Biology subjects

Leibenluft, E.

Publications and source records attributed to Leibenluft, E..

7 recordsLinked to original sources

bioRxiv2023

A New Behavioral Paradigm for Studying Frustrative Non-reward in Juvenile Mice

BackgroundIrritability, defined as proneness to anger, can reach a pathological extent. It is a defining symptom of Disruptive Mood Dysregulation Disorder (DMDD) and one of the most common reasons youth presents for psychiatric evaluation and care. Aberrant responses to frustrative non-reward (FNR, the response to omission of expected reward) are central to the pathophysiology of irritability. FNR is a translational construct to study irritability across species. The development of preclinical FNR models would advance mechanistic studies of the important and relatively understudied clinical phenomenon of irritability. MethodsWe used FNR as a conceptual framework to develop a novel mouse behavioral paradigm named Alternate Poking Reward Omission (APRO). After APRO, mice were examined with a battery of behavioral tests and processed for whole brain c-Fos imaging. FNR increases locomotion and aggression in mice regardless of sex. These behavioral changes resemble the symptoms observed in youth with severe irritability. There is no change in anxiety-like, depression-like, or non-aggressive social behaviors. FNR increases c-Fos+ neurons in 13 subregions of thalamus, iso-cortex and hippocampus including the prelimbic, ACC, hippocampus, dorsal thalamus, cuneiform nucleus, pons, and pallidum areas. FNR also shifts the brain network towards a more global processing mode. ConclusionOur novel FNR paradigm produces a frustration effect and alters brain processing in ways resembling the symptoms and brain network reconfiguration observed in youth with severe irritability. The novel behavioral paradigm and identified brain regions lay the groundwork for further mechanistic studies of frustration and irritability in rodents.

neuroscience↗

bioRxiv2023

BOLD response is more than just magnitude: improving detection sensitivity through capturing hemodynamic profiles

Typical FMRI analyses assume a canonical hemodynamic response function (HRF) with a focus on the overshoot peak height, while other morphological aspects are largely ignored. Thus, in most reported analyses, the overall effect is reduced from a curve to a single scalar. Here, we adopt a data-driven approach to HRF estimation at the whole-brain voxel level, without assuming a response profile at the individual level. Then, we estimate the response in its entirety with a roughness penalty at the population level to improve predictive accuracy, inferential efficiency, and cross-study reproducibility. Using a fast event-related FMRI dataset, we demonstrate the extent of under-fitting and information loss that occurs when adopting the canonical approach. We also address the following questions: O_LIHow much does the HRF shape vary across regions, conditions, and groups? C_LIO_LIDoes an agnostic approach improve sensitivity to detect an effect compared to an assumed HRF? C_LIO_LICan examining HRF shape help validate the presence of an effect complementing statistical evidence? C_LIO_LICould the HRF shape provide evidence for whole-brain BOLD response during a simple task? C_LI

neuroscience↗

bioRxiv2022

Systematic Review and Meta-Analysis: Task-based fMRI Studies in Youths with Irritability

ObjectiveChildhood irritability, operationalized as disproportionate and frequent temper tantrums and low frustration tolerance relative to peers, is a transdiagnostic symptom across many pediatric disorders. Studies using task-dependent functional magnetic resonance imaging (fMRI) to probe neural dysfunction in irritability have increased. However, an integrated review summarizing the published methods and synthesized fMRI results remains lacking. MethodWe conducted a systematic search using irritability terms and task functional neuroimaging in key databases in March 2021, and identified 30 studies for our systematic review. Sample characteristics and fMRI methods were summarized. A subset of 28 studies met the criteria for extracting coordinate-based data for quantitative meta-analysis. Ten activation-likelihood estimations were performed to examine neural convergence across irritability measures and fMRI task domains. ResultsSystematic review revealed small sample sizes (median = 58, mean age range = 8-16 years) with heterogeneous sample characteristics, irritability measures, tasks, and analytical procedures. Meta-analyses found no evidence for neural activation convergence of irritability across neurocognitive functions related to emotional reactivity, cognitive control, and reward processing, nor within each domain. Sensitivity analyses partialing out variances driven by heterogeneous tasks, irritability measures, stimulus types, and developmental ages all yielded null findings. ConclusionThe lack of neural convergence suggests a need for common, standardized irritability assessments and more homogeneous fMRI tasks. Thoughtfully designed fMRI studies probing commonly defined neurocognitive functions may be more fruitful to elucidate the neural mechanisms of irritability. Open science practices, data mining in large neuroscience databases, and standardized analytical methods promote meaningful collaboration in irritability research.

neuroscience↗

bioRxiv2021

Mobile Footprinting: Linking Individual Distinctiveness in Mobility Patterns to Mood, Sleep, and Brain Functional Connectivity

Mapping individual differences in behavior is fundamental to personalized neuroscience. Here, we establish that statistical patterns of smartphone-based mobility features represent unique "footprints" that allow individual identification. Critically, mobility footprints exhibit varying levels of person-specific distinctiveness and are associated with individual differences in affective instability, circadian irregularity, and brain functional connectivity. Together, this work suggests that real-world mobility patterns may provide an individual-specific signature linking brain, behavior, and mood.

neuroscience↗

bioRxiv2019

Accelerated Cortical Thinning within Structural Brain Networks is Associated with Irritability in Youth

BackgroundIrritability is an important dimension of psychopathology that spans multiple clinical diagnostic categories, yet its relationship to patterns of brain development remains sparsely explored. Here, we examined how trans-diagnostic symptoms of irritability relate to the development of structural brain networks.\n\nMethodsAll participants (n=144, 87 females) completed structural brain imaging with 3 Tesla MRI at two timepoints (mean age at follow-up: 20.9 years, mean inter-scan interval: 5.1 years). Irritability at follow-up was assessed using the Affective Reactivity Index, and cortical thickness was quantified using Advanced Normalization Tools software. Structural covariance networks were delineated using non-negative matrix factorization, a multivariate analysis technique. Both cross-sectional and longitudinal associations with irritability at follow-up were evaluated using generalized additive models with penalized splines. The False Discovery Rate (q<0.05) was used to correct for multiple comparisons.\n\nResultsCross-sectional analysis of follow-up data revealed that 11 of the 24 covariance networks were associated with irritability, with higher levels of irritability being associated with thinner cortex. Longitudinal analyses further revealed that accelerated cortical thinning within 9 networks was related to irritability at follow-up. Effects were particularly prominent in brain regions implicated in emotion regulation, including the orbitofrontal, lateral temporal, and medial temporal cortex.\n\nConclusionsCollectively, these findings suggest that irritability is associated with widespread cortical thickness reductions and accelerated cortical thinning, particularly within frontal and temporal cortex. Aberrant structural maturation of regions important for emotional regulation may in part underlie symptoms of irritability.

neuroscience↗

bioRxiv2018

Identifying novel subtypes of irritability using a developmental genetic approach

ObjectiveIrritability is a common reason for referral to services, strongly associated with impairment and negative outcomes, but is a nosological and treatment challenge. A major issue is how irritability should be conceptualized. This study used a developmental approach to test the hypothesis that there are several forms of irritability, including a neurodevelopmental/ADHD-like subtype with onset in childhood and a depression/mood subtype with onset in adolescence.\n\nMethodData were analyzed in the Avon Longitudinal Study of Parents and Children, a prospective UK population-based cohort. Irritability trajectory-classes were estimated for 7924 individuals with data at multiple time-points across childhood and adolescence (4 possible time-points from approximately ages 7 to 15 years). Psychiatric diagnoses were assessed at approximately ages 7 and 15 years. Psychiatric genetic risk was indexed by polygenic risk scores (PRS) for attention-deficit/hyperactivity disorder (ADHD) and major depressive disorder (MDD) derived using large genome-wide association study results.\n\nResultsFive irritability trajectory classes were identified: low (81.2%), decreasing (5.6%), increasing (5.5%), late-childhood limited (5.2%) and high-persistent (2.4%). The early-onset, high-persistent trajectory was associated with male preponderance, childhood ADHD (OR=108.64 (57.45-204.41), p<0.001) and ADHD PRS (OR=1.31 (1.09-1.58), p=0.005); the adolescent-onset, increasing trajectory was associated with female preponderance, adolescent MDD (OR=5.14 (2.47-10.73), p<0.001) and MDD PRS (OR=1.20, (1.05-1.38), p=0.009). Both trajectory classes were associated with MDD diagnosis and ADHD genetic risk.\n\nConclusionsThe developmental context of irritability may be important in its conceptualization: early-onset persistent irritability maybe more neurodevelopmental/ADHD-like and later-onset irritability more depression/mood-like. This has implications for treatment as well as nosology.

genetics↗

bioRxiv2017

Intraclass correlation: improved modeling approaches and applications for neuroimaging

Intraclass correlation (ICC) is a reliability metric that gauges similarity when, for example, entities are measured under similar, or even the same, well-controlled conditions, which in MRI applications include runs/sessions, twins, parent/child, scanners, sites, etc. The popular definition and interpretation of ICC are usually framed statistically under the conventional ANOVA platform. Here, we show that the ANOVA frame-work is often limited, rigid, and inflexible in modeling capabilities. Additionally we provide a comprehensive overview of ICC analysis in its prior usage in neuroimaging. These intrinsic limitations motivate several novel improvements. Specifically, we start with the conventional ICC model under the ANOVA platform, and extend it along two dimensions: first, fixing the failure in ICC estimation when negative values occur under degen-erative circumstance, and second, incorporating reliability information of effect estimates into the ICC model. These endeavors lead to four modeling strategies: linear mixed-effects (LME), Bayesian mixed-effects (BME), multilevel mixed-effects (MME), and Bayesian multilevel mixed-effects (BMME). Compared to ANOVA, each of these four models directly provides estimates for fixed effects as well as their statistical significances, in addition to the ICC estimate. These new modeling approaches can also accommodate missing data as well as fixed effects for confounding variables. More importantly, we show that the novel MME and BMME approaches offer more accurate characterization and decomposition among the variance components, leading to more robust ICC computation. Based on these theoretical considerations and model performance comparisons with a real experimental dataset, we offer the following general-purpose recommendations. First, ICC estimation through MME or BMME is preferable when precision information is available for the effect estimate; precision information provides weights that more accurately allocate the variances in the data. When precision information is unavailable, ICC estimation through LME or the novel BME is the preferred option. Second, even though the absolute agreement version, ICC(2,1), is presently more popular in the field, the consistency version, ICC(3,1), is a practical and informative choice for whole-brain ICC analysis that achieves a well-balanced compromise when all potential fixed effects are accounted for. Third, approaches for clear, meaningful, and useful result reporting in ICC analysis are discussed. All models, ICC formulations, and related statistical testing methods have been implemented in an open source program 3dICC, which is publicly available as part of the AFNI suite. Even though our work here focuses on the whole brain level, the modeling strategy and recommendations can be equivalently applied to other situations such as voxel, region, and network levels.

neuroscience↗