Developmental differences in functional organization of multispectral networks.

Assessing brain connectivity during rest has become a widely used approach to identify changes in functional brain organization during development. Generally, previous works have demonstrated that brain activity shifts from more local to more distributed processing from childhood into adolescence. However, the majority of those works have been based on functional magnetic resonance imaging measures, whereas multispectral functional connectivity, as measured using magnetoencephalography (MEG), has been far less characterized. In our study, we examined spontaneous cortical activity during eyes-closed rest using MEG in 101 typically developing youth (9-15 years old; 51 females, 50 males). Multispectral MEG images were computed, and connectivity was estimated in the canonical delta, theta, alpha, beta, and gamma bands using the imaginary part of the phase coherence, which was computed between 200 brain regions defined by the Schaefer cortical atlas. Delta and alpha connectivity matrices formed more communities as a function of increasing age. Connectivity weights predominantly decreased with age in both frequency bands; delta-band differences largely implicated limbic cortical regions and alpha band differences in attention and cognitive networks. These results are consistent with previous work, indicating the functional organization of the brain becomes more segregated across development, and highlight spectral specificity across different canonical networks.

Resting state network connectivity alterations in HIV: Parallels with aging.

The increasing incidence of age-related comorbidities in people with HIV (PWH) has led to accelerated aging theories. Functional neuroimaging research, including functional connectivity (FC) using resting-state functional magnetic resonance imaging (rs-fMRI), has identified neural aberrations related to HIV infection. Yet little is known about the relationship between aging and resting-state FC in PWH. This study included 86 virally suppressed PWH and 99 demographically matched controls spanning 22-72 years old who underwent rs-fMRI. The independent and interactive effects of HIV and aging on FC were investigated both within- and between-network using a 7-network atlas. The relationship between HIV-related cognitive deficits and FC was also examined. We also conducted network-based statistical analyses using a brain anatomical atlas (n = 512 regions) to ensure similar results across independent approaches. We found independent effects of age and HIV in between-network FC. The age-related increases in FC were widespread, while PWH displayed further increases above and beyond aging, particularly between-network FC of the default-mode and executive control networks. The results were overall similar using the regional approach. Since both HIV infection and aging are associated with independent increases in between-network FC, HIV infection may be associated with a reorganization of the major brain networks and their functional interactions in a manner similar to aging.

Changes of creative ability and underlying brain network connectivity throughout the lifespan.

Creativity, or divergent thinking, is essential to and supported by cognitive functions necessary for everyday tasks. The current study investigates divergent thinking and its neural mechanisms from adolescence to late adulthood. To do this, 180 healthy participants completed a creativity task called the egg task including 86 adolescents (mean age (SD) = 13.62 (1.98)), 52 young adults (24.92 (3.60), and 42 older adults (62.84 (7.02)). Additionally, a subsample of 111 participants completed a resting-state fMRI scan. After investigating the impact of age on different divergent thinking metrics, we investigated the impact of age on the association between divergent thinking and resting-state functional connectivity within and between major resting-state brain networks associated with creative thinking: the DMN, ECN, and SN. Adolescents tended to be less creative than both young and older adults in divergent thinking scores related to expansion creativity, and not in persistent creativity, while young and older adults performed relatively similar. We found that adolescents' functional integrity of the executive control network (ECN) was positively associated with expansion creativity, which was significantly different from the negative association in both the young and older adults. These results suggest that creative performance and supporting brain networks change throughout the lifespan.

Person-Based Similarity Index for Cognition and Its Neural Correlates in Late Adulthood: Implications for Cognitive Reserve.

Healthy aging is typically associated with some level of cognitive decline, but there is substantial variation in such decline among older adults. The mechanisms behind such heterogeneity remain unclear but some have suggested a role for cognitive reserve. In this work, we propose the "person-based similarity index" for cognition (PBSI-Cog) as a proxy for cognitive reserve in older adults, and use the metric to quantify similarity between the cognitive profiles of healthy older and younger participants. In the current study, we computed this metric in 237 healthy older adults (55-88 years) using a reference group of 156 younger adults (18-39 years) taken from the Cambridge Center for Ageing and Neuroscience dataset. Our key findings revealed that PBSI-Cog scores in older adults were: 1) negatively associated with age (rho = -0.25, P = 10-4) and positively associated with higher education (t = 2.4, P = 0.02), 2) largely explained by fluid intelligence and executive function, and 3) predicted more by functional connectivity between lower- and higher-order resting-state networks than brain structural morphometry or education. Particularly, we found that higher segregation between the sensorimotor and executive networks predicted higher PBSI-Cog scores. Our results support the notion that brain network functional organization may underly variability in cognitive reserve in late adulthood.

Role of Emotion Reactivity to Predict Facial Emotion Recognition Changes with Aging.

Emotional intelligence includes an assortment of factors related to emotion function. Such factors involve emotion recognition (in this case via facial expression), emotion trait, reactivity, and regulation. We aimed to investigate how the subjective appraisals of emotional intelligence (i.e. trait, reactivity, and regulation) are associated with objective emotion recognition accuracy, and how these associations differ between young and older adults. Data were extracted from the CamCAN dataset (189 adults: 57 young/118 older) from assessments measuring these emotion constructs. Using linear regression models, we found that greater negative reactivity was associated with better emotion recognition accuracy among older adults, though the pattern was opposite for young adults with the greatest difference in disgust and surprise recognition. Positive reactivity and depression level predicted surprise recognition, with the associations significantly differing between the age groups. The present findings suggest the level to which older and young adults react to emotional stimuli differentially predicts their ability to correctly identify facial emotion expressions. Older adults with higher negative reactivity may be able to integrate their negative emotions effectively in order to recognize other's negative emotions more accurately. Alternatively, young adults may experience interference from negative reactivity, lowering their ability to recognize other's negative emotions.

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Greater male than female variability in regional brain structure across the lifespan.

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.

Linked patterns of biological and environmental covariation with brain structure in adolescence: a population-based longitudinal study.

Adolescence is a period of major brain reorganization shaped by biologically timed and by environmental factors. We sought to discover linked patterns of covariation between brain structural development and a wide array of these factors by leveraging data from the IMAGEN study, a longitudinal population-based cohort of adolescents. Brain structural measures and a comprehensive array of non-imaging features (relating to demographic, anthropometric, and psychosocial characteristics) were available on 1476 IMAGEN participants aged 14 years and from a subsample reassessed at age 19 years (n = 714). We applied sparse canonical correlation analyses (sCCA) to the cross-sectional and longitudinal data to extract modes with maximum covariation between neuroimaging and non-imaging measures. Separate sCCAs for cortical thickness, cortical surface area and subcortical volumes confirmed that each imaging phenotype was correlated with non-imaging features (sCCA r range: 0.30-0.65, all PFDR < 0.001). Total intracranial volume and global measures of cortical thickness and surface area had the highest canonical cross-loadings (|ρ| = 0.31-0.61). Age, physical growth and sex had the highest association with adolescent brain structure (|ρ| = 0.24-0.62); at baseline, further significant positive associations were noted for cognitive measures while negative associations were observed at both time points for prenatal parental smoking, life events, and negative affect and substance use in youth (|ρ| = 0.10-0.23). Sex, physical growth and age are the dominant influences on adolescent brain development. We highlight the persistent negative influences of prenatal parental smoking and youth substance use as they are modifiable and of relevance for public health initiatives.

Atlas55+: Brain Functional Atlas of Resting-State Networks for Late Adulthood.

Currently, several human brain functional atlases are used to define the spatial constituents of the resting-state networks (RSNs). However, the only brain atlases available are derived from samples of young adults. As brain networks are continuously reconfigured throughout life, the lack of brain atlases derived from older populations may influence RSN results in late adulthood. To address this gap, the aim of the study was to construct a reliable brain atlas derived only from older participants. We leveraged resting-state functional magnetic resonance imaging data from three cohorts of healthy older adults (total N = 563; age = 55-95 years) and a younger-adult cohort (N = 128; age = 18-35 years). We identified the major RSNs and their subdivisions across all older-adult cohorts. We demonstrated high spatial reproducibility of these RSNs with an average spatial overlap of 67%. Importantly, the RSNs derived from the older-adult cohorts were spatially different from those derived from the younger-adult cohort (P = 2.3 × 10-3). Lastly, we constructed a novel brain atlas, called Atlas55+, which includes the consensus of the major RSNs and their subdivisions across the older-adult cohorts. Thus, Atlas55+ provides a reliable age-appropriate template for RSNs in late adulthood and is publicly available. Our results confirm the need for age-appropriate functional atlases for studies investigating aging-related brain mechanisms.

Link between fluid/crystallized intelligence and global/local visual abilities across adulthood.

Human visual processing involves the extraction of both global and local information from a visual stimulus. Such processing may be related to cognitive abilities, which is likely going to change over time as we age. We aimed to investigate the impact of healthy aging on the association between visual global vs local processing and intelligence. In this context, we collected behavioral data during a visual search task in 103 adults (50 younger/53 older). We extracted three metrics reflecting global advantage (faster global than local processing), and visual interference in detecting either local or global features (based on interfering visual distractors). We found that older, but not younger, adults with higher levels of fluid and crystallized intelligence showed stronger signs of global advantage and interference effects during local processing, respectively. The present findings also provide promising clues regarding how participants consider and process their visual world in healthy aging.

Associations of cannabis use disorder with cognition, brain structure, and brain function in African Americans.

Although previous studies have highlighted associations of cannabis use with cognition and brain morphometry, critical questions remain with regard to the association between cannabis use and brain structural and functional connectivity. In a cross-sectional community sample of 205 African Americans (age 18-70) we tested for associations of cannabis use disorder (CUD, n = 57) with multi-domain cognitive measures and structural, diffusion, and resting state brain-imaging phenotypes. Post hoc model evidence was computed with Bayes factors (BF) and posterior probabilities of association (PPA) to account for multiple testing. General cognitive functioning, verbal intelligence, verbal memory, working memory, and motor speed were lower in the CUD group compared with non-users (p < .011; 1.9 < BF < 3,217). CUD was associated with altered functional connectivity in a network comprising the motor-hand region in the superior parietal gyri and the anterior insula (p < .04). These differences were not explained by alcohol, other drug use, or education. No associations with CUD were observed in cortical thickness, cortical surface area, subcortical or cerebellar volumes (0.12 < BF < 1.5), or graph-theoretical metrics of resting state connectivity (PPA < 0.01). In a large sample collected irrespective of cannabis used to minimize recruitment bias, we confirm the literature on poorer cognitive functioning in CUD, and an absence of volumetric brain differences between CUD and non-CUD. We did not find evidence for or against a disruption of structural connectivity, whereas we did find localized resting state functional dysconnectivity in CUD. There was sufficient proof, however, that organization of functional connectivity as determined via graph metrics does not differ between CUD and non-user group.

Baseline brain structural and functional predictors of clinical outcome in the early course of schizophrenia.

Although schizophrenia is considered a brain disorder, the role of brain organization for symptomatic improvement remains inadequately defined. We investigated the relationship between baseline brain morphology, resting-state network connectivity and clinical response after 24-weeks of antipsychotic treatment in patients with schizophrenia (n = 95) using integrated multivariate analyses. There was no significant association between clinical response and measures of cortical thickness (r = 0.37, p = 0.98) and subcortical volume (r = 0.56, p = 0.15). By contrast, we identified a strong mode of covariation linking functional network connectivity to clinical response (r = 0.70; p = 0.04), and particularly to improvement in positive (weight = 0.62) and anxious/depressive symptoms (weight = 0.49). Higher internal cohesiveness of the default mode network was the single most important positive predictor. Key negative predictors involved the functional cohesiveness of central executive subnetworks anchored in the frontoparietal cortices and subcortical regions (including the thalamus and striatum) and the inter-network integration between the default mode and sensorimotor networks. The present findings establish links between clinical response and the functional organization of brain networks involved both in perception and in spontaneous and goal-directed cognition, thereby advancing our understanding of the pathophysiology of schizophrenia.

Person-Based Brain Morphometric Similarity is Heritable and Correlates With Biological Features.

The characterization of the functional significance of interindividual variation in brain morphometry is a core aim of cognitive neuroscience. Prior research has focused on interindividual variation at the level of regional brain measures thus overlooking the fact that each individual brain is a person-specific ensemble of interdependent regions. To expand this line of inquiry we introduce the person-based similarity index (PBSI) for brain morphometry. The conceptual unit of the PBSI is the individual person's brain structural profile which considers all relevant morphometric measures as features of a single vector. In 2 independent cohorts (total of 1756 healthy participants), we demonstrate the foundational validity of this approach by affirming that the PBSI scores for subcortical volume and cortical thickness in healthy individuals differ between men and women, are heritable, and robust to variation in neuroimaging parameters, sample composition, and regional brain morphometry. Moreover, the PBSI scores correlate with age, body mass index, and fluid intelligence. Collectively, these results suggest that the person-based measures of brain morphometry are biologically and functionally meaningful and have the potential to advance the study of human variation in multivariate brain imaging phenotypes in healthy and clinical populations.

Evaluation of the spatial variability in the major resting-state networks across human brain functional atlases.

The human brain is intrinsically organized into resting-state networks (RSNs). Currently, several human brain functional atlases are used to define the spatial constituents of these RSNs. However, there are significant concerns about interatlas variability. In response, we undertook a quantitative comparison of the five major RSNs (default mode [DMN], salience, central executive, sensorimotor, and visual networks) across currently available brain functional atlases (n = 6) in which we demonstrated that (a) similarity between atlases was modest and positively linked to the size of the sample used to construct them; (b) across atlases, spatial overlap among major RSNs ranged between 17 and 76% (mean = 39%), which resulted in variability in their functional connectivity; (c) lower order RSNs were generally spatially conserved across atlases; (d) among higher order RSNs, the DMN was the most conserved across atlases; and (e) voxel-wise flexibility (i.e., the likelihood of a voxel to change network assignment across atlases) was high for subcortical regions and low for the sensory, motor and medial prefrontal cortices, and the precuneus. In order to facilitate RSN reproducibility in future studies, we provide a new freely available Consensual Atlas of REsting-state Networks, based on the most reliable atlases.

Elevated Body Mass Index is Associated with Increased Integration and Reduced Cohesion of Sensory-Driven and Internally Guided Resting-State Functional Brain Networks.

Elevated body mass index (BMI) is associated with increased multi-morbidity and mortality. The investigation of the relationship between BMI and brain organization has the potential to provide new insights relevant to clinical and policy strategies for weight control. Here, we quantified the association between increasing BMI and the functional organization of resting-state brain networks in a sample of 496 healthy individuals that were studied as part of the Human Connectome Project. We demonstrated that higher BMI was associated with changes in the functional connectivity of the default-mode network (DMN), central executive network (CEN), sensorimotor network (SMN), visual network (VN), and their constituent modules. In siblings discordant for obesity, we showed that person-specific factors contributing to obesity are linked to reduced cohesiveness of the sensory networks (SMN and VN). We conclude that higher BMI is associated with widespread alterations in brain networks that balance sensory-driven (SMN, VN) and internally guided (DMN, CEN) states which may augment sensory-driven behavior leading to overeating and subsequent weight gain. Our results provide a neurobiological context for understanding the association between BMI and brain functional organization while accounting for familial and person-specific influences.

Reproducibility of myelin content-based human habenula segmentation at 3 Tesla.

In vivo morphological study of the human habenula, a pair of small epithalamic nuclei adjacent to the dorsomedial thalamus, has recently gained significant interest for its role in reward and aversion processing. However, segmenting the habenula from in vivo magnetic resonance imaging (MRI) is challenging due to the habenula's small size and low anatomical contrast. Although manual and semi-automated habenula segmentation methods have been reported, the test-retest reproducibility of the segmented habenula volume and the consistency of the boundaries of habenula segmentation have not been investigated. In this study, we evaluated the intra- and inter-site reproducibility of in vivo human habenula segmentation from 3T MRI (0.7-0.8 mm isotropic resolution) using our previously proposed semi-automated myelin contrast-based method and its fully-automated version, as well as a previously published manual geometry-based method. The habenula segmentation using our semi-automated method showed consistent boundary definition (high Dice coefficient, low mean distance, and moderate Hausdorff distance) and reproducible volume measurement (low coefficient of variation). Furthermore, the habenula boundary in our semi-automated segmentation from 3T MRI agreed well with that in the manual segmentation from 7T MRI (0.5 mm isotropic resolution) of the same subjects. Overall, our proposed semi-automated habenula segmentation showed reliable and reproducible habenula localization, while its fully-automated version offers an efficient way for large sample analysis.

From "rest" to language task: Task activation selects and prunes from broader resting-state network.

Resting-state networks (RSNs) show spatial patterns generally consistent with networks revealed during cognitive tasks. However, the exact degree of overlap between these networks has not been clearly quantified. Such an investigation shows promise for decoding altered functional connectivity (FC) related to abnormal language functioning in clinical populations such as temporal lobe epilepsy (TLE). In this context, we investigated the network configurations during a language task and during resting state using FC. Twenty-four healthy controls, 24 right and 24 left TLE patients completed a verb generation (VG) task and a resting-state fMRI scan. We compared the language network revealed by the VG task with three FC-based networks (seeding the left inferior frontal cortex (IFC)/Broca): two from the task (ON, OFF blocks) and one from the resting state. We found that, for both left TLE patients and controls, the RSN recruited regions bilaterally, whereas both VG-on and VG-off conditions produced more left-lateralized FC networks, matching more closely with the activated language network. TLE brings with it variability in both task-dependent and task-independent networks, reflective of atypical language organization. Overall, our findings suggest that our RSN captured bilateral activity, reflecting a set of prepotent language regions. We propose that this relationship can be best understood by the notion of pruning or winnowing down of the larger language-ready RSN to carry out specific task demands. Our data suggest that multiple types of network analyses may be needed to decode the association between language deficits and the underlying functional mechanisms altered by disease. Hum Brain Mapp 38:2540-2552, 2017. © 2017 Wiley Periodicals, Inc.

Resting-state functional connectivity in epilepsy: growing relevance for clinical decision making.

PURPOSE OF REVIEW: Seizures produce dysfunctional, maladaptive networks, making functional connectivity an ideal technique for identifying complex brain effects of epilepsy. We review the current status of resting-state functional connectivity (rsFC) research, highlighting its potential added value to epilepsy surgery programs. RECENT FINDINGS: RsFC research has demonstrated that the brain impact of seizures goes beyond the epileptogenic zone, changing connectivity patterns in widespread cortical regions. There is evidence for abnormal connectivity, but the degree to which these represent adaptive or maladaptive plasticity responses is unclear. Empirical associations with cognitive performance and psychiatric symptoms have helped understand deleterious impacts of seizures outside the epileptogenic zone. Studies in the prediction of outcome suggest that there are identifiable presurgical patterns of functional connectivity associated with a greater likelihood of positive cognitive or seizure outcomes. SUMMARY: The role of rsFC remains limited in most clinical settings, but shows great promise for identifying epileptic circuits and foci, predicting outcomes following surgery, and explaining cognitive deficits and psychiatric symptoms of epilepsy. RsFC has demonstrated that even focal epilepsies constitute a network and brain systems disorder. By providing a tool to both identify and characterize the brain network impact of epileptiform activity, rsFC can make a strong contribution to presurgical algorithms in epilepsy.