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How often a researcher is cited usually plays a decisive role in that person's career advancement, because academic institutions often use citation metrics, either explicitly or implicitly, to estimate research impact and productivity. Research has shown, however, that citation patterns and practices are affected by various biases, including the prestige of the authors being cited and their gender, race, and nationality, whether self-attested or perceived. Some commentators have proposed that researchers can address biases related to social identity or position by including a Citation Diversity Statement in a manuscript submitted for publication. A Citation Diversity Statement is a paragraph placed before the reference section of a manuscript in which the authors address the diversity and equitability of their references in terms of gender, race, ethnicity, or other factors and affirm a commitment to promoting equity and diversity in sources and references. The present commentary considers arguments in favor of Citation Diversity Statements, and some practical and ethical issues that these statements raise. [ABSTRACT FROM AUTHOR]

The deleterious effects of adversity are likely intergenerational, such that one generation's adverse experiences can affect the next. Epidemiological studies link maternal adversity to offspring depression and anxiety, possibly via transmission mechanisms that influence offspring fronto-limbic connectivity. However, studies have not thoroughly disassociated postnatal exposure effects nor considered the role of offspring sex. We utilized infant neuroimaging to test the hypothesis that maternal childhood maltreatment (CM) would be associated with increased fronto-limbic connectivity in infancy and tested brain-behavior associations in childhood. Ninety-two dyads participated (32 mothers with CM, 60 without; 52 infant females, 40 infant males). Women reported on their experiences of CM and non-sedated sleeping infants underwent MRIs at 2.44 ± 2.74 weeks. Brain volumes were estimated via structural MRI and white matter structural connectivity (fiber counts) via diffusion MRI with probabilistic tractography. A subset of parents (n = 36) reported on children's behaviors at age 5.17 ± 1.73 years. Males in the maltreatment group demonstrated greater intra-hemispheric fronto-limbic connectivity (b = 0.96, p= 0.008, [95%CI 0.25, 1.66]), no differences emerged for females. Fronto-limbic connectivity was related to somatic complaints in childhood only for males (r = 0.673, p = 0.006). Our findings suggest that CM could have intergenerational associations to offspring brain development, yet mechanistic studies are needed. [ABSTRACT FROM AUTHOR]

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high‐resolution, high‐quality magnetic resonance images. These methods, however, may not translate to low‐resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low‐resource settings where low‐field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image‐based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low‐field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high‐resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray‐to‐white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to CM. [ABSTRACT FROM AUTHOR]

Objective Given the association between essential tremor (ET) and higher rates of cognitive decline, assessing this decline is an important element of research and clinical care. The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are two broad, brief measures that are widely used to monitor cognitive impairment in various neurological disorders. We sought to determine the relative ability of the MoCA and the MMSE to detect cognitive decline in elderly patients with ET. Methods We administered a neuropsychological battery to an ET cohort every 18 months over 4.5 years. We defined the gold standard for change in cognition as the change in neuropsychological test scores over consecutive evaluations and applied the Reliable Change Method to detect meaningful increases/decreases in test scores. We performed receiver operating characteristics (ROC) analysis to quantify the area under the curve (AUC) and compare the ability of the MoCA and the MMSE to detect cognitive decline. Results The AUCs for the MoCA and the MMSE did not differ significantly at any interval or when all intervals were pooled for analysis. Across all intervals, the ability of the MoCA and the MMSE to detect cognitive decline was consistently fair or poor. Conclusions We found that the ability of the MoCA and the MMSE to detect cognitive decline in ET patients over 18-month intervals is inadequate. Unchanged scores on the MoCA and the MMSE in ET over time should be approached with caution. We propose that these screening tools should be supplemented with additional neuropsychological tests. [ABSTRACT FROM AUTHOR]

Introduction: This study aims to measure frequency and correlates of initial idiopathic psychiatric diagnosis in a cohort of 147 patients with Frontotemporal Dementia (FTD)-spectrum disorders. Methods: Participants were evaluated at the National Institutes of Health in Bethesda, Maryland. Initial participant diagnoses were determined by chart review and patient and informant interviews. Logistic regression was used to assess the relationships between diagnosis and age of symptom onset, gender, education, family history of psychiatric illness, and family history of dementia. Additional exploratory analyses investigated patients' first symptom type. Results: 25% (n=43) of all the patients reviewed were initially misdiagnosed with an idiopathic psychiatric illness, which is less than half the commonly cited 50% rate.3 Depression was the most common misdiagnosis (46.5%). Family history of dementia, family history of mental illness and an exploratory analysis of behavioral first symptoms suggested significant association with a greater likelihood of initial idiopathic psychiatric diagnosis in FTD patients. Discussion: This data confirms patterns of initial idiopathic psychiatric diagnosis in FTD and elucidates potential factors underlying misdiagnosis. Potential implications for patient outcomes, caregiver burden and healthcare costs are discussed. [ABSTRACT FROM AUTHOR]

A great deal of neuroimaging research focuses on voxel-wise analysis or segmentation of damaged tissue, yet many diseases are characterized by diffuse or non-regional neuropathology. In simple cases, these processes can be quantified using summary statistics of voxel intensities. However, the manifestation of a disease process in imaging data is often unknown, or appears as a complex and nonlinear relationship between the voxel intensities on various modalities. When the relevant pattern is unknown, summary statistics are often unable to capture differences between disease groups, and their use may encourage post hoc searches for the optimal summary measure. In this study, we introduce the multi-modal density testing (MMDT) framework for the naive discovery of group differences in voxel intensity profiles. MMDT operationalizes multi-modal magnetic resonance imaging (MRI) data as multivariate subject-level densities of voxel intensities and utilizes kernel density estimation to develop a local two-sample test for individual points within the density space. Through simulations, we show that this method controls type I error and recovers relevant differences when applied to a specified point. Additionally, we demonstrate the ability to maintain power while controlling the family-wise error rate and false discovery rate when applying the test over a grid of points within the density space. Finally, we apply this method to a study of subjects with either multiple sclerosis (MS) or conditions that tend to mimic MS on MRI, and find significant differences between the two groups in their voxel intensity profiles within the thalamus. [ABSTRACT FROM AUTHOR]