Brain Structural Networks Associated with Intelligence and Visuomotor Ability

Increasing evidence indicates that multiple structures in the brain are associated with intelligence and cognitive function at the network level. The association between the grey matter (GM) structural network and intelligence and cognition is not well understood. We applied a multivariate approach to identify the pattern of GM and link the structural network to intelligence and cognitive functions. Structural magnetic resonance imaging was acquired from 92 healthy individuals. Source-based morphometry analysis was applied to the imaging data to extract GM structural covariance. We assessed the intelligence, verbal fluency, processing speed, and executive functioning of the participants and further investigated the correlations of the GM structural networks with intelligence and cognitive functions. Six GM structural networks were identified. The cerebello-parietal component and the frontal component were significantly associated with intelligence. The parietal and frontal regions were each distinctively associated with intelligence by maintaining structural networks with the cerebellum and the temporal region, respectively. The cerebellar component was associated with visuomotor ability. Our results support the parieto-frontal integration theory of intelligence by demonstrating how each core region for intelligence works in concert with other regions. In addition, we revealed how the cerebellum is associated with intelligence and cognitive functions

Brain Structural Networks Associated with Intelligence and Visuomotor Ability

Increasing evidence indicates that multiple structures in the brain are associated with intelligence and cognitive function at the network level. The association between the grey matter (GM) structural network and intelligence and cognition is not well understood. We applied a multivariate approach to identify the pattern of GM and link the structural network to intelligence and cognitive functions. Structural magnetic resonance imaging was acquired from 92 healthy individuals. Source-based morphometry analysis was applied to the imaging data to extract GM structural covariance. We assessed the intelligence, verbal fluency, processing speed, and executive functioning of the participants and further investigated the correlations of the GM structural networks with intelligence and cognitive functions. Six GM structural networks were identified. The cerebello-parietal component and the frontal component were significantly associated with intelligence. The parietal and frontal regions were each distinctively associated with intelligence by maintaining structural networks with the cerebellum and the temporal region, respectively. The cerebellar component was associated with visuomotor ability. Our results support the parieto-frontal integration theory of intelligence by demonstrating how each core region for intelligence works in concert with other regions. In addition, we revealed how the cerebellum is associated with intelligence and cognitive functions

Capturing brain‐cognition relationship: Integrating task‐based fMRI across tasks markedly boosts prediction and test‐retest reliability

Capturing individual differences in cognition is central to human neuroscience. Yet our ability to estimate cognitive abilities via brain MRI is still poor in both prediction and reliability. Our study tested if this inability can be improved by integrating MRI signals across the whole brain and across modalities, including task-based functional MRI (tfMRI) of different tasks along with other non-task MRI modalities, such as structural MRI, resting-state functional connectivity. Using the Human Connectome Project (n = 873, 473 females, after quality control), we directly compared predictive models comprising different sets of MRI modalities (e.g., seven tasks vs. non-task modalities). We applied two approaches to integrate multimodal MRI, stacked vs. flat models, and implemented 16 combinations of machine-learning algorithms. The stacked model integrating all modalities via stacking Elastic Net provided the best prediction (r = 0.57), relatively to other models tested, as well as excellent test-retest reliability (ICC=∼.85) in capturing general cognitive abilities. Importantly, compared to the stacked model integrating across non-task modalities (r = 0.27), the stacked model integrating tfMRI across tasks led to significantly higher prediction (r = 0.56) while still providing excellent test-retest reliability (ICC=∼.83). The stacked model integrating tfMRI across tasks was driven by frontal and parietal areas and by tasks that are cognition-related (working-memory, relational processing, and language). This result is consistent with the parieto-frontal integration theory of intelligence. Accordingly, our results contradict the recently popular notion that tfMRI is not reliable enough to capture individual differences in cognition. Instead, our study suggests that tfMRI, when used appropriately (i.e., by drawing information across the whole brain and across tasks and by integrating with other modalities), provides predictive and reliable sources of information for individual differences in cognitive abilities, more so than non-task modalities

Mapping of Structure-Function Age-Related Connectivity Changes on Cognition Using Multimodal MRI

The relationship between age-related changes in brain structural connectivity (SC) and functional connectivity (FC) with cognition is not well understood. Furthermore, it is not clear whether cognition is represented via a similar spatial pattern of FC and SC or instead is mapped by distinct sets of distributed connectivity patterns. To this end, we used a longitudinal, within-subject, multimodal approach aiming to combine brain data from diffusion-weighted MRI (DW-MRI), and functional MRI (fMRI) with behavioral evaluation, to better understand how changes in FC and SC correlate with changes in cognition in a sample of older adults. FC and SC measures were derived from the multimodal scans acquired at two time points. Change in FC and SC was correlated with 13 behavioral measures of cognitive function using Partial Least Squares Correlation (PLSC). Two of the measures indicate an age-related change in cognition and the rest indicate baseline cognitive performance. FC and SC—cognition correlations were expressed across several cognitive measures, and numerous structural and functional cortical connections, mainly cingulo-opercular, dorsolateral prefrontal, somatosensory and motor, and temporo-parieto-occipital, contributed both positively and negatively to the brain-behavior relationship. Whole-brain FC and SC captured distinct and independent connections related to the cognitive measures. Overall, we examined age-related function-structure associations of the brain in a comprehensive and integrated manner, using a multimodal approach. We pointed out the behavioral relevance of age-related changes in FC and SC. Taken together, our results highlight that the heterogeneity in distributed FC and SC connectivity patterns provide unique information about the variable nature of healthy cognitive aging

Auditory experience modulates frontoparietal theta activity serving fluid intelligence

Children who are hard of hearing are at risk for developmental language and academic delays compared with children with normal hearing. Some work suggests that high-order cognitive function, including fluid intelligence, may relate to language and academic outcomes in children with hearing loss, but findings in these studies have been mixed and to date, there have been no studies of the wholebrain neural dynamics serving fluid intelligence in the context of hearing loss. To this end, this study sought to identify the impact of hearing loss and subsequent hearing aid use on the neural dynamics serving abstract reasoning in children who are hard of hearing relative to children with normal hearing using magnetoencephalography. We found significant elevations in occipital and parietal theta activity during early stimulus evaluation in children who are hard of hearing relative to normal-hearing peers. In addition, we found that greater hearing aid use was significantly related to reduced activity throughout the fronto-parietal network. Notably, there were no differences in alpha dynamics between groups during later-stage processing nor did alpha activity correlate with hearing aid use. These cross-sectional data suggest that differences in auditory experience lead to widespread alterations in the neural dynamics serving initial stimulus processing in fluid intelligence in children

Activity in the fronto-parietal multiple-demand network is robustly associated with individual differences in working memory and fluid intelligence.

Numerous brain lesion and fMRI studies have linked individual differences in executive abilities and fluid intelligence to brain regions of the fronto-parietal "multiple-demand" (MD) network. Yet, fMRI studies have yielded conflicting evidence as to whether better executive abilities are associated with stronger or weaker MD activations and whether this relationship is restricted to the MD network. Here, in a large-sample (n = 216) fMRI investigation, we found that stronger activity in MD regions - functionally defined in individual participants - was robustly associated with more accurate and faster responses on a spatial working memory task performed in the scanner, as well as fluid intelligence measured independently (n = 114). In line with some prior claims about a relationship between language and fluid intelligence, we also found a weak association between activity in the brain regions of the left fronto-temporal language network during an independent passive reading task, and performance on the working memory task. However, controlling for the level of MD activity abolished this relationship, whereas the MD activity-behavior association remained highly reliable after controlling for the level of activity in the language network. Finally, we demonstrate how unreliable MD activity measures, coupled with small sample sizes, could falsely lead to the opposite, negative, association that has been reported in some prior studies. Taken together, these results demonstrate that a core component of individual differences variance in executive abilities and fluid intelligence is selectively and robustly positively associated with the level of activity in the MD network, a result that aligns well with lesion studies

structural connectivity analysis in children with segmental callosal agenesis

BACKGROUND AND PURPOSE: Segmental callosal agenesis is characterized by the absence of the intermediate callosal portion. We aimed to evaluate the structural connectivity of segmental callosal agenesis by using constrained spherical deconvolution tractography and connectome analysis. MATERIALS AND METHODS: We reviewed the clinical-radiologic features of 8 patients (5 males; mean age, 3.9 years). Spherical deconvolution and probabilistic tractography were performed on diffusion data. Structural connectivity analysis, including summary network metrics, modularity analysis, and network consistency measures, was applied in 5 patients and 10 age-/sex-matched controls. RESULTS: We identified 3 subtypes based on the position of the hippocampal commissure: beneath the anterior callosal remnant in 3 patients (type I), beneath the posterior callosal remnant in 3 patients (type II), and between the anterior and posterior callosal remnants in 2 patients (type III). In all patients, the agenetic segment corresponded to fibers projecting to the parietal lobe, and segmental Probst bundles were found at that level. Ectopic callosal bundles were identified in 3 patients. Topology analysis revealed reduced global connectivity in patients compared with controls. The network topology of segmental callosal agenesis was more variable across patients than that of the control connectomes. Modularity analysis revealed disruption of the structural core organization in the patients. CONCLUSIONS: Three malformative subtypes of segmental callosal agenesis were identified. Even the absence of a small callosal segment may impact global brain connectivity and modularity organization. The presence of ectopic callosal bundles may explain the greater interindividual variation in the connectomes of patients with segmental callosal agenesis

Brain connectivity studied by fMRI: homologous network organization in the rat, monkey, and human

The mammalian brain is composed of functional networks operating at different spatial and temporal scales — characterized by patterns of interconnections linking sensory, motor, and cognitive systems. Assessment of brain connectivity has revealed that the structure and dynamics of large-scale network organization are altered in multiple disease states suggesting their use as diagnostic or prognostic indicators. Further investigation into the underlying mechanisms, organization, and alteration of large-scale brain networks requires homologous animal models that would allow neurophysiological recordings and experimental manipulations. My current dissertation presents a comprehensive assessment and comparison of rat, macaque, and human brain networks based on evaluation of intrinsic low-frequency fluctuations of the blood oxygen-level-dependent (BOLD) fMRI signal. The signal fluctuations, recorded in the absence of any task paradigm, have been shown to reflect anatomical connectivity and are presumed to be a hemodynamic manifestation of slow fluctuations in neuronal activity. Importantly, the technique circumvents many practical limitations of other methodologies and can be compared directly between multiple species. Networks of all species were found underlying multiple levels of sensory, motor, and cognitive processing. Remarkable homologous functional connectivity was found across all species, however network complexity was dramatically increased in primate compared to rodent species. Spontaneous temporal dynamics of the resting-state networks were also preserved across species. The results demonstrate that rats and macaques share remarkable homologous network organization with humans, thereby providing strong support for their use as an animal model in the study of normal and abnormal brain connectivity as well as aiding the interpretation of electrophysiological recordings within the context of large-scale brain networks

Autistic traits,ADHD symptoms,neurological soft signs and regional cerebral blood flow in adults with autism spectrum disorders

The resting regional cerebral blood flow (rCBF) patterns related to co-occurring symptoms such as inattention, hyperactivity, neurological soft signs and motor problems have not yet been disclosed in autism spectrum disorders (ASD). In this study thirteen adults with ASD and ten matched neurotypical controls underwent PET. The scores of rating scales for autistic traits, attention deficit hyperactivity disorder (ADHD) and neurological soft signs were included in a factorial analysis and correlated with rCBF. Factors corresponding to \u27\u27autistic/ADHD traits\u27\u27, \u27\u27sensory-motor integration\u27\u27 and \u27\u27Intelligence/Motor sequencing\u27\u27 were identified. In the ASD group, positive correlations with CBF were found for \u27\u27autistic/ADHD traits\u27\u27 in caudate bilaterally and the inferior parietal lobule, for \u27\u27sensory-motor integration\u27\u27 in parieto-occipital cortex and for \u27\u27Intelligence/Motor sequencing\u27\u27 in the right temporal cortex. Notably, CBF in the left thalamus correlated negatively with all three factors. Autistic traits and ADHD symptoms were associated with shared neural substrates. The correlation between \u27\u27autistic/ADHD traits\u27\u27 and rCBF in the caudate is possibly associated with the executive impairments and ritualistic/stereotyped behaviors apparent in ASD. Furthermore, sensory-motor deficits were correlated with rCBF in the occipital visual cortex, involved in atypical visual perception in ASD. Various behavioral and neurological symptoms are suggested to converge into the ASD phenotype

Characterization of the Community Structure of Large Scale Functional Brain Networks During Ketamine-Medetomidine Anesthetic Induction

One of the central questions in neuroscience is to understand the way communication is organized in the brain, trying to comprehend how cognitive capacities or physiological states of the organism are potentially related to brain activities involving interactions of several brain areas. One important characteristic of the functional brain networks is that they are modularly structured, being this modular architecture regarded to account for a series of properties and functional dynamics. In the neurobiological context, communities may indicate brain regions that are involved in one same activity, representing neural segregated processes. Several studies have demonstrated the modular character of organization of brain activities. However, empirical evidences regarding to its dynamics and relation to different levels of consciousness have not been reported yet. Within this context, this research sought to characterize the community structure of functional brain networks during an anesthetic induction process. The experiment was based on intra-cranial recordings of neural activities of an old world macaque of the species Macaca fuscata during a Ketamine-Medetomidine anesthetic induction process. Networks were serially estimated in time intervals of five seconds. Changes were observed within about one and a half minutes after the administration of the anesthetics, revealing the occurrence of a transition on the community structure. The awake state was characterized by the presence of large clusters involving frontal and parietal regions, while the anesthetized state by the presence of communities in the primary visual and motor cortices, being the areas of the secondary associative cortex most affected. The results report the influence of general anesthesia on the structure of functional clusters, contributing for understanding some new aspects of neural correlates of consciousness.Comment: 24 pages, 8 figures. arXiv admin note: text overlap with arXiv:1604.0000