Correlation Between the Wechsler Adult Intelligence Scale- 3 rd Edition Metrics and Brain Structure in Healthy Individuals: A Whole-Brain Magnetic Resonance Imaging Study

Inferring neurocognition using artificial intelligence on brain MRIs

Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition. A core mystery is the MRI explanation of individual differences in neurocognition and its manifestation in intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation must be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on artificial intelligence inferring human neurocognition.

Evaluation of behavioral variance/covariance explained by the neuroimaging data through a pattern-based regression

Neuroimaging data have been widely used to understand the neural bases of human behaviors. However, most studies were either based on a few predefined regions of interest or only able to reveal limited vital regions, hence not providing an overarching description of the relationship between neuroimaging and behaviors. Here, we proposed a voxel-based pattern regression that not only could investigate the overall brain-associated variance (BAV) for a given behavioral measure but could also evaluate the shared neural bases between different behaviors across multiple neuroimaging data. The proposed method demonstrated consistently high reliability and accuracy through comprehensive simulations. We further implemented this approach on real data of adolescents (IMAGEN project, n = 2089) and adults (HCP project, n = 808) to investigate brain-based variances of multiple behavioral measures, for instance, cognitive behaviors, substance use, and psychiatric disorders. Notably, intelligence-related scores showed similar high BAVs with the gray matter volume across both datasets. Further, our approach allows us to reveal the latent brain-based correlation across multiple behavioral measures, which are challenging to obtain otherwise. For instance, we observed a shared brain architecture underlying depression and externalizing problems in adolescents, while the symptom comorbidity may only emerge later in adults. Overall, our approach will provide an important statistical tool for understanding human behaviors using neuroimaging data.

Mind the gap: trajectory of cognitive development in young individuals with sickle cell disease: a cross-sectional study

STUDY OBJECTIVES

Compared to typically developing children and young adults (CYA-TD), those living with Sickle Cell Disease (CYA-SCD) experience more cognitive difficulties, particularly with executive function. Few studies have examined the relative importance of silent cerebral infarction (SCI), haemoglobin and arterial oxygen content on age-related cognitive changes using cross-sectional or longitudinal (developmental trajectory) data. This study presents cohort data from a single timepoint to inform studies with multiple timepoints.

METHODS

We compared cross-sectional raw and scaled scores as age-related changes in cognition (trajectories) in CYA-SCD and age-and ethnicity-matched CYA-TD. We also compared cross-sectional age-related changes in cognition (trajectories) in CYA-SCD with and without SCI to CYA-TD. General cognitive abilities were assessed using Wechsler Intelligence Scales, including the Verbal Comprehension Index (VCI) and Perceptual Reasoning Index (PRI) underpinning IQ. Executive function was evaluated using the Delis-Kaplan Executive Function System (D-KEFS) Tower subtest and the Behaviour Rating Inventory of Executive Function (BRIEF) questionnaire. SCI were identified from contemporaneous 3 T MRI; participants with overt stroke were excluded. Recent haemoglobin was available and oxygen saturation (SpO2) was measured on the day of the MRI.

RESULTS

Data were available for 120 CYA-SCD [62 male; age = 16.78 ± 4.79 years; 42 (35%) with SCI] and 53 CYA-TD (23 male; age = 17.36 ± 5.16). Compared with CYA-TD, CYA-SCD experienced a delayed onset in VCI and slower rate of development for BRIEF Global Executive Composite, Metacognition Index (MI), and Behaviour Regulation Index. The rate of executive function development for the BRIEF MI differed significantly between CYA-TD and CYA-SCD, with those with SCI showing a 26% delay compared with CYA-TD. For CYA-SCD with SCI, arterial oxygen content explained 22% of the variance in VCI and 37% in PRI, while haemoglobin explained 29% of the variance in PRI.

CONCLUSION

Age-related cognitive trajectories of CYA-SCD may not be impaired but may progress more slowly. Longitudinal studies are required, using tests unaffected by practice. In addition to initiation of medical treatment, including measures to improve arterial oxygen content, early cognitive intervention, educational support, and delivery of extracurricular activities could support cognitive development for CYA-SCD.Graphical Abstract.

Association between the Pittsburgh sleep quality index and white matter integrity in healthy adults: a whole-brain magnetic resonance imaging study

To disclose possible associations between poorer sleep quality and structural brain alterations in a non-psychiatric healthy population, this study investigated the association between the Pittsburgh sleep quality index (PSQI) and brain correlates, using a whole-brain approach. This study included 371 right-handed healthy adults (138 males, mean age: 46.4 ± 14.0 years [range: 18-75]) who were right-handed. Subjective sleep quality was assessed using the Japanese version of the PSQI (PSQI-J), and the cutoff score for poor subjective sleep quality was set at ≥ 6. Voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) were performed to examine whether a higher score of the PSQI-J indicates, poorer sleep quality is associated with gray matter volume and white matter microstructure alternations, respectively. Among the participants, 38.8% had a PSQI-J cutoff score of ≥ 6. VBM did not reveal any correlation between PSQI-J scores and gray matter volume. However, DTI revealed that PSQI-J global scores were significantly and negatively correlated with diffuse white matter fractional anisotropy (FA) values (p < 0.05, corrected). Moreover, the PSQI-J sleep disturbance and use of sleep medication component scores were significantly and negatively correlated with right anterior thalamic radiation and diffuse white matter FA values, respectively (p < 0.05, corrected). There were no significant differences in gray matter volume and white matter metrics (FA, axial, radial, and mean diffusivities) between the groups with PSQI-J scores above or below the cutoff. Our findings suggest that lower sleep quality, especially the use of sleep medication, is associated with impaired white matter integrity in healthy adults. Limitations of this study are relatively small number of participants and cross-sectional design. Fine sleep quality, possibly preventing the use of sleep medication, may contribute to preserve white matter integrity in the brain of healthy adults.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41105-022-00442-0.

Robust associations between white matter microstructure and general intelligence

Few tract-based spatial statistics (TBSS) studies have investigated the relations between intelligence and white matter microstructure in healthy (young) adults, and those have yielded mixed observations, yet white matter is fundamental for efficient and accurate information transfer throughout the human brain. We used a multicenter approach to identify white matter regions that show replicable structure-function associations, employing data from 4 independent samples comprising over 2000 healthy participants. TBSS indicated 188 voxels exhibited significant positive associations between g factor scores and fractional anisotropy (FA) in all 4 data sets. Replicable voxels formed 3 clusters, located around the left-hemispheric forceps minor, superior longitudinal fasciculus, and cingulum-cingulate gyrus with extensions into their surrounding areas (anterior thalamic radiation, inferior fronto-occipital fasciculus). Our results suggested that individual differences in general intelligence are robustly associated with white matter FA in specific fiber bundles distributed across the brain, consistent with the Parieto-Frontal Integration Theory of intelligence. Three possible reasons higher FA values might create links with higher g are faster information processing due to greater myelination, more direct information processing due to parallel, homogenous fiber orientation distributions, or more parallel information processing due to greater axon density.

Association of body mass index and its classifications with gray matter volume in individuals with a wide range of body mass index group: A whole-brain magnetic resonance imaging study

Aim

To examine the association of body mass index (BMI) [kg/m²] and its classifications (underweight [BMI < 18.5], normal [18.5 ≤ BMI < 25], overweight [25 ≤ BMI < 30], and obese [BMI ≥ 30]) with brain structure in individuals with a wide range of BMI group.

Materials and methods

The participants included 382 right-handed individuals (mean age: 46.9 ± 14.3 years, 142 men and 240 women). The intelligence quotient was assessed using the Japanese Adult Reading Test. Voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) were performed to analyze the association of BMI and its classifications with gray and white matter structures, respectively.

Results

According to VBM, BMI was significantly and negatively correlated with the bilateral cerebellum exterior volumes. In group comparisons, the right cerebellum exterior volume was significantly lower in the overweight or obese group than in the underweight or normal group, while the bilateral cuneus and calcarine cortex, left cuneus, and left precuneus volume was significantly lower in the underweight group than in the non-underweight group. Sex-related stratification analyses for VBM revealed that BMI was significantly and negatively correlated with the bilateral cerebellum exterior volumes only in women. In group comparisons, the left cerebellum exterior volume was significantly lower in obese women than in non-obese women. The left thalamus proper and the right cerebellum exterior volumes were significantly lower in overweight or obese group than in underweight or normal group in men and women, respectively. The bilateral cuneus and calcarine cortex, left cuneus and carcarine cortex, and bilateral cuneus volume was significantly lower in underweight men than in non-underweight men. In contrast, there were no notable findings on DTI.

Conclusion

Our results suggest association of continuous BMI, being overweight or obese, and being underweight with decreased gray matter volume in individuals with a wide range of BMI group. Furthermore, sex-related differences are seen in the association of BMI and its classifications with regional gray matter volume reductions. Abnormally high or low BMIs may have a negative influence on regional gray matter volumes.

Effects of Transcranial Direct Current Stimulation Over the Left Primary Motor Cortex on Verbal Intelligence

Previous studies have shown that changes in gray matter density and volume in the left primary motor cortex are significantly associated with changes in individuals’ verbal intelligence quotient (VIQ), but not with their performance intelligence quotient (PIQ). In the present study, we examined the effects of transcranial direct current stimulation (tDCS) over the left primary motor cortex on performance in intelligence tests. We chose four subtests (two each for VIQ and PIQ) of the Wechsler Adult Intelligence Scale-Chinese Revised version and randomized participants into anodal, cathodal, and sham groups. We found that anodal stimulation significantly improved performance in verbal intelligence subtests compared to cathodal and sham stimulation, while performance intelligence subtest scores did not change in any stimulation condition. These findings suggest that the excitation level of the left primary motor cortex has a unique effect on verbal intelligence.

Dissociated brain functional connectivity of fast versus slow frequencies underlying individual differences in fluid intelligence: a DTI and MEG study

Brain network analysis represents a powerful technique to gain insights into the connectivity profile characterizing individuals with different levels of fluid intelligence (Gf). Several studies have used diffusion tensor imaging (DTI) and slow-oscillatory resting-state fMRI (rs-fMRI) to examine the anatomical and functional aspects of human brain networks that support intelligence. In this study, we expand this line of research by investigating fast-oscillatory functional networks. We performed graph theory analyses on resting-state magnetoencephalographic (MEG) signal, in addition to structural brain networks from DTI data, comparing degree, modularity and segregation coefficient across the brain of individuals with high versus average Gf scores. Our results show that high Gf individuals have stronger degree and lower segregation coefficient than average Gf participants in a significantly higher number of brain areas with regards to structural connectivity and to the slower frequency bands of functional connectivity. The opposite result was observed for higher-frequency (gamma) functional networks, with higher Gf individuals showing lower degree and higher segregation across the brain. We suggest that gamma oscillations in more intelligent individuals might support higher local processing in segregated subnetworks, while slower frequency bands would allow a more effective information transfer between brain subnetworks, and stronger information integration.

TENSOR QUANTILE REGRESSION WITH APPLICATION TO ASSOCIATION BETWEEN NEUROIMAGES AND HUMAN INTELLIGENCE

Human intelligence is usually measured by well-established psychometric tests through a series of problem solving. The recorded cognitive scores are continuous but usually heavy-tailed with potential outliers and violating the normality assumption. Meanwhile, magnetic resonance imaging (MRI) provides an unparalleled opportunity to study brain structures and cognitive ability. Motivated by association studies between MRI images and human intelligence, we propose a tensor quantile regression model, which is a general and robust alternative to the commonly used scalar-on-image linear regression. Moreover, we take into account rich spatial information of brain structures, incorporating low-rankness and piece-wise smoothness of imaging coefficients into a regularized regression framework. We formulate the optimization problem as a sequence of penalized quantile regressions with a generalized Lasso penalty based on tensor decomposition, and develop a computationally efficient alternating direction method of multipliers algorithm (ADMM) to estimate the model components. Extensive numerical studies are conducted to examine the empirical performance of the proposed method and its competitors. Finally, we apply the proposed method to a large-scale important dataset: the Human Connectome Project. We find that the tensor quantile regression can serve as a prognostic tool to assess future risk of cognitive impairment progression. More importantly, with the proposed method, we are able to identify the most activated brain subregions associated with quantiles of human intelligence. The prefrontal and anterior cingulate cortex are found to be mostly associated with lower and upper quantile of fluid intelligence. The insular cortex associated with median of fluid intelligence is a rarely reported region.

White Matter Alterations Between Brain Network Hubs Underlie Processing Speed Impairment in Patients With Schizophrenia

Processing speed (PS) impairment is one of the most severe and common cognitive deficits in schizophrenia. Previous studies have reported correlations between PS and white matter diffusion properties, including fractional anisotropy (FA), in several fiber bundles in schizophrenia, suggesting that white matter alterations could underpin decreased PS. In schizophrenia, white matter alterations are most prevalent within inter-hub connections of the rich club. However, the spatial and topological characteristics of this association between PS and FA have not been investigated in patients. In this context, we tested whether structural connections comprising the rich club network would underlie PS impairment in 298 patients with schizophrenia or schizoaffective disorder and 190 healthy controls from the Australian Schizophrenia Research Bank. PS, measured using the digit symbol coding task, was largely (Cohen’s d = 1.33) and significantly (P < .001) reduced in the patient group when compared with healthy controls. Significant associations between PS and FA were widespread in the patient group, involving all cerebral lobes. FA was not associated with other cognitive measures of phonological fluency and verbal working memory in patients, suggesting specificity to PS. A topological analysis revealed that despite being spatially widespread, associations between PS and FA were over-represented among connections forming the rich club network. These findings highlight the need to consider brain network topology when investigating high-order cognitive functions that may be spatially distributed among several brain regions. They also reinforce the evidence that brain hubs and their interconnections may be particularly vulnerable parts of the brain in schizophrenia.