Locus Coeruleus magnetic resonance imaging in cognitively intact elderly subjects

The locus coeruleus is the main noradrenergic nucleus of the brain and is often affected in neurodegenerative diseases. Recently, magnetic resonance imaging with specific T1-weighted sequences for neuromelanin has been used to evaluate locus coeruleus integrity in patients with these conditions. In some of these studies, abnormalities in locus coeruleus signal have also been found in healthy controls and related to ageing. However, this would be at variance with recent post-mortem studies showing that the nucleus is not affected during normal ageing. The present study aimed at evaluating locus coeruleus features in a well-defined cohort of cognitively healthy subjects who remained cognitively intact on a one-year follow-up. An ad-hoc semiautomatic analysis of locus coeruleus magnetic resonance was applied. Sixty-two cognitively intact subjects aged 60-80 years, without significant comorbidities, underwent 3 T magnetic resonance with specific sequences for locus coeruleus. A semi-automatic tool was used to estimate the number of voxels belonging to locus coeruleus and its intensity was obtained for each subject. Each subject underwent extensive neuropsychological testing at baseline and 12 months after magnetic resonance scan. Based on neuropsychological testing 53 subjects were cognitively normal at baseline and follow up. No significant age-related differences in locus coeruleus parameters were found in this cohort. In line with recent post-mortem studies, our in vivo study confirms that locus coeruleus magnetic resonance features are not statistically significantly affected by age between 60 and 80 years, the age range usually evaluated in studies on neurodegenerative diseases. A significant alteration of locus coeruleus features in a cognitively intact elderly subject might be an early sign of pathology.

Data-driven derivation of natural EEG frequency components: An optimised example assessing resting EEG in healthy ageing

BACKGROUND

The majority of electroencephalographic (EEG) investigations in normal ageing have determined EEG spectra from epochs recorded in the eyes-closed (EC) and/or eyes-open (EO) resting states, and summed amplitudes or power estimates within somewhat-arbitrary and/or inconsistently defined traditional frequency band limits.

NEW METHOD

Natural frequency components were sought using a data-driven frequency Principal Components Analysis (f-PCA) approach, optimised to reduce between-condition and between-group misallocation of variance. Frequency component correspondence was screened using the Congruence Coefficient and topographic correlations for potential matches on Condition and/or Group. The amplitudes of corresponding natural components were then explored as a function of these independent variables.

RESULTS

Separate f-PCAs with Young and Older adults' EC and EO data each yielded between six and nine components that peaked across the traditional delta to beta band ranges. Across these, two components were matched on Group and Condition, while a further six were matched on Condition (within-groups), and four on Group (within-conditions).

COMPARISON WITH EXISTING METHODS

Multiple frequency components were found within the traditional bands, and provided a wider perspective in terms of additional natural component details. In addition to novel insights, the well-documented age-related spectral reductions were seen in the common delta component, and in one EC (but no EO) alpha component.

CONCLUSIONS

This combination of optimised f-PCA approach and component screening procedure has wide potential in the EEG field beyond the ageing topic explored here, being applicable across an extensive range of studies using EEG oscillations to explore aspects of cognitive processing and individual differences.

Longitudinal Assessment of Hippocampal Atrophy in Midlife and Early Old Age: Contrasting Manual Tracing and Semi-automated Segmentation (FreeSurfer)

It is important to have accurate estimates of normal age-related brain structure changes and to understand how the choice of measurement technique may bias those estimates. We compared longitudinal change in hippocampal volume, laterality and atrophy measured by manual tracing and FreeSurfer (version 5.3) in middle age (n = 244, 47.2[1.4] years) and older age (n = 199, 67.0[1.4] years) individuals over 8 years. The proportion of overlap (Dice coefficient) between the segmented hippocampi was calculated and we hypothesised that the proportion of overlap would be higher for older individuals as a consequence of higher atrophy. Hippocampal volumes produced by FreeSurfer were larger than manually traced volumes. Both methods produced a left less than right volume laterality difference. Over time this laterality difference increased for manual tracing and decreased for FreeSurfer leading to laterality differences in left and right estimated atrophy rates. The overlap proportion between methods was not significantly different for older individuals, but was greater for the right hippocampus. Estimated middle age annualised atrophy rates were - 0.39(1.0) left, 0.07(1.01) right, - 0.17(0.88) total for manual tracing and - 0.15(0.69) left, - 0.20(0.63) right, - 0.18(0.57) total for FreeSurfer. Older age atrophy rates were - 0.43(1.32) left, - 0.15(1.41) right, - 0.30 (1.23) total for manual tracing and - 0.34(0.79) left, - 0.68(0.78) right, - 0.51(0.65) total for FreeSurfer. FreeSurfer reliably segments the hippocampus producing atrophy rates that are comparable to manual tracing with some biases that need to be considered in study design. FreeSurfer is suited for use in large longitudinal studies where it is not cost effective to use manual tracing.

Callosal circularity as an early marker for Alzheimer's disease

Background

Although brain atrophy is considered to be a downstream marker of Alzheimer's disease (AD), subtle changes may allow to identify healthy subjects at risk of developing AD. As the ability to select at-risk persons is considered to be important to assess the efficacy of drugs and as MRI is a widely available imaging technique we have recently developed a reliable segmentation algorithm for the corpus callosum (CC). Callosal atrophy within AD has been hypothesized to reflect both myelin breakdown and Wallerian degeneration.

Methods

We applied our fully automated segmentation and feature extraction algorithm to two datasets: the OASIS database consisting of 316 healthy controls (HC) and 100 patients affected by either mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD) and a second database that was collected at the Memory Clinic of Hospital Network Antwerp and consists of 181 subjects, including healthy controls, subjects with subjective cognitive decline (SCD), MCI, and ADD. All subjects underwent (among others) neuropsychological testing including the Mini-Mental State Examination (MMSE). The extracted features were the callosal area (CCA), the circularity (CIR), the corpus callosum index (CCI) and the thickness profile.

Results

CIR and CCI differed significantly between most groups. Furthermore, CIR allowed us to discriminate between SCD and HC with an accuracy of 77%. The more detailed callosal thickness profile provided little added value towards the discrimination of the different AD stages. The largest effect of normal ageing on callosal thickness was found in the frontal callosal midbody.

Conclusions

To the best of our knowledge, this is the first study investigating changes in corpus callosum morphometry in normal ageing and AD by exploring both summarizing features (CCA, CIR and CCI) and the complete CC thickness profile in two independent cohorts using a completely automated algorithm. We showed that callosal circularity allows to discriminate between an important subgroup of the early AD spectrum (SCD) and age and sex matched healthy controls.

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Callosal circularity allows to discriminate between subjects with subjective cognitive decline and matched healthy controls

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Callosal circularity is smaller in subjects with AD dementia as compared to matched subjects with mild cognitive impairment

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The callosal thickness profile differs between AD and HC, but not between the different clinical AD stages

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The AD thickness profile strongly correlates with age in HCs

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Callosal circularity correlates with CSF biomarkers (T-tau and P-tau) in MCI.

The impact of ageing and gender on visual mental imagery processes: A study of performance on tasks from the Complete Visual Mental Imagery Battery (CVMIB)

In this study we aim to evaluate the impact of ageing and gender on different visual mental imagery processes. Two hundred and fifty-one participants (130 women and 121 men; age range = 18-77 years) were given an extensive neuropsychological battery including tasks probing the generation, maintenance, inspection, and transformation of visual mental images (Complete Visual Mental Imagery Battery, CVMIB). Our results show that all mental imagery processes with the exception of the maintenance are affected by ageing, suggesting that other deficits, such as working memory deficits, could account for this effect. However, the analysis of the transformation process, investigated in terms of mental rotation and mental folding skills, shows a steeper decline in mental rotation, suggesting that age could affect rigid transformations of objects and spare non-rigid transformations. Our study also adds to previous ones in showing gender differences favoring men across the lifespan in the transformation process, and, interestingly, it shows a steeper decline in men than in women in inspecting mental images, which could partially account for the mixed results about the effect of ageing on this specific process. We also discuss the possibility to introduce the CVMIB in clinical assessment in the context of theoretical models of mental imagery.

A brain imaging repository of normal structural MRI across the life course: Brain Images of Normal Subjects (BRAINS)

The Brain Images of Normal Subjects (BRAINS) Imagebank (http://www.brainsimagebank.ac.uk) is an integrated repository project hosted by the University of Edinburgh and sponsored by the Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) collaborators. BRAINS provide sharing and archiving of detailed normal human brain imaging and relevant phenotypic data already collected in studies of healthy volunteers across the life-course. It particularly focusses on the extremes of age (currently older age, and in future perinatal) where variability is largest, and which are under-represented in existing databanks. BRAINS is a living imagebank where new data will be added when available. Currently BRAINS contains data from 808 healthy volunteers, from 15 to 81years of age, from 7 projects in 3 centres. Additional completed and ongoing studies of normal individuals from 1st to 10th decades are in preparation and will be included as they become available. BRAINS holds several MRI structural sequences, including T1, T2, T2* and fluid attenuated inversion recovery (FLAIR), available in DICOM (http://dicom.nema.org/); in future Diffusion Tensor Imaging (DTI) will be added where available. Images are linked to a wide range of 'textual data', such as age, medical history, physiological measures (e.g. blood pressure), medication use, cognitive ability, and perinatal information for pre/post-natal subjects. The imagebank can be searched to include or exclude ranges of these variables to create better estimates of 'what is normal' at different ages.

Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements

Cognitive decline in normal ageing and Alzheimer's disease (AD) emerges from functional disruption in the coordination of large-scale brain systems sustaining cognition. Integrity of these systems can be examined by correlation methods based on analysis of resting state functional magnetic resonance imaging (fMRI). Here we investigate functional connectivity within the default mode network (DMN) in normal ageing and AD using resting state fMRI. Images from young and elderly controls, and patients with AD were processed using spatial independent component analysis to identify the DMN. Functional connectivity was quantified using integration and indices derived from graph theory. Four DMN sub-systems were identified: Frontal (medial and superior), parietal (precuneus-posterior cingulate, lateral parietal), temporal (medial temporal), and hippocampal (bilateral). There was a decrease in antero-posterior interactions (lower global efficiency), but increased interactions within the frontal and parietal sub-systems (higher local clustering) in elderly compared to young controls. This decreased antero-posterior integration was more pronounced in AD patients compared to elderly controls, particularly in the precuneus-posterior cingulate region. Conjoint knowledge of integration measures and graph indices in the same data helps in the interpretation of functional connectivity results, as comprehension of one measure improves with understanding of the other. The approach allows for complete characterisation of connectivity changes and could be applied to other resting state networks and different pathologies.