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Wolf, D. H.

Publications and source records attributed to Wolf, D. H..

At least 19 recordsLinked to original sources

Effects of dopamine and serotonin on persistence for delayed rewards in humans

People often must decide how long to continue waiting for rewards that will arrive at an uncertain time in the future. We propose that these persistence decisions involve weighing the benefits of continued waiting against opportunity costs of waiting, a balance that may shift over time. This framework suggests that persistence decisions share neural mechanisms with foraging decisions, which require ongoing comparisons between a current resource and possible alternatives. Dopamine and serotonin have been proposed to play opposing roles in foraging, with dopamine promoting exploration and serotonin promoting exploitation. Here we investigated their roles in persistence. In a within-subjects, double-blind, placebo-controlled study in young adults (n = 42), we examined the effects of increasing dopamine with L-dopa and increasing serotonin with escitalopram. We predicted that L-dopa would decrease persistence and escitalopram would increase it. Participants also completed patch-foraging, time perception, risk tolerance, and temporal discounting tasks to explore potential mechanisms of drug effects on persistence. Escitalopram increased persistence, after adjusting for the effects of anxiety and condition order, such that participants waited longer for rewards after taking the serotonergic drug. L-dopa did not influence persistence. In exploratory analyses controlling for age, however, L-dopa reduced persistence and increased exploration in foraging.

neuroscience↗

The PMADS Project: A Longitudinal Multimodal Cohort Study to Understand Risk for Perinatal Mood and Anxiety Disorders

BackgroundPerinatal mood and anxiety disorders (PMADs) are among the most common and consequential complications of pregnancy. The perinatal period is also characterized by profound hormonal fluctuations and large-scale brain plasticity. However, the mechanisms linking these neurobiological changes to psychiatric risk are poorly understood. Prospective, clinically informed studies are needed to identify quantitative biomarkers and clarify pathways linking perinatal neurobiology to PMADs risk. MethodsThis report describes the design of a prospective, longitudinal cohort study integrating multimodal neuroimaging, biofluid sampling, and deep clinical phenotyping to enable precision characterization of neurobiological trajectories of PMADs risk. Twenty-five individuals at elevated risk for PMADs will be recruited prior to conception and followed across six in-person timepoints spanning the menstrual cycle, pregnancy, and early postpartum, with additional remote follow-ups through the first postpartum year. Data collection includes high-resolution structural MRI, functional brain mapping using multi-echo resting-state fMRI, diffusion MRI, arterial spin labeling, ultra-high field MR-based techniques for measuring glutamate (GluCEST and 1HMRS), biofluid sampling, and comprehensive clinical, behavioral, and cognitive assessments. Structured clinical interviews assess categorical diagnoses while dimensional symptom measures capture heterogeneity and transdiagnostic features of perinatal psychopathology. Longitudinal analyses will model nonlinear trajectories of brain and symptom change across the perinatal period as well as evaluate whether preconception network features and menstrual cycle-related brain changes are associated with subsequent perinatal symptom emergence. DiscussionThis cohort study establishes a longitudinal, multimodal framework for investigating neurobiological changes across the transition to pregnancy in individuals at elevated risk for PMADs. By anchoring pregnancy-related brain changes to preconception and menstrual cycle-related variability within the same individuals, this study is designed to evaluate associations between preconception hormone sensitivity, pregnancy-induced neuroplasticity, and PMADs risk. The resulting dataset will provide a deeply phenotyped longitudinal resource for investigating brain-behavior relationships across the perinatal period. Findings are expected to inform future larger-scale studies aimed at advancing mechanistic understanding of PMADs, improving individualized risk stratification, and supporting development of personalized preventive and neuromodulatory interventions.

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An Open, Fully-processed, Longitudinal Data Resource to Study Brain Development and Transdiagnostic Executive Function

Executive function (EF) develops rapidly during adolescence. However, deficits in EF also emerge in adolescence, representing a transdiagnostic symptom associated with many forms of psychopathology. To promote transdiagnostic research on EF during development, we introduce a new data resource - the Penn Longitudinal Executive functioning in Adolescent Development study (Penn LEAD) - that combines longitudinal multimodal imaging data with rich clinical and cognitive phenotyping. These data include 225 imaging sessions from 132 individuals (8-16 years old at the time of enrollment) who are typically developing (27.3%), or meet criteria for attention-deficit hyperactivity disorder (20.5%) or the psychosis-spectrum (52.3%). In addition to phenotypic data from multiple cognitive tasks focused on EF, the study includes data from structural MRI, diffusion MRI, n-back task fMRI, resting-state fMRI, and arterial spin-labeled MRI. Notably, all raw data, fully-processed derived data, and detailed quality control recommendations are publicly shared on OpenNeuro. We anticipate that such analysis-ready data will accelerate research on EF development in psychiatry.

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Cannabis Use and Glutamate across the Psychosis Spectrum: In Vivo Evidence from 7T Proton Magnetic Resonance Spectroscopy

Cannabis use is linked to elevated psychosis risk, yet the neurobiological mechanisms that couple use to symptom expression remain unclear. Because glutamatergic dysregulation has been implicated in both cannabis effects and psychosis vulnerability, we examined whether brain glutamate relates to dimensional symptoms as a function of cannabis use across the psychosis spectrum. Seventy-nine participants--typically developing controls, clinical high-risk individuals, and patients with psychosis--completed dimensional clinical assessments, detailed cannabis surveys, urine toxicology, and ultra-high-field 7T 1HMRS quantification of anterior cingulate cortex (ACC) glutamate levels. Linear models assessed the main and interactive effects of ACC glutamate and cannabis use on positive and negative symptoms. Self-reported cannabis use showed strong concordance with urine toxicology. Cannabis use was associated with higher positive and negative symptoms. Independently, higher ACC glutamate predicted greater positive and negative symptoms. Notably, lower glutamate levels were associated with higher positive symptoms in cannabis users. Exploratory analyses suggested interactions for depressive and manic symptoms, indicating that glutamatergic abnormalities may amplify the overall severity of cannabis-related symptoms. Sensitivity analyses revealed lower ACC glutamate in psychosis patients--especially cannabis users--highlighting diagnostic group differences and reinforcing the link between cannabis exposure and glutamatergic dysfunction. These findings implicate ACC glutamatergic dysfunction as a transdiagnostic correlate of symptom burden, particularly in those with psychosis who are cannabis users. Glutamate-targeted interventions and longitudinal designs will be needed to examine causal pathways linking cannabis exposure to psychosis-relevant outcomes.

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An open, fully-processed data resource for studying mood and sleep variability in the developing brain

Brain development during adolescence and early adulthood coincides with shifts in emotion regulation and sleep. Despite this, few existing datasets simultaneously characterize affective dynamics, sleep variation, and multimodal measures of brain development. Here, we describe the study protocol and initial release (n = 10) of an open data resource of neuroimaging paired with densely sampled behavioral measures in adolescents and young adults. All participants complete multi-echo functional MRI, compressed-sensing diffusion MRI, and advanced arterial spin-labeled MRI. Behavioral measures include ecological momentary assessment, actigraphy, extensive cognitive assessments, and detailed clinical phenotyping focused on emotion regulation. Raw and processed data are openly available without a data use agreement and will be regularly updated as accrual continues. Together, this resource will accelerate research on the links between mood, sleep, and brain development.

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Reproducible Sex Differences in Personalized Functional Network Topography in Youth

BackgroundA key step towards understanding psychiatric disorders that disproportionately impact female mental health is delineating the emergence of sex-specific patterns of brain organization at the critical transition from childhood to adolescence. Prior work suggests that individual differences in the spatial organization of functional brain networks across the cortex are associated with psychopathology and differ systematically by sex. AimsWe aimed to evaluate the impact of sex on the spatial organization of person-specific functional brain networks. MethodWe leveraged person-specific atlases of functional brain networks defined using non-negative matrix factorization in a sample of n = 6437 youths from the Adolescent Brain Cognitive Development Study. Across independent discovery and replication samples, we used generalized additive models to uncover associations between sex and the spatial layout ("topography") of personalized functional networks (PFNs). Next, we trained support vector machines to classify participants sex from multivariate patterns of PFN topography. Finally, we leveraged transcriptomic data from the Allen Human Brain Atlas to evaluate spatial correlations between sex differences in PFN topography and gene expression. ResultsSex differences in PFN topography were greatest in association networks including the fronto-parietal, ventral attention, and default mode networks. Machine learning models trained on participants PFNs were able to classify participant sex with high accuracy. Brain regions with the greatest sex differences in PFN topography were enriched in expression of X-linked genes as well as genes expressed in astrocytes and excitatory neurons. ConclusionsSex differences in PFN topography are robust, replicate across large-scale samples of youth, and are associated with expression patterns of X-linked genes. These results suggest a potential contributor to the female-biased risk in depressive and anxiety disorders that emerge at the transition from childhood to adolescence.

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pNet: A toolbox for personalized functional networks modeling

Personalized functional networks (FNs) derived from functional magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain functional topography associated with the brain development, aging, and disorders. To facilitate applications of the personalized FNs with enhanced reliability and reproducibility, we develop an open-source toolbox that is user-friendly, extendable, and includes rigorous quality control (QC), featuring multiple user interfaces (graphics, command line, and a step-by-step guideline) and job-scheduling for high performance computing (HPC) clusters. Particularly, the toolbox, named personalized functional network modeling (pNet), takes fMRI inputs in either volumetric or surface type, ensuring compatibility with multiple fMRI data formats, and computes personalized FNs using two distinct modeling methods: one method optimizes the functional coherence of FNs, while the other enhances their independence. Additionally, the toolbox provides HTML-based reports for QC and visualization of personalized FNs. The toolbox is developed in both MATLAB and Python platforms with a modular design to facilitate extension and modification by users familiar with either programming language. We have evaluated the toolbox on two fMRI datasets and demonstrated its effectiveness and user-friendliness with interactive and scripting examples. pNet is publicly available at https://github.com/MLDataAnalytics/pNet.

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In-vivo whole-cortex estimation of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth

A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I ratio remains unknown. Here we non-invasively estimate a putative marker of whole-cortex E/I ratio by fitting a large-scale biophysically-plausible circuit model to resting-state functional MRI (fMRI) data. We first confirm that our model generates realistic brain dynamics in the Human Connectome Project. Next, we show that the estimated E/I ratio marker is sensitive to the GABA-agonist benzodiazepine alprazolam during fMRI. Alprazolam-induced E/I changes are spatially consistent with positron emission tomography measurement of benzodiazepine receptor density. We then investigate the relationship between the E/I ratio marker and neurodevelopment. We find that the E/I ratio marker declines heterogeneously across the cerebral cortex during youth, with the greatest reduction occurring in sensorimotor systems relative to association systems. Importantly, among children with the same chronological age, a lower E/I ratio marker (especially in association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 years old) and Asian (7.2 to 7.9 years old) cohorts, suggesting that a more mature E/I ratio indexes improved cognition during normative development. Overall, our findings open the door to studying how disrupted E/I trajectories may lead to cognitive dysfunction in psychopathology that emerges during youth. SignificanceHealthy brain function requires a delicate balance of neural excitation (E) and inhibition (I). In animals, this balance - the E/I ratio - is known to decrease with the maturation of inhibitory circuitry during healthy development. However, in humans, the normative development of cortex-wide E/I ratio remains unclear. Here, we use a biophysical model and non-invasive brain scans to estimate a marker of E/I ratio. Spatial changes in our E/I ratio marker are consistent with a drug that decreases E/I ratio. We also find that our cortex-wide E/I ratio marker decreases during development. Furthermore, North American and Asian children with lower E/I ratio, especially in higher-order cortex, have better cognitive performance. Overall, E/I ratio is a potential index of healthy neurocognitive development.

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Individual Differences in Delay Discounting are Associated with Dorsal Prefrontal Cortex Connectivity in Youth

Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including substance use disorders, obesity, and academic achievement. However, the functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of youth. A total of 293 youth (9-23 years) completed a delay discounting task and underwent resting-state fMRI at 3T. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity was then performed. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a hub of the default mode network. Delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other parts of the default mode network, and reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest that delay discounting in youth is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.

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Asymmetric Signaling Across the Hierarchy of Cytoarchitecture within the Human Connectome

Cortical variations in cytoarchitecture form a sensory-fugal axis that systematically shapes regional profiles of extrinsic connectivity. Additionally, this axis is thought to guide signal propagation and integration across the cortical hierarchy. While human neuroimaging work has shown that this axis constrains local properties of the human connectome, it remains unclear whether it also shapes the asymmetric signaling that arises from higher-order connectome topology. Here, we used network control theory to examine the amount of energy required to propagate dynamics across the sensory-fugal axis. Our results revealed an asymmetry in this energy indicating that bottom-up transitions were easier to complete compared to top-down transitions. Supporting analyses demonstrated that this asymmetry was underpinned by a connectome topology that is wired to support efficient bottom-up signaling. Finally, we found that this asymmetry correlated with changes in intrinsic neuronal timescales and lessened throughout youth. Our results show that cortical variation in cytoarchitecture may guide the formation of macroscopic connectome topology.

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Association between Neighborhood Socioeconomic Status and Executive System Activation in Youth

Low socioeconomic status has been shown to have detrimental effects on cognitive performance, including working memory (WM). As executive systems that support WM undergo functional neurodevelopment during adolescence, environmental stressors at both individual and community levels may have a particularly strong impact on cognitive outcomes. Here, we sought to examine how neighborhood socioeconomic status (SES) impacts task-related activation of the executive system during adolescence, and to determine whether this effect mediates the relationship between neighborhood SES and WM performance. To address these questions, we studied 1,158 youths (age 8-22) that completed a fractal n-back WM task during fMRI at 3T as part of the Philadelphia Neurodevelopmental Cohort. We found that higher neighborhood SES was associated with greater activation of the executive system to WM load, including the bilateral dorsolateral prefrontal cortex, posterior parietal cortex, and precuneus. These associations remained significant when controlling for related factors like parental education and exposure to traumatic events. Furthermore, high dimensional multivariate mediation analysis identified two distinct patterns of brain activity within the executive system that significantly mediated the relationship between neighborhood SES and task performance. Together, these findings underscore the importance of the neighborhood environment in shaping executive system function and WM in youth.

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Sex Differences in Functional Topography of Association Networks

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here we leveraged an advanced machine learning method (sparsity-regularized nonnegative matrix factorization) to define individualized functional networks in 693 youth (ages 8-23 years) who underwent functional magnetic resonance imaging as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 83% accuracy (p<0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention and default mode networks. Mass-univariate analyses using generalized additive models with penalized splines provided convergent results. Comparative analysis using transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography correlated with the expression of genes on the X-chromosome. These results identify normative developmental sex differences in the functional topography of association networks and highlight the role of sex as a biological variable in shaping brain development in youth. SIGNIFICANCE STATEMENTWe identify normative developmental sex differences in the functional topography of personalized association networks including the ventral attention network and default mode network. Furthermore, chromosomal enrichment analyses revealed that sex differences in multivariate patterns of functional topography were spatially coupled to the expression of X-linked genes as well as astrocytic and excitatory neuronal cell-type signatures. These results highlight the role of sex as a biological variable in shaping functional brain development in youth.

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A Developmental Reduction of the Excitation:Inhibition Ratio in Association Cortex during Adolescence.

Adolescence is hypothesized to be a critical period for the development of association cortex. A reduction of the excitation:inhibition (E:I) ratio is a hallmark of critical period development; however it has been unclear how to assess the development of the E:I ratio using non-invasive neuroimaging techniques. Here, we used pharmacological fMRI with a GABAergic benzodiazepine challenge to empirically generate a model of E:I ratio based on multivariate patterns of functional connectivity. In an independent sample of 879 youth (ages 8-22 years), this model predicted reductions in the E:I ratio during adolescence, which were specific to association cortex and related to psychopathology. These findings support hypothesized shifts in E:I balance of association cortices during a neurodevelopmental critical period in adolescence. TeaserInhibitory maturation of the association cortex reflects an adolescent critical period.

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Alprazolam modulates persistence energy during emotion processing in first-degree relatives of individuals with schizophrenia: a network control study

Schizophrenia is marked by deficits in facial affect processing associated with abnormalities in GABAergic circuitry, deficits also found in first-degree relatives. Facial affect processing involves a distributed network of brain regions including limbic regions like amygdala and visual processing areas like fusiform cortex. Pharmacological modulation of GABAergic circuitry using benzodiazepines like alprazolam can be useful for studying this facial affect processing network and associated GABAergic abnormalities in schizophrenia. Here, we use pharmacological modulation and computational modeling to study the contribution of GABAergic abnormalities toward emotion processing deficits in schizophrenia. Specifically, we apply principles from network control theory to model persistence energy - the control energy required to maintain brain activation states - during emotion identification and recall tasks, with and without administration of alprazolam, in a sample of first-degree relatives and healthy controls. Here, persistence energy quantifies the magnitude of theoretical external inputs during the task. We find that alprazolam increases persistence energy in relatives but not in controls during threatening face processing, suggesting a compensatory mechanism given the relative absence of behavioral abnormalities in this sample of unaffected relatives. Further, we demonstrate that regions in the fusiform and occipital cortices are important for facilitating state transitions during facial affect processing. Finally, we uncover spatial relationships (i) between regional variation in differential control energy (alprazolam versus placebo) and (ii) both serotonin and dopamine neurotransmitter systems, indicating that alprazolam may exert its effects by altering neuromodulatory systems. Together, these findings reveal differences in emotion-processing circuitry associated with genetic vulnerability to schizophrenia.

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Altered functional brain dynamics in chromosome 22q11.2 deletion syndrome during facial affect processing

Chromosome 22q11.2 deletion syndrome (22q11.2DS) is a multisystem disorder associated with multiple congenital anomalies, variable medical features, and neurodevelopmental differences resulting in diverse psychiatric phenotypes, including marked deficits in facial memory and social cognition. Neuroimaging in individuals with 22q11.2DS has revealed differences relative to matched controls in BOLD fMRI activation during facial affect processing tasks, but time-varying interactions between brain areas during facial affect processing have not yet been studied in 22q11.2DS. We applied constrained principal component analysis to identify temporally overlapping brain activation patterns from BOLD fMRI data acquired during an emotion identification task from 58 individuals with 22q11.2DS and 58 age-, race-, and sex-matched healthy controls. Delayed frontal-motor feedback signals were diminished in individuals with 22q11.2DS, as were delayed emotional memory signals engaging amygdala, hippocampus, and entorhinal cortex. Early task-related engagement of motor and visual cortices and salience-related insular activation were relatively preserved in 22q11.2DS. Insular activation was associated with task performance within the 22q11.2DS sample. Differences in cortical surface area, but not cortical thickness, showed spatial alignment with an activation pattern associated with face processing. These findings suggest that relative to matched controls, primary visual processing and insular function are relatively intact in individuals with 22q11.22DS, while motor feedback, face processing, and emotional memory processes are more affected. Such insights may help inform potential interventional targets and enhance the specificity of neuroimaging indices of cognitive dysfunction in 22q11.2DS.

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Transdiagnostic dimensions of psychopathology explain individuals' unique deviations from normative neurodevelopment in brain structure

Psychopathology is rooted in neurodevelopment. However, clinical and biological heterogeneity, together with a focus on case-control approaches, have made it difficult to link dimensions of psychopathology to abnormalities of neurodevelopment. Here, using the Philadelphia Neurodevelopmental Cohort, we built normative models of cortical volume and tested whether deviations from these models better predicted psychiatric symptoms compared to raw cortical volume. Specifically, drawing on the p-factor hypothesis, we distilled 117 clinical symptom measures into six orthogonal psychopathology dimensions: overall psychopathology, anxious-misery, externalizing disorders, fear, positive psychotic symptoms, and negative psychotic symptoms. We found that multivariate patterns of deviations yielded improved out-of-sample prediction of psychopathology dimensions compared to multivariate patterns of raw cortical volume. We also found that correlations between overall psychopathology and deviations in ventromedial prefrontal, inferior temporal, dorsal anterior cingulate, and insular cortices were stronger than those observed for specific dimensions of psychopathology (e.g., anxious-misery). Notably, these same regions are consistently implicated in a range of putatively distinct disorders. Finally, we performed conventional case-control comparisons of deviations in a group of individuals with depression and a group with attention-deficit hyperactivity disorder (ADHD). We observed spatially overlapping effects between these groups that diminished when controlling for overall psychopathology. Together, our results suggest that modeling cortical brain features as deviations from normative neurodevelopment improves prediction of psychiatric symptoms in out-of-sample testing, and that p-factor models of psychopathology may assist in separating biomarkers that are disorder-general from those that are disorder-specific.

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Neurocognitive and Functional Heterogeneity in Depressed Youth

OBJECTIVEDepression is a common psychiatric illness that often begins in youth, and is associated with cognitive symptoms. However, there is significant variability in the cognitive burden, likely reflecting biological heterogeneity. This study sought to identify neurocognitive subtypes in a large sample of depressed youth, and evaluated the neural signatures of these subtypes.\n\nMETHODSParticipants were drawn from the Philadelphia Neurodevelopmental Cohort, including 712 youth with a lifetime history of a major depressive episode and 712 typically developing (TD) youth matched on age and sex. A subset (n=368, TD=200) also completed neuroimaging. Cognition was assessed with the Penn Computerized Neurocognitive Battery. A semi-supervised machine-learning algorithm, HYDRA (Heterogeneity through Discriminative Analysis), was used to delineate neurocognitive subtypes. Subtypes were evaluated for differences in both clinical psychopathology and brain activation during an n-back working memory fMRI task.\n\nRESULTSHYDRA identified three neurocognitive subtypes in the depressed group. Overall, Subtype 1 had better performance than TD comparators across many cognitive tasks (high accuracy, moderate speed), Subtype 2 was cognitively impaired (low accuracy, slow speed), whereas Subtype 3 was impulsive (low accuracy, fast speed). While subtypes did not differ in clinical psychopathology, they diverged in their activation profiles in regions critical for executive function, which mirrored differences in cognition.\n\nCONCLUSIONSUsing a data-driven approach, three neurocognitive subtypes of depression were identified that differed in neural signatures despite similar clinical psychopathology. These data suggest disparate mechanisms of cognitive vulnerability and resilience in depression, which may inform the identification of biomarkers for prognosis and treatment response.

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Individual Variation in Control Network Topography Supports Executive Function in Youth

The spatial distribution of large-scale functional networks on the anatomic cortex differs between individuals, and is particularly variable in networks responsible for executive function. However, it remains unknown how this functional topography evolves in development and supports cognition. Capitalizing upon advances in machine learning and a large sample of youth (n=693, ages 8-23y) imaged with 27 minutes of high-quality fMRI data, we delineate how functional topography evolves during youth. We found that the functional topography of association networks is refined with age, allowing accurate prediction of an unseen individuals brain maturity. Furthermore, the cortical representation of executive networks predicts individual differences in executive function. Finally, variability of functional topography is associated with fundamental properties of brain organization including evolutionary expansion, cortical myelination, and cerebral blood flow. Our results emphasize the importance of considering both the plasticity and diversity of functional neuroanatomy during development, and suggest advances in personalized therapeutics.

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