Association of structural global brain network properties with intelligence in normal aging

Quantifying the deficit-imaging neurobehavioural impairment in childhood epilepsy

BACKGROUND

Neurobehavioral impairments such as learning difficulty, autism, attention deficit hyperactivity disorder (ADHD) and mood or behavioural problems are known to be increased in children with epilepsy; however, they remain under-recognised and often cause considerable morbidity. Quantitative neuroimaging techniques offer a potential avenue to improving our understanding of the underlying pathological basis for these disorders, aiding with diagnosis and risk stratification.

METHODS

A systematic review was undertaken for original research articles involving magnetic resonance imaging in children with epilepsy and one or more neurobehavioural impairments. Studies were reviewed with respect to patient population, methodology and magnetic resonance imaging (MRI) findings.

RESULTS

A total of 25 studies were identified and included in this review. The majority of studies looked at single impairments, commonly cognitive impairment or ADHD, with few studies reporting on other impairments. Reductions in cortical grey matter and disruptions of functional and structural brain networks were associated with poorer cognitive performance and disruptions of grey and white matter within a fronto-striatal-cerebellar network associated with ADHD. Insufficient studies were available to report on other impairments.

CONCLUSIONS

Relatively few studies exist in this field and those that do are methodologically diverse. Further investigation is required to determine if the changes reported to date are epilepsy syndrome specific or have broader applicability.

Atlasing the frontal lobe connections and their variability due to age and education: a spherical deconvolution tractography study

In neuroscience, there is a growing consensus that higher cognitive functions may be supported by distributed networks involving different cerebral regions, rather than by single brain areas. Communication within these networks is mediated by white matter tracts and is particularly prominent in the frontal lobes for the control and integration of information. However, the detailed mapping of frontal connections remains incomplete, albeit crucial to an increased understanding of these cognitive functions. Based on 47 high-resolution diffusion-weighted imaging datasets (age range 22-71 years), we built a statistical normative atlas of the frontal lobe connections in stereotaxic space, using state-of-the-art spherical deconvolution tractography. We dissected 55 tracts including U-shaped fibers. We further characterized these tracts by measuring their correlation with age and education level. We reported age-related differences in the microstructural organization of several, specific frontal fiber tracts, but found no correlation with education level. Future voxel-based analyses, such as voxel-based morphometry or tract-based spatial statistics studies, may benefit from our atlas by identifying the tracts and networks involved in frontal functions. Our atlas will also build the capacity of clinicians to further understand the mechanisms involved in brain recovery and plasticity, as well as assist clinicians in the diagnosis of disconnection or abnormality within specific tracts of individual patients with various brain diseases.

Aging alterations in whole-brain networks during adulthood mapped with the minimum spanning tree indices: the interplay of density, connectivity cost and life-time trajectory

The organizational network changes in the human brain across the lifespan have been mapped using functional and structural connectivity data. Brain network changes provide valuable insights into the processes underlying senescence. Nonetheless, the altered network density in the elderly severely compromises the usefulness of network analysis to study the aging brain. We successfully circumvented this problem by focusing on the critical structural network backbone, using a robust tree representation. Whole-brain networks' minimum spanning trees were determined in a dataset of diffusion-weighted images from 382 healthy subjects, ranging in age from 20.2 to 86.2 years. Tree-based metrics were compared with classical network metrics. In contrast to the tree-based metrics, classical metrics were highly influenced by age-related changes in network density. Tree-based metrics showed linear and non-linear correlation across adulthood and are in close accordance with results from previous histopathological characterizations of the changes in white matter integrity in the aging brain.

The smarter, the stronger: intelligence level correlates with brain resilience to systematic insults

Neuroimaging evidences posit human intelligence as tightly coupled with several structural and functional brain properties, also suggesting its potential protective role against aging and neurodegenerative conditions. However, whether higher order cognition might in fact lead to a more resilient brain has not been quantitatively demonstrated yet. Here we document a relationship between individual intelligence quotient (IQ) and brain resilience to targeted and random attacks, as measured through resting-state fMRI graph-theoretical analysis in 102 healthy individuals. In this modeling context, enhanced brain robustness to targeted attacks (TA) in individuals with higher IQ is supported by an increased distributed processing capacity despite the systematic loss of the most important node(s) of the system. Moreover, brain resilience in individuals with higher IQ is supported by a set of neocortical regions mainly belonging to language and memory processing network(s), whereas regions related to emotional processing are mostly responsible for lower IQ individuals. Results suggest intelligence level among the predictors of post-lesional or neurodegenerative recovery, also promoting the evolutionary role of higher order cognition, and simultaneously suggesting a new framework for brain stimulation interventions aimed at counteract brain deterioration over time.

Low cognitive reserve is associated with chronic migraine with medication overuse and poor quality of life

Objective

The objective of this article is to test the hypothesis that cognitive reserve (CR) is related to migraine chronification, medication overuse and poor quality of life in migraineurs.

Design/methods

A cross-sectional study on patients with chronic migraine with medication overuse (CM-MOH), episodic migraine (EM), and controls, matched by sex, age and education, was carried out. CR was assessed by a specific questionnaire, and quality of life was measured by general and specific questionnaires (SF-36 and MSQoL). Migraine Disability Assessment Scale and Beck questionnaires for depression and anxiety were used. Medication dependence was evaluated by the medication-dependence questionnaire in headache (MDQ-H).

Results

Fifty-five individuals were enrolled: 18 CM-MOH patients (32.73%), 22 EM patients (40%) and 15 controls (27.27%). Fifty (90.91%) of them were females and aged 43.53 (7.54) years. Univariate analysis showed a significant association between the study group and CR, and all items of the SF-36, anxiety and depression questionnaires, MSQoL and MDQ-H. The lower CR and CM-MOH group were related to a worse quality of life, more anxiety and depression and the highest medication dependence scores. Multivariate analysis showed that higher CR scores were related to higher quality of life as measured by the physical and mental composite scores of the SF-36, and to lower anxiety (beta = −1.08, p = 0.001) and depression (beta = −0.56, p = 0.03) levels. Focusing on MSQoL, the increase in CR was predictive of a better quality of life (beta = 1.88, p < 0.0001). By all the models, the explained variance of the sample ranged from 39% (mental composite score) to 58% (MSQoL).

Conclusions

Low CR appears to be an independent factor associated with the deterioration of quality of life, the presence of anxiety and depression, and drug dependence and medication overuse in CM-MOH.