Serotonin signaling modulates the effects of familial risk for depression on cortical thickness

Use of random matrix theory in the discovery of resting state brain networks

Connectomics identifies brain networks in vivo in resting state functional MRI. However, the presence of noise produces spurious identification of brain networks, which have low test-retest reliability. A Network Based Statistics approach to network identification has been previously proposed that affords much better statistical power relative to Bonferroni method but nevertheless provides a sufficiently conservative, family-wise control for false positives. We propose the use of Random Matrix Theory (RMT) to discover brain networks and to associate those networks with demographic and clinical variables. We parcellated the brain into cortical and subcortical regions using either an anatomical or a functional brain atlas. We applied RMT to study functional connectivity across brain regions by first computing the correlation matrix for time courses in those brain regions and then identifying eigenvalues that deviate from the theoretical random distribution that RMT predicts, on the assumption that real brain networks would produce eigenvalues that differ significantly from the random distribution. We assessed the specificity and test-retest reliability of identified networks through application of this RMT-based approach to (1) synthetic data generated under the null-hypothesis, (2) resting state functional MRI data from 4 real-world cohorts of patients and healthy controls, and (3) synthetic data generated by the addition of increasing amounts of noise to real-world datasets. Our findings showed that RMT method was robust to the atlas used for parcellating the brain and did not discover a brain network in synthetic data when in fact a network was not present (i.e., specificity was high); RMT-identified networks in the real-world dataset had high test-retest reliability; and RMT-based method consistently discovered the same network in the presence of increasing noise in the real-world dataset.

Application of a diathesis-stress model to the interplay of cortical structural development and emerging depression in youth

Cross-sectional studies in adults have long identified differences in cortical structure in adults with depression compared to healthy adults, with most studies identifying reductions in grey matter volume, cortical thickness, and surface area in primarily frontal cortical regions including the OFC, ACC, and variable sub-regions of the PFC. However, when, why, and for whom these neural correlates of depression emerge remains poorly understood, necessitating developmental study of associations between depression and cortical structure. We systematically reviewed studies examining these associations in child/adolescent samples, and applied a developmentally-focused diathesis-stress model to understand the impacts of depressogenic risk-factors and stressors on the development of structural neural correlates of depression. Cross-sectional findings in youth are generally similar to those found in adults, but vary in magnitude and direction of effects. Preliminary evidence suggests that age, sex, severity, and comorbidity moderate these associations. Longitudinal studies show depression prospectively predicting cortical structure and structure predicting emerging depression. Consistent with a diathesis-stress model, associations have been noted between risk-factors for depression (e.g., genetic risk, family risk) and environmental stressors (e.g., early life stress) and structural neural correlates. Further investigation of these associations across development with attention to vulnerability factors and stressors is indicated.

5HTTLPR Genetic Variant and Major Depressive Disorder: A Review

Major Depressive Disorder (MDD) is a disease that involves biological, psychological, and social interactions. Studies have shown the importance of genetics contribution to MDD development. The SCL6A4 protein (5HTTLPR) functions transporting serotonin, a neurotransmitter linked to mood and emotion, to the synaptic cleft. Hence, this study seeks, through a literature review, a better comprehension of the 5HTTLPR genetic variant association with MDD. For this purpose, a search was performed on the Virtual Health Library Portal for articles that related 5HTTLPR to MDD. Most of the articles found were conducted in the American continent, with one (1) study implemented in Brazil. 5HTTLPR associations were found regarding changes in the nervous system, pharmacology, and risk factors seen in MDD patients. When verifying the allelic distribution, the S allele had a higher frequency in most of the studies analyzed. Despite not finding a commonality in the different studies, the tremendous genetic variation found demonstrates the MDD complexity. For this reason, further studies in diverse populations should be conducted to assist in the understanding and treatment of the disease.

Fluoxetine-induced recovery of serotonin and norepinephrine projections in a mouse model of post-stroke depression

Chronic treatment with fluoxetine (FLX) is required for its antidepressant effects, but the role of serotonin (5-HT) axonal plasticity in FLX action is unknown. To address this, we examined mice with a stroke in the left medial prefrontal cortex (mPFC) resulting in persistent anxiety-like and depression-like behaviors and memory deficits as a model of post-stroke depression. Chronic treatment with FLX (but not exercise) completely reversed the behavioral phenotype and partially reversed changes in FosB-labeled cells in the mPFC, nucleus accumbens, septum, hippocampus, basolateral amygdala (BLA), and dorsal raphe. In these regions, 5-HT or norepinephrine (NE) innervation was quantified by staining for 5-HT or NE transporters, respectively. 5-HT synapses and synaptic triads were identified as synaptophysin-stained sites on 5-HT axons located proximal to gephyrin-stained or PSD95-stained spines. A week after stroke, 5-HT innervation was greatly reduced at the stroke site (left cingulate gyrus (CG) of the mPFC) and the left BLA. Chronically, 5-HT and NE innervation was reduced at the left CG, nucleus accumbens, and BLA, with no changes in other regions. In these areas, pre-synaptic and post-synaptic 5-HT synapses and triads to inhibitory (gephyrin+) sites were reduced, while 5-HT contacts at excitatory (PSD95+) sites were reduced in the CG and prelimbic mPFC. Chronic FLX, but not exercise, reversed these reductions in 5-HT innervation but incompletely restored NE projections. Changes in 5-HT innervation were verified using YFP staining in mice expressing YFP-tagged channelrhodopsin in 5-HT neurons. Thus, FLX-induced 5-HT axonal neuroplasticity of forebrain projections may help mediate recovery from brain injury.

Cluster-level statistical inference in fMRI datasets: The unexpected behavior of random fields in high dimensions

Identifying regional effects of interest in MRI datasets usually entails testing a priori hypotheses across many thousands of brain voxels, requiring control for false positive findings in these multiple hypotheses testing. Recent studies have suggested that parametric statistical methods may have incorrectly modeled functional MRI data, thereby leading to higher false positive rates than their nominal rates. Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools. To understand better why parametric methods may yield excessive false positives, we assessed their performance when applied both to simulated datasets of 1D, 2D, and 3D Gaussian Random Fields (GRFs) and to 710 real-world, resting-state fMRI datasets. We showed that both the simulated 2D and 3D GRFs and the real-world data contain a small percentage (<6%) of very large clusters (on average 60 times larger than the average cluster size), which were not present in 1D GRFs. These unexpectedly large clusters were deemed statistically significant using parametric methods, leading to empirical familywise error rates (FWERs) as high as 65%: the high empirical FWERs were not a consequence of parametric methods failing to model spatial smoothness accurately, but rather of these very large clusters that are inherently present in smooth, high-dimensional random fields. In fact, when discounting these very large clusters, the empirical FWER for parametric methods was 3.24%. Furthermore, even an empirical FWER of 65% would yield on average less than one of those very large clusters in each brain-wide analysis. Nonparametric methods, in contrast, estimated distributions from those large clusters, and therefore, by construct rejected the large clusters as false positives at the nominal FWERs. Those rejected clusters were outlying values in the distribution of cluster size but cannot be distinguished from true positive findings without further analyses, including assessing whether fMRI signal in those regions correlates with other clinical, behavioral, or cognitive measures. Rejecting the large clusters, however, significantly reduced the statistical power of nonparametric methods in detecting true findings compared with parametric methods, which would have detected most true findings that are essential for making valid biological inferences in MRI data. Parametric analyses, in contrast, detected most true findings while generating relatively few false positives: on average, less than one of those very large clusters would be deemed a true finding in each brain-wide analysis. We therefore recommend the continued use of parametric methods that model nonstationary smoothness for cluster-level, familywise control of false positives, particularly when using a Cluster Defining Threshold of 2.5 or higher, and subsequently assessing rigorously the biological plausibility of the findings, even for large clusters. Finally, because nonparametric methods yielded a large reduction in statistical power to detect true positive findings, we conclude that the modest reduction in false positive findings that nonparametric analyses afford does not warrant a re-analysis of previously published fMRI studies using nonparametric techniques.

Serotonin in psychiatry: in vitro disease modeling using patient-derived neurons

Several lines of evidence implicate serotonin in the etiology of multiple psychiatric disorders, especially mood disorders, such as major depressive disorder (MDD) and bipolar disorder (BD). Much of our current understanding of biological mechanisms underlying serotonergic alterations in mood disorders comes from animal studies. Innovation in induced pluripotent stem cell and transdifferentiation technologies for deriving neurons from adult humans has enabled the study of disease-relevant cellular phenotypes in vitro. In this context, human serotonergic neurons can now be generated using three recently published methodologies. In this mini-review, we broadly discuss evidence linking altered serotonergic neurotransmission in MDD and BD and focus on recently published methods for generating human serotonergic neurons in vitro.