Patterns of small involuntary fixation saccades (SIFSs) in different neurodegenerative diseases: the role of noise.

During the attempt to steadily fixate at a single spot, sequences of small involuntary fixation saccades (SIFSs, known also as microsaccades οr intrusions) occur which form spatio-temporal patterns such as square wave jerks (SWJs), a pattern characterised by alternating centrifugal and centripetal movements of similar magnitude. In many neurodegenerative disorders, SIFSs exhibit elevated amplitudes and frequencies. Elevated SIFS amplitudes have been shown to favour the occurrence of SWJs ("SWJ coupling"). We analysed SIFSs in different subject groups comprising both healthy controls (CTR) and patients with amyotrophic lateral sclerosis (ALS) and progressive supranuclear palsy (PSP), i.e. two neurodegenerative diseases with completely different neuropathological basis and different clinical phenotypes. We show that, across these groups, the relations between SIFS amplitude and the relative frequency of SWJ-like patterns and other SIFS characteristics follow a common law. As an explanation, we propose that physiological and technical noise comprises a small, amplitude-independent component that has little effect on large SIFSs, but causes considerable deviations from the intended amplitude and direction of small ones. Therefore, in contrast to large SIFSs, successive small SIFSs have a lower chance to meet the SWJ similarity criteria. In principle, every measurement of SIFSs is affected by an amplitude-independent noise background. Therefore, the dependence of SWJ coupling on SIFS amplitude will probably be encountered in almost any group of subjects. In addition, we find a positive correlation between SIFS amplitude and frequency in ALS, but none in PSP, suggesting that the elevated amplitudes might arise at different sites in the two disorders.

Diffusion Tensor Imaging in Amyotrophic Lateral Sclerosis: Machine Learning for Biomarker Development.

Diffusion tensor imaging (DTI) allows the in vivo imaging of pathological white matter alterations, either with unbiased voxel-wise or hypothesis-guided tract-based analysis. Alterations of diffusion metrics are indicative of the cerebral status of patients with amyotrophic lateral sclerosis (ALS) at the individual level. Using machine learning (ML) models to analyze complex and high-dimensional neuroimaging data sets, new opportunities for DTI-based biomarkers in ALS arise. This review aims to summarize how different ML models based on DTI parameters can be used for supervised diagnostic classifications and to provide individualized patient stratification with unsupervised approaches in ALS. To capture the whole spectrum of neuropathological signatures, DTI might be combined with additional modalities, such as structural T1w 3-D MRI in ML models. To further improve the power of ML in ALS and enable the application of deep learning models, standardized DTI protocols and multi-center collaborations are needed to validate multimodal DTI biomarkers. The application of ML models to multiparametric MRI/multimodal DTI-based data sets will enable a detailed assessment of neuropathological signatures in patients with ALS and the development of novel neuroimaging biomarkers that could be used in the clinical workup.

Sequential alterations in diffusion metrics as correlates of disease severity in amyotrophic lateral sclerosis.

BACKGROUND AND OBJECTIVE: The neuropathology of amyotrophic lateral sclerosis (ALS) follows a regional distribution pattern in the brain with four stages. Using diffusion tensor imaging (DTI), this pattern can be translated into a tract-based staging scheme to assess cerebral progression in vivo. This study investigates the association between the sequential alteration pattern and disease severity in patients with ALS. METHODS: DTI data of 325 patients with ALS and 130 healthy controls were analyzed in a tract of interest (TOI)-based approach. Patients were categorized according to their ALS-FRS-R scores into groups with declining functionality. The fractional anisotropy (FA) values in the tracts associated with neuropathological stages were group-wise compared with healthy controls. RESULTS: The FA in the tracts associated with ALS stages showed a decrease which could be related to the disease severity stratification, i.e., at the group level, the lower the ALS-FRS-R of the categorized patient group, the higher was the effect size of the stage-related tract. In the patient group with the highest ALS-FRS-R, Cohen's d showed a medium effect size in the corticospinal tract and small effect sizes in the other stage-related tracts. Overall, the lower the ALS-FRS-R of the categorized patient group the higher was the effect size of the comparison with healthy controls. CONCLUSION: The progression of white matter alterations across tracts according to the model of sequential tract involvement is associated with clinical disease severity in patients with ALS, suggesting the use of staging-based DTI as a technical marker for disease progression.

Segmental alterations of the corpus callosum in motor neuron disease: A DTI and texture analysis in 575 patients.

INTRODUCTION: Within the core neuroimaging signature of amyotrophic lateral sclerosis (ALS), the corpus callosum (CC) is increasingly recognized as a consistent feature. The aim of this study was to investigate the sensitivity and specificity of the microstructural segmental CC morphology, assessed by diffusion tensor imaging (DTI) and high-resolution T1-weighted (T1w) imaging, in a large cohort of ALS patients including different clinical phenotypes. METHODS: In a single-centre study, 575 patients with ALS (classical phenotype, N  = 432; restricted phenotypes primary lateral sclerosis (PLS) N = 55, flail arm syndrome (FAS) N = 45, progressive bulbar palsy (PBP) N = 22, lower motor neuron disease (LMND) N = 21) and 112 healthy controls underwent multiparametric MRI, i.e. volume-rendering T1w scans and DTI. Tract-based fractional anisotropy statistics (TFAS) was applied to callosal tracts of CC areas I-V, identified from DTI data (tract-of-interest (TOI) analysis), and texture analysis was applied to T1w data. In order to further specify the callosal alterations, a support vector machine (SVM) algorithm was used to discriminate between motor neuron disease patients and controls. RESULTS: The analysis of white matter integrity revealed predominantly FA reductions for tracts of the callosal areas I, II, and III (with highest reductions in callosal area III) for all ALS patients and separately for each phenotype when compared to controls; texture analysis demonstrated significant alterations of the parameters entropy and homogeneity for ALS patients and all phenotypes for the CC areas I, II, and III (with again highest reductions in callosal area III) compared to controls. With SVM applied on multiparametric callosal parameters of area III, a separation of all ALS patients including phenotypes from controls with 72% sensitivity and 73% specificity was achieved. These results for callosal area III parameters could be improved by an SVM of six multiparametric callosal parameters of areas I, II, and III, achieving a separation of all ALS patients including phenotypes from controls with 84% sensitivity and 85% specificity. DISCUSSION: The multiparametric MRI texture and DTI analysis demonstrated substantial alterations of the frontal and central CC with most significant alterations in callosal area III (motor segment) in ALS and separately in all investigated phenotypes (PLS, FAS, PBP, LMND) in comparison to controls, while no significant differences were observed between ALS and its phenotypes. The combination of the texture and the DTI parameters in an unbiased SVM-based approach might contribute as a neuroimaging marker for the assessment of the CC in ALS, including subtypes.

A multivariate Bayesian classification algorithm for cerebral stage prediction by diffusion tensor imaging in amyotrophic lateral sclerosis.

BACKGROUND AND OBJECTIVE: Diffusion tensor imaging (DTI) can be used to tract-wise map correlates of the sequential disease progression and, therefore, to assess disease stages of amyotrophic lateral sclerosis (ALS) in vivo. According to a threshold-based sequential scheme, a classification of ALS patients into disease stages is possible, however, several patients cannot be staged for methodological reasons. This study aims to implement a multivariate Bayesian classification algorithm for disease stage prediction at an individual ALS patient level based on DTI metrics of involved tract systems to improve disease stage mapping. METHODS: The analysis of fiber tracts involved in each stage of ALS was performed in 325 ALS patients and 130 age- and gender-matched healthy controls. Based on Bayes' theorem and in accordance with the sequential disease progression, a multistage classifier was implemented. Patients were categorized into in vivo DTI stages using the threshold-based method and the Bayesian algorithm. By the margin of confidence, the reliability of the Bayesian categorizations was accessible. RESULTS: Based on the Bayesian multistage classifier, 88% of all ALS patients could be assigned into an ALS stage compared to 77% using the threshold-based staging scheme. Additionally, the confidence of all classifications could be estimated. CONCLUSIONS: By the application of the multi-stage Bayesian classifier, an individualized in vivo cerebral staging of ALS patients was possible based on the sequentially involved tract systems and, furthermore, the reliability of the respective classifications could be determined. The Bayesian classification algorithm is an improvement of the threshold-based staging method and could provide a framework for extending the DTI-based in vivo cerebral staging in ALS.

Morphological alterations of the hypothalamus in idiopathic intracranial hypertension.

Background: The pathophysiology of idiopathic intracranial hypertension (IIH), a condition characterized by raised intracranial pressure, is not well understood. Objectives: We hypothesized that the hypothalamus might exhibit alterations in patients with IIH, based on its established association with obesity and the potential role of hormonal and metabolic factors in IIH. Design: Retrospective single-center cohort study. Methods: Thirty-three individuals with IIH and 40 matched healthy individuals were studied, including levels of the hormones and proteins leptin, adiponectin, ghrelin, insulin, growth/differentiation factor 15 (GDF15), somatostatin, and melatonin. In vivo high-resolution T1-weighted magnetic resonance imaging (MRI) data were analyzed by quantification of hypothalamic volumes using a well-established segmentation method, separate for the anterior and the posterior hypothalamic subvolumes. An additional analysis was performed using age, gender, and BMI-matched subgroups of 20 IIH patients and 20 controls. Results: The analysis of laboratory values showed significantly increased insulin, leptin, and melatonin levels for IIH patients in comparison to controls, while adiponectin levels were decreased in IIH; however, only melatonin level differences could be confirmed in the analysis with BMI matching. There was no statistical difference in total hypothalamus volumes between IIH and controls, but the hypothalamic morphology was altered in IIH patients with a lower volume of the anterior part of the hypothalamus and a higher volume of the posterior part; these results were identical in the analysis of the BMI-matched groups. The correlation analyses between hormonal changes and hypothalamic morphology did not achieve significant results. Conclusion: In this exploratory study, morphological abnormalities of the hypothalamus were observed to be associated with the IIH complex, although the mechanism remains to be unraveled. These findings expand the metabolic phenotype of IIH, but further functional studies are necessary to corroborate these data.

Longitudinal monitoring of amyotrophic lateral sclerosis by diffusion tensor imaging: Power calculations for group studies.

Introduction: Diffusion tensor imaging (DTI) can be used to map disease progression in amyotrophic lateral sclerosis (ALS) and therefore is a promising candidate for a biomarker in ALS. To this end, longitudinal study protocols need to be optimized and validated regarding group sizes and time intervals between visits. The objective of this study was to assess the influences of sample size, the schedule of follow-up measurements, and measurement uncertainties on the statistical power to optimize longitudinal DTI study protocols in ALS. Patients and methods: To estimate the measurement uncertainty of a tract-of-interest-based DTI approach, longitudinal test-retest measurements were applied first to a normal data set. Then, DTI data sets of 80 patients with ALS and 50 healthy participants were analyzed in the simulation of longitudinal trajectories, that is, longitudinal fractional anisotropy (FA) values for follow-up sessions were simulated for synthetic patient and control groups with different rates of FA decrease in the corticospinal tract. Monte Carlo simulations of synthetic longitudinal study groups were used to estimate the statistical power and thus the potentially needed sample sizes for a various number of scans at one visit, different time intervals between baseline and follow-up measurements, and measurement uncertainties. Results: From the simulation for different longitudinal FA decrease rates, it was found that two scans per session increased the statistical power in the investigated settings unless sample sizes were sufficiently large and time intervals were appropriately long. The positive effect of a second scan per session on the statistical power was particularly pronounced for FA values with high measurement uncertainty, for which the third scan per session increased the statistical power even further. Conclusion: With more than one scan per session, the statistical power of longitudinal DTI studies can be increased in patients with ALS. Consequently, sufficient statistical power can be achieved even with limited sample sizes. An improved longitudinal DTI study protocol contributes to the detection of small changes in diffusion metrics and thereby supports DTI as an applicable and reliable non-invasive biomarker in ALS.

Multimodal in vivo staging in amyotrophic lateral sclerosis using artificial intelligence.

BACKGROUND: The underlying neuropathological process of amyotrophic lateral sclerosis (ALS) can be classified in a four-stage sequential pTDP-43 cerebral propagation scheme. Using diffusion tensor imaging (DTI), in vivo imaging of these stages has already been shown to be feasible for the specific corticoefferent tract systems. Because both cognitive and oculomotor dysfunctions are associated with microstructural changes at the brain level in ALS, a cognitive and an oculomotor staging classification were developed, respectively. The association of these different in vivo staging schemes has not been attempted to date. METHODS: A total of 245 patients with ALS underwent DTI, video-oculography, and cognitive testing using Edinburgh Cognitive and Behavioral ALS Screen (ECAS). A set of tract-related diffusion metrics, cognitive, and oculomotor parameters was selected for further analysis. Hierarchical and k-means clustering algorithms were used to obtain an optimal cluster solution. RESULTS: According to cluster analysis, differentiation of patients with ALS into four clusters resulted: Cluster A showed the highest fractional anisotropy (FA) values and thereby the best performances in executive oculomotor tasks and cognitive tests, whereas cluster D showed the lowest FA values, the lowest ECAS scores, and the worst executive oculomotor performance across all clusters. Clusters B and C showed intermediate results regarding parameter values. DISCUSSION: In a multimodal dataset of technical assessments of brain structure and function in ALS, an artificial intelligence-based cluster analysis showed high congruence of DTI, executive oculomotor function, and neuropsychological performance for mapping in vivo correlates of neuropathological spreading.

Artificial neural networks for non-linear age correction of diffusion metrics in the brain.

Human aging is characterized by progressive loss of physiological functions. To assess changes in the brain that occur with increasing age, the concept of brain aging has gained momentum in neuroimaging with recent advancements in statistical regression and machine learning (ML). A common technique to assess the brain age of a person is, first, fitting a regression model to neuroimaging data from a group of healthy subjects, and then, using the resulting model for age prediction. Although multiparametric MRI-based models generally perform best, models solely based on diffusion tensor imaging have achieved similar results, with the benefits of faster data acquisition and better replicability across scanners and field strengths. In the present study, we developed an artificial neural network (ANN) for brain age prediction based upon tract-based fractional anisotropy (FA). Consequently, we investigated if this age-prediction model could also be used for non-linear age correction of white matter diffusion metrics in healthy adults. The brain age prediction accuracy of the ANN (R 2 = 0.47) was similar to established multimodal models. The comparison of the ANN-based age-corrected FA with the tract-wise linear age-corrected FA resulted in an R 2 value of 0.90 [0.82; 0.93] and a mean difference of 0.00 [-0.04; 0.05] for all tract systems combined. In conclusion, this study demonstrated the applicability of complex ANN models to non-linear age correction of tract-based diffusion metrics as a proof of concept.

Age-Related Alterations in DTI Metrics in the Human Brain-Consequences for Age Correction.

Background: Over the life span, the diffusion metrics in brain MRI show different, partly nonlinear changes. These age-dependent changes also seem to exhibit regional differences with respect to the brain anatomy. The age correction of a study cohort's diffusion metrics might thus require consideration of age-related factors. Methods: Diffusion tensor imaging data sets were acquired from 219 healthy participants at ages between 19 and 81 years. Fractional anisotropy (FA), mean diffusivity (MD), and axial and radial diffusivity (AD and RD, respectively) maps were analyzed by a tract of interest-based fiber tracking approach. To describe diffusion metrics as a function of the participant age, linear splines were used to perform curve fitting in 21 specific tract systems covering different functional areas and diffusion directions. Results: In the majority of tracts, an interpolation with a change of alteration rate during adult life described the diffusion properties more accurately than a linear model. Consequently, the diffusion properties remained relatively stable until a decrease (of FA) or increase (of MD, AD, and RD) started at a region-specific time point, whereas a uniform change of diffusion properties was observed only in a few tracts. Single tracts, e.g., located in the cerebellum, remained nearly unaltered throughout the ages between 19 and 81 years. Conclusions: Age corrections of diffusion properties should not be applied to all white matter regions and all age spans in the same way. Therefore, we propose three different approaches for age correction based on fiber tracking techniques, i.e., no correction for areas that do not experience age-related changes and two variants of an age correction depending on the age range of the cohort and the tracts considered.

Making America Great Again? National Nostalgia's Effect on Outgroup Perceptions.

Nostalgia is a fond longing for the past that has been shown to increase feelings of meaning, social connectedness, and self-continuity. Although nostalgia for personal memories provides intra- and interpersonal benefits, there may be negative consequences of group-based nostalgia on the perception and acceptance of others. The presented research examined national nostalgia (a form of collective nostalgia), and its effects on group identification and political attitudes in the United States. In a sample of US voters (N = 252), tendencies to feel personal and national nostalgia are associated with markedly different emotional and attitudinal profiles. Higher levels of national nostalgia predicted both positive attitudes toward President Trump and racial prejudice, though there was no evidence of such relationships with personal nostalgia. National nostalgia most strongly predicted positive attitudes toward president Trump among those high in racial prejudice. Furthermore, nostalgia's positive relationship with racial prejudice was partially mediated by perceived outgroup threat. Results from this study will help us better understand how the experience of national nostalgia can influence attitudes and motivate political behavior.

Why Can't I Resist Those "Puppy Dog" (or "Kitty Cat") Eyes? A Study of Owner Attachment and Factors Associated with Pet Obesity.

Attachment theory posits that patterns of interaction derived from the attachment system provide a starting point for understanding how people both receive and provide care. Extending this theory to human-animal interactions provides insights into how human psychology affects pets, such as pet obesity. The goal of this study was to determine how attachment anxiety and avoidance might contribute to pet obesity. We assessed 563 pet owners' attachment-related anxiety and avoidance, as well as additional attachment-related constructs (emotional rejection, evaluation concern, caregiving, and attentiveness to a pet). We also assessed various factors associated with pet obesity, including weight, body condition, daily treats, and daily interaction. The results indicate that dog owners high in attachment anxiety are concerned about how their pet may evaluate them, leading to more caregiving and attentiveness that results in more treats given per day, and a larger body condition (but not weight). In addition, owners high in attachment avoidance may seek to downplay the possibility of the dog negatively evaluating them, thus providing more negligent care. These findings suggest that attachment plays a unique role in shaping the pet-caregiver relationship and influences various elements that contribute to pet obesity, particularly in dogs. As such, the findings may lend a novel perspective to strategies for reducing pet obesity and provide a framework for future research into pet health.

Multiparametric Microstructural MRI and Machine Learning Classification Yields High Diagnostic Accuracy in Amyotrophic Lateral Sclerosis: Proof of Concept.

The potential of multiparametric quantitative neuroimaging has been extensively discussed as a diagnostic tool in amyotrophic lateral sclerosis (ALS). In the past, the integration of multimodal, quantitative data into a useful diagnostic classifier was a major challenge. With recent advances in the field, machine learning in a data driven approach is a potential solution: neuroimaging biomarkers in ALS are mainly observed in the cerebral microstructure, with diffusion tensor imaging (DTI) and texture analysis as promising approaches. We set out to combine these neuroimaging markers as age-corrected features in a machine learning model with a cohort of 502 subjects, divided into 404 patients with ALS and 98 healthy controls. We calculated a linear support vector classifier (SVC) which is a very robust model and then verified the results with a multilayer perceptron (MLP)/neural network. Both classifiers were able to separate ALS patients from controls with receiver operating characteristic (ROC) curves showing an area under the curve (AUC) of 0.87-0.88 ("good") for the SVC and 0.88-0.91 ("good" to "excellent") for the MLP. Among the coefficients of the SVC, texture data contributed the most to a correct classification. We consider these results as a proof of concept that demonstrated the power of machine learning in the application of multiparametric quantitative neuroimaging data to ALS.

Segmental involvement of the corpus callosum in C9orf72-associated ALS: a tract of interest-based DTI study.

BACKGROUND: C9orf72 hexanucleotide repeat expansions are associated with widespread cerebral alterations, including white matter alterations. However, there is lack of information on changes in commissure fibres. Diffusion tensor imaging (DTI) can identify amyotrophic lateral sclerosis (ALS)-associated patterns of regional brain alterations at the group level. The objective of this study was to investigate the structural connectivity of the corpus callosum (CC) in ALS patients with C9orf72 expansions. METHODS: DTI-based white matter mapping was performed by a hypothesis-guided tractwise analysis of fractional anisotropy (FA) maps for 25 ALS patients with C9orf72 expansion versus 25 matched healthy controls. Furthermore, a comparison with a patient control group of 25 sporadic ALS patients was performed. DTI-based tracts that originate from callosal sub-areas I to V were identified and correlated with clinical data. RESULTS: The analysis of white matter integrity demonstrated regional FA reductions for tracts of the callosal areas II and III for ALS patients with C9orf72 expansions while FA reductions in sporadic ALS patients were observed only for tracts of the callosal area III; these reductions were correlated with clinical parameters. CONCLUSION: The tract-of-interest-based analysis showed a microstructural callosal involvement pattern in C9orf72-associated ALS that included the motor segment III together with frontal callosal connections, as an imaging signature of the C9orf72-associated overlap of motor neuron disease and frontotemporal pathology.

Diagnostic value of video-oculography in progressive supranuclear palsy: a controlled study in 100 patients.

BACKGROUND: The eponymous feature of progressive supranuclear palsy (PSP) is oculomotor impairment which is one of the relevant domains in the Movement Disorder Society diagnostic criteria. OBJECTIVE: We aimed to investigate the value of specific video-oculographic parameters for the use as diagnostic markers in PSP. METHODS: An analysis of video-oculography recordings of 100 PSP patients and 49 age-matched healthy control subjects was performed. Gain of smooth pursuit eye movement and latency, gain, peak eye velocity, asymmetry of downward and upward velocities of saccades as well as rate of saccadic intrusions were analyzed. RESULTS: Vertical saccade velocity and saccadic intrusions allowed for the classification of about 70% and 56% of the patients, respectively. By combining both parameters, almost 80% of the PSP patients were covered, while vertical velocity asymmetry was observed in approximately 34%. All parameters had a specificity of above 95%. The sensitivities were lower with around 50-60% for the velocity and saccadic intrusions and only 27% for vertical asymmetry. CONCLUSIONS: In accordance with oculomotor features in the current PSP diagnostic criteria, video-oculographic assessment of vertical saccade velocity and saccadic intrusions resulted in very high specificity. Asymmetry of vertical saccade velocities, in the opposite, did not prove to be useful for diagnostic purposes.

How to Arrange Follow-Up Time-Intervals for Longitudinal Brain MRI Studies in Neurodegenerative Diseases.

BACKGROUND: Longitudinal brain MRI monitoring in neurodegeneration potentially provides substantial insights into the temporal dynamics of the underlying biological process, but is time- and cost-intensive and may be a burden to patients with disabling neurological diseases. Thus, the conceptualization of follow-up time-intervals in longitudinal MRI studies is an essential challenge and substantial for the results. The objective of this work is to discuss the association of time-intervals and the results of longitudinal trends in the frequently used design of one baseline and two follow-up scans. METHODS: Different analytical approaches for calculating the linear trend of longitudinal parameters were studied in simulations including their performance of dealing with outliers; these simulations were based on the longitudinal striatum atrophy in MRI data of Huntington's disease patients, detected by atlas-based volumetry (ABV). RESULTS: For the design of one baseline and two follow-up visits, the simulations with outliers revealed optimum results for identical time-intervals between baseline and follow-up scans. However, identical time-intervals between the three acquisitions lead to the paradox that, depending on the fit method, the first follow-up scan results do not influence the final results of a linear trend analysis. CONCLUSIONS: This theoretical study analyses how the design of longitudinal imaging studies with one baseline and two follow-up visits influences the results. Suggestions for the analysis of longitudinal trends are provided.

Eye movement alterations in presymptomatic C9orf72 expansion gene carriers.

OBJECTIVE: The clinical manifestation of amyotrophic lateral sclerosis (ALS) is characterized by motor neuron degeneration, whereas frontotemporal dementia (FTD) patients show alterations of behavior and cognition. Both share repeat expansions in C9orf72 as the most prevalent genetic cause. Before disease-defining symptoms onset, structural and functional changes at cortical level may emerge in C9orf72 carriers. Here, we characterized oculomotor parameters and their association to neuropsychological domains in apparently asymptomatic individuals with mutations in ALS/FTD genes. PATIENTS AND METHODS: Forty-eight carriers of ALS genes, without any clinical symptoms underwent video-oculographic examination, including 22 subjects with C9orf72 mutation, 17 with SOD1, and 9 with other ALS associated gene mutations (n = 3 KIF5A; n = 3 FUS/FUS + TBK1; n = 1 NEK1; n = 1 SETX; n = 1 TDP43). A total of 17 subjects underwent a follow-up measurement. Data were compared to 54 age- and gender-matched healthy controls. Additionally, mutation carriers performed a neuropsychological assessment. RESULTS: In comparison to controls, the presymptomatic subjects performed significantly worse in executive oculomotor tasks such as the ability to perform correct anti-saccades. A gene mutation subgroup analysis showed that dysfunctions in C9orf72 carriers were much more pronounced than in SOD1 carriers. The anti-saccade error rate of ALS mutation carriers was associated with cognitive deficits: this correlation was increased in subjects with C9orf72 mutation, whereas SOD1 carriers showed no associations. CONCLUSION: In C9orf72 carriers, executive eye movement dysfunctions, especially the increased anti-saccade error rate, were associated with cognitive impairment and unrelated to time. These oculomotor impairments are in support of developmental deficits in these mutations, especially in prefrontal areas.

To Help or To Harm? Assessing the Impact of Envy on Prosocial and Antisocial Behaviors.

Two studies examined how envy influences prosocial and antisocial behavior. In Experiment 1, participants in an envious state (relative to a neutral state) were less helpful: They picked up fewer dropped pencils in their immediate vicinity. We expanded upon these findings by examining how envy affected both helping and harming behavior in a competitive scenario. In Experiment 2, individuals in envious or neutral states assigned puzzle tasks to another student in a prisoner's dilemma style scenario. Prosocial and antisocial behaviors were assessed via the difficulty of the assigned puzzles (easy puzzles were considered helpful and difficult puzzles were harmful). We hypothesized that experiencing envy would result in greater motive to harm as well as greater likelihood of engaging in harmful behavior. The hypothesis was supported, suggesting that envy has detrimental ramifications that go beyond the individual and extend to interpersonal relationships.

Gray (Literature) Matters: Evidence of Selective Hypothesis Reporting in Social Psychological Research.

Selective reporting practices (SRPs)-adding, dropping, or altering study elements when preparing reports for publication-are thought to increase false positives in scientific research. Yet analyses of SRPs have been limited to self-reports or analyses of pre-registered and published studies. To assess SRPs in social psychological research more broadly, we compared doctoral dissertations defended between 1999 and 2017 with the publications based on those dissertations. Selective reporting occurred in nearly 50% of studies. Fully supported dissertation hypotheses were 3 times more likely to be published than unsupported hypotheses, while unsupported hypotheses were nearly 4 times more likely to be dropped from publications. Few hypotheses were found to be altered or added post hoc. Dissertation studies with fewer supported hypotheses were more likely to remove participants or measures from publications. Selective hypothesis reporting and dropped measures significantly predicted greater hypothesis support in published studies, supporting concerns that SRPs may increase Type 1 error risk.