A central challenge in the fMRI based study of functional connectivity is distinguishing neuronally related signal fluctuations from the effects of motion, physiology, and other nuisance sources. Conventional techniques for removing nuisance effects include modeling of noise time courses based on external measurements followed by temporal filtering. These techniques have limited effectiveness. Previous studies have shown using multi-echo fMRI that neuronally related fluctuations are Blood Oxygen Level Dependent (BOLD) signals that can be characterized in terms of changes in R2* and initial signal intensity (S0) based on the analysis of echo-time (TE) dependence. We hypothesized that if TE-dependence could be used to differentiate BOLD and non-BOLD signals, non-BOLD signal could be removed to denoise data without conventional noise modeling. To test this hypothesis, whole brain multi-echo data were acquired at 3 TEs and decomposed with Independent Components Analysis (ICA) after spatially concatenating data across space and TE. Components were analyzed for the degree to which their signal changes fit models for R2* and S0 change, and summary scores were developed to characterize each component as BOLD-like or not BOLD-like. These scores clearly differentiated BOLD-like “functional network” components from non BOLD-like components related to motion, pulsatility, and other nuisance effects. Using non BOLD-like component time courses as noise regressors dramatically improved seed-based correlation mapping by reducing the effects of high and low frequency non-BOLD fluctuations. A comparison with seed-based correlation mapping using conventional noise regressors demonstrated the superiority of the proposed technique for both individual and group level seed-based connectivity analysis, especially in mapping subcortical-cortical connectivity. The differentiation of BOLD and non-BOLD components based on TE-dependence was highly robust, which allowed for the identification of BOLD-like components and the removal of non BOLD-like components to be implemented as a fully automated procedure.

Letter and category fluency tasks are used to assess semantic knowledge, retrieval ability, and executive functioning. They appear to be useful in detecting different types of dementia, but accurate detection of neuropsychological impairment relies on appropriate normative data. Multiple regression analysis was used to develop demographically corrected norms for letter and category fluency in 768 normal adults. T-score equations were developed on a base subsample of 403, and crossvalidated on a separate subsample (n = 365). Participants ranged in age from 20 years to 101 years; in educational level from 0 to 20 years; 55% were Caucasian and 45% were African American. Together, age, education, and ethnicity were significant predictors of letter and category fluency performance, accounting for 15% and 25% of variance, respectively. Formulas and tables for converting raw fluency scores to demographically corrected T scores are presented.

Neuropsychiatric symptoms (NPS) in Alzheimer’s disease (AD) are widespread and disabling. This has been known since Dr. Alois Alzheimer’s first case, Frau Auguste D., presented with emotional distress and delusions of infidelity/excessive jealousy, followed by cognitive symptoms. Being cognizant of this, in 2010 the Alzheimer’s Association convened a Research Roundtable on the topic of NPS in AD. A major outcome of the Roundtable was the founding of a Professional Interest Area (PIA) within the International Society to Advance Alzheimer’s Research and Treatment (ISTAART). The NPS-PIA has prepared a series of documents that are intended to summarize the literature and provide more detailed specific recommendations for NPS research. This overview paper is the first of these living documents that will be updated periodically as the science advances. The overview is followed by syndrome specific synthetic reviews and recommendations prepared by NPS-PIA Workgroups on depression, apathy, sleep, agitation, and psychosis.

A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of largescale data sharing for mental health research.

Connectivity changes in the insula in subjects with MDD suggest that ketamine may normalize the interaction between the DMN and salience networks, supporting the triple network dysfunction model of MDD.

The California Verbal Learning Test (CVLT) is designed to quantify components of verbal learning, retention and retrieval. The present study used multiple regression analyses to correct for demographic characteristics on CVLT performance measures. There were 906 subjects, of whom 549 were Caucasians (61%) and 357 were African Americans (39%). Age, education, ethnicity, and gender were found to be significant predictors of performance on several CVLT indices, including Total Words Recalled, Trial 1, Trial 5, List B, Short Delay Free Recall (SDFR), and Long Delay Free Recall (LDFR). Demographically corrected T-scores were calculated for a base sample of 672 subjects and cross-validated on 234 separate subjects. Tables and regression equations are offered to convert raw scores into T-scores corrected for age, gender, education, and ethnicity. Demographically corrected Recognition Discriminability cutoff scores were calculated for age and education levels. In order to provide some indices of important memory processes, we also computed indices of retrieval, Short-Delay forgetting and Long-Delay forgetting and present normative information for them.

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