Effects of aging on functional and structural brain connectivity.

This study uses Monte Carlo simulations to investigate the optimal value of the diffusion weighting factor b for estimating white-matter fiber orientations using diffusion MRI with a standard spherical sampling scheme. We devise an algorithm for determining the optimal echo time, pulse width, and pulse separation in the pulsed-gradient spin-echo sequence for a specific value of b. The Monte Carlo simulations provide an estimate of the optimal value of b for recovering one and two fiber orientations. We show that the optimum is largely independent of the noise level in the measurements and the number of gradient directions and that the optimum depends only weakly on the diffusion anisotropy, the maximum gradient strength, and the spin-spin relaxation time. The optimum depends strongly on the mean diffusivity. In brain tissue, the optima we estimate are in the ranges [0.7, 1.0] x 10(9) s m(-2) and [2.2, 2.8] x 10(9) s m(-2) for the one- and two-fiber cases, respectively. The best b for estimating the fractional anisotropy is slightly higher than for estimating fiber directions in the one-fiber case and slightly lower in the two-fiber case. To estimate Tr(D) in the one-fiber case, the optimal setting is higher still. Simulations suggest that a ratio of high to low b measurements of 5 to 1 is a good compromise for measuring fiber directions and size and shape indices.

Optimal imaging parameters for fibre-orientation estimation in diffusion MRI

Understanding how the brain matures in healthy individuals is critical for evaluating deviations from normal development in psychiatric and neurodevelopmental disorders. The brain's anatomical networks are profoundly re-modeled between childhood and adulthood, and diffusion tractography offers unprecedented power to reconstruct these networks and neural pathways in vivo. Here we tracked changes in structural connectivity and network efficiency in 439 right-handed individuals aged 12 to 30 (211 female/126 male adults, mean age=23.6, SD=2.19; 31 female/24 male 12 year olds, mean age=12.3, SD=0.18; and 25 female/22 male 16 year olds, mean age=16.2, SD=0.37). All participants were scanned with high angular resolution diffusion imaging (HARDI) at 4 T. After we performed whole brain tractography, 70 cortical gyral-based regions of interest were extracted from each participant's co-registered anatomical scans. The proportion of fiber connections between all pairs of cortical regions, or nodes, was found to create symmetric fiber density matrices, reflecting the structural brain network. From those 70 × 70 matrices we computed graph theory metrics characterizing structural connectivity. Several key global and nodal metrics changed across development, showing increased network integration, with some connections pruned and others strengthened. The increases and decreases in fiber density, however, were not distributed proportionally across the brain. The frontal cortex had a disproportionate number of decreases in fiber density while the temporal cortex had a disproportionate number of increases in fiber density. This large-scale analysis of the developing structural connectome offers a foundation to develop statistical criteria for aberrant brain connectivity as the human brain matures.

Development of brain structural connectivity between ages 12 and 30: A 4-Tesla diffusion imaging study in 439 adolescents and adults

The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer's Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.

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Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?

Regular cannabis use is associated with adverse cognitive and mental health outcomes that have been ascribed to aberrant neuroanatomy in brain regions densely innervated with cannabinoid receptors. Neuroanatomical differences between cannabis users and controls have been assessed in multiple structural magnetic resonance imaging (sMRI) studies. However, there is heterogeneity in the results leading to cautious interpretation of the data so far. We examined the sMRI evidence to date in human cannabis users, to establish more definitely whether neuroanatomical alterations are associated with regular cannabis use. The regional specificity and association with cannabis use indices (i.e. cumulative dosage, duration) were also explored. We systematically reviewed and meta-analysed published sMRI studies investigating regional brain volumes (cortical, subcortical and global) in cannabis users and non-user controls. Three electronic databases were searched (PubMed, Scopus, and PsycINFO). A total of 17 meta-analyses were conducted (one for each cortical, subcortical and global volume) using the generic inverse variance method, whereby standardised mean difference in volume was calculated between users and non-users. Exploratory meta-regressions were conducted to investigate the association between cannabis use indices and regional brain volumes. A total of 30 articles were eligible for inclusion, contributing 106 effect sizes across 17 meta-analyses. Regular cannabis users had significantly smaller volumes of the hippocampus (SMD = 0.14, 95% CIs [0.02, 0.27]; Z = 2.29, p = 0.02, I2 = 74%) and orbitofrontal cortex {medial (SMD = 0.30, 95% CIs [0.15, 0.45]; Z = 3.89, p = 0.0001, I2 = 51%), lateral (SMD = 0.19, 95% CIs [0.07, 0.32]; Z = 3.10, p = 0.002, I2 = 26%)} relative to controls. The volumes of the hippocampus and orbitofrontal cortex were not significantly associated with cannabis duration and dosage. Our findings are consistent with evidence of aberrance in brain regions involved in reward, learning and memory, and motivation circuits in the regular use of substances other than cannabis, pointing to commonality in neurobiological abnormalities between regular users of cannabis and of other substances.

Does regular cannabis use affect neuroanatomy? An updated systematic review and meta-analysis of structural neuroimaging studies

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

BACKGROUND Although alterations in medial temporal lobe structures have been previously associated with use of cannabis, one of the most widely used illicit drugs, whether such alterations are a cause or effect of cannabis use has been unclear. METHODS In this cross-sectional observational study involving 404 twins/siblings, we have compared cortical thickness and surface area between groups of gender-matched sibling-pairs (concordant cannabis unexposed, concordant exposed and discordant for cannabis exposure) using permutation tests after controlling for potential confounds. Bi-variate polygenic model was used to assess the genetic and environmental contributions underlying cortical morphological phenotypes and frequency of cannabis use. RESULTS Cortical thickness of the right entorhinal cortex was significantly lower in the concordant exposed siblings compared to both discordant unexposed and discordant exposed groups [false discovery rate (FDR)-corrected, q < 0.05]. The association between the right entorhinal cortex thickness and frequency of cannabis use is due to the contribution of significant shared additive genetic (ρg = -0.19 ± 0.08; p = 0.02) factors but not unique environment (ρe = 0.05 ± 0.09; p = 0.53). Significantly lower surface area of the right entorhinal cortex in discordant exposed group compared with the discordant unexposed group furnishes preliminary evidence in support of causal effect of cannabis use (FDR-corrected, q < 0.05). However, bi-variate polygenic model-based analysis did not show any significant effect. CONCLUSIONS Shared genetic liability may underlie the association between cannabis exposure and thinner right entorhinal cortex. Prospective longitudinal studies are necessary to definitively disentangle the cause-effect relationships of cannabis use.

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Subhadip Paul

Papers

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

Does thinner right entorhinal cortex underlie genetic liability to cannabis use

Non-isothermal and isothermal reduction kinetics of iron ore agglomerates

Youth with elevated psychopathic traits exhibit structural integrity deficits in the uncinate fasciculus.

Nanocomposite-based smart fertilizers: A boon to agricultural and environmental sustainability.