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.

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.

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.

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.

Visceral obesity relates to deep white matter hyperintensities via inflammation.

OBJECTIVE: White matter hyperintensities (WMHs) are linked to vascular risk factors and increase the risk of cognitive decline, dementia, and stroke. We here aimed to determine whether obesity contributes to regional WMHs using a whole-brain approach in a well-characterized population-based cohort. METHODS: Waist-to-hip ratio (WHR), body mass index (BMI), systolic/diastolic blood pressure, hypertension, diabetes and smoking status, blood glucose and inflammatory markers, as well as distribution of WMH were assessed in 1,825 participants of the LIFE-adult study (age, 20-82 years; BMI, 18.4-55.4 kg/m2 ) using high-resolution 3-Tesla magnetic resonance imaging. Voxel-wise analyses tested if obesity predicts regional probability of WMH. Additionally, mediation effects of high-sensitive C-reactive protein and interleukin-6 (IL6) measured in blood were related to obesity and WMH using linear regression and structural equation models. RESULTS: WHR related to higher WMH probability predominantly in the deep white matter, even after adjusting for effects of age, sex, and systolic blood pressure (mean ß = 0.0043 [0.0008 SE], 95% confidence interval, [0.00427, 0.0043]; threshold-free cluster enhancement, family-wise error-corrected p < 0.05). Conversely, higher systolic blood pressure was associated with WMH in periventricular white matter regions. Mediation analyses indicated that both higher WHR and higher BMI contributed to increased deep-to-periventricular WMH ratio through elevated IL6. INTERPRETATION: Our results indicate an increased WMH burden selectively in the deep white matter in obese subjects with high visceral fat accumulation, independent of common obesity comorbidities such as hypertension. Mediation analyses proposed that visceral obesity contributes to deep white matter lesions through increases in proinflammatory cytokines, suggesting a pathomechanistic link. Longitudinal studies need to confirm this hypothesis. ANN NEUROL 2019;85:194-203.

Effects of resveratrol on memory performance, hippocampus connectivity and microstructure in older adults - A randomized controlled trial.

INTRODUCTION: The polyphenol resveratrol has been suggested to exert beneficial effects on memory and the aging hippocampus due to calorie-restriction mimicking effects. However, the evidence based on human interventional studies is scarce. We therefore aimed to determine the effects of resveratrol on memory performance, and to identify potential underlying mechanisms using a broad array of blood-based biomarkers as well as hippocampus connectivity and microstructure assessed with ultra-high field magnetic resonance imaging (UHF-MRI). METHODS: In this double-blind, randomized controlled trial, 60 elderly participants (60-79 years) with a wide body-mass index (BMI) range of 21-37 kg/m2 were randomized to receive either resveratrol (200 mg/day) or placebo for 26 weeks (registered at ClinicalTrials.gov: NCT02621554). Baseline and follow-up assessments included the California Verbal Learning Task (CVLT, main outcome), the ModBent task, anthropometry, markers of glucose and lipid metabolism, inflammation and neurotrophins derived from fasting blood, multimodal neuroimaging at 3 and 7 T, and questionnaires to assess confounding factors. RESULTS: Multivariate repeated-measures ANOVA did not detect significant time by group effects for CVLT performance. There was a trend for preserved pattern recognition memory after resveratrol, while performance decreased in the placebo group (n.s., p = 0.07). Further exploratory analyses showed increases in both groups over time in body fat, cholesterol, fasting glucose, interleukin 6, high sensitive C-reactive protein, tumor necrosis factor alpha and in mean diffusivity of the subiculum and presubiculum, as well as decreases in physical activity, brain-derived neurotrophic factor and insulin-like growth factor 1 at follow-up, which were partly more pronounced after resveratrol. DISCUSSION: This interventional study failed to show significant improvements in verbal memory after 6 months of resveratrol in healthy elderly with a wide BMI range. A non-significant trend emerged for positive effects on pattern recognition memory, while possible confounding effects of unfavorable changes in lifestyle behavior, neurotrophins and inflammatory markers occurred. Our findings also indicate the feasibility to detect (un)healthy aging-related changes in measures of hippocampus microstructure after 6 months using 7T diffusion MRI. More studies incorporating a longer duration and larger sample size are needed to determine if resveratrol enhances memory performance in healthy older adults.

White matter microstructural variability mediates the relation between obesity and cognition in healthy adults.

Obesity has been linked with structural and functional brain changes. However, the impact of obesity on brain and cognition in aging remains debatable, especially for white matter. We therefore aimed to determine the effects of obesity on white matter microstructure and potential implications for cognition in a well-characterized large cohort of healthy adults. In total, 1255 participants (50% females, 19-80 years, BMI 16.8-50.2 kg/m2) with diffusion-weighted magnetic resonance imaging at 3T were analysed. Tract-based spatial statistics (TBSS) probed whether body mass index (BMI) and waist-to-hip ratio (WHR) were related to fractional anisotropy (FA). We conducted partial correlations and mediation analyses to explore whether obesity or regional FA were related to cognitive performance. Analyses were adjusted for demographic, genetic, and obesity-associated confounders. Results showed that higher BMI and higher WHR were associated with lower FA in multiple white matter tracts (p < 0.05, FWE-corrected). Mediation analyses provided evidence for indirect negative effects of higher BMI and higher WHR on executive functions and processing speed through lower FA in fiber tracts connecting (pre)frontal, visual, and associative areas (indirect paths, |ß| ≥ 0.01; 99% |CI| > 0). This large cross-sectional study showed that obesity is correlated with lower FA in multiple white matter tracts in otherwise healthy adults, independent of confounders. Moreover, although effect sizes were small, mediation results indicated that visceral obesity was linked to poorer executive functions and lower processing speed through lower FA in callosal and associative fiber tracts. Longitudinal studies are needed to support this hypothesis.

Pathological glutamatergic neurotransmission in Gilles de la Tourette syndrome.

Gilles de la Tourette syndrome is a hereditary, neuropsychiatric movement disorder with reported abnormalities in the neurotransmission of dopamine and γ-aminobutyric acid (GABA). Spatially focalized alterations in excitatory, inhibitory and modulatory neurochemical ratios within specific functional subdivisions of the basal ganglia, may lead to the expression of diverse motor and non-motor features as manifested in Gilles de la Tourette syndrome. Current treatment strategies are often unsatisfactory thus provoking the need for further elucidation of the underlying pathophysiology. In view of (i) the close spatio-temporal synergy exhibited between excitatory, inhibitory and modulatory neurotransmitter systems; (ii) the crucial role played by glutamate (Glu) in tonic/phasic dopaminergic signalling; and (iii) the interdependent metabolic relationship exhibited between Glu and GABA via glutamine (Gln); we postulated that glutamatergic signalling is related to the pathophysiology of Gilles de la Tourette syndrome. As such, we examined the neurochemical profile of three cortico-striato-thalamo-cortical regions in 37 well-characterized, drug-free adult patients and 36 age/gender-matched healthy control subjects via magnetic resonance spectroscopy at 3 T. To interrogate the influence of treatment on metabolite concentrations, spectral data were acquired from 15 patients undergoing a 4-week treatment with aripiprazole. Test-retest reliability measurements in 23 controls indicated high repeatability of voxel localization and metabolite quantitation. We report significant reductions in striatal concentrations of Gln, Glu + Gln (Glx) and the Gln:Glu ratio, and thalamic concentrations of Glx in Gilles de la Tourette syndrome in comparison to controls. ON-treatment patients exhibited no significant metabolite differences when compared to controls but significant increases in striatal Glu and Glx, and trends for increases in striatal Gln and thalamic Glx compared to baseline measurements. Multiple regression analysis revealed a significant negative correlation between (i) striatal Gln and actual tic severity; and (ii) thalamic Glu and premonitory urges. Our results indicate that patients with Gilles de la Tourette syndrome exhibit an abnormality in the flux of metabolites in the GABA-Glu-Gln cycle, thus implying perturbations in astrocytic-neuronal coupling systems that maintain the subtle balance between excitatory and inhibitory neurotransmission within subcortical nuclei.

Predicting brain-age from multimodal imaging data captures cognitive impairment.

The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brain-imaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N=2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N=475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders.

Higher body mass index is associated with reduced posterior default mode connectivity in older adults.

Obesity is a complex neurobehavioral disorder that has been linked to changes in brain structure and function. However, the impact of obesity on functional connectivity and cognition in aging humans is largely unknown. Therefore, the association of body mass index (BMI), resting-state network connectivity, and cognitive performance in 712 healthy, well-characterized older adults of the Leipzig Research Center for Civilization Diseases (LIFE) cohort (60-80 years old, mean BMI 27.6 kg/m2 ± 4.2 SD, main sample: n = 521, replication sample: n = 191) was determined. Statistical analyses included a multivariate model selection approach followed by univariate analyses to adjust for possible confounders. Results showed that a higher BMI was significantly associated with lower default mode functional connectivity in the posterior cingulate cortex and precuneus. The effect remained stable after controlling for age, sex, head motion, registration quality, cardiovascular, and genetic factors as well as in replication analyses. Lower functional connectivity in BMI-associated areas correlated with worse executive function. In addition, higher BMI correlated with stronger head motion. Using 3T neuroimaging in a large cohort of healthy older adults, independent negative associations of obesity and functional connectivity in the posterior default mode network were observed. In addition, a subtle link between lower resting-state connectivity in BMI-associated regions and cognitive function was found. The findings might indicate that obesity is associated with patterns of decreased default mode connectivity similar to those seen in populations at risk for Alzheimer's disease. Hum Brain Mapp 38:3502-3515, 2017. © 2017 Wiley Periodicals, Inc.

Lamina-dependent calibrated BOLD response in human primary motor cortex.

Disentangling neural activity at different cortical depths during a functional task has recently generated growing interest, since this would allow to separate feedforward and feedback activity. The majority of layer-dependent studies have, so far, relied on gradient-recalled echo (GRE) blood-oxygenation-level dependent (BOLD) acquisitions, which are weighted towards the large draining veins at the cortical surface. The current study aims to obtain quantitative brain activity responses in the primary motor cortex on a laminar scale without the contamination due to accompanying secondary vascular effects. Evoked oxidative metabolism was evaluated using the Davis model, to investigate its applicability, advantages, and limits in lamina-dependent fMRI. Average values for the calibration parameter, M, and for changes in the cerebral metabolic rate of oxygen consumption (CMRO2) during a unilateral finger-tapping task were (11±2)% and (30±7)%, respectively, with distinct variation features across the cortical depth. The results presented here showed an uncoupling between BOLD-based functional magnetic resonance imaging (fMRI) and metabolic changes across cortical depth, while the tight coupling between CMRO2 and CBV was conserved across cortical layers. We conclude that the Davis model can help to obtain estimates of lamina-dependent metabolic changes without contamination from large draining veins, with high consistency and reproducibility across participants.

Baseline oxygenation in the brain: Correlation between respiratory-calibration and susceptibility methods.

New MRI methods for noninvasive imaging of baseline oxygen extraction fraction (OEF) in the brain show great promise. Quantitative O2 imaging (QUO2) applies a biophysical model to measure OEF in tissue from BOLD, cerebral blood flow (CBF), and end-tidal O2 (ETO2) signals acquired during two or more gas manipulations. Alternatively, quantitative susceptibility mapping (QSM) maps baseline OEF along cerebral vessels based on the deoxyhemoblogin (dHb) susceptibility shift between veins and water. However, these approaches have not been carefully compared to each other or to known physiological signals. The aims of this study were to compare OEF values by QUO2 and QSM; and to see if baseline OEF relates to BOLD and CBF changes during a visual task. Simultaneous BOLD and arterial spin labeling (ASL) scans were acquired at 7T in 11 healthy subjects continuously during hypercapnia (5% CO2, 21% O2), hyperoxia (100% O2), and carbogen (5% CO2, 95% O2) for QUO2 analysis. Separate BOLD-ASL scans were acquired during a checkerboard stimulus to identify functional changes in the visual cortex. Gradient echo phase images were also collected at rest for QSM reconstruction of OEF along cerebral veins draining the visual cortex. Mean baseline OEF was (43.5±14)% for QUO2 with two gases, (42.3±17)% for QUO2 with three gases, and (29.4±3)% for QSM across volunteers. Three-gas QUO2 values of OEF correlated with QSM values of OEF (P=0.03). However, Bland-Altman analysis revealed that QUO2 tended to measure higher baseline OEF with respect to QSM, which likely results from underestimation of the hyperoxic BOLD signal and low signal-to-noise ratio of the ASL acquisitions. Across subjects, the percent CBF change during the visual task correlated with OEF measured by 3-gas QUO2 (P<0.04); and by QSM (P=0.035), providing evidence that the new methods measure true variations in brain physiology across subjects.

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.

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.

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).

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.

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.

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.