Why is a flamingo named as pelican and asparagus as celery? Understanding the relationship between targets and errors in a speeded picture naming task.

Speakers sometimes make word production errors, such as mistakenly saying pelican instead of flamingo. This study explored which properties of an error influence the likelihood of its selection over the target word. Analysing real-word errors in speeded picture naming, we investigated whether, relative to the target, naming errors were more typical representatives of the semantic category, were associated with more semantic features, and/or were semantically more closely related to the target than its near semantic neighbours were on average. Results indicated that naming errors tended to be more typical category representatives and possess more semantic features than the targets. Moreover, while not being the closest semantic neighbours, errors were largely near semantic neighbours of the targets. These findings suggest that typicality, number of semantic features, and semantic similarity govern activation levels in the production system, and we discuss possible mechanisms underlying these effects in the context of word production theories.

Differences in white matter hyperintensities in socioeconomically deprived groups: results of the population-based LIFE Adult Study.

OBJECTIVE: Previous studies have shown that socioeconomically deprived groups exhibit higher lesion load of the white matter (WM) in aging. The aim of this study was to (i) investigate to what extent education and income may contribute to differences in white matter hyperintensities (WMHs) and (ii) identify risk profiles related to a higher prevalence of age-associated WMH. DESIGN AND SETTING: Population-based adult study of the Leipzig Research Centre for Civilization Diseases (LIFE) in Leipzig, Germany. PARTICIPANTS: Dementia-free sample aged 40-80 years (n = 1,185) derived from the population registry. MEASUREMENTS: Information was obtained in standardized interviews. WMH (including the derived Fazekas scores) were assessed using automated segmentation of high-resolution T1-weighted anatomical and fluid-attenuated inversion recovery (FLAIR) MRI acquired at 3T. RESULTS: Despite a significant association between income and WMH in univariate analyses, results from adjusted models (age, gender, arterial hypertension, heart disease, and APOE e4 allele) indicated no association between income and WMH. Education was associated with Fazekas scores, but not with WMH and not after Bonferroni correction. Prevalence of some health-related risk factors was significantly higher among low-income/education groups. After combining risk factors in a factor analysis, results from adjusted models indicated significant associations between higher distress and more WMH as well as between obesity and more deep WMH. CONCLUSIONS: Previously observed differences in WMH between socioeconomically deprived groups might stem from differences in health-related risk factors. These risk factors should be targeted in prevention programs tailored to socioeconomically deprived individuals.

Multiclass prediction of different dementia syndromes based on multi-centric volumetric MRI imaging.

INTRODUCTION: Dementia syndromes can be difficult to diagnose. We aimed at building a classifier for multiple dementia syndromes using magnetic resonance imaging (MRI). METHODS: Atlas-based volumetry was performed on T1-weighted MRI data of 426 patients and 51 controls from the multi-centric German Research Consortium of Frontotemporal Lobar Degeneration including patients with behavioral variant frontotemporal dementia, Alzheimer's disease, the three subtypes of primary progressive aphasia, i.e., semantic, logopenic and nonfluent-agrammatic variant, and the atypical parkinsonian syndromes progressive supranuclear palsy and corticobasal syndrome. Support vector machine classification was used to classify each patient group against controls (binary classification) and all seven diagnostic groups against each other in a multi-syndrome classifier (multiclass classification). RESULTS: The binary classification models reached high prediction accuracies between 71 and 95% with a chance level of 50%. Feature importance reflected disease-specific atrophy patterns. The multi-syndrome model reached accuracies of more than three times higher than chance level but was far from 100%. Multi-syndrome model performance was not homogenous across dementia syndromes, with better performance in syndromes characterized by regionally specific atrophy patterns. Whereas diseases generally could be classified vs controls more correctly with increasing severity and duration, differentiation between diseases was optimal in disease-specific windows of severity and duration. DISCUSSION: Results suggest that automated methods applied to MR imaging data can support physicians in diagnosis of dementia syndromes. It is particularly relevant for orphan diseases beside frequent syndromes such as Alzheimer's disease.

No negative impact of word retraining on vocabulary use or clarity of communication in semantic dementia.

Word retraining techniques can improve picture naming of treated items in people with semantic dementia (SD). The utility of this, however, has been questioned given the propensity for under- and overgeneralization errors in naming in SD. Few studies have investigated the occurrence of such errors. This study examined whether, following tailored word retraining: (1) misuse of words increases, (2) the type of naming errors changes, and/or (3) clarity of communication is reduced. Performance on trained and untrained word naming from nine participants with SD who completed a word retraining programme were analysed. Responses from baseline and post-intervention assessments were coded for misuse (i.e., trained word produced for another target item), error type, and communication clarity. All participants showed significant improvement for trained vocabulary. There was no significant increase in misuse of words, with such errors occurring rarely. At a group level, there was an increased tendency toward omission errors for untrained items, and a reduction in semantically related responses. However, this did not impact on clarity scores with no consistent change across participants. In sum, we found no negative impacts following tailored word retraining, providing further evidence of the benefit of these programmes for individuals with SD.

Are they really stronger? Comparing effects of semantic variables in speeded deadline and standard picture naming.

Investigations of effects of semantic variables on picture naming have often been inconclusive, with some studies reporting significant and others non-significant effects. One potential explanation may relate to the specific naming tasks used: While most previous studies have used standard picture naming, others have used speeded naming that requires participants to prioritise naming speed over accuracy. Speeded naming has been suggested to cause enhanced effects of item-inherent word characteristics due to disruptions of cognitive control and resulting modulations of responsiveness to input. Consequently, this study investigated whether effects are stronger in speeded compared to standard picture naming, focusing on six feature-based semantic variables: number of semantic features, intercorrelational density, number of near semantic neighbours, semantic similarity, typicality, and distinctiveness. The results showed few differences in the variables' effects between the two naming tasks: In the naming latency analysis, the inhibitory effect of distinctiveness was stronger in the speeded naming task, while in the accuracy analysis the effect of number of semantic features was stronger in the standard naming task. These findings cannot, therefore, be exclusively accounted for by increased responsiveness to input in speeded naming and we discuss possible underlying mechanisms. We conclude that, while some differences in effects of semantic variables between previous studies may have been caused by the specific naming task used, differences between studies more likely depend on statistical power and control of other influential variables in the experiment.

Increased Serum NSE and S100B Indicate Neuronal and Glial Alterations in Subjects Under 71 Years With Mild Neurocognitive Disorder/Mild Cognitive Impairment.

Background: Mild cognitive impairment (MCI) is considered a pre-stage of different dementia syndromes. Despite diagnostic criteria refined by DSM-5 and a new term for MCI - "mild neurocognitive disorder" (mild NCD) - this diagnosis is still based on clinical criteria. Methods: To link mild NCD to the underlying pathophysiology we assessed the degree of white matter hyperintensities (WMH) in the brain and peripheral biomarkers for neuronal integrity (neuron-specific enolase, NSE), plasticity (brain-derived neurotrophic factor, BDNF), and glial function (S100B) in 158 community-dwelling subjects with mild NCD and 82 healthy controls. All participants (63-79 years old) were selected from the Leipzig-population-based study of adults (LIFE). Results: Serum S100B levels were increased in mild NCD in comparison to controls (p = 0.007). Serum NSE levels were also increased but remained non-significant after Bonferroni-Holm correction (p = 0.04). Furthermore, age by group interaction was significant for S100B. In an age-stratified sub-analysis, NSE and S100B were higher in younger subjects with mild NCD below 71 years of age. Some effects were inconsistent after controlling for potentially confounding factors. The discriminatory power of the two biomarkers NSE and S100B was insufficient to establish a pathologic threshold for mild NCD. In subjects with mild NCD, WMH load correlated with serum NSE levels (r = 0.20, p = 0.01), independently of age. Conclusion: Our findings might indicate the presence of neuronal (NSE) and glial (S100B) injury in mild NCD. Future studies need to investigate whether younger subjects with mild NCD with increased biomarker levels are at risk of developing major NCD.

Investigating the influence of semantic factors on word retrieval: Reservations, results and recommendations.

There is consensus that word retrieval starts with activation of semantic representations. However, in adults without language impairment, relatively little attention has been paid to the effects of the semantic attributes of to-be-retrieved words. This paper, therefore, addresses the question of which item-inherent semantic factors influence word retrieval. Specifically, it reviews the literature on a selection of these factors: imageability, concreteness, number of semantic features, typicality, intercorrelational density, featural distinctiveness, concept distinctiveness, animacy, semantic neighbourhood density, semantic similarity, operativity, valence, and arousal. It highlights several methodological challenges in this field, and has a focus on the insights from studies with people with aphasia where the effects of these variables are more prevalent. The paper concludes that further research simultaneously examining the effects of different semantic factors that are likely to affect lexical co-activation, and the interaction of these variables, would be fruitful, as would suitably scaled computational modelling of these effects in unimpaired language processing and in language impairment. Such research would enable the refinement of theories of semantic processing and word production, and potentially have implications for diagnosis and treatment of semantic and lexical impairments.

The influence of white matter lesions on the electric field in transcranial electric stimulation.

BACKGROUND: Transcranial direct current stimulation (tDCS) is a promising tool to enhance therapeutic efforts, for instance, after a stroke. The achieved stimulation effects exhibit high inter-subject variability, primarily driven by perturbations of the induced electric field (EF). Differences are further elevated in the aging brain due to anatomical changes such as atrophy or lesions. Informing tDCS protocols by computer-based, individualized EF simulations is a suggested measure to mitigate this variability. OBJECTIVE: While brain anatomy in general and specifically atrophy as well as stroke lesions are deemed influential on the EF in simulation studies, the influence of the uncertainty in the change of the electrical properties of the white matter due to white matter lesions (WMLs) has not been quantified yet. METHODS: A group simulation study with 88 subjects assigned into four groups of increasing lesion load was conducted. Due to the lack of information about the electrical conductivity of WMLs, an uncertainty analysis was employed to quantify the variability in the simulation when choosing an arbitrary conductivity value for the lesioned tissue. RESULTS: The contribution of WMLs to the EF variance was on average only one tenth to one thousandth of the contribution of the other modeled tissues. While the contribution of the WMLs significantly increased (p≪.01) in subjects exhibiting a high lesion load compared to low lesion load subjects, typically by a factor of 10 and above, the total variance of the EF didnot change with the lesion load. CONCLUSION: Our results suggest that WMLs do not perturb the EF globally and can thus be omitted when modeling subjects with low to medium lesion load. However, for high lesion load subjects, the omission of WMLs may yield less robust local EF estimations in the vicinity of the lesioned tissue. Our results contribute to the efforts of accurate modeling of tDCS for treatment planning.

Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes.

IMPORTANCE: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context. OBJECTIVE: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging. DESIGN, SETTING, AND PARTICIPANTS: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes. INTERVENTIONS: N.A. MAIN OUTCOMES AND MEASURES: Cohen's kappa, accuracy, and F1-score to assess model performance. RESULTS: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy. CONCLUSIONS AND RELEVANCE: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.

Semantic variables both help and hinder word production: Behavioral evidence from picture naming.

This research investigated how word production is influenced by six feature-based semantic variables (number of semantic features, intercorrelational density, number of near semantic neighbors, semantic similarity, typicality, and distinctiveness). We simultaneously investigated effects of the six semantic variables on spoken picture naming in a large group of participants (n = 87), while controlling for other psycholinguistic variables. Across analyses, number of semantic features was the most consistent predictor with a facilitatory effect on naming latency and accuracy. In addition, inhibitory effects were found on naming accuracy for intercorrelational density and on naming latency for distinctiveness. The facilitatory effect of number of semantic features is suggested to stem from stronger semantic activation with an increasing number of semantic features, which results in facilitated selection of the word's lexical representation. In contrast, the inhibitory effect of intercorrelational density is most easily accounted for by increased competition at the lexical level. The mechanism underlying the inhibitory effect of distinctiveness is unclear. These findings indicate that future research on factors affecting word retrieval should also control for effects of number of semantic features, intercorrelational density, and distinctiveness. They also suggest that effects of the other semantic variables (e.g., semantic neighbors) reported in the literature were potentially overestimated due to insufficient control of other semantic and/or psycholinguistic variables. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network.

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all "visible" PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.

Effects of semantic variables on word production in aphasia.

Words differ in the complexity of their semantic representations and their relationships to other words and these differences can be operationalised as a variety of semantic variables. The research presented here investigates how word production in aphasia is influenced by six feature-based semantic variables (number of near semantic neighbours, semantic similarity, number of semantic features, typicality, intercorrelational density, and distinctiveness). Previous research has reported inconsistent findings for some of the semantic variables, while others have not been previously studied in aphasia. Spoken picture naming data from a large group of individuals with aphasia with mixed spoken word production impairments (n = 175) and a sub-group who produced few phonological errors (n = 60) was analysed. We examined effects of the semantic variables on overall naming accuracy and on the occurrence of different error types (semantic errors overall, coordinate errors, omissions), while controlling for other psycholinguistic variables using generalised linear mixed effects models and Bayesian correlations. Across analyses, number of semantic features was the most important predictor with a facilitatory main effect on naming accuracy in the sub-group analysis. Number of semantic features, along with typicality and semantic similarity, also predicted error types and in some analyses these effects depended on the integrity of semantic processing. Effects of the semantic variables and their theoretical explanations and implications are discussed in light of previous research and models of word production.

Domain-specific cues improve robustness of deep learning-based segmentation of CT volumes.

Machine learning has considerably improved medical image analysis in the past years. Although data-driven approaches are intrinsically adaptive and thus, generic, they often do not perform the same way on data from different imaging modalities. In particular computed tomography (CT) data poses many challenges to medical image segmentation based on convolutional neural networks (CNNs), mostly due to the broad dynamic range of intensities and the varying number of recorded slices of CT volumes. In this paper, we address these issues with a framework that adds domain-specific data preprocessing and augmentation to state-of-the-art CNN architectures. Our major focus is to stabilise the prediction performance over samples as a mandatory requirement for use in automated and semi-automated workflows in the clinical environment. To validate the architecture-independent effects of our approach we compare a neural architecture based on dilated convolutions for parallel multi-scale processing (a modified Mixed-Scale Dense Network: MS-D Net) to traditional scaling operations (a modified U-Net). Finally, we show that an ensemble model combines the strengths across different individual methods. Our framework is simple to implement into existing deep learning pipelines for CT analysis. It performs well on a range of tasks such as liver and kidney segmentation, without significant differences in prediction performance on strongly differing volume sizes and varying slice thickness. Thus our framework is an essential step towards performing robust segmentation of unknown real-world samples.

Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities.

BACKGROUND AND PURPOSE: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. METHODS: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. RESULTS: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 (NBEAL), 10q23.1 (TSPAN14/FAM231A), and 10q24.33 (SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 (NOS3) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. CONCLUSIONS: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.

Cortical laminar resting-state signal fluctuations scale with the hypercapnic blood oxygenation level-dependent response.

Calibrated functional magnetic resonance imaging can remove unwanted sources of signal variability in the blood oxygenation level-dependent (BOLD) response. This is achieved by scaling, using information from a perfusion-sensitive scan during a purely vascular challenge, typically induced by a gas manipulation or a breath-hold task. In this work, we seek for a validation of the use of the resting-state fluctuation amplitude (RSFA) as a scaling factor to remove vascular contributions from the BOLD response. Given the peculiarity of depth-dependent vascularization in gray matter, BOLD and vascular space occupancy (VASO) data were acquired at submillimeter resolution and averaged across cortical laminae. RSFA from the primary motor cortex was, thus, compared to the amplitude of hypercapnia-induced signal changes (tSDhc ) and with the M factor of the Davis model on a laminar level. High linear correlations were observed for RSFA and tSDhc ( R2 = 0.92 ± 0.06) and somewhat reduced for RSFA and M ( R2 = 0.62 ± 0.19). Laminar profiles of RSFA-normalized BOLD signal changes yielded good agreement with corresponding VASO profiles. Overall, this suggests that RSFA contains strong vascular components and is also modulated by baseline quantities contained in the M factor. We conclude that RSFA may replace the scaling factor tSDhc for normalizing the laminar BOLD response.

Unraveling corticobasal syndrome and alien limb syndrome with structural brain imaging.

Alien limb phenomenon is a rare syndrome associated with a feeling of non-belonging and disowning toward one's limb. In contrast, anarchic limb phenomenon leads to involuntary but goal-directed movements. Alien/anarchic limb phenomena are frequent in corticobasal syndrome (CBS), an atypical parkinsonian syndrome characterized by rigidity, akinesia, dystonia, cortical sensory deficit, and apraxia. The structure-function relationship of alien/anarchic limb was investigated in multi-centric structural magnetic resonance imaging (MRI) data. Whole-group and single-subject comparisons were made in 25 CBS and eight CBS-alien/anarchic limb patients versus controls. Support vector machine was used to see if CBS with and without alien/anarchic limb could be distinguished by structural MRI patterns. Whole-group comparison of CBS versus controls revealed asymmetric frontotemporal atrophy. CBS with alien/anarchic limb syndrome versus controls showed frontoparietal atrophy including the supplementary motor area contralateral to the side of the affected limb. Exploratory analysis identified frontotemporal regions encompassing the pre-/and postcentral gyrus as compromised in CBS with alien limb syndrome. Classification of CBS patients yielded accuracies of 79%. CBS-alien/anarchic limb syndrome was differentiated from CBS patients with an accuracy of 81%. Predictive differences were found in the cingulate gyrus spreading to frontomedian cortex, postcentral gyrus, and temporoparietoocipital regions. We present the first MRI-based group analysis on CBS-alien/anarchic limb. Results pave the way for individual clinical syndrome prediction and allow understanding the underlying neurocognitive architecture.

Functional characterization of a novel CSF1R mutation causing hereditary diffuse leukoencephalopathy with spheroids.

BACKGROUND: Colony-stimulating factor 1 receptor is a tyrosine kinase transmembrane protein that mediates proliferation, differentiation, and survival of monocytes/macrophages and microglia. CSF1R gene mutations cause hereditary diffuse leukoencephalopathy with spheroids (HDLS), an autosomal-dominantly inherited microgliopathy, leading to early onset dementia with high lethality. METHODS: By interdisciplinary assessment of a complex neuropsychiatric condition in a 44-year old female patient, we narrowed down the genetic diagnostic to CSF1R gene sequencing. Flow cytometric analyses of uncultivated peripheral blood monocytes were conducted sequentially to measure the cell surface CSF1 receptor and autophosphorylation levels. Monocyte subpopulations were monitored during disease progression. RESULTS: We identified a novel heterozygous deletion-insertion mutation c.2527_2530delinsGGCA, p.(Ile843_Leu844delinsGlyIle) in our patient with initial signs of HDLS. Marginally elevated cell surface CSF1 receptor levels with increased Tyr723 autophosphorylation suggest an enhanced receptor activity. Furthermore, we observed a shift in monocyte subpopulations during disease course. CONCLUSION: Our data indicate a mutation-related CSF1R gain-of-function, accompanied by an altered composition of the peripheral innate immune cells in our patient with HDLS. Since pharmacological targeting of CSF1R with tyrosine kinase inhibitors prevents disease progression in mouse models of neurodegenerative disorders, a potential pharmacological benefit of CSF1R inhibition remains to be elucidated for patients with HDLS.

A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults.

We present a publicly available dataset of 227 healthy participants comprising a young (N=153, 25.1±3.1 years, range 20-35 years, 45 female) and an elderly group (N=74, 67.6±4.7 years, range 59-77 years, 37 female) acquired cross-sectionally in Leipzig, Germany, between 2013 and 2015 to study mind-body-emotion interactions. During a two-day assessment, participants completed MRI at 3 Tesla (resting-state fMRI, quantitative T1 (MP2RAGE), T2-weighted, FLAIR, SWI/QSM, DWI) and a 62-channel EEG experiment at rest. During task-free resting-state fMRI, cardiovascular measures (blood pressure, heart rate, pulse, respiration) were continuously acquired. Anthropometrics, blood samples, and urine drug tests were obtained. Psychiatric symptoms were identified with Standardized Clinical Interview for DSM IV (SCID-I), Hamilton Depression Scale, and Borderline Symptoms List. Psychological assessment comprised 6 cognitive tests as well as 21 questionnaires related to emotional behavior, personality traits and tendencies, eating behavior, and addictive behavior. We provide information on study design, methods, and details of the data. This dataset is part of the larger MPI Leipzig Mind-Brain-Body database.

Association of peripheral blood pressure with gray matter volume in 19- to 40-year-old adults.

OBJECTIVE: To test whether elevated blood pressure (BP) relates to gray matter (GM) volume (GMV) changes in young adults who had not previously been diagnosed with hypertension (systolic BP [SBP]/diastolic BP [DBP] ≥140/90 mm Hg). METHODS: We associated BP with GMV from structural 3T T1-weighted MRI of 423 healthy adults between 19 and 40 years of age (mean age 27.7 ± 5.3 years, 177 women, SBP/DBP 123.2/73.4 ± 12.2/8.5 mm Hg). Data originated from 4 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 1 of 4 categories: (1) SBP <120 and DBP <80 mm Hg, (2) SBP 120-129 or DBP 80-84 mm Hg, (3) SBP 130-139 or DBP 85-89 mm Hg, (4) SBP ≥140 or DBP ≥90 mm Hg. RESULTS: IBMA yielded the following results: (1) lower regional GMV was correlated with higher peripheral BP; (2) lower GMV was found with higher BP when comparing individuals in subhypertensive categories 3 and 2, respectively, to those in category 1; (3) lower BP-related GMV was found in regions including hippocampus, amygdala, thalamus, frontal, and parietal structures (e.g., precuneus). CONCLUSION: BP ≥120/80 mm Hg 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 pathophysiologic cascade of asymptomatic cerebrovascular disease to symptomatic end-organ damage, such as stroke or dementia.

Lesion location matters: The relationships between white matter hyperintensities on cognition in the healthy elderly.

White matter hyperintensities (WMH) are associated with cognitive decline. We aimed to identify the spatial specificity of WMH impact on cognition in non-demented, healthy elderly. We quantified WMH volume among healthy participants of a community dwelling cohort ( n = 702, age range 60 - 82 years, mean age = 69.5 years, 46% female) and investigated the effects of WMH on cognition and behavior, specifically for executive function, memory, and motor speed performance. Lesion location influenced their effect on cognition and behavior: Frontal WMH in the proximity of the frontal ventricles mainly affected executive function and parieto-temporal WMH in the proximity of the posterior horns deteriorated memory, while WMH in the upper deep white matter-including the corticospinal tract-compromised motor speed performance. This study exposes the subtle and subclinical yet detrimental effects of WMH on cognition in healthy elderly, and strongly suggests a causal influence of WMH on cognition by demonstrating the spatial specificity of these effects.