To investigate whether associations of smoking with depression and anxiety are likely to be causal, using a Mendelian randomisation approach.
Mendelian randomisation meta-analyses using a genetic variant (rs16969968/rs1051730) as a proxy for smoking heaviness, and observational meta-analyses of the associations of smoking status and smoking heaviness with depression, anxiety and psychological distress.
Current, former and never smokers of European ancestry aged ≥16 years from 25 studies in the Consortium for Causal Analysis Research in Tobacco and Alcohol (CARTA).
Primary outcome measures
Binary definitions of depression, anxiety and psychological distress assessed by clinical interview, symptom scales or self-reported recall of clinician diagnosis.
The analytic sample included up to 58 176 never smokers, 37 428 former smokers and 32 028 current smokers (total N=127 632). In observational analyses, current smokers had 1.85 times greater odds of depression (95% CI 1.65 to 2.07), 1.71 times greater odds of anxiety (95% CI 1.54 to 1.90) and 1.69 times greater odds of psychological distress (95% CI 1.56 to 1.83) than never smokers. Former smokers also had greater odds of depression, anxiety and psychological distress than never smokers. There was evidence for positive associations of smoking heaviness with depression, anxiety and psychological distress (ORs per cigarette per day: 1.03 (95% CI 1.02 to 1.04), 1.03 (95% CI 1.02 to 1.04) and 1.02 (95% CI 1.02 to 1.03) respectively). In Mendelian randomisation analyses, there was no strong evidence that the minor allele of rs16969968/rs1051730 was associated with depression (OR=1.00, 95% CI 0.95 to 1.05), anxiety (OR=1.02, 95% CI 0.97 to 1.07) or psychological distress (OR=1.02, 95% CI 0.98 to 1.06) in current smokers. Results were similar for former smokers.
Findings from Mendelian randomisation analyses do not support a causal role of smoking heaviness in the development of depression and anxiety.
Mendelian randomisation; Smoking; Depression; Anxiety
Pubertal dynamics plays an important role in physical and psychological development of children and adolescents. We aim to provide reference ranges of plasma testosterone in a large longitudinal sample. Furthermore, we describe a measure of testosterone trajectories during adolescence that can be used in future investigations of development.
We carried out longitudinal measurements of plasma testosterone in 2,216 samples obtained from 513 males (9 to 17 years of age) from the Avon Longitudinal Study of Parents and Children. We used integration of a model fitted to each participant’s testosterone trajectory to calculate a measure of average exposure to testosterone over adolescence. We pooled these data with corresponding values reported in the literature to provide a reference range of testosterone levels in males between the ages of 6 and 19 years.
The average values of total testosterone in the ALSPAC sample range from 0.82 nmol/L (Standard Deviation [SD]: 0.09) at 9 years of age to 16.5 (SD: 2.65) nmol/L at 17 years of age; these values are congruent with other reports in the literature. The average exposure to testosterone is associated with different features of testosterone trajectories such as Peak Testosterone Change, Age at Peak Testosterone Change, and Testosterone at 17 years of age as well as the timing of the growth spurt during puberty.
The average exposure to testosterone is a useful measure for future investigations using testosterone trajectories to examine pubertal dynamics.
Face expressions are a rich source of social signals. Here we estimated the proportion of phenotypic variance in the brain response to facial expressions explained by common genetic variance captured by ∼500,000 single nucleotide polymorphisms. Using genomic-relationship-matrix restricted maximum likelihood (GREML), we related this global genetic variance to that in the brain response to facial expressions, as assessed with functional magnetic resonance imaging (fMRI) in a community-based sample of adolescents (n = 1,620). Brain response to facial expressions was measured in 25 regions constituting a face network, as defined previously. In 9 out of these 25 regions, common genetic variance explained a significant proportion of phenotypic variance (40–50%) in their response to ambiguous facial expressions; this was not the case for angry facial expressions. Across the network, the strength of the genotype-phenotype relationship varied as a function of the inter-individual variability in the number of functional connections possessed by a given region (R2 = 0.38, p<0.001). Furthermore, this variability showed an inverted U relationship with both the number of observed connections (R2 = 0.48, p<0.001) and the magnitude of brain response (R2 = 0.32, p<0.001). Thus, a significant proportion of the brain response to facial expressions is predicted by common genetic variance in a subset of regions constituting the face network. These regions show the highest inter-individual variability in the number of connections with other network nodes, suggesting that the genetic model captures variations across the adolescent brains in co-opting these regions into the face network.
We measured brain response to facial expressions in a large sample of typically developing adolescents (n = 1,620) and assessed “heritability” of the response using common genetic variations across the genome. In a subset of brain regions, we explained 40–50% of phenotypic variance by genetic variance. These brain regions appear to differ from the rest of the face network in the degree of inter-individual variations in their functional connectivity. We propose that these regions, including the prefrontal and premotor cortex, represent “Optional” part of the network co-opted by its “Obligatory” members, including the posterior part of the superior temporal sulcus, fusiform face area and the lateral occipital cortex, concerned with processing complex visual stimuli.
Genetic variations in fat mass- and obesity (FTO)-associated gene, a well-replicated gene locus of obesity, appear to be associated also with reduced regional brain volumes in elderly. Here, we examined whether FTO is associated with total brain volume in adolescence, thus exploring possible developmental effects of FTO. We studied a population-based sample of 598 adolescents recruited from the French Canadian founder population in whom we measured brain volume by magnetic resonance imaging. Total fat mass was assessed with bioimpedance and body mass index was determined with anthropometry. Genotype–phenotype associations were tested with Merlin under an additive model. We found that the G allele of FTO (rs9930333) was associated with higher total body fat [TBF (P = 0.002) and lower brain volume (P = 0.005)]. The same allele was also associated with higher lean body mass (P = 0.03) and no difference in height (P = 0.99). Principal component analysis identified a shared inverse variance between the brain volume and TBF, which was associated with FTO at P = 5.5 × 10−6. These results were replicated in two independent samples of 413 and 718 adolescents, and in a meta-analysis of all three samples (n = 1729 adolescents), FTO was associated with this shared inverse variance at P = 1.3 × 10−9. Co-expression networks analysis supported the possibility that the underlying FTO effects may occur during embryogenesis. In conclusion, FTO is associated with shared inverse variance between body adiposity and brain volume, suggesting that this gene may exert inverse effects on adipose and brain tissues. Given the completion of the overall brain growth in early childhood, these effects may have their origins during early development.
Visceral fat (VF) promotes the development of metabolic syndrome (MetS), which emerges as early as in adolescence. The clustering of MetS components suggests shared etiologies, but these are largely unknown and may vary between males and females. Here, we investigated the latent structure of pre-clinical MetS in a community-based sample of 286 male and 312 female adolescents, assessing their abdominal adiposity (VF) directly with magnetic resonance imaging. Principal component analysis of the five MetS-defining variables (VF, blood pressure [BP], fasting serum triglycerides, HDL-cholesterol and glucose) identified two independent components in both males and females. The first component was sex-similar; it explained >30% of variance and was loaded by all but BP variables. The second component explained >20% of variance; it was loaded by BP similarly in both sexes but additional loading by metabolic variables was sex-specific. This sex-specificity was not detected in analyses that used waist circumference instead of VF. In adolescence, MetS-defining variables cluster into at least two sub-syndromes: (1) sex-similar metabolic abnormalities of obesity-induced insulin resistance and (2) sex-specific metabolic abnormalities associated with BP elevation. These results suggest that the etiology of MetS may involve more than one pathway and that some of the pathways may differ between males and females. Further, the sex-specific metabolic abnormalities associated with BP elevation suggest the need for sex-specific prevention and treatment strategies of MetS.
The most dramatic growth of the human brain occurs in utero and during the first 2 years of postnatal life. Genesis of the cerebral cortex involves cell proliferation, migration, and apoptosis, all of which may be influenced by prenatal environment. Here, we show that variation in KCTD8 (potassium channel tetramerization domain 8) is associated with brain size in female adolescents (rs716890, P = 5.40 × 10−09). Furthermore, we found that the KCTD8 locus interacts with prenatal exposure to maternal cigarette smoking vis-à-vis cortical area and cortical folding: In exposed girls only, the KCTD8 locus explains up to 21% of variance. Using head circumference as a proxy of brain size at 7 years of age, we have replicated this gene–environment interaction in an independent sample. We speculate that KCTD8 might modulate adverse effects of smoking during pregnancy on brain development via apoptosis triggered by low intracellular levels of potassium, possibly reducing the number of progenitor cells.
adolescence; brain; GWAS; pregnancy
To study whether maternal cigarette smoking during pregnancy is associated with alterations in the growth of fetal lungs, kidneys, liver, brain, and placenta.
A case-control study, with operators performing the image analysis blinded.
Study performed on a research-dedicated magnetic resonance imaging (MRI) scanner (1.5 T) with participants recruited from a large teaching hospital in the United Kingdom.
A total of 26 pregnant women (13 current smokers, 13 non smokers) were recruited; 18 women (10 current smokers, 8 nonsmokers) returned for the second scan later in their pregnancy.
Each fetus was scanned with MRI at 22–27 weeks and 33–38 weeks gestational age (GA).
Main outcome measures
Images obtained with MRI were used to measure volumes of the fetal brain, kidneys, lungs, liver and overall fetal size, as well as placental volumes.
Exposed fetuses showed lower brain volumes, kidney volumes, and total fetal volumes, with this effect being greater at visit 2 than at visit 1 for brain and kidney volumes, and greater at visit 1 than at visit 2 for total fetal volume. Exposed fetuses also demonstrated lower lung volume and placental volume, and this effect was similar at both visits. No difference was found between the exposed and nonexposed fetuses with regards to liver volume.
Magnetic resonance imaging has been used to show that maternal smoking is associated with reduced growth of fetal brain, lung and kidney; this effect persists even when the volumes are corrected for maternal education, gestational age, and fetal sex. As expected, the fetuses exposed to maternal smoking are smaller in size. Similarly, placental volumes are smaller in smoking versus nonsmoking pregnant women.
Dynamics of brain signals such as electroencephalogram (EEG) can be characterized as a sequence of quasi-stable patterns. Such patterns in the brain signals can be associated with coordinated neural oscillations, which can be modeled by non-linear systems. Further, these patterns can be quantified through dynamical non-stationarity based on detection of qualitative changes in the state of the systems underlying the observed brain signals. This study explored age-related changes in dynamical non-stationarity of the brain signals recorded at rest, longitudinally with 128-channel EEG during early adolescence (10 to 13 years of age, 56 participants). Dynamical non-stationarity was analyzed based on segmentation of the time series with subsequent grouping of the segments into clusters with similar dynamics. Age-related changes in dynamical non-stationarity were described in terms of the number of stationary states and the duration of the stationary segments. We found that the EEG signal became more non-stationary with age. Specifically, the number of states increased whereas the mean duration of the stationary segment decreased with age. These two effects had global and parieto-occipital distribution, respectively, with the later effect being most dominant in the alpha (around 10 Hz) frequency band.
Adolescence places high demands on inter-personal interactions and, hence, on the extraction and processing of social cues. Here we assess longitudinally the development of brain activity within a network implicated in social cognition—the action observation network. We performed activation likelihood estimation meta-analyses to define regions of interest based upon the mature action observation network of adults. Using functional magnetic resonance imaging, we then examined developmental trajectories of functional brain activity within these brain regions. Using this approach, we reveal quadratic trajectories within a fronto-parietal network previously shown to demonstrate correlated morphological development.
fMRI; development; adolescence; emotion; action observation
Musicians are trained in melodic transposition, the skill of extracting the pitch interval structure (i.e., the frequency ratios between pitches) and moving it into different keys (i.e., different pitch levels). This ability to recognize whether a melody is the same or altered when it is played back in a different key is correlated with both greater neural activation and cortical thickness in bilateral intraparietal sulcus (IPS). Musical training only explains part of this finding, suggesting that the ability to transpose a melody may have innate predispositions. The current study was designed to address this question: are the anatomical correlates of musical transposition already present in non-musician children at 14 years of age? If so, is there any evidence that those traits were already in place at earlier ages? To answer this question, we recruited 47 adolescents (age 14.5 years) from a longitudinal study and tested them on a melodic transposition task. These adolescents had already undergone anatomical magnetic resonance imaging (MRI) at the ages of 10 (Time 1), 11.5 (Time 2), 13 (Time 3) years, as well as at age 14.5 years (Time 4) They were tested on the transposition task during Time-4 visit. During this visit, we found a relationship between cortical thickness in left IPS and performance on the transposed melody task in the girls and not the boys; no such relationship was observed at any of the earlier ages. Given that girls reach more advanced staged of pubertal maturation earlier than boys, it is possible that the relationship between cortical thickness in IPS and skill at melodic transposition only emerges once the brain has reached a certain degree of maturity. This claim is supported by a lack of similar sex differences in the adults: the degree of correlation between cortical thickness and performance on the same transposed melody task did not differ between men and women in a previous study. Taken together, our results suggest that the relationship between cortical thickness and the ability to transpose a melody is not fixed, and that the effects observed in adults are neither due exclusively to training nor to predisposition.
musical abilities; mental transformations; intraparietal sulcus; fMRI; development; adolescent
Inter-individual variation in facial shape is one of the most noticeable phenotypes in humans, and it is clearly under genetic regulation; however, almost nothing is known about the genetic basis of normal human facial morphology. We therefore conducted a genome-wide association study for facial shape phenotypes in multiple discovery and replication cohorts, considering almost ten thousand individuals of European descent from several countries. Phenotyping of facial shape features was based on landmark data obtained from three-dimensional head magnetic resonance images (MRIs) and two-dimensional portrait images. We identified five independent genetic loci associated with different facial phenotypes, suggesting the involvement of five candidate genes—PRDM16, PAX3, TP63, C5orf50, and COL17A1—in the determination of the human face. Three of them have been implicated previously in vertebrate craniofacial development and disease, and the remaining two genes potentially represent novel players in the molecular networks governing facial development. Our finding at PAX3 influencing the position of the nasion replicates a recent GWAS of facial features. In addition to the reported GWA findings, we established links between common DNA variants previously associated with NSCL/P at 2p21, 8q24, 13q31, and 17q22 and normal facial-shape variations based on a candidate gene approach. Overall our study implies that DNA variants in genes essential for craniofacial development contribute with relatively small effect size to the spectrum of normal variation in human facial morphology. This observation has important consequences for future studies aiming to identify more genes involved in the human facial morphology, as well as for potential applications of DNA prediction of facial shape such as in future forensic applications.
Monozygotic twins look more alike than dizygotic twins or other siblings, and siblings in turn look more alike than unrelated individuals, indicating that human facial morphology has a strong genetic component. We quantitatively assessed human facial shape phenotypes based on statistical shape analyses of facial landmarks obtained from three-dimensional magnetic resonance images of the head. These phenotypes turned out to be highly promising for studying the genetic basis of human facial variation in that they showed high heritability in our twin data. A subsequent genome-wide association study (GWAS) identified five candidate genes affecting facial shape in Europeans: PRDM16, PAX3, TP63, C5orf50, and COL17A1. In addition, our data suggest that genetic variants associated with NSCL/P also influence normal facial shape variation. Overall, this study provides novel and confirmatory links between common DNA variants and normal variation in human facial morphology. Our results also suggest that the high heritability of facial phenotypes seems to be explained by a large number of DNA variants with relatively small individual effect size, a phenomenon well known for other complex human traits, such as adult body height.
Women and men differ in a wide variety of behavioral traits and in their vulnerability to developing certain mental disorders. This review endeavors to explore how recent preclinical and clinical research findings have enhanced our understanding of the factors that underlie these disparities. We start with a brief overview of some of the important genetic, molecular, and hormonal determinants that contribute to the process of sexual differentiation. We then discuss the importance of animal models in studying the mechanisms responsible for sex differences in neuropsychiatric disorders (e.g., drug dependence) – with a special emphasis on experimental models based on the neurodevelopmental and “three hits” hypotheses. Next, we describe the most common brain phenotypes observed in vivo with magnetic resonance imaging. We discuss the challenges in interpreting these phenotypes vis-à-vis the underlying neurobiology and revisit the known sex differences in brain structure from birth, through adolescence, and into adulthood. This is followed by a presentation of pertinent clinical and epidemiological data that point to important sex differences in the prevalence, course, and expression of psychopathologies such as schizophrenia, and mood disorders including major depression and posttraumatic stress disorder. Recent evidence implies that mood disorders and psychosis share some common genetic predispositions and neurobiological bases. Therefore, modern research is emphasizing dimensional representation of mental disorders and conceptualization of schizophrenia and major depression as a continuum of cognitive deficits and neurobiological abnormalities. Herein, we examine available evidence on cerebral sexual dimorphism to verify if sex differences vary quantitatively and/or qualitatively along the psychoses-depression continuum. Finally, sex differences in the prevalence of posttraumatic disorder and drug abuse have been described, and we consider the genomic and molecular data supporting these differences.
sexual differentiation; adolescence; schizophrenia; depression; bipolar disorders; posttraumatic disorders; addiction; animal models
Variation in brain structure may reflect variation in functional properties of specific brain systems. Structural variation may therefore reflect variation in behavioural performance. Here, we use diffusion-weighted magnetic resonance imaging to show that variation in white matter integrity in a specific region in the body of the corpus callosum is associated with variation in performance of a bimanual co-ordination task. When the callosal region showing this association is used as a seed for probabilistic tractography, inter-hemispheric pathways are generated to the supplementary motor area and caudal cingulate motor area. This provides further evidence for the role of medial wall motor areas in bimanual co-ordination and supports the idea that variation in brain structure reflects inter-individual differences in skilled performance.
The complex connectivity of the cerebral cortex is a topic of much study, yet the link between structure and function is still unclear. The processing capacity and throughput of information at individual brain regions remains an open question and one that could potentially bridge these two aspects of neural organization. The rate at which information is emitted from different nodes in the network and how this output process changes under different external conditions are general questions that are not unique to neuroscience, but are of interest in multiple classes of telecommunication networks. In the present study we show how some of these questions may be addressed using tools from telecommunications research. An important system statistic for modeling and performance evaluation of distributed communication systems is the time between successive departures of units of information at each node in the network. We describe a method to extract and fully characterize the distribution of such inter-departure times from the resting-state electroencephalogram (EEG). We show that inter-departure times are well fitted by the two-parameter Gamma distribution. Moreover, they are not spatially or neurophysiologically trivial and instead are regionally specific and sensitive to the presence of sensory input. In both the eyes-closed and eyes-open conditions, inter-departure time distributions were more dispersed over posterior parietal channels, close to regions which are known to have the most dense structural connectivity. The biggest differences between the two conditions were observed at occipital sites, where inter-departure times were significantly more variable in the eyes-open condition. Together, these results suggest that message departure times are indicative of network traffic and capture a novel facet of neural activity.
The brain may be thought of as a network of regions that communicate with each other to produce emergent phenomena such as perception and cognition. Many potentially interesting aspects of brain networks, such as how information is emitted at different nodes, also tend to be of interest in various types of telecommunication systems, such as telephony. Thus, network properties that are relevant in the context of brain function may be important for telecommunication networks in general. Here we show how neural activity can be partitioned into units of information and analyzed from the perspective of a telecommunication system. We demonstrate that the inter-departure times of such units of information have very similar probability distributions across subjects and that they are sensitive both to regional variation and cognitive state. The approach we describe can be applied in a wide variety of experimental paradigms to generate novel indices of neural activity and open new avenues for network analysis of the brain.
Quantitative analysis of craniofacial morphology is of interest to scholars
working in a wide variety of disciplines, such as anthropology, developmental
biology, and medicine. T1-weighted (anatomical) magnetic resonance images (MRI)
provide excellent contrast between soft tissues. Given its three-dimensional
nature, MRI represents an ideal imaging modality for the analysis of
craniofacial structure in living individuals. Here we describe how T1-weighted
MR images, acquired to examine brain anatomy, can also be used to analyze facial
features. Using a sample of typically developing adolescents from the Saguenay
Youth Study (N = 597; 292 male, 305 female, ages: 12 to 18
years), we quantified inter-individual variations in craniofacial structure in
two ways. First, we adapted existing nonlinear registration-based morphological
techniques to generate iteratively a group-wise population average of
craniofacial features. The nonlinear transformations were used to map the
craniofacial structure of each individual to the population average. Using
voxel-wise measures of expansion and contraction, we then examined the effects
of sex and age on inter-individual variations in facial features. Second, we
employed a landmark-based approach to quantify variations in face surfaces. This
approach involves: (a) placing 56 landmarks (forehead, nose, lips, jaw-line,
cheekbones, and eyes) on a surface representation of the MRI-based group
average; (b) warping the landmarks to the individual faces using the inverse
nonlinear transformation estimated for each person; and (3) using a principal
components analysis (PCA) of the warped landmarks to identify facial features
(i.e. clusters of landmarks) that vary in our sample in a correlated fashion. As
with the voxel-wise analysis of the deformation fields, we examined the effects
of sex and age on the PCA-derived spatial relationships between facial features.
Both methods demonstrated significant sexual dimorphism in craniofacial
structure in areas such as the chin, mandible, lips, and nose.
Neural activity is irregular and unpredictable, yet little is known about why this is the case and how this property relates to the functional architecture of the brain. Here we show that the variability of a region’s activity systematically varies according to its topological role in functional networks. We recorded the resting-state electroencephalogram (EEG) and constructed undirected graphs of functional networks. We measured the centrality of each node in terms of the number of connections it makes (degree), the ease with which the node can be reached from other nodes in the network (efficiency) and the tendency of the node to occupy a position on the shortest paths between other pairs of nodes in the network (betweenness). As a proxy for variability, we estimated the information content of neural activity using multiscale entropy analysis. We found that the rate at which information was generated was largely predicted by centrality. Namely, nodes with greater degree, betweenness, and efficiency were more likely to have high information content, while peripheral nodes had relatively low information content. These results suggest that the variability of regional activity reflects functional embedding.
variability; entropy; degree; efficiency; connectivity; centrality; functional integration
Previous studies have shown smaller brain volume and less gray matter in children with attention-deficit/hyperactivity disorder (ADHD). Relatively few morphological studies have examined structures thought to subserve inhibitory control, one of the diagnostic features of ADHD. We examined one such region, the pars opercularis, predicting a thinner cortex of the inferior frontal gyrus (IFG) in children with ADHD.
Structural images were obtained from 49 children (24 control; 25 ADHD combined subtype) aged 9 though 15 years. Images were processed using a volumetric pipeline to provide a fully automated estimate of regional volumes of gray and white matter. A further analysis using FreeSurfer provided measures of cortical thickness for each lobe, and for 13 regions in the frontal lobe.
Relative to controls, children with ADHD had smaller whole brain volume and lower gray matter, but not white matter, volumes in all lobes. An analysis of frontal regions showed a significant interaction of group by region. Planned contrasts showed bilateral thinner cortex in the pars opercularis in children with ADHD.
Children with ADHD showed both diffuse and regional gray matter abnormalities. Consistent with its putative role in response inhibition, the cortex of the pars opercularis was thinner in children with ADHD who, as expected, had significantly poorer inhibitory performance on a Go/No-go task. These differences held for both hemispheres raising the possibility that a developmental abnormality of IFG might drive development of inhibition difficulties.
Attention-deficit/hyperactivity disorder; MRI; Cortical thickness; Inferior frontal gyrus; Gray matter
The purpose of this paper is twofold. First, we will review different approaches that one can use with transcranial magnetic stimulation (TMS) to study both its effects on motor behavior and on neural connections in the human brain. Second, we will present evidence obtained in TMS-based studies showing that the dorsal premotor area (PMd), the ventral premotor area (PMv), the supplementary motor area (SMA), and the pre-supplementary motor area (pre-SMA) each have different roles to play in motor behavior. We highlight the importance of the PMd in response selection based on arbitrary cues and in the control of arm movements, the PMv in grasping and in the discrimination of bodily actions, the SMA in movement sequencing and in bimanual coordination, and the pre-SMA in cognitive control. We will also discuss ways in which TMS can be used to chart “true” cerebral reorganization in clinical populations and how TMS might be used as a therapeutic tool to facilitate motor recovery after stroke. We will end our review by discussing some of the methodological challenges and future directions for using this tool in basic and clinical neuroscience.
transcranial magnetic stimulation; motor system; functional connectivity; effective connectivity; premotor area; supplementary motor area; stroke recovery; functional neuroimaging
Understanding the interactions among different brain regions is fundamental to our understanding of brain function. Here we describe a complete map of functional connections in the human brain derived by an automatic meta-analysis of 825 neuroimaging articles, representing 3402 experiments. The likelihood of a functional connection between regions was estimated by studying the interdependence of their “activity,” as reported in each experiment, across all experiments. We obtained a dense coactivation map that recovers some fundamental principles of the brain's functional connectivity, such as the symmetric interhemispheric connections, and important functional networks, such as the fronto-parietal attention network, the resting state network and the motor network.
brain mapping; database; functional connectivity; human brain; meta-analysis
What do we know about the maturation of the human brain during adolescence? Do structural changes in cerebral cortex reflect synaptic pruning? Are increases in white-matter volume driven by myelination? Is the adolescent brain more or less sensitive to reward? These are but a few questions we ask in this review while attempting to indicate how findings obtained in the healthy brain help in furthering our understanding of mental health during adolescence.
Recent studies with magnetic resonance imaging suggest that age-related changes in white matter during male adolescence may indicate an increase in g ratio wherein the radial growth of an axon outpaces a corresponding increase in myelin thickness. We review the original Rushton (1951) model where a g ratio of ∼0.6 represents an optimal relationship between the axon and fibre diameters vis-à-vis conduction velocity, and point out evidence indicating slightly higher g ratio in large-diameter fibres. We estimate that fibres with a diameter larger than 9.6 μm will have a relatively thinner myelin sheath, and brains with increasingly larger proportions of such large-diameter fibres will have progressively lower concentration of myelin. We conclude by pointing out possible implications of “suboptimal” g ratio for the emergence of “disconnection” disorders, such as schizophrenia, in late adolescence.
myelin; axon; connectivity; adolescence; schizophrenia; brain development
When engaged by a stimulus, different nodes of a neural circuit respond in a coordinated fashion. We often ask whether there is a cause and effect in such interregional interactions. This paper proposes that we can infer causality in functional connectivity by employing a ‘perturb and measure’ approach. In the human brain, this has been achieved by combining transcranial magnetic stimulation (TMS) with positron emission tomography (PET), functional magnetic resonance imaging or electroencephalography. Here, I will illustrate this approach by reviewing some of our TMS/PET work, and will conclude by discussing a few methodological and theoretical challenges facing those studying neural connectivity using a perturbation.
transcranial magnetic stimulation; positron emission tomography; neural connectivity; cerebral cortex
Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive tool used to manipulate activity in specific neural circuits of the human brain. Clinical studies suggest that, in some patients with major depression, rTMS has the potential to alleviate symptoms that may be related to functional abnormalities in a frontocingulate circuit. This paper reviews the rationale for the use of rTMS in this context. The following topics are discussed: symptoms and cognition in major depression, with special emphasis on the initiation of speech; neuroimaging studies of depression; rTMS as treatment for depression; structure and function of the mid-dorsolateral frontal and anterior cingulate cortices; and combined TMS/positron emission tomography studies of frontocortical connectivity.
brain; cognition; depression; electroconvulsive therapy; frontal lobe; speech; tomography, emission computed; transcranial magnetic stimulation