Modulation of functional network properties in major depressive disorder following electroconvulsive therapy (ECT): a resting-state EEG analysis

Electroconvulsive therapy (ECT) is a highly effective neuromodulatory intervention for treatment-resistant major depressive disorder (MDD). Presently, however, understanding of its neurophysiological effects remains incomplete. In the present study, we utilised resting-state electroencephalography (RS-EEG) to explore changes in functional connectivity, network topology, and spectral power elicited by an acute open-label course of ECT in a cohort of 23 patients with treatment-resistant MDD. RS-EEG was recorded prior to commencement of ECT and again within 48 h following each patient’s final treatment session. Our results show that ECT was able to enhance connectivity within lower (delta and theta) frequency bands across subnetworks largely confined to fronto-central channels, while, conversely, more widespread subnetworks of reduced connectivity emerged within faster (alpha and beta) bands following treatment. Graph-based topological analyses revealed changes in measures of functional segregation (clustering coefficient), integration (characteristic path length), and small-world architecture following ECT. Finally, post-treatment enhancement of delta and theta spectral power was observed, which showed a positive association with the number of ECT sessions received. Overall, our findings indicate that RS-EEG can provide a sensitive measure of dynamic neural activity following ECT and highlight network-based analyses as a promising avenue for furthering mechanistic understanding of the effects of convulsive therapies

Myelin-Associated Glycoprotein Gene and Brain Morphometry in Schizophrenia

Myelin and oligodendrocyte disruption may be a core feature of schizophrenia pathophysiology. The purpose of the present study was to localize the effects of previously identified risk variants in the myelin-associated glycoprotein (MAG) gene on brain morphometry in schizophrenia patients and healthy controls. Forty-five schizophrenia patients and 47 matched healthy controls underwent clinical, structural magnetic resonance imaging, and genetics procedures. Gray and white matter cortical lobe volumes along with hippocampal volumes were calculated from T1-weighted MRI scans. Each subject was also genotyped for the two disease-associated MAG single nucleotide polymorphisms (rs720308 and rs720309). Repeated measures general linear model (GLM) analysis found significant region by genotype and region by genotype by diagnosis interactions for the effects of MAG risk variants on lobar gray matter volumes. No significant associations were found with lobar white matter volumes or hippocampal volumes. Follow-up univariate GLMs found the AA genotype of rs720308 predisposed schizophrenia patients to left temporal and parietal gray matter volume deficits. These results suggest that the effects of the MAG gene on cortical gray matter volume in schizophrenia patients can be localized to temporal and parietal cortices. Our results support a role for MAG gene variation in brain morphometry in schizophrenia, align with other lines of evidence implicating MAG in schizophrenia, and provide genetically based insight into the heterogeneity of brain imaging findings in this disorder

The SORL1 gene and convergent neural risk for Alzheimer\u27s disease across the human lifespan

Prior to intervention trials in individuals genetically at-risk for late-onset Alzheimer\u27s disease, critical first steps are identifying where (neuroanatomic effects), when (timepoint in the lifespan) and how (gene expression and neuropathology) Alzheimer\u27s risk genes impact the brain. We hypothesized that variants in the sortilin-like receptor (SORL1) gene would affect multiple Alzheimer\u27s phenotypes before the clinical onset of symptoms. Four independent samples were analyzed to determine effects of SORL1 genetic risk variants across the lifespan at multiple phenotypic levels: (1) microstructural integrity of white matter using diffusion tensor imaging in two healthy control samples (n = 118, age 18-86; n = 68, age 8-40); (2) gene expression using the Braincloud postmortem healthy control sample (n = 269, age 0-92) and (3) Alzheimer\u27s neuropathology (amyloid plaques and tau tangles) using a postmortem sample of healthy, mild cognitive impairment (MCI) and Alzheimer\u27s individuals (n = 710, age 66-108). SORL1 risk variants predicted lower white matter fractional anisotropy in an age-independent manner in fronto-temporal white matter tracts in both samples at 5% family-wise error-corrected thresholds. SORL1 risk variants also predicted decreased SORL1 mRNA expression, most prominently during childhood and adolescence, and significantly predicted increases in amyloid pathology in postmortem brain. Importantly, the effects of SORL1 variation on both white matter microstructure and gene expression were observed during neurodevelopmental phases of the human lifespan. Further, the neuropathological mechanism of risk appears to primarily involve amyloidogenic pathways. Interventions targeted toward the SORL1 amyloid risk pathway may be of greatest value during early phases of the lifespan

Fuzzy Fibers: Uncertainty in dMRI Tractography

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research

The XVth World Congress of Psychiatric Genetics, October 7–11, 2007: Rapporteur summaries of oral presentations

The World Congress of Psychiatric Genetics (WCPG) has become an annual event since the early 1990's sponsored by the International Society of Psychiatric Genetics (ISPG). Each year the latest published and unpublished findings are aired for discussion by representatives of the majority of research programs on this topic world-wide. The 2007 congress was held in New York City and attracted over 1000 researchers. The topics emphasized included results from whole genome association studies, the significance of copy number variation and the important contributions of epigenetic events to psychiatric disorders. There were over 20 oral sessions devoted to these and other topics of interest. Young investigator recipients of travel awards served as rapporteurs to summarize sessions and these summaries follow.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58040/1/30711_ftp.pd

Genetic epistasis regulates amyloid deposition in resilient aging

AbstractIntroduction The brain-derived neurotrophic factor (BDNF) interacts with important genetic Alzheimer's disease (AD) risk factors. Specifically, variants within the SORL1 gene determine BDNF's ability to reduce amyloid β (Aβ) in vitro. We sought to test whether functional BDNF variation interacts with SORL1 genotypes to influence expression and downstream AD-related processes in humans. Methods We analyzed postmortem brain RNA sequencing and neuropathological data for 441 subjects from the Religious Orders Study/Memory and Aging Project and molecular and structural neuroimaging data for 1285 subjects from the Alzheimer's Disease Neuroimaging Initiative. Results We found one SORL1 RNA transcript strongly regulated by SORL1-BDNF interactions in elderly without pathological AD and showing stronger associations with diffuse than neuritic Aβ plaques. The same SORL1-BDNF interactions also significantly influenced Aβ load as measured with [18F]Florbetapir positron emission tomography. Discussion Our results bridge the gap between risk and resilience factors for AD, demonstrating interdependent roles of established SORL1 and BDNF functional genotypes

Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning?:A multi-method and multi-dataset study

Machine learning is a powerful tool that has previously been used to classify schizophrenia (SZ) patients from healthy controls (HC) using magnetic resonance images. Each study, however, uses different datasets, classification algorithms, and validation techniques. Here, we perform a critical appraisal of the accuracy of machine learning methodologies used in SZ/HC classifications studies by comparing three machine learning algorithms (logistic regression [LR], support vector machines [SVMs], and linear discriminant analysis [LDA]) on three independent datasets (435 subjects total) using two tissue density estimates and cortical thickness (CT). Performance is assessed using 10-fold cross-validation, as well as a held-out validation set. Classification using CT outperformed tissue densities, but there was no clear effect of dataset. LR, SVMs, and LDA each yielded the highest accuracies for a different feature set and validation paradigm, but most accuracies were between 55 and 70%, well below previously reported values. The highest accuracy achieved was 73.5% using CT data and an SVM. Taken together, these results illustrate some of the obstacles to constructing effective disease classifiers, and suggest that tissue densities and CT may not be sufficiently sensitive for SZ/HC classification given current available methodologies and sample sizes

Recurring alcohol-related care between 1998 and 2007 among people treated for an alcohol-related disorder in 1997: A register study in Stockholm County

<p>Abstract</p> <p>Background</p> <p>Inpatient care for alcohol intoxication is increasing in Sweden, especially among young women. Since it is well known that alcohol disorder is a chronic relapsing illness, this study examines the extent to which people return for more care.</p> <p>Method</p> <p>All inpatients with alcohol-related diagnoses in Stockholm County during 1997 were followed prospectively to 2007 through registers. The proportion reappearing for the same diagnosis, other alcohol-related inpatient, or outpatient care each year after baseline, as well as the number of years the inpatients reappeared were calculated (n = 2735). Three diagnoses were examined separately; alcohol dependence, harmful use of alcohol, and alcohol intoxication.</p> <p>Results</p> <p>Three out of five inpatients with an alcohol diagnoses reappeared for more alcohol-related inpatient care during the following decade. The proportion returning was largest the year after baseline and then decreased curvilinearly over time. The inclusion of outpatient care increased proportions, but did not change patterns. Of those with an alcohol dependence diagnosis at baseline 42 percent returned for more alcohol-related inpatient care the first, 28 percent the fifth, and 25 percent the tenth year. Corresponding proportions for harmful use and intoxication were smaller. One in five among those with an alcohol dependence returned for more than five of the ten years. Ordered logistic regressions confirmed that besides diagnosis, age and gender were independently related to the number of years returning to care.</p> <p>Conclusions</p> <p>While middle-aged males with alcohol dependence were in a revolving door, young female inpatients with intoxication diagnosis returned to a comparably lower degree.</p