The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB’s ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures were assessed using timeseries (amplitude and spectra), network matrix and spatial map analyses. For timeseries and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.

HighlightsThis meta-analysis confirms a robust link between IL-6, CRP and major depression.The role of TNF-α and IL-1β in major depression remains uncertain.Further mechanistic and immunotherapeutic studies on IL-6 and CRP are needed.

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Objectives To estimate 10 year decline in cognitive function from longitudinal data in a middle aged cohort and to examine whether age cohorts can be compared with cross sectional data to infer the effect of age on cognitive decline.Design Prospective cohort study. At study inception in 1985-8, there were 10 308 participants, representing a recruitment rate of 73%.Setting Civil service departments in London, United Kingdom.Participants 5198 men and 2192 women, aged 45-70 at the beginning of cognitive testing in 1997-9.Main outcome measure Tests of memory, reasoning, vocabulary, and phonemic and semantic fluency, assessed three times over 10 years.Results All cognitive scores, except vocabulary, declined in all five age categories (age 45-49, 50-54, 55-59, 60-64, and 65-70 at baseline), with evidence of faster decline in older people. In men, the 10 year decline, shown as change/range of test×100, in reasoning was −3.6% (95% confidence interval −4.1% to −3.0%) in those aged 45-49 at baseline and −9.6% (−10.6% to −8.6%) in those aged 65-70. In women, the corresponding decline was −3.6% (−4.6% to −2.7%) and −7.4% (−9.1% to −5.7%). Comparisons of longitudinal and cross sectional effects of age suggest that the latter overestimate decline in women because of cohort differences in education. For example, in women aged 45-49 the longitudinal analysis showed reasoning to have declined by −3.6% (−4.5% to −2.8%) but the cross sectional effects suggested a decline of −11.4% (−14.0% to −8.9%). ConclusionsCognitive decline is already evident in middle age (age 45-49).

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Depressive symptoms in the early phase of the study corresponding to midlife, even when chronic/recurring, do not increase the risk for dementia. Along with our analysis of depressive trajectories over 28 years, these results suggest that depressive symptoms are a prodromal feature of dementia or that the 2 share common causes. The findings do not support the hypothesis that depressive symptoms increase the risk for dementia.