Hippocampal morphology and cognitive functions in community-dwelling older people: the Lothian Birth Cohort 1936

Altered Sex Differences in Hippocampal Subfield Volumes in Schizophrenia

BACKGROUND AND HYPOTHESIS

The hippocampus is a heterogenous brain structure that differs between the sexes and has been implicated in the pathophysiology of psychiatric illnesses. Here, we explored sex and diagnostic group differences in hippocampal subfield volumes, in individuals with schizophrenia spectrum disorder (SZ), bipolar disorders (BD), and healthy controls (CTL).

STUDY DESIGN

One thousand and five hundred and twenty-one participants underwent T1-weighted magnetic resonance imaging (SZ, n = 452, mean age 30.7 ± 9.2 [SD] years, males 59.1%; BD, n = 316, 33.7 ± 11.4, 41.5%; CTL, n = 753, 34.1 ± 9.1, 55.6%). Total hippocampal, subfield, and intracranial volumes were estimated with Freesurfer (v6.0.0). Analysis of covariance and multiple regression models were fitted to examine sex-by-diagnostic (sub)group interactions in volume. In SZ and BD, separately, associations between volumes and clinical as well as cognitive measures were examined between the sexes using regression models.

STUDY RESULTS

Significant sex-by-group interactions were found for the total hippocampus, dentate gyrus, molecular layer, presubiculum, fimbria, hippocampal-amygdaloid transition area, and CA4, indicating a larger volumetric deficit in male patients relative to female patients when compared with same-sex CTL. Subgroup analyses revealed that this interaction was driven by males with schizophrenia. Effect sizes were overall small (partial η < 0.02). We found no significant sex differences in the associations between hippocampal volumes and clinical or cognitive measures in SZ and BD.

CONCLUSIONS

Using a well-powered sample, our findings indicate that the pattern of morphological sex differences in hippocampal subfields is altered in individuals with schizophrenia relative to CTL, due to higher volumetric deficits in males.

Is Hippocampal Volume a Relevant Early Marker of Dementia?

OBJECTIVE

Hippocampal volume (HV) is a key imaging marker to improve Alzheimer's disease risk prediction. However, longitudinal studies are rare, and hippocampus may also be implicated in the subtle aging-related cognitive decline observed in dementia-free individuals. Our aim was to determine whether HV, measured by manual or automatic segmentation, is associated with dementia risk and cognitive decline in participants with and without incident dementia.

METHODS

At baseline, 510 dementia-free participants from the French longitudinal ESPRIT cohort underwent magnetic resonance imaging. HV was measured by manual and by automatic segmentation (FreeSurfer 6.0). The presence of dementia and cognitive functions were investigated at each follow-up (2, 4, 7, 10, 12, and 15 years). Cox proportional hazards models and linear mixed models were used to assess the association of HV with dementia risk and with cognitive decline, respectively.

RESULTS

During the 15-years follow-up, 42 participants developed dementia. Reduced HV (regardless of the measurement method) was significantly associated with higher dementia risk and cognitive decline in the whole sample. However, only the automatically measured HV was associated with cognitive decline in dementia-free participants.

CONCLUSION

These results suggest that HV can be used to predict the long-term risk of dementia but also cognitive decline in a dementia-free population. This raises the question of the relevance of HV measurement as an early marker of dementia in the general population.

Piecing it together: atrophy profiles of hippocampal subfields relate to cognitive impairment along the Alzheimer's disease spectrum

Introduction

People with Alzheimer's disease (AD) experience more rapid declines in their ability to form hippocampal-dependent memories than cognitively normal healthy adults. Degeneration of the whole hippocampal formation has previously been found to covary with declines in learning and memory, but the associations between subfield-specific hippocampal neurodegeneration and cognitive impairments are not well characterized in AD. To improve prognostic procedures, it is critical to establish in which hippocampal subfields atrophy relates to domain-specific cognitive declines among people along the AD spectrum. In this study, we examine high-resolution structural magnetic resonance imaging (MRI) of the medial temporal lobe and extensive neuropsychological data from 29 amyloid-positive people on the AD spectrum and 17 demographically-matched amyloid-negative healthy controls.

Methods

Participants completed a battery of neuropsychological exams including select tests of immediate recollection, delayed recollection, and general cognitive status (i.e., performance on the Mini-Mental State Examination [MMSE] and Montreal Cognitive Assessment [MoCA]). Hippocampal subfield volumes (CA1, CA2, CA3, dentate gyrus, and subiculum) were measured using a dedicated MRI slab sequence targeting the medial temporal lobe and used to compute distance metrics to quantify AD spectrum-specific atrophic patterns and their impact on cognitive outcomes.

Results

Our results replicate prior studies showing that CA1, dentate gyrus, and subiculum hippocampal subfield volumes were significantly reduced in AD spectrum participants compared to amyloid-negative controls, whereas CA2 and CA3 did not exhibit such patterns of atrophy. Moreover, degeneration of the subiculum along the AD spectrum was linked to a significant decline in general cognitive status measured by the MMSE, while degeneration scores of the CA1 and dentate gyrus were more widely associated with declines on the MMSE and tests of learning and memory.

Discussion

These findings provide evidence that subfield-specific patterns of hippocampal degeneration, in combination with cognitive assessments, may constitute a sensitive prognostic approach and could be used to better track disease trajectories among individuals on the AD spectrum.

Predictive models demonstrate age-dependent association of subcortical volumes and cognitive measures

Whether brain matter volume is correlated with cognitive functioning and higher intelligence is controversial. We explored this relationship by analysis of data collected on 193 healthy young and older adults through the "Leipzig Study for Mind-Body-Emotion Interactions" (LEMON) study. Our analysis involved four cognitive measures: fluid intelligence, crystallized intelligence, cognitive flexibility, and working memory. Brain subregion volumes were determined by magnetic resonance imaging. We normalized each subregion volume to the estimated total intracranial volume and conducted training simulations to compare the predictive power of normalized volumes of large regions of the brain (i.e., gray matter, cortical white matter, and cerebrospinal fluid), normalized subcortical volumes, and combined normalized volumes of large brain regions and normalized subcortical volumes. Statistical tests showed significant differences in the performance accuracy and feature importance of the subregion volumes in predicting cognitive skills for young and older adults. Random forest feature selection analysis showed that cortical white matter was the key feature in predicting fluid intelligence in both young and older adults. In young adults, crystallized intelligence was best predicted by caudate nucleus, thalamus, pallidum, and nucleus accumbens volumes, whereas putamen, amygdala, nucleus accumbens, and hippocampus volumes were selected for older adults. Cognitive flexibility was best predicted by the caudate, nucleus accumbens, and hippocampus in young adults and caudate and amygdala in older adults. Finally, working memory was best predicted by the putamen, pallidum, and nucleus accumbens in the younger group, whereas amygdala and hippocampus volumes were predictive in the older group. Thus, machine learning predictive models demonstrated an age-dependent association between subcortical volumes and cognitive measures. These approaches may be useful in predicting the likelihood of age-related cognitive decline and in testing of approaches for targeted improvement of cognitive functioning in older adults.

Structural brain imaging correlates of general intelligence in UK Biobank

The associations between indices of brain structure and measured intelligence are unclear. This is partly because the evidence to-date comes from mostly small and heterogeneous studies. Here, we report brain structure-intelligence associations on a large sample from the UK Biobank study. The overall N = 29,004, with N = 18,426 participants providing both brain MRI and at least one cognitive test, and a complete four-test battery with MRI data available in a minimum N = 7201, depending upon the MRI measure. Participants' age range was 44–81 years (M = 63.13, SD = 7.48). A general factor of intelligence (g) was derived from four varied cognitive tests, accounting for one third of the variance in the cognitive test scores. The association between (age- and sex- corrected) total brain volume and a latent factor of general intelligence is r = 0.276, 95% C.I. = [0.252, 0.300]. A model that incorporated multiple global measures of grey and white matter macro- and microstructure accounted for more than double the g variance in older participants compared to those in middle-age (13.6% and 5. 4%, respectively). There were no sex differences in the magnitude of associations between g and total brain volume or other global aspects of brain structure. The largest brain regional correlates of g were volumes of the insula, frontal, anterior/superior and medial temporal, posterior and paracingulate, lateral occipital cortices, thalamic volume, and the white matter microstructure of thalamic and association fibres, and of the forceps minor. Many of these regions exhibited unique contributions to intelligence, and showed highly stable out of sample prediction.

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We used a large sample from UK Biobank (N = 29,004, age range = 44–81 years).

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The association between brain volume and intelligence (‘g’) was r = 0.276.

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Multiple global tissue measures explained twice the g variance in older than middle age.

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The size of the association between g and global brain measures did not vary by sex.

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We investigate the regional cortical, subcortical and white matter correlates of g.

Hierarchical complexity of the adult human structural connectome

The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have largely confounded attempts towards comprehensive constructive modelling. This calls for new perspectives. Hierarchical complexity is an emerging paradigm of complex network topology based on the observation that complex systems are composed of hierarchies within which the roles of hierarchically equivalent nodes display highly variable connectivity patterns. Here we test the hierarchical complexity of the human structural connectomes of a group of seventy-nine healthy adults. Binary connectomes are found to be more hierarchically complex than three benchmark random network models. This provides a new key description of brain structure, revealing a rich diversity of connectivity patterns within hierarchically equivalent nodes. Dividing the connectomes into four tiers based on degree magnitudes indicates that the most complex nodes are neither those with the highest nor lowest degrees but are instead found in the middle tiers. Spatial mapping of the brain regions in each hierarchical tier reveals consistency with the current anatomical, functional and neuropsychological knowledge of the human brain. The most complex tier (Tier 3) involves regions believed to bridge high-order cognitive (Tier 1) and low-order sensorimotor processing (Tier 2). We then show that such diversity of connectivity patterns aligns with the diversity of functional roles played out across the brain, demonstrating that hierarchical complexity can characterise functional diversity strictly from the network topology.

Effects of short and long term electromagnetic fields exposure on the human hippocampus

The increasing use of mobile phones may have a number of physiological and psychological effects on human health. Many animal and human studies have reported various effects on the central nervous system and cognitive performance from of exposure to electromagnetic fields (EMF) emitted by mobile phones. The aim of the present study was to evaluate the effects of mobile phones on the morphology of the human brain and on cognitive performance using stereological and spectroscopic methods and neurocognitive tests. Sixty healthy female medical school students aged 18-25 years were divided into a low exposure group (30 subjects, <30 min daily use by the head) and high exposure group (30 subjects, >90 min daily use by the head). Magnetic resonance images (MRI) of the brain analysed on OsiriX 3.2.1 workstation. Neuropsychological tests were performed for each subject. In addition, three dominant specific metabolites were analysed, choline at 3.21 ppm, creatine at 3.04 ppm and N-acetyl aspartate at 2.02 ppm. Analysis of the spectroscopic results revealed no significant difference in specific metabolites between the groups (p > 0.05). There was also no significant difference in terms of hippocampal volume between the groups (p > 0.05). In contrast, the results of the stroop and digit span (backward) neurocognitive tests of high exposure group for evaluating attention were significantly poorer from low exposure group (p < 0.05). Based on these results, we conclude that a lack of attention and concentration may occur in subjects who talk on mobile phones for longer times, compared to those who use phones relatively less.