Miray Erbey

The age-dependent relationship between resting heart rate variability and functional brain connectivity

&NA; Resting heart rate variability (HRV), an index of parasympathetic cardioregulation and an individual trait marker related to mental and physical health, decreases with age. Previous studies have associated resting HRV with structural and functional properties of the brain – mainly in cortical midline and limbic structures. We hypothesized that aging affects the relationship between resting HRV and brain structure and function. In 388 healthy subjects of three age groups (140 younger: 26.0 ± 4.2 years, 119 middle‐aged: 46.3 ± 6.2 years, 129 older: 66.9 ± 4.7 years), gray matter volume (GMV, voxel‐based morphometry) and resting state functional connectivity (eigenvector centrality mapping and exploratory seed‐based functional connectivity) were related to resting HRV, measured as the root mean square of successive differences (RMSSD). Confirming previous findings, resting HRV decreased with age. For HRV‐related GMV, there were no statistically significant differences between the age groups, nor similarities across all age groups. In whole‐brain functional connectivity analyses, we found an age‐dependent association between resting HRV and eigenvector centrality in the bilateral ventromedial prefrontal cortex (vmPFC), driven by the younger adults. Across all age groups, HRV was positively correlated with network centrality in the bilateral posterior cingulate cortex. Seed‐based functional connectivity analysis using the vmPFC cluster revealed an HRV‐related cortico‐cerebellar network in younger but not in middle‐aged or older adults. Our results indicate that the decrease of HRV with age is accompanied by changes in functional connectivity along the cortical midline. This extends our knowledge of brain‐body interactions and their changes over the lifespan.

A functional connectome phenotyping dataset including cognitive state and personality measures

The dataset enables exploration of higher-order cognitive faculties, self-generated mental experience, and personality features in relation to the intrinsic functional architecture of the brain. We provide multimodal magnetic resonance imaging (MRI) data and a broad set of state and trait phenotypic assessments: mind-wandering, personality traits, and cognitive abilities. Specifically, 194 healthy participants (between 20 and 75 years of age) filled out 31 questionnaires, performed 7 tasks, and reported 4 probes of in-scanner mind-wandering. The scanning session included four 15.5-min resting-state functional MRI runs using a multiband EPI sequence and a high-resolution structural scan using a 3D MP2RAGE sequence. This dataset constitutes one part of the MPI-Leipzig Mind-Brain-Body database.

Association of Peripheral Blood Pressure with Grey Matter Volume in 19- to 40-Year-Old Adults

Objective To test whether elevated blood pressure (BP) relates to grey matter volume (GMV) changes in young adults who had not previously been diagnosed as hypertensive (systolic BP (SBP)/diastolic BP (DBP)≥140/90 mmHg). Methods We associated BP with GMV from structural 3 Tesla T1-weighted MRI of 423 healthy adults between 19-40 years (mean age=27.7±5.3 years, 177 women, SBP/DBP=123.2/73.4±12.2/8.5 mmHg). Data originated from four previously unpublished cross-sectional studies conducted in Leipzig, Germany. We performed voxel-based morphometry on each study separately and combined results in image-based meta-analyses (IBMA) to assess cumulative effects across studies. Resting BP was assigned to one of four categories: (1) SBP<120 and DBP<80 mmHg, (2) SBP 120-129 or DBP 80-84 mmHg, (3) SBP 130-139 or DBP 85-89 mmHg, (4) SBP≥140 or DBP≥90 mmHg. Results IBMA yielded: (a) lower regional GMV was correlated with higher peripheral BP; (b) lower GMV with higher BP when comparing individuals in sub-hypertensive categories 3 and 2, respectively, to those in category 1; (c) lower BP-related GMV was found in regions including hippocampus, amygdala, thalamus, frontal and parietal structures (e.g. precuneus). Conclusions BP≥120/80 mmHg was associated with lower GMV in regions that have previously been related to GM decline in older individuals with manifest hypertension. Our study shows that BP-associated GM alterations emerge continuously across the range of BP and earlier in adulthood than previously assumed. This suggests that treating hypertension or maintaining lower BP in early adulthood might be essential for preventing the pathophysiological cascade of asymptomatic cerebrovascular disease to symptomatic end-organ damage, such as stroke or dementia.

Neural dynamics of stress recovery and their relation to hormonal, cardiac, and subjective changes

N = 93 healthy subjects (39 f, 54 m, age = 28 ± 8) completed the Maastricht Vital Exhaustion Questionnaire (Short Form) and reported their physical exercise activities. Multiple linear regression analyses were conducted. The results show a significant influence of physical exercise on vital exhaustion (ˇ = −.300, p < .05). Posthoc analyses show that this association is only evident in female (ˇ = −.368, p < .05), not in male participants (ˇ = .006, p = .966). In the current study the influence of physical exercise on VE in healthy subjects was confirmed only for women. This might be due to the higher range of vital exhaustion scores in women. Further implications of this gender differential effect will be discussed.

Allostatic load and its connection to the brain.

Although individual stress reactions are adaptive, chronic stress hasnegative effects onpsychological andphysiological functioning. It is therefore important to investigate the dysregulation associated with chronic stress – in the brain and in the rest of the body. Oneway to conceptualize the causes and effects of chronic stress is “allostatic load”, which describes the bodily cost an organism has to carry for adapting to changing environments (McEwen, 1998). Allostatic load can be quantified through a multi-systemic and cumulative measurement, an allostatic load index. We collected data from 96 young (45 female; age: 24.8±3.3, range: 20–35) and 74 elderly (37 female; age: 67.6±4.7, range: 59–77) healthy participants that were part of the Leipzig Cohort for Mind-BodyEmotion Interactions (LEMON). In addition to blood samples, we acquired psychoand anthropometric information as well as measures of the central and the autonomic nervous system, through 3T-MRI and 3-lead ECG recordings, respectively. Neuroendocrine, immune, and metabolic markers were combined with cardiovascular and anthropometric measures to yield individual allostatic load indices. In between-subject analyses, we find that such a composite marker of allostatic load can be related to subjective stress reports, measures of heart rate variability during a 5-min resting period, brain structure (i.e., regional differences in greymatter density) as well as brain function (i.e., network connectivity during task-free resting-state fMRI).We show that an allostatic load index can help investigate inter-individual differences in psychophysiological markers of chronic stress and the brain’s adaptation to it.

Emotion recognition based on spatially smooth spectral features of the EEG

The primary aim of this study was to select the optimal feature subset for discrimination of three dimensions of emotions (arousal, valence, liking) from subjects using electroencephalogram (EEG) signals. The EEG signals were collected from 25 channels on 21 healthy subjects whilst they were watching movie segments with emotional content. The band power values extracted from eleven frequency bands, namely delta (0.5-3.5 Hz), theta (4-7.5 Hz), alpha (8-12 Hz), beta (13-30 Hz), gamma (30-50 Hz), low theta (4-6 Hz), high theta (6-8 Hz), low alpha (8-10 Hz), high alpha (10-12 Hz), low beta (13-18 Hz) and high beta (18-30 Hz) bands, were used as EEG features. The most discriminative features for classification of EEG feature sets were selected using sequential floating forward search (SFFS) algorithm and a modified version of SFFS algorithm, which imposes the topographical smoothness of spectral features, along with linear discriminant analysis (LDA) classifier. The best classification accuracies for three emotional dimensions were obtained for liking (72.22%) followed by arousal (67.50%) and valence (66.67%). SFFS-LDA and modified SFFS-LDA algorithms produced slightly different classification accuracies. However, the findings suggested that the use of modified SFFS-LDA algorithm provides more robust feature subsets for understanding of underlying functional neuroanatomic mechanisms corresponding to distinct emotional states.