exhibit the long term effects of premature labour on lateral temporal cortices, which changed in both ongoing BOLD fluctuations and decreased cerebral structural volumes. This thesis further provided evidence that multivariate pattern analysis such as support vector machine (SVM) may identify imaging-based biomarkers and reliably detect signatures of preterm birth

Regional homogeneity, resting-state functional connectivity and amplitude of low frequency fluctuation associated with creativity measured by divergent thinking in a sex-specific manner

Brain connectivity is traditionally thought to be important for creativity. Here we investigated the associations of creativity measured by divergent thinking (CMDT) with resting-state functional magnetic imaging (fMRI) measures and their sex differences. We examined these relationships in the brains of 1277 healthy young adults. Whole-brain analyses revealed a significant interaction between verbal CMDT and sex on (a) regional homogeneity within an area from the left anterior temporal lobe (b) on the resting state functional connectivity (RSFC) between the mPFC and the left inferior frontal gyrus and (c) on fractional amplitude of low frequency fluctuations (fALFF) in several distinct areas, including the precuneus and middle cingulate gyrus, left middle temporal gyrus, right middle frontal gyrus, and cerebellum. These interactions were mediated by positive correlations in females and negative correlations in males. These findings suggest that greater CMDT in females is reflected by (a) regional coherence (regional homogeneity) of brain areas responsible for representing and combining concepts as well as (b) the efficient functional connection (RSFC) between the key areas for the default state of cognitive activity and speech production, and (c) greater spontaneous neural activity (fALFF) during the resting of brain areas involved in frontal lobe functions, default cognitive activities, and language functions. Furthermore, these findings suggest that the associations between creativity and resting state brain connectivity patterns are different between males and females

Altered brain spontaneous activity in patients with cerebral small vessel disease using the amplitude of low-frequency fluctuation of different frequency bands

BackgroundPrevious studies showed that cerebral small vessel disease (cSVD) is a leading cause of cognitive decline in elderly people and the development of Alzheimer’s disease. Although brain structural changes of cSVD have been documented well, it remains unclear about the properties of brain intrinsic spontaneous activity in patients with cSVD.MethodsWe collected resting-state fMRI (rs-fMRI) and T1-weighted 3D high-resolution brain structural images from 41 cSVD patients and 32 healthy controls (HC). By estimating the amplitude of low-frequency fluctuation (ALFF) under three different frequency bands (typical band: 0.01–0.1 Hz; slow-4: 0.027–0.073 Hz; and slow-5: 0.01–0.027 Hz) in the whole-brain, we analyzed band-specific ALFF differences between the cSVD patients and controls.ResultsThe cSVD patients showed uniformly lower ALFF than the healthy controls in the typical and slow-4 bands (pFWE < 0.05). In the typical band, cSVD patients showed lower ALFF involving voxels of the fusiform, hippocampus, inferior occipital cortex, middle occipital cortex, insula, inferior frontal cortex, rolandic operculum, and cerebellum compared with the controls. In the slow-4 band, cSVD patients showed lower ALFF involving voxels of the cerebellum, hippocampus, occipital, and fusiform compared with the controls. However, there is no significant between-group difference of ALFF in the slow-5 band. Moreover, we found significant “group × frequency” interactions in the left precuneus.ConclusionOur results suggested that brain intrinsic spontaneous activity of cSVD patients was abnormal and showed a frequency-specific characteristic. The ALFF in the slow-4 band may be more sensitive to detecting a malfunction in cSVD patients

Graph-Based Network Analysis of Resting-State Functional MRI

In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain's spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain's intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future