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Clarifying the relationship between depression symptoms and cardiometabolic and related health could clarify risk factors and treatment targets. The objective of this study was to assess whether depression symptoms in midlife are associated with the subsequent onset of cardiometabolic health problems.
The study sample comprised 787 male twin veterans with polygenic risk score data who participated in the Harvard Twin Study of Substance Abuse (‘baseline’) and the longitudinal Vietnam Era Twin Study of Aging (‘follow-up’). Depression symptoms were assessed at baseline [mean age 41.42 years (s.d. = 2.34)] using the Diagnostic Interview Schedule, Version III, Revised. The onset of eight cardiometabolic conditions (atrial fibrillation, diabetes, erectile dysfunction, hypercholesterolemia, hypertension, myocardial infarction, sleep apnea, and stroke) was assessed via self-reported doctor diagnosis at follow-up [mean age 67.59 years (s.d. = 2.41)].
Total depression symptoms were longitudinally associated with incident diabetes (OR 1.29, 95% CI 1.07–1.57), erectile dysfunction (OR 1.32, 95% CI 1.10–1.59), hypercholesterolemia (OR 1.26, 95% CI 1.04–1.53), and sleep apnea (OR 1.40, 95% CI 1.13–1.74) over 27 years after controlling for age, alcohol consumption, smoking, body mass index, C-reactive protein, and polygenic risk for specific health conditions. In sensitivity analyses that excluded somatic depression symptoms, only the association with sleep apnea remained significant (OR 1.32, 95% CI 1.09–1.60).
A history of depression symptoms by early midlife is associated with an elevated risk for subsequent development of several self-reported health conditions. When isolated, non-somatic depression symptoms are associated with incident self-reported sleep apnea. Depression symptom history may be a predictor or marker of cardiometabolic risk over decades.
Extracellular recordings have been, and still are, the main workhorse when measuring neural activity in vivo. In single-unit recordings sharp electrodes are positioned close to a neuronal soma, and the firing rate of this particular neuron is measured by counting spikes, that is, the standardized extracellular signatures of action potentials (Gold et al., 2006). For such recordings the interpretation of the measurements is straightforward, but complications arise when more than one neuron contributes to the recorded extracellular potential. For example, if two firing neurons of the same type are at about the same distance from their somas to the tip of the recording electrode, it may be very difficult to sort the spikes according to from which neuron they originate.
The use of two (stereotrode (McNaughton et al., 1983)), four (tetrode (Recce and O'Keefe, 1989;Wilson andMcNaughton, 1993; Gray et al., 1995; Jog et al., 2002)) or more (Buzsáki, 2004) close-neighbored recording sites allows for improved spike sorting, since the different distances from the electrode tips or contacts allow for triangulation. With present recording techniques and clustering methods one can sort out spike trains from tens of neurons from single tetrodes and from hundreds of neurons with multi-shank electrodes (Buzsáki, 2004).
Information about spiking is typically extracted from the high-frequency band (≳500 Hz) of extracellular potentials. Since these high-frequency signals generally stem from an unknown number of spiking neurons in the immediate vicinity of the electrode contact, this is called multi-unit activity (MUA).
Understanding the genetic and environmental contributions to measures of brain structure such as surface area and cortical thickness is important for a better understanding of the nature of brain-behavior relationships and changes due to development or disease. Continuous spatial maps of genetic influences on these structural features can contribute to our understanding of regional patterns of heritability, since it remains to be seen whether genetic contributions to brain structure respect the boundaries of any traditional parcellation approaches. Using data from magnetic resonance imaging scans collected on a large sample of monozygotic and dizygotic twins in the Vietnam Era Twin Study of Aging, we created maps of the heritability of areal expansion (a vertex-based area measure) and cortical thickness and examined the degree to which these maps were affected by adjustment for total surface area and mean cortical thickness. We also compared the approach of estimating regional heritability based on the average heritability of vertices within the region to the more traditional region-of-interest (ROI)-based approach. The results suggested high heritability across the cortex for areal expansion and, to a slightly lesser degree, for cortical thickness. There was a great deal of genetic overlap between global and regional measures for surface area, so maps of region-specific genetic influences on surface area revealed more modest heritabilities. There was greater inter-regional variability in heritabilities when calculated using the traditional ROI-based approach compared to summarizing vertex-by-vertex heritabilities within regions. Discrepancies between the approaches were greatest in small regions and tended to be larger for surface area than for cortical thickness measures. Implications regarding brain phenotypes for future genetic association studies are discussed.
The aim of the study was to investigate whether age affects visual
memory retention across extended time intervals. In addition, we wanted
to study how memory capabilities across different time intervals are
related to the volume of different neuroanatomical structures (right
hippocampus, right cortex, right white matter). One test of recognition
(CVMT) and one test of recall (Rey-Osterrieth Complex Figure Test) were
administered, giving measures of immediate recognition/recall,
20–30 min recognition/recall, and recognition/recall at a
mean of 75 days. Volumetric measures of right hemisphere hippocampus,
cortex, and white matter were obtained through an automated labelling
procedure of MRI recordings. Results did not demonstrate a steeper rate
of forgetting for older participants when the retention intervals were
increased, indicating that older people have spared ability to retain
information in the long-term store. Differences in neuroanatomical
volumes could explain up to 36% of the variance in memory performance,
but were not significantly related to rates of forgetting. Cortical
volume and hippocampal volume were in some cases independent as
predictors of memory function. Generally, cortical volume was a better
predictor of recognition memory than hippocampal volume, while the 2
structures did not differ in their predictive power of recall
abilities. While neuroanatomical volumetric differences can explain
some of the differences in memory functioning between younger and older
persons, the hippocampus does not seem to be unique in this respect.
(JINS, 2005, 11, 2–15.)
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