Neuroanatomical Correlates of Intelligence

Reliability of measuring regional callosal atrophy in neurodegenerative diseases

The Corpus Callosum (CC) is an important structure connecting the two brain hemispheres. As several neurodegenerative diseases are known to alter its shape, it is an interesting structure to assess as biomarker. Yet, currently, the CC-segmentation is often performed manually and is consequently an error prone and time-demanding procedure. In this paper, we present an accurate and automated method for corpus callosum segmentation based on T1-weighted MRI images.

After the initial construction of a CC atlas based on healthy controls, a new image is subjected to a mid-sagittal plane (MSP) detection algorithm and a 3D affine registration in order to initialise the CC within the extracted MSP. Next, an active shape model is run to extract the CC. We calculated the reliability of most popular CC features (area, circularity, corpus callosum index and thickness profile) in healthy controls, Alzheimer's Disease patients and Multiple Sclerosis patients. Importantly, we also provide inter-scanner reliability estimates.

We obtained an intra-class correlation coefficient (ICC) of over 0.95 for most features and most datasets. The inter-scanner reliability assessed on the MS patients was remarkably well and ranged from 0.77 to 0.97.

In summary, we have constructed an algorithm that reliably detects the CC in 3D T1 images in a fully automated way in healthy controls and different neurodegenerative diseases. Although the CC area and the circularity are the most reliable features (ICC > 0.97); the reliability of the thickness profile (ICC > 0.90; excluding the tip) is sufficient to warrant its inclusion in future clinical studies.

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A completely automated segmentation of the Corpus Callosum

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Both traditional features and the thickness profile using Laplace's equation are calculated.

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Excellent reproducibility and accuracy in healthy controls

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Excellent reproducibility and accuracy in Alzheimer's Dementia and Multiple Sclerosis patients

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Excellent inter-scanner reliability enabling the pooling of multi-center data

Quantitative Classification of Cerebellar Foliation in Cartilaginous Fishes (Class: Chondrichthyes) Using Three-Dimensional Shape Analysis and Its Implications for Evolutionary Biology

A true cerebellum appeared at the onset of the chondrichthyan (sharks, batoids, and chimaerids) radiation and is known to be essential for executing fast, accurate, and efficient movement. In addition to a high degree of variation in size, the corpus cerebellum in this group has a high degree of variation in convolution (or foliation) and symmetry, which ranges from a smooth cerebellar surface to deep, branched convexities and folds, although the functional significance of this trait is unclear. As variation in the degree of foliation similarly exists throughout vertebrate evolution, it becomes critical to understand this evolutionary process in a wide variety of species. However, current methods are either qualitative and lack numerical rigor or they are restricted to two dimensions. In this paper, a recently developed method for the characterization of shapes embedded within noisy, three-dimensional data called spherical wave decomposition (SWD) is applied to the problem of characterizing cerebellar foliation in cartilaginous fishes. The SWD method provides a quantitative characterization of shapes in terms of well-defined mathematical functions. An additional feature of the SWD method is the construction of a statistical criterion for the optimal fit, which represents the most parsimonious choice of parameters that fits to the data without overfitting to background noise. We propose that this optimal fit can replace a previously described qualitative visual foliation index (VFI) in cartilaginous fishes with a quantitative analog, i.e. the cerebellar foliation index (CFI). The capability of the SWD method is demonstrated in a series of volumetric images of brains from different chondrichthyan species that span the range of foliation gradings currently described for this group. The CFI is consistent with the qualitative grading provided by the VFI, delivers a robust measure of cerebellar foliation, and can provide a quantitative basis for brain shape characterization across taxa.

Underlying sources of cognitive-anatomical variation in multi-modal neuroimaging and cognitive testing

Healthy adults have robust individual differences in neuroanatomy and cognitive ability not captured by demographics or gross morphology (Luders, Narr, Thompson, & Toga, 2009). We used a hierarchical independent component analysis (hICA) to create novel characterizations of individual differences in our participants (N=190). These components fused data across multiple cognitive tests and neuroanatomical variables. The first level contained four independent, underlying sources of phenotypic variance that predominately modeled broad relationships within types of data (e.g., "white matter," or "subcortical gray matter"), but were not reflective of traditional individual difference measures such as sex, age, or intracranial volume. After accounting for the novel individual difference measures, a second level analysis identified two underlying sources of phenotypic variation. One of these made strong, joint contributions to both the anatomical structures associated with the core fronto-parietal "rich club" network (van den Heuvel & Sporns, 2011), and to cognitive factors. These findings suggest that a hierarchical, data-driven approach is able to identify underlying sources of individual difference that contribute to cognitive-anatomical variation in healthy young adults.

Hyper-connectivity of functional networks for brain disease diagnosis

Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help discover disease-related biomarkers important for disease diagnosis.

Association of maternal thyroid function during early pregnancy with offspring IQ and brain morphology in childhood: a population-based prospective cohort study

BACKGROUND

Thyroid hormone is involved in the regulation of early brain development. Since the fetal thyroid gland is not fully functional until week 18-20 of pregnancy, neuronal migration and other crucial early stages of intrauterine brain development largely depend on the supply of maternal thyroid hormone. Current clinical practice mostly focuses on preventing the negative consequences of low thyroid hormone concentrations, but data from animal studies have shown that both low and high concentrations of thyroid hormone have negative effects on offspring brain development. We aimed to investigate the association of maternal thyroid function with child intelligence quotient (IQ) and brain morphology.

METHODS

In this population-based prospective cohort study, embedded within the Generation R Study (Rotterdam, Netherlands), we investigated the association of maternal thyroid function with child IQ (assessed by non-verbal intelligence tests) and brain morphology (assessed on brain MRI scans). Eligible women were those living in the study area at their delivery date, which had to be between April 1, 2002, and Jan 1, 2006. For this study, women with available serum samples who presented in early pregnancy (<18 weeks) were included. Data for maternal thyroid-stimulating hormone, free thyroxine, thyroid peroxidase antibodies (at weeks 9-18 of pregnancy), and child IQ (assessed at a median of 6·0 years of age [95% range 5·6-7·9 years]) or brain MRI scans (done at a median of 8·0 years of age [6·2-10·0]) were obtained. Analyses were adjusted for potential confounders including concentrations of human chorionic gonadotropin and child thyroid-stimulating hormone and free thyroxine.

FINDINGS

Data for child IQ were available for 3839 mother-child pairs, and MRI scans were available from 646 children. Maternal free thyroxine concentrations showed an inverted U-shaped association with child IQ (p=0·0044), child grey matter volume (p=0·0062), and cortex volume (p=0·0011). For both low and high maternal free thyroxine concentrations, this association corresponded to a 1·4-3·8 points reduction in mean child IQ. Maternal thyroid-stimulating hormone was not associated with child IQ or brain morphology. All associations remained similar after the exclusion of women with overt hypothyroidism and overt hyperthyroidism, and after adjustment for concentrations of human chorionic gonadotropin, child thyroid-stimulating hormone and free thyroxine or thyroid peroxidase antibodies (continuous or positivity).

INTERPRETATION

Both low and high maternal free thyroxine concentrations during pregnancy were associated with lower child IQ and lower grey matter and cortex volume. The association between high maternal free thyroxine and low child IQ suggests that levothyroxine therapy during pregnancy, which is often initiated in women with subclinical hypothyroidism during pregnancy, might carry the potential risk of adverse child neurodevelopment outcomes when the aim of treatment is to achieve high-normal thyroid function test results.

FUNDING

The Netherlands Organisation for Health Research and Development (ZonMw) and the European Community's Seventh Framework Programme.

Topography of Slow Sigma Power during Sleep is Associated with Processing Speed in Preschool Children

Cognitive development is influenced by maturational changes in processing speed, a construct reflecting the rapidity of executing cognitive operations. Although cognitive ability and processing speed are linked to spindles and sigma power in the sleep electroencephalogram (EEG), little is known about such associations in early childhood, a time of major neuronal refinement. We calculated EEG power for slow (10-13 Hz) and fast (13.25-17 Hz) sigma power from all-night high-density electroencephalography (EEG) in a cross-sectional sample of healthy preschool children (n = 10, 4.3 ± 1.0 years). Processing speed was assessed as simple reaction time. On average, reaction time was 1409 ± 251 ms; slow sigma power was 4.0 ± 1.5 μV²; and fast sigma power was 0.9 ± 0.2 μV². Both slow and fast sigma power predominated over central areas. Only slow sigma power was correlated with processing speed in a large parietal electrode cluster (p < 0.05, r ranging from -0.6 to -0.8), such that greater power predicted faster reaction time. Our findings indicate regional correlates between sigma power and processing speed that are specific to early childhood and provide novel insights into the neurobiological features of the EEG that may underlie developing cognitive abilities.

Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques

Magnetic resonance imaging (MRI) of the brain provides exceptional image quality for visualization and neuroanatomical classification of brain structure. A variety of image analysis techniques provide both qualitative as well as quantitative methods to relate brain structure with neuropsychological outcome and are reviewed herein. Of particular importance are more automated methods that permit analysis of a broad spectrum of anatomical measures including volume, thickness and shape. The challenge for neuropsychology is which metric to use, for which disorder and the timing of when image analysis methods are applied to assess brain structure and pathology. A basic overview is provided as to the anatomical and pathoanatomical relations of different MRI sequences in assessing normal and abnormal findings. Some interpretive guidelines are offered including factors related to similarity and symmetry of typical brain development along with size-normalcy features of brain anatomy related to function. The review concludes with a detailed example of various quantitative techniques applied to analyzing brain structure for neuropsychological outcome studies in traumatic brain injury.

Whole-brain functional hypoconnectivity as an endophenotype of autism in adolescents

Endophenotypes are heritable and quantifiable markers that may assist in the identification of the complex genetic underpinnings of psychiatric conditions. Here we examined global hypoconnectivity as an endophenotype of autism spectrum conditions (ASCs). We studied well-matched groups of adolescent males with autism, genetically-related siblings of individuals with autism, and typically-developing control participants. We parcellated the brain into 258 regions and used complex-network analysis to detect a robust hypoconnectivity endophenotype in our participant group. We observed that whole-brain functional connectivity was highest in controls, intermediate in siblings, and lowest in ASC, in task and rest conditions. We identified additional, local endophenotype effects in specific networks including the visual processing and default mode networks. Our analyses are the first to show that whole-brain functional hypoconnectivity is an endophenotype of autism in adolescence, and may thus underlie the heritable similarities seen in adolescents with ASC and their relatives.

Beyond a bigger brain: Multivariable structural brain imaging and intelligence

People with larger brains tend to score higher on tests of general intelligence (g). It is unclear, however, how much variance in intelligence other brain measurements would account for if included together with brain volume in a multivariable model. We examined a large sample of individuals in their seventies (n = 672) who were administered a comprehensive cognitive test battery. Using structural equation modelling, we related six common magnetic resonance imaging-derived brain variables that represent normal and abnormal features-brain volume, cortical thickness, white matter structure, white matter hyperintensity load, iron deposits, and microbleeds-to g and to fluid intelligence. As expected, brain volume accounted for the largest portion of variance (~ 12%, depending on modelling choices). Adding the additional variables, especially cortical thickness (+~ 5%) and white matter hyperintensity load (+~ 2%), increased the predictive value of the model. Depending on modelling choices, all neuroimaging variables together accounted for 18-21% of the variance in intelligence. These results reveal which structural brain imaging measures relate to g over and above the largest contributor, total brain volume. They raise questions regarding which other neuroimaging measures might account for even more of the variance in intelligence.

When larger brains do not have more neurons: increased numbers of cells are compensated by decreased average cell size across mouse individuals

There is a strong trend toward increased brain size in mammalian evolution, with larger brains composed of more and larger neurons than smaller brains across species within each mammalian order. Does the evolution of increased numbers of brain neurons, and thus larger brain size, occur simply through the selection of individuals with more and larger neurons, and thus larger brains, within a population? That is, do individuals with larger brains also have more, and larger, neurons than individuals with smaller brains, such that allometric relationships across species are simply an extension of intraspecific scaling? Here we show that this is not the case across adult male mice of a similar age. Rather, increased numbers of neurons across individuals are accompanied by increased numbers of other cells and smaller average cell size of both types, in a trade-off that explains how increased brain mass does not necessarily ensue. Fundamental regulatory mechanisms thus must exist that tie numbers of neurons to numbers of other cells and to average cell size within individual brains. Finally, our results indicate that changes in brain size in evolution are not an extension of individual variation in numbers of neurons, but rather occur through step changes that must simultaneously increase numbers of neurons and cause cell size to increase, rather than decrease.

Attentional Control and Intelligence: MRI Orbital Frontal Gray Matter and Neuropsychological Correlates

Attentional control is a key function of working memory that is hypothesized to play an important role in psychometric intelligence. To test the neuropsychological underpinnings of this hypothesis, we examined full-scale IQ, as measured by the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III), and attentional control, as measured by Trails B response time and Wisconsin Card Sorting (WCS) test perseverative errors in 78 healthy participants, 25 of whom also had available magnetic resonance imaging (MRI) gray matter volume studies of the orbital frontal cortex (OFC) parcellated into three regions: gyrus rectus, middle orbital gyrus, and lateral orbital gyrus. Hierarchical regression indicated that Trails B response time specifically explained 15.13% to 19.18% of the variation in IQ and WCS perseverative errors accounted for an additional 8.12% to 11.29% of the variance. Full-scale IQ correlated very strongly with right middle orbital gyrus gray matter volume (r = 0.610, p = 0.002), as did Trails B response time with left middle orbital gyrus gray matter volume (r = −0.608, p = 0.003). Trails B response time and right middle orbital gyrus gray matter volume jointly accounted for approximately 32.95% to 54.82% of the variance in IQ scores. These results provided evidence of the unique contributions of attentional control and OFC gray matter to intelligence.

Brain structure-function associations in multi-generational families genetically enriched for bipolar disorder

Recent theories regarding the pathophysiology of bipolar disorder suggest contributions of both neurodevelopmental and neurodegenerative processes. While structural neuroimaging studies indicate disease-associated neuroanatomical alterations, the behavioural correlates of these alterations have not been well characterized. Here, we investigated multi-generational families genetically enriched for bipolar disorder to: (i) characterize neurobehavioural correlates of neuroanatomical measures implicated in the pathophysiology of bipolar disorder; (ii) identify brain-behaviour associations that differ between diagnostic groups; (iii) identify neurocognitive traits that show evidence of accelerated ageing specifically in subjects with bipolar disorder; and (iv) identify brain-behaviour correlations that differ across the age span. Structural neuroimages and multi-dimensional assessments of temperament and neurocognition were acquired from 527 (153 bipolar disorder and 374 non-bipolar disorder) adults aged 18-87 years in 26 families with heavy genetic loading for bipolar disorder. We used linear regression models to identify significant brain-behaviour associations and test whether brain-behaviour relationships differed: (i) between diagnostic groups; and (ii) as a function of age. We found that total cortical and ventricular volume had the greatest number of significant behavioural associations, and included correlations with measures from multiple cognitive domains, particularly declarative and working memory and executive function. Cortical thickness measures, in contrast, showed more specific associations with declarative memory, letter fluency and processing speed tasks. While the majority of brain-behaviour relationships were similar across diagnostic groups, increased cortical thickness in ventrolateral prefrontal and parietal cortical regions was associated with better declarative memory only in bipolar disorder subjects, and not in non-bipolar disorder family members. Additionally, while age had a relatively strong impact on all neurocognitive traits, the effects of age on cognition did not differ between diagnostic groups. Most brain-behaviour associations were also similar across the age range, with the exception of cortical and ventricular volume and lingual gyrus thickness, which showed weak correlations with verbal fluency and inhibitory control at younger ages that increased in magnitude in older subjects, regardless of diagnosis. Findings indicate that neuroanatomical traits potentially impacted by bipolar disorder are significantly associated with multiple neurobehavioural domains. Structure-function relationships are generally preserved across diagnostic groups, with the notable exception of ventrolateral prefrontal and parietal association cortex, volumetric increases in which may be associated with cognitive resilience specifically in individuals with bipolar disorder. Although age impacted all neurobehavioural traits, we did not find any evidence of accelerated cognitive decline specific to bipolar disorder subjects. Regardless of diagnosis, greater global brain volume may represent a protective factor for the effects of ageing on executive functioning.

Relationship Between Surface-Based Brain Morphometric Measures and Intelligence in Autism Spectrum Disorders: Influence of History of Language Delay

Autism spectrum disorders (ASD) are a group of conditions that show abnormalities in the neuroanatomy of multiple brain regions. The variability in the development of intelligence and language among individuals on the autism spectrum has long been acknowledged, but it remains unknown whether these differences impact on the neuropathology of ASD. In this study, we aimed to compare associations between surface-based regional brain measures and general intelligence (IQ) scores in ASD individuals with and without a history of language delay. We included 64 ASD adults of normal intelligence (37 without a history of language delay and 27 with a history of language delay and 80 neurotypicals). Regions with a significant association between verbal and nonverbal IQ and measures of cortical thickness (CT), surface area, and cortical volume were first identified in the combined sample of individuals with ASD and controls. Thicker dorsal frontal and temporal cortices, and thinner lateral orbital frontal and parieto-occipital cortices were associated with greater and lower verbal IQ scores, respectively. Correlations between cortical volume and verbal IQ were observed in similar regions as revealed by the CT analysis. A significant difference between ASD individuals with and without a history of language delay in the association between CT and verbal IQ was evident in the parieto-occipital region. These results indicate that ASD subgroups defined on the basis of differential language trajectories in childhood can have different associations between verbal IQ and brain measures in adulthood despite achieving similar levels of cognitive performance.

Diffuse alterations in grey and white matter associated with cognitive impairment in Shwachman-Diamond syndrome: evidence from a multimodal approach

Shwachman-Diamond syndrome is a rare recessive genetic disease caused by mutations in SBDS gene, at chromosome 7q11. Phenotypically, the syndrome is characterized by exocrine pancreatic insufficiency, bone marrow dysfunction, skeletal dysplasia and variable cognitive impairments. Structural brain abnormalities (smaller head circumference and decreased brain volume) have also been reported. No correlation studies between brain abnormalities and neuropsychological features have yet been performed. In this study we investigate neuroanatomical findings, neurofunctional pathways and cognitive functioning of Shwachman-Diamond syndrome subjects compared with healthy controls. To be eligible for inclusion, participants were required to have known SBDS mutations on both alleles, no history of cranial trauma or any standard contraindication to magnetic resonance imaging. Appropriate tests were used to assess cognitive functions. The static images were acquired on a 3 × 0 T magnetic resonance scanner and blood oxygen level-dependent functional magnetic resonance imaging data were collected both during the execution of the Stroop task and at rest. Diffusion tensor imaging was used to assess brain white matter. The Tract-based Spatial Statistics package and probabilistic tractography were used to characterize white matter pathways. Nine participants (5 males), half of all the subjects aged 9-19 years included in the Italian Shwachman-Diamond Syndrome Registry, were evaluated and compared with nine healthy subjects, matched for sex and age. The patients performed less well than norms and controls on cognitive tasks (p = 0.0002). Overall, cortical thickness was greater in the patients, both in the left (+10%) and in the right (+15%) hemisphere, significantly differently increased in the temporal (left and right, p = 0.04), and right parietal (p = 0.03) lobes and in Brodmann area 44 (p = 0.04) of the right frontal lobe. The greatest increases were observed in the left limbic-anterior cingulate cortex (≥43%, p < 0.0004). Only in Broca's area in the left hemisphere did the patients show a thinner cortical thickness than that of controls (p = 0.01). Diffusion tensor imaging showed large, significant difference increases in both fractional anisotropy (+37%, p < 0.0001) and mean diffusivity (+35%, p < 0.005); the Tract-based Spatial Statistics analysis identified six abnormal clusters of white matter fibres in the fronto-callosal, right fronto-external capsulae, left fronto-parietal, right pontine, temporo-mesial and left anterior-medial-temporal regions. Brain areas activated during the Stroop task and those active during the resting state, are different, fewer and smaller in patients and correlate with worse performance (p = 0.002). Cognitive impairment in Shwachman-Diamond syndrome subjects is associated with diffuse brain anomalies in the grey matter (verbal skills with BA44 and BA20 in the right hemisphere; perceptual skills with BA5, 37, 20, 21, 42 in the left hemisphere) and white matter connectivity (verbal skills with alterations in the fronto-occipital fasciculus and with the inferior-longitudinal fasciculus; perceptual skills with the arcuate fasciculus, limbic and ponto-cerebellar fasciculus; memory skills with the arcuate fasciculus; executive functions with the anterior cingulated and arcuate fasciculus).

Longitudinal volumetric brain changes in autism spectrum disorder ages 6-35 years

Since the impairments associated with autism spectrum disorder (ASD) tend to persist or worsen from childhood into adulthood, it is of critical importance to examine how the brain develops over this growth epoch. We report initial findings on whole and regional longitudinal brain development in 100 male participants with ASD (226 high-quality magnetic resonance imaging [MRI] scans; mean inter-scan interval 2.7 years) compared to 56 typically developing controls (TDCs) (117 high-quality scans; mean inter-scan interval 2.6 years) from childhood into adulthood, for a total of 156 participants scanned over an 8-year period. This initial analysis includes between one and three high-quality scans per participant that have been processed and segmented to date, with 21% having one scan, 27% with two scans, and 52% with three scans in the ASD sample; corresponding percentages for the TDC sample are 30%, 30%, and 40%. The proportion of participants with multiple scans (79% of ASDs and 68% of TDCs) was high in comparison to that of large longitudinal neuroimaging studies of typical development. We provide volumetric growth curves for the entire brain, total gray matter (GM), frontal GM, temporal GM, parietal GM, occipital GM, total cortical white matter (WM), corpus callosum, caudate, thalamus, total cerebellum, and total ventricles. Mean volume of cortical WM was reduced significantly. Mean ventricular volume was increased in the ASD sample relative to the TDCs across the broad age range studied. Decreases in regional mean volumes in the ASD sample most often were due to decreases during late adolescence and adulthood. The growth curve of whole brain volume over time showed increased volumes in young children with autism, and subsequently decreased during adolescence to meet the TDC curve between 10 and 15 years of age. The volume of many structures continued to decline atypically into adulthood in the ASD sample. The data suggest that ASD is a dynamic disorder with complex changes in whole and regional brain volumes that change over time from childhood into adulthood.

Endocasts: possibilities and limitations for the interpretation of human brain evolution

Brains are not preserved in the fossil record but endocranial casts are. These are casts of the internal bony braincase, revealing approximate brain size and shape, and they are also informative about brain surface morphology. Endocasts are the only direct evidence of human brain evolution, but they provide only limited data ('paleoneurology'). This review discusses some new fossil endocasts and recent methodological advances that have allowed novel analyses of old endocasts, leading to intriguing findings and hypotheses. The interpretation of paleoneurological data always relies on comparative information from living species whose brains and behavior can be directly investigated. It is therefore important that future studies attempt to better integrate different approaches. Only then will we be able to gain a better understanding about hominin brain evolution. © 2014 S. Karger AG, Basel.

Brain size, sex, and the aging brain

This study was conducted to examine the statistical influence of brain size on cortical, subcortical, and cerebellar compartmental volumes. This brain size influence was especially studied to delineate interactions with Sex and Age. Here, we studied 856 healthy subjects of which 533 are classified as young and 323 as old. Using an automated segmentation procedure cortical (gray and white matter [GM and WM] including the corpus callosum), cerebellar (GM and WM), and subcortical (thalamus, putamen, pallidum, caudatus, hippocampus, amygdala, and accumbens) volumes were measured and subjected to statistical analyses. These analyses revealed that brain size and age exert substantial statistical influences on nearly all compartmental volumes. Analyzing the raw compartmental volumes replicated the frequently reported Sex differences in compartmental volumes with men showing larger volumes. However, when statistically controlling for brain size Sex differences and Sex × Age interactions practically disappear. Thus, brain size is more important than Sex in explaining interindividual differences in compartmental volumes. The influence of brain size is discussed in the context of an allometric scaling of the compartmental volumes.

Subcortical regional morphology correlates with fluid and spatial intelligence

Neuroimaging studies have revealed associations between intelligence and brain morphology. However, researchers have focused primarily on the anatomical features of the cerebral cortex, whereas subcortical structures, such as the basal ganglia (BG), have often been neglected despite extensive functional evidence on their relation with higher-order cognition. Here we performed shape analyses to understand how individual differences in BG local morphology account for variability in cognitive performance. Structural MRI was acquired in 104 young adults (45 men, 59 women, mean age = 19.83, SD = 1.64), and the outer surface of striatal structures (caudate, nucleus accumbens, and putamen), globus pallidus, and thalamus was estimated for each subject and hemisphere. Further, nine cognitive tests were used to measure fluid (Gf), crystallized (Gc), and spatial intelligence (Gv). Latent scores for these factors were computed by means of confirmatory factor analysis and regressed vertex-wise against subcortical shape (local displacements of vertex position), controlling for age, sex, and adjusted for brain size. Significant results (FDR < 5%) were found for Gf and Gv, but not Gc, for the right striatal structures and thalamus. The main results show a relative enlargement of the rostral putamen, which is functionally connected to the right dorsolateral prefrontal cortex and other intelligence-related prefrontal areas.

The effects of a single night of sleep deprivation on fluency and prefrontal cortex function during divergent thinking

The dorsal and ventral aspects of the prefrontal cortex (PFC) are the two regions most consistently recruited in divergent thinking tasks. Given that frontal tasks have been shown to be vulnerable to sleep loss, we explored the impact of a single night of sleep deprivation on fluency (i.e., number of generated responses) and PFC function during divergent thinking. Participants underwent functional magnetic resonance imaging scanning twice while engaged in the Alternate Uses Task (AUT) - once following a single night of sleep deprivation and once following a night of normal sleep. They also wore wrist activity monitors, which enabled us to quantify daily sleep and model cognitive effectiveness. The intervention was effective, producing greater levels of fatigue and sleepiness. Modeled cognitive effectiveness and fluency were impaired following sleep deprivation, and sleep deprivation was associated with greater activation in the left inferior frontal gyrus (IFG) during AUT. The results suggest that an intervention known to temporarily compromise frontal function can impair fluency, and that this effect is instantiated in the form of an increased hemodynamic response in the left IFG.

Neuroanatomical correlates of intelligence in healthy young adults: the role of basal ganglia volume

BACKGROUND

In neuropsychiatric diseases with basal ganglia involvement, higher cognitive functions are often impaired. In this exploratory study, we examined healthy young adults to gain detailed insight into the relationship between basal ganglia volume and cognitive abilities under non-pathological conditions.

METHODOLOGY/PRINCIPAL FINDINGS

We investigated 137 healthy adults that were between the ages of 21 and 35 years with similar educational backgrounds. Magnetic resonance imaging (MRI) was performed, and volumes of basal ganglia nuclei in both hemispheres were calculated using FreeSurfer software. The cognitive assessment consisted of verbal, numeric and figural aspects of intelligence for either the fluid or the crystallised intelligence factor using the intelligence test Intelligenz-Struktur-Test (I-S-T 2000 R). Our data revealed significant correlations of the caudate nucleus and pallidum volumes with figural and numeric aspects of intelligence, but not with verbal intelligence. Interestingly, figural intelligence associations were dependent on sex and intelligence factor; in females, the pallidum volumes were correlated with crystallised figural intelligence (r = 0.372, p = 0.01), whereas in males, the caudate volumes were correlated with fluid figural intelligence (r = 0.507, p = 0.01). Numeric intelligence was correlated with right-lateralised caudate nucleus volumes for both females and males, but only for crystallised intelligence (r = 0.306, p = 0.04 and r = 0.459, p = 0.04, respectively). The associations were not mediated by prefrontal cortical subfield volumes when controlling with partial correlation analyses.

CONCLUSIONS/SIGNIFICANCE

The findings of our exploratory analysis indicate that figural and numeric intelligence aspects, but not verbal aspects, are strongly associated with basal ganglia volumes. Unlike numeric intelligence, the type of figural intelligence appears to be related to distinct basal ganglia nuclei in a sex-specific manner. Subcortical brain structures thus may contribute substantially to cognitive performance.

Early adolescent cortical thinning is related to better neuropsychological performance

Adolescence is characterized by significant neuromaturation, including extensive cortical thinning, particularly in frontal regions. The goal of this study was to examine the behavioral correlates of neurostructural development in early adolescence. Participants were 185 healthy 12- to 14-year-olds (44% female) recruited from local schools. Participants completed a comprehensive neuropsychological test battery and magnetic resonance imaging session. Cortical surface reconstruction and thickness estimates were performed via FreeSurfer. Age and cortical thickness were negatively correlated in 10 brain regions, 7 of which were in frontal areas (β = −.15 to −.25, ps ≤ .05). Hierarchical linear regressions examined the influence of cortical thickness on working memory, attention, verbal learning and memory, visuospatial functioning, spatial planning and problem solving, and inhibition, controlling for age and intracranial volume. Thinner parietal cortices predicted better performances on tests of verbal learning and memory, visuospatial functioning, and spatial planning and problem solving (β = −.14 to −.24, ps ≤ .05). Age, spanning from 12 to 14 years, accounted for up to 6% of cortical thickness, suggesting substantial thinning during early adolescence, with males showing more accelerated thinning than females between ages 12 and 14. For both males and females, thinner parietal association cortices corresponded with better neurocognitive functioning above and beyond age alone.

Associations between cortical thickness and general intelligence in children, adolescents and young adults

Neuroimaging research indicates that human intellectual ability is related to brain structure including the thickness of the cerebral cortex. Most studies indicate that general intelligence is positively associated with cortical thickness in areas of association cortex distributed throughout both brain hemispheres. In this study, we performed a cortical thickness mapping analysis on data from 182 healthy typically developing males and females ages 9 to 24 years to identify correlates of general intelligence (g) scores. To determine if these correlates also mediate associations of specific cognitive abilities with cortical thickness, we regressed specific cognitive test scores on g scores and analyzed the residuals with respect to cortical thickness. The effect of age on the association between cortical thickness and intelligence was examined. We found a widely distributed pattern of positive associations between cortical thickness and g scores, as derived from the first unrotated principal factor of a factor analysis of Wechsler Abbreviated Scale of Intelligence (WASI) subtest scores. After WASI specific cognitive subtest scores were regressed on g factor scores, the residual score variances did not correlate significantly with cortical thickness in the full sample with age covaried. When participants were grouped at the age median, significant positive associations of cortical thickness were obtained in the older group for g-residualized scores on Block Design (a measure of visual-motor integrative processing) while significant negative associations of cortical thickness were observed in the younger group for g-residualized Vocabulary scores. These results regarding correlates of general intelligence are concordant with the existing literature, while the findings from younger versus older subgroups have implications for future research on brain structural correlates of specific cognitive abilities, as well as the cognitive domain specificity of behavioral performance correlates of normative gray matter thinning during adolescence.

Systemic inflammation in non-demented elderly human subjects: brain microstructure and cognition

The purpose of this study was to test the hypothesis that higher levels of systemic inflammation in a community sample of non-demented subjects older than seventy years of age are associated with reduced diffusion anisotropy in brain white matter and lower cognition. Ninety-five older persons without dementia underwent detailed clinical and cognitive evaluation and magnetic resonance imaging, including diffusion tensor imaging. Systemic inflammation was assessed with a composite measure of commonly used circulating inflammatory markers (C-reactive protein and tumor necrosis factor-alpha). Tract-based spatial statistics analyses demonstrated that diffusion anisotropy in the body and isthmus of the corpus callosum was negatively correlated with the composite measure of systemic inflammation, controlling for demographic, clinical and radiologic factors. Visuospatial ability was negatively correlated with systemic inflammation, and diffusion anisotropy in the body and isthmus of the corpus callosum was shown to mediate this association. The findings of the present study suggest that higher levels of systemic inflammation may be associated with lower microstructural integrity in the corpus callosum of non-demented elderly individuals, and this may partially explain the finding of reduced higher-order visual cognition in aging.

Alzheimer's disease neurodegenerative biomarkers are associated with decreased cognitive function but not β-amyloid in cognitively normal older individuals

β-Amyloid (Aβ) plaque deposition and neurodegeneration within temporoparietal and hippocampal regions may indicate increased risk of Alzheimer's disease (AD). This study examined relationships between AD biomarkers of Aβ and neurodegeneration as well as cognitive performance in cognitively normal older individuals. Aβ burden was quantified in 72 normal older human subjects from the Berkeley Aging Cohort (BAC) using [(11)C] Pittsburgh compound B (PIB) positron emission tomography. In the same individuals, we measured hippocampal volume, as well as glucose metabolism and cortical thickness, which were extracted from a template of cortical AD-affected regions. The three functional and structural biomarkers were merged into a highly AD-sensitive multimodality biomarker reflecting neural integrity. In the normal older individuals, there was no association between elevated PIB uptake and either the single-modality or the multimodality neurodegenerative biomarkers. Lower neural integrity within the AD-affected regions and a control area (the visual cortex) was related to lower scores on memory and executive function tests; the same association was not found with PIB retention. The relationship between cognition and the multimodality AD biomarker was stronger in individuals with the highest PIB uptake. The findings indicate that neurodegeneration occurs within AD regions regardless of Aβ deposition and accounts for worse cognition in cognitively normal older people. The impact of neural integrity on cognitive functions is, however, enhanced in the presence of high Aβ burden for brain regions that are most affected in AD.

Prediction for human intelligence using morphometric characteristics of cortical surface: partial least square analysis

A number of imaging studies have reported neuroanatomical correlates of human intelligence with various morphological characteristics of the cerebral cortex. However, it is not yet clear whether these morphological properties of the cerebral cortex account for human intelligence. We assumed that the complex structure of the cerebral cortex could be explained effectively considering cortical thickness, surface area, sulcal depth and absolute mean curvature together. In 78 young healthy adults (age range: 17-27, male/female: 39/39), we used the full-scale intelligence quotient (FSIQ) and the cortical measurements calculated in native space from each subject to determine how much combining various cortical measures explained human intelligence. Since each cortical measure is thought to be not independent but highly inter-related, we applied partial least square (PLS) regression, which is one of the most promising multivariate analysis approaches, to overcome multicollinearity among cortical measures. Our results showed that 30% of FSIQ was explained by the first latent variable extracted from PLS regression analysis. Although it is difficult to relate the first derived latent variable with specific anatomy, we found that cortical thickness measures had a substantial impact on the PLS model supporting the most significant factor accounting for FSIQ. Our results presented here strongly suggest that the new predictor combining different morphometric properties of complex cortical structure is well suited for predicting human intelligence.

Splenium of corpus callosum: patterns of interhemispheric interaction in children and adults

The splenium of the corpus callosum connects the posterior cortices with fibers varying in size from thin late-myelinating axons in the anterior part, predominantly connecting parietal and temporal areas, to thick early-myelinating fibers in the posterior part, linking primary and secondary visual areas. In the adult human brain, the function of the splenium in a given area is defined by the specialization of the area and implemented via excitation and/or suppression of the contralateral homotopic and heterotopic areas at the same or different level of visual hierarchy. These mechanisms are facilitated by interhemispheric synchronization of oscillatory activity, also supported by the splenium. In postnatal ontogenesis, structural MRI reveals a protracted formation of the splenium during the first two decades of human life. In doing so, the slow myelination of the splenium correlates with the formation of interhemispheric excitatory influences in the extrastriate areas and the EEG synchronization, while the gradual increase of inhibitory effects in the striate cortex is linked to the local inhibitory circuitry. Reshaping interactions between interhemispherically distributed networks under various perceptual contexts allows sparsification of responses to superfluous information from the visual environment, leading to a reduction of metabolic and structural redundancy in a child's brain.

Working memory training is associated with lower prefrontal cortex activation in a divergent thinking task

Working memory (WM) training has been shown to lead to improvements in WM capacity and fluid intelligence. Given that divergent thinking loads on WM and fluid intelligence, we tested the hypothesis that WM training would improve performance and moderate neural function in the Alternate Uses Task (AUT)-a classic test of divergent thinking. We tested this hypothesis by administering the AUT in the functional magnetic resonance imaging scanner following a short regimen of WM training (experimental condition), or engagement in a choice reaction time task not expected to engage WM (active control condition). Participants in the experimental group exhibited significant improvement in performance in the WM task as a function of training, as well as a significant gain in fluid intelligence. Although the two groups did not differ in their performance on the AUT, activation was significantly lower in the experimental group in ventrolateral prefrontal and dorsolateral prefrontal cortices-two brain regions known to play dissociable and critical roles in divergent thinking. Furthermore, gain in fluid intelligence mediated the effect of training on brain activation in ventrolateral prefrontal cortex. These results indicate that a short regimen of WM training is associated with lower prefrontal activation-a marker of neural efficiency-in divergent thinking.

Mapping changes of in vivo connectivity patterns in the human mediodorsal thalamus: correlations with higher cognitive and executive functions

The mediodorsal thalamic nucleus is recognized as an association hub mediating interconnections with mainly the prefrontal cortex. Tracer studies in primates and in vivo diffusion tensor tractography findings in both humans and monkeys confirm its role in relaying networks that connect to the dorsolateral prefrontal, orbitofrontal, frontal medial and cingulate cortex. Our study was designed to use in vivo probabilistic tractography to describe the pathways emerging from or projecting to the mediodorsal nucleus; moreover, to use such information to automatically define subdivisions based on the divergence of remote structural connections. Diffusion tensor MR imaging data of 156 subjects were utilized to perform connectivity-based segmentation of the mediodorsal nucleus by employing a k-means clustering algorithm. Two domains were revealed (medial and lateral) that are separated from each other by a sagittally oriented plane. For each subject, general assessment of cognitive performance by means of the Wechsler Abbreviated Scale of Intelligence and measures of Delis-Kaplan Executive Function System (D-KEFS) test was utilized. Inter-subject variability in terms of connectivity-based cluster sizes was discovered and the relative sizes of the lateral mediodorsal domain correlated with the individuals' performance in the D-KEFS Sorting test (r = 0.232, p = 0.004). Our results show that the connectivity-based parcellation technique applied to the mediodorsal thalamic nucleus delivers a single subject level descriptor of connectional topography; furthermore, we revealed a possible weak interaction between executive performance and the size of the thalamic area from which pathways converge to the lateral prefrontal cortex.

Brain white matter tract integrity as a neural foundation for general intelligence

General intelligence is a robust predictor of important life outcomes, including educational and occupational attainment, successfully managing everyday life situations, good health and longevity. Some neuronal correlates of intelligence have been discovered, mainly indicating that larger cortices in widespread parieto-frontal brain networks and efficient neuronal information processing support higher intelligence. However, there is a lack of established associations between general intelligence and any basic structural brain parameters that have a clear functional meaning. Here, we provide evidence that lower brain-wide white matter tract integrity exerts a substantial negative effect on general intelligence through reduced information-processing speed. Structural brain magnetic resonance imaging scans were acquired from 420 older adults in their early 70s. Using quantitative tractography, we measured fractional anisotropy and two white matter integrity biomarkers that are novel to the study of intelligence: longitudinal relaxation time (T1) and magnetisation transfer ratio. Substantial correlations among 12 major white matter tracts studied allowed the extraction of three general factors of biomarker-specific brain-wide white matter tract integrity. Each was independently associated with general intelligence, together explaining 10% of the variance, and their effect was completely mediated by information-processing speed. Unlike most previously established neurostructural correlates of intelligence, these findings suggest a functionally plausible model of intelligence, where structurally intact axonal fibres across the brain provide the neuroanatomical infrastructure for fast information processing within widespread brain networks, supporting general intelligence.

Individual differences in EEG spectral power reflect genetic variance in gray and white matter volumes

The human electroencephalogram (EEG) consists of oscillations that reflect the summation of postsynaptic potentials at the dendritic tree of cortical neurons. The strength of the oscillations (EEG power) is a highly genetic trait that has been related to individual differences in many phenotypes, including intelligence and liability for psychopathology. Here, we investigated whether brain anatomy underlies these EEG power differences by correlating it to gray and white matter volumes (GMV, WMV), and additionally investigated whether this association can be attributed to genes or environmental factors. EEG was measured in a sample of 405 young adult twins and their siblings, and power in the theta (~4 Hz), alpha (~10 Hz), and beta (~20 Hz) frequency bands determined. A subset of 121 subjects were also scanned in a 1.5 T MRI scanner, and gray and white matter volumes defined as the total of cortical and subcortical volumes, excluding cerebellum. Both MRI-based volumes and EEG power spectra were highly heritable. GMV and WMV correlated .25 to .29 with EEG power for the slower oscillations (theta, alpha). Moreover, these phenotypic correlations largely reflected genetic covariation, irrespective of oscillation frequency and volume type. Genetic correlations (.31 < rA < .43) revealed that only moderate proportions of the heritable variance overlapped between MRI volumes and EEG power. The results suggest that MRI volumes and EEG power share genetic sources of variation, which may reflect such processes as myelination, synaptic density, and dendritic outgrowth.

Meditation as a potential therapy for autism: a review

Autism is a chronic neurodevelopmental disorder of unknown cause that affects approximately 1–3 percent of children and four times more boys than girls. Its prevalence is global and its social impact is devastating. In autism, the brain is unable to process sensory information normally. Instead, simple stimuli from the outside world are experienced as overwhelmingly intense and strain the emotional centers of the brain. A stress response to the incoming information is initiated that destabilizes cognitive networks and short-circuits adequate behavioral output. As a result, the child is unable to respond adequately to stimulation and initiate social behavior towards family, friends, and peers. In addition, these children typically face immune-digestive disorders that heighten social fears, anxieties, and internal conflicts. While it is critical to treat the physical symptoms, it is equally vital to offer an evidence-based holistic solution that harmonizes both their emotional and physical well-being as they move from childhood into adult life. Here, we summarize evidence from clinical studies and neuroscience research that suggests that an approach built on yogic principles and meditative tools is worth pursuing. Desired outcomes include relief of clinical symptoms of the disease, greater relaxation, and facilitated expression of feelings and skills, as well as improved family and social quality of life.

Sleep electroencephalography topography and children's intellectual ability

The aim of the study was to investigate the relationship between regional aspects of the children's sleep electroencephalogram (EEG) (high-density EEG recordings) and their intellectual ability. The spectral power in the α, σ, and β frequency ranges of 109 EEG derivations was correlated with the scores of full-scale intelligence quotient, fluid intelligence quotient, and working memory (14 participants, mean age: 10.5±1.0 years; six girls). The previously reported relationship (derivation C3/A2) between spectral band power and intellectual ability could further be refined, particular spatial patterns over central and parietal areas with positive correlations were found. Thus, neurobiological correlates of intelligence during sleep may exhibit brain region-specific patterns.

A single g factor is not necessary to simulate positive correlations between cognitive tests

In the area of abilities testing, one issue of continued dissent is whether abilities are best conceptualized as manifestations of a single underlying general factor or as reflecting the combination of multiple traits that may be dissociable. The fact that diverse cognitive tests tend to be positively correlated has been taken as evidence for a single general ability or "g" factor. In the present study, simulations of test performance were run to evaluate the hypothesis that multiple independent abilities that affect test performance in a consistent manner will produce a positive manifold. Correlation matrices were simulated from models using either one or eight independent factors. The extent to which these factors operated in a consistent manner across tests (i.e., that a factor that facilitates performance on one test tends to facilitate performance on other tests) was manipulated by varying the mean value of the randomly selected weights. The tendency of both a single factor and eight independent factors to produce positive correlations increased as the randomly selected weights operated in a more consistent fashion. Thus the presence of a positive manifold in the correlations between diverse cognitive tests does not provide differential support for either single factor or multiple factor models of general abilities.

Intelligence and the brain: A model-based approach

Various biological correlates of general intelligence (g) have been reported. Despite this, however, the relationship between neurological measurements and g is not fully clear. We use structural equation modeling to model the relationship between behavioral Wechsler Adult Intelligence Scale (WAIS) estimates of g and neurological measurements (voxel-based morphometry and diffusion tensor imaging of eight regions of interest). We discuss psychometric models that explicate the relationship between g and the brain in a manner in line with the scientific study of g. Fitting the proposed models to the data, we find that a MIMIC model (for multiple indicators, multiple causes), where the contributions of different brain regions to a unidimensional g are estimated separately, provides the best fit against the data.

Neuroanatomical substrates of age-related cognitive decline

There are many reports of relations between age and cognitive variables and of relations between age and variables representing different aspects of brain structure and a few reports of relations between brain structure variables and cognitive variables. These findings have sometimes led to inferences that the age-related brain changes cause the age-related cognitive changes. Although this conclusion may well be true, it is widely recognized that simple correlations are not sufficient to warrant causal conclusions, and other types of correlational information, such as mediation and correlations between longitudinal brain changes and longitudinal cognitive changes, also have limitations with respect to causal inferences. These issues are discussed, and the existing results on relations of regional volume, white matter hyperintensities, and diffusion tensor imaging measures of white matter integrity to age and to measures of cognitive functioning are reviewed. It is concluded that at the current time the evidence that these aspects of brain structure are neuroanatomical substrates of age-related cognitive decline is weak. The final section contains several suggestions concerning measurement and methodology that may lead to stronger conclusions in the future.

A uniquely modern human pattern of endocranial development. Insights from a new cranial reconstruction of the Neandertal newborn from Mezmaiskaya

The globular braincase of modern humans is distinct from all fossil human species, including our closest extinct relatives, the Neandertals. Such adult shape differences must ultimately be rooted in different developmental patterns, but it is unclear at which point during ontogeny these group characteristics emerge. Here we compared internal shape changes of the braincase from birth to adulthood in Neandertals (N = 10), modern humans (N = 62), and chimpanzees (N = 62). Incomplete fossil specimens, including the two Neandertal newborns from Le Moustier 2 and Mezmaiskaya, were reconstructed using reference-based estimation methods. We used 3D geometric morphometrics to statistically compare shapes of virtual endocasts extracted from computed-tomographic scans. Throughout the analysis, we kept track of possible uncertainties due to the missing data values and small fossil sample sizes. We find that some aspects of endocranial development are shared by the three species. However, in the first year of life, modern humans depart from this presumably ancestral pattern of development. Newborn Neandertals and newborn modern humans have elongated braincases, and similar endocranial volumes. During a 'globularization-phase' modern human endocasts change to the globular shape that is characteristic for Homo sapiens. This phase of early development is unique to modern humans, and absent from chimpanzees and Neandertals. Our results support the notion that Neandertals and modern humans reach comparable adult brain sizes via different developmental pathways. The differences between these two human groups are most prominent directly after birth, a critical phase for cognitive development.

How many neurons do you have? Some dogmas of quantitative neuroscience under revision

Owing to methodological shortcomings and a certain conservatism that consolidates wrong assumptions in the literature, some dogmas have become established and reproduced in papers and textbooks, derived from quantitative features of the brain. The first dogma states that the cerebral cortex is the pinnacle of brain evolution - based on the observations that its volume is greater in more 'intelligent' species, and that cortical surface area grows more than any other brain region, to reach the largest proportion in higher primates and humans. The second dogma claims that the human brain contains 100 billion neurons, plus 10-fold more glial cells. These round numbers have become widely adopted, although data provided by different authors have led to a broad range of 75-125 billion neurons in the whole brain. The third dogma derives from the second, and states that our brain is structurally special, an outlier as compared with other primates. Being so large and convoluted, it is a special construct of nature, unrelated to evolutionary scaling. Finally, the fourth dogma appeared as a tentative explanation for the considerable growth of the brain throughout development and evolution - being modular in structure, the brain (and particularly the cerebral cortex) grows by tangential addition of modules that are uniform in neuronal composition. In this review, we sought to examine and challenge these four dogmas, and propose other interpretations or simply their replacement with alternative views.

Cognition and lobar morphology in full mutation boys with fragile X syndrome

The aims of the present study are twofold: (1) to examine cortical morphology (CM) associated with alterations in cognition in fragile X syndrome (FXS); (2) to characterize the CM profile of FXS versus FXS with an autism diagnosis (FXS+Aut) as a preliminary attempt to further elucidate the behavioral distinctions between the two sub-groups. We used anatomical magnetic resonance imaging surface-based morphometry in 21 male children (FXS N=11 and age [2.27-13.3] matched controls [C] N=10). We found (1) increased whole hemispheric and lobar cortical volume, cortical thickness and cortical complexity bilaterally, yet insignificant changes in hemispheric surface area and gyrification index in FXS compared to C; (2) linear regression analyses revealed significant negative correlations between CM and cognition; (3) significant CM differences between FXS and FXS+Aut associated with their distinctive behavioral phenotypes. These findings are critical in understanding the neuropathophysiology of one of the most common intellectual deficiency syndromes associated with altered cognition as they provide human in vivo information about genetic control of CM and cognition.

N-acetylaspartate concentration in corpus callosum is positively correlated with intelligence in adolescents

The corpus callosum is the largest white matter bundle in the brain and integrates inter-hemispheric cortices during sensory-motor and high-order cognitive processes. The aim of the present study was to investigate the associations between the metabolite concentrations in the corpus callosum and intelligence among adolescents. Thirty male adolescents aged between 14 and 16 years were included into the study. We measured the intelligence quotient (IQ) scores of the subjects by using the Wechsler Intelligence Scale for Children-Revised (verbal, performance and full-scale IQ) test. We used proton MR spectroscopy to measure the absolute concentrations of N-acetylasparate (NAA), creatine (Cr) and choline (Cho) in the genu, midbody and isthmus/splenium regions of the corpus callosum. We also measured the whole brain parenchymal size and used it as a confounding factor in the statistical analyses. We assessed the correlations between neurometabolite concentrations and verbal, performance and full-scale IQ scores. We found a significant positive correlation between the whole brain parenchymal size and the full-scale IQ scores. And, the NAA concentration in the isthmus/splenium region was positively correlated with the performance IQ and full-scale IQ scores. NAA is a marker of neuro/axonal integrity. NAA concentration in white matter is related to the structural and functional integrity of axonal fibers. The positive correlation of the IQ scores with the NAA concentrations in the isthmus/splenium region indicates that more efficient inter-hemispheric data transfer between parieto-occipital cortices may enhance intellectual performance.

The brain dynamics of intellectual development: waxing and waning white and gray matter

Distributed brain areas support intellectual abilities in adults. How structural maturation of these areas in childhood enables development of intelligence is not established. Neuroimaging can be used to monitor brain development, but studies to date have typically considered single imaging modalities. To explore the impact of structural brain maturation on the development of intelligence, we used a combination of cortical thickness, white matter (WM) volume and WM microstructure in 168 healthy participants aged 8-30 years. Principal component analyses (PCAs) were conducted separately for cortical thickness, WM volume, fractional anisotropy (FA) and mean diffusivity (MD) in 64 different brain regions. For all four parameters, the PCAs revealed a general factor explaining between 40% and 53% of the variance across regions. When tested separately, negative age-independent relationships were found between intellectual abilities and cortical thickness and MD, respectively, while WM volume and FA were positively associated with intellectual abilities. The relationships between intellectual abilities and brain structure varied with age, with stronger relationships seen in children and adolescents than in young adults. Multiple regression analysis with the different imaging measures as simultaneous predictors, showed that cortical thickness, WM volume and MD all yielded unique information in explaining intellectual abilities in development. The present study demonstrates that different imaging modalities and measures give complementary information about the neural substrates of intellectual abilities in development, emphasizing the importance of multimodal imaging in investigations of neurocognitive development.

Intellectual abilities and white matter microstructure in development: a diffusion tensor imaging study

Higher-order cognitive functions are supported by distributed networks of multiple interconnected cortical and subcortical regions. Efficient cognitive processing depends on fast communication between these regions, so the integrity of the connections between them is of great importance. It is known that white matter (WM) development is a slow process, continuing into adulthood. While the significance of cortical maturation for intellectual development is described, less is known about the relationships between cognitive functions and maturation of WM connectivity. In this cross-sectional study, we investigated the associations between intellectual abilities and development of diffusion tensor imaging (DTI) derived measures of WM microstructure in 168 right-handed participants aged 8-30 years. Independently of age and sex, both verbal and performance abilities were positively related to fractional anisotropy (FA) and negatively related to mean diffusivity (MD) and radial diffusivity (RD), predominantly in the left hemisphere. Further, verbal, but not performance abilities, were associated with developmental differences in DTI indices in widespread regions in both hemispheres. Regional analyses showed relations with both FA and RD bilaterally in the anterior thalamic radiation and the cortico-spinal tract and in the right superior longitudinal fasciculus. In these regions, our results suggest that participants with high verbal abilities may show accelerated WM development in late childhood and a subsequent earlier developmental plateau, in contrast to a steadier and prolonged development in participants with average verbal abilities. Longitudinal data are needed to validate these interpretations. The results provide insight into the neurobiological underpinnings of intellectual development.