Collection ID: 3445

Project Overview

Schizotypal Personality Disorder (SPD) shares similarities with schizophrenia (SZ), but with attenuated symptoms, making it a valuable intermediate SZ-spectrum phenotype. Investigating SPD provides insights into etiology, genetics, risk factors, and protective mechanisms associated with psychosis, without the confounding effects of antipsychotic medication or hospitalization often seen in SZ.

This project represents the first longitudinal study to:

• Incorporate individuals with SPD into a comprehensive multimodal neuroimaging and neurocognitive investigation.
• Leverage the Research Domain Criteria (RDoC) framework to characterize aberrant neural circuitry and identify risk and protective factors across the SZ spectrum.

Study Design

• Participants:

  • Healthy controls (HCs): 80 (no Axis I or personality disorders)
  • SPD (unmedicated, no Axis I disorders): 80
  • Early-onset SZ (first 2 years of illness): 80
  • Age range: 18–40 years
  • Timepoints: Baseline, 9-month, and 18-month follow-ups

• Multimodal Neuroimaging:

  • Structural MRI
  • Diffusion Tensor Imaging (DTI)
  • Resting-state fMRI
  • Task-based fMRI (nonverbal working memory task at baseline and 18 months)

• Neurocognitive and Clinical Assessments:

  • Conducted at all three time points

• Advanced Analytic Methods:

  • Dynamic causal modeling (DCM) to evaluate frontotemporal connectivity hypotheses
  • Machine learning to integrate multimodal imaging, neurocognitive, and clinical data

Research Objectives

1. Map the longitudinal course of frontotemporal circuitry abnormalities across the SZ spectrum.
2. Track the longitudinal course of neurocognitive, clinical, and functional outcomes in SZ-spectrum disorders.
3. Identify factors (or combinations thereof) that differentiate groups in the SZ spectrum—highlighting risk and protective factors for SZ.

Significance

By leveraging longitudinal and multimodal data, this project will:

• Clarify neural mechanisms that protect SPD individuals from full-blown psychosis.
• Identify biomarkers of risk and resilience in SZ-spectrum disorders.
• Advance precision interventions for individuals at heightened risk of schizophrenia.

Data Access and Licensing

• The .fz files are shared under a Creative Commons Attribution-NonCommercial-ShareAlike License (CC BY-NC-SA).
• Access to the .sz and structural images requires submission of a Data Use Agreement (DUA) approved by the NDA’s Data Use Permission Group of the NIMH Data Archive.

Investigator

• Erin Hazlett

  • Institution: Icahn School of Medicine at Mount Sinai

Collection ID: 2818

Project Overview

While neighborhood disadvantage has long been associated with negative physical, socioeconomic, and mental health outcomes, many children growing up in such contexts exhibit remarkable adaptive competence. This project seeks to identify the neurobehavioral and neurodevelopmental pathways through which familial and community-level protective factors support these resilient outcomes.

Study Design

• Participants:

  • 500 adolescent twin pairs (ages 11–16)
  • Previously assessed at ages 6–10
  • Residing in modestly-to-severely disadvantaged neighborhoods

• Neuroimaging:

  • Joint models that integrate:

    • Task-based fMRI
    • Resting-state fMRI
    • Diffusion Tensor Imaging (DTI)
    • Structural MRI (sMRI)

• Key Constructs:

  • Resilience: Defined as adaptive competence and absence of psychopathology
  • Protective processes: Familial and community-level supports

Research Objectives

1. Identify neural markers of resilience

   • Determine which synergistic neural networks are linked to adaptive outcomes in the face of disadvantage.

2. Illuminate multilevel pathways

   • Leverage the longitudinal and genetically-informed twin sample to explore epigenetic, environmental, and genetic influences on resilience.

3. Understand protective processes

   • Test the hypothesis that positive parenting and community factors foster normative neural architecture in children exposed to adversity, enabling them to thrive.

Significance

This genetically-informed developmental neuroscience approach aims to:

• Reveal multilevel biobehavioral pathways that promote resilience to adversity.
• Advance preventive and early intervention strategies for children in disadvantaged environments.
• Deepen our understanding of how brain development supports resilience in the face of chronic neighborhood disadvantage.

Data Access and Licensing

• The .fz files are shared under a Creative Commons Attribution-NonCommercial-ShareAlike License (CC BY-NC-SA).
• Access to the .sz and structural images requires submission of a Data Use Agreement (DUA) approved by the NDA’s Data Use Permission Group of the NIMH Data Archive.

Investigator

• S. Burt

  • Institution: Michigan State University

Collection ID: 2690

Project Overview

Early life stress and variations in caregiving—particularly insecure or disorganized attachment—are known risk factors for behavioral and emotional maladaptation in childhood. Yet, neurobiological mechanisms linking early caregiving, attachment security, and brain development in infancy remain poorly understood.

This project uses resting-state fMRI and diffusion tensor imaging (DTI) to investigate how the integration of neural networks during the first year of life varies as a function of maternal caregiving and how these neural changes relate to attachment security at 12 months.

Study Design

• Longitudinal Assessments:

  • Infant brain scans at 3 and 12 months during natural sleep (resting-state fMRI and DTI).

  • Infant-mother interactions at 3, 6, and 9 months:

    • Traditional interaction paradigms.
    • New technology for naturalistic home environment capture using an automated system.

  • Infant-mother attachment assessed at 12 months using the Strange Situation Procedure (gold standard for attachment measurement).

• Neural Network Analysis:

  • Intra-network and inter-network connectivity.
  • Dynamic, whole-brain, data-driven approaches to capture changes in functional and structural brain networks.

Significance

By integrating multiple levels of analysis across multiple time points, this project aims to:

• Uncover transactions among maternal caregiving, infant behavior, and brain network development.
• Provide conceptual advances in understanding how early caregiving shapes trajectories of brain and behavioral development.
• Identify potential targets for early preventive interventions to promote healthy socio-emotional and neural development.

Data Access and Licensing

• The .fz files are shared under a Creative Commons Attribution-NonCommercial-ShareAlike License (CC BY-NC-SA).
• Access to the .sz and structural images requires submission of a Data Use Agreement (DUA) approved by the NDA’s Data Use Permission Group of the NIMH Data Archive.

Investigator

• Nancy McElwain

  • Institution: University of Illinois at Urbana-Champaign

Collection ID: 2329

Project Overview

The C-MIND dataset is part of the Pediatric Functional Neuroimaging Research Network, a collaboration led by the Pediatric Neuroimaging Research Consortium at Cincinnati Children's Hospital Medical Center (CCHMC), with partners including:

• Laboratory of Neuroimaging (LONI) at the University of Southern California
• Brain Mapping Center at UCLA
• Children’s Hospital of Pittsburgh
• University of Michigan

This initiative was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) and aimed to:

1. Develop standardized recruitment, scanning, and processing protocols for pediatric neuroimaging across development.
2. Investigate brain development from infancy through adolescence.

Dataset Composition

• Imaging Data:

  • T1-weighted (T1-W) and T2-weighted (T2-W) 3D anatomical scans
  • BOLD-fMRI
  • Diffusion Tensor Imaging (DTI)
  • Arterial Spin Labeling (ASL) perfusion imaging
  • A novel concurrent ASL/BOLD-fMRI dataset for exploring neurovascular coupling and BOLD response relationships

• Behavioral Data:

  • Collected in conjunction with imaging to allow investigation of brain-behavior relationships during development

• Cohort Details:

  • Over 200 typically developing children for cross-sectional analyses

  • Longitudinal imaging and behavioral data:

    • 40 infants and toddlers (0–3 years)
    • 30 children (7–9 years)

Research Significance

The C-MIND dataset enables investigation of:

• Developmental changes in white and gray matter
• Structural and functional connectivity
• Neurovascular coupling/reactivity
• Relationships between brain development and cognitive changes

This comprehensive dataset offers unique insights into normal brain maturation from infancy through adolescence.

Data Access and Licensing

• The .fz files are shared under a Creative Commons Attribution-NonCommercial-ShareAlike License (CC BY-NC-SA).
• Access to the .sz and structural images requires submission of a Data Use Agreement (DUA) approved by the NDA’s Data Use Permission Group of the NIMH Data Archive.

Investigators

• Scott Holland

• Jennifer Vannest

  • Organization: Cincinnati Children’s Hospital Medical Center

Collection ID: 2285

Project Overview

Emerging evidence from functional connectivity MRI (fcMRI) and diffusion tensor imaging (DTI) studies suggests that cognitive, socio-communicative, and sensorimotor impairments in autism spectrum disorders (ASD) are linked to abnormal brain network connectivity. However, most studies have yet to integrate functional and anatomical connectivity measures—let alone dynamic processing metrics using magnetoencephalography (MEG).

This project aims to fill this gap by combining fcMRI, DTI, and MEG to comprehensively investigate network integrity and processing dynamics in ASD.

Study Design

• Participants:

  • 60 adolescents with ASD
  • 60 matched typically developing (TD) participants

• Imaging and Analysis Approaches:

  • fMRI (functional connectivity):

    • During lexical-semantic decision tasks to identify key nodes of a Visual, Lexico-Semantic, Executive, and Motor (VLSEM) circuit.
    • Examine both within-circuit and whole-brain connectivity.

  • DTI:

    • Measure structural connectivity between VLSEM circuit nodes.

  • MEG:

    • Assess dynamic processing efficiency and real-time network dynamics within these circuits.

Study Objectives

1. Identify VLSEM Circuit Nodes:

   • Using fMRI data during lexical-semantic decision tasks.

2. Characterize Connectivity:

   • Map functional and anatomical connectivity within VLSEM circuits and with the broader brain.

3. Evaluate Dynamic Processing:

   • Use MEG to assess temporal aspects of network engagement.

4. Integrate Multi-Modal Data:

   • Provide a holistic understanding of how circuit-level abnormalities contribute to ASD symptoms.

Significance

By integrating structural, functional, and dynamic neural measures, this project will:

• Advance our understanding of neural network dysfunction in ASD.
• Highlight potential targets for early intervention to address cognitive and socio-communicative challenges.

Data Access and Licensing

• The .fz files are shared under a Creative Commons Attribution-NonCommercial-ShareAlike License (CC BY-NC-SA).
• Access to the .sz and structural images requires submission of a Data Use Agreement (DUA) approved by the NDA’s Data Use Permission Group of the NIMH Data Archive.

Investigator

• Ralph-Axel Mueller

  • Institution: San Diego State University

Collection ID: 2138

Project Overview

Eating disorders (EDs) such as anorexia nervosa (AN) and bulimia nervosa (BN) are severe psychiatric conditions with some of the highest mortality rates of any mental illnesses. Despite differences in weight and behavior between AN and BN, both disorders share core features like food restriction, binge eating, and purging—and little is known about the biological mechanisms linking these symptoms to brain function.

This project leverages the NIMH RDoC framework, focusing on the Prediction Error construct to explore how brain reward circuitry underlies dimensions of ED pathology.

Research Aims

1. Functional Neural Reward Circuits (fMRI)

   • Examine taste-reward prediction error responses in adolescents and young adults (ages 16–29), a group at highest risk for severe ED outcomes.

   • Brain regions of interest:

     • Anteroventral striatum (caudate/putamen)
     • Insula
     • Anterior cingulate cortex (ACC)

   • Hypotheses:

     • Lower BMI in ED subjects will be linked to heightened ACC activation to reward-predicting stimuli.
     • Prediction error responses in these circuits will be associated with ED-relevant behaviors.

2. Structural MRI and DTI

   • Assess gray and white matter volume and white matter integrity.

   • Hypotheses:

     • Severe ED behaviors will correlate with lower caudate and insula volumes.
     • Structural metrics will predict functional reward circuit activation from Aim 1.

3. RDoC-based Clustering

   • Cluster ED individuals based on prediction error responses, contrasting with traditional DSM criteria.

   • Hypotheses:

     • Prediction error-based clustering will outperform DSM diagnoses in grouping ED-relevant behaviors.
     • Findings will highlight EDs as disorders of altered salience response.

Significance

• Identifies biological underpinnings of ED dimensions, transcending categorical diagnoses.
• Advances precision psychiatry by linking brain-based prediction error signals to specific ED behaviors.
• Informs the next generation of treatment targets for EDs based on reward circuitry vulnerabilities.

Data Access and Licensing

• The .fz files are shared under a Creative Commons Attribution-NonCommercial-ShareAlike License (CC BY-NC-SA).
• Access to the .sz and structural images requires submission of a Data Use Agreement (DUA) approved by the NDA’s Data Use Permission Group of the NIMH Data Archive.

Investigator

• Joel Stoddard

  • Institution: University of Colorado

Collection ID: 2106

Project Overview

Poverty affects 1 in 5 children in the US, increasing the risk of lifelong psychopathology that perpetuates low socioeconomic status. Poverty-related stressors can biologically embed themselves in a child’s development, altering brain function and the hypothalamic-pituitary-adrenal (HPA) axis, thereby predisposing to anxiety and depression.

This project aims to elucidate how chronic poverty-related stress impacts biological mechanisms and ultimately increases psychopathology risk.

Key Research Questions

• How do exposure to danger, family conflict, residential instability, and neglect impact:

  • HPA axis regulation
  • Amygdala reactivity
  • Regulatory connections from the ventromedial prefrontal cortex to the amygdala

• How do these stressors shape a heightened sensitivity to negative events and contribute to anxiety and depression symptoms?

Study Design

• Participants:

  • Teens from the Fragile Families and Child Wellbeing Study (FFCWS), a nationally representative sample of children born to predominantly low-income families.
  • Longitudinal data from birth to age 15, with follow-up at age 17.

• Assessments at Age 15:

  • Functional MRI (fMRI):

    • Activation and connectivity in response to emotional faces

  • Diffusion Tensor Imaging (DTI):

    • Structural connectivity

  • HPA Axis Measures:

    • Cortisol and DHEA responses to stress

  • Behavioral Measures:

    • Attention bias

  • Self- and Parent-Report:

    • Negative affect, anxiety, and depression symptoms

• Integration:

  • Developmental history from FFCWS (economic status, parenting, early symptoms) mapped onto neurobiological and psychological outcomes.

Significance

This study will:

• Advance understanding of the biological embedding of poverty and its neurodevelopmental consequences.
• Identify critical pathways linking poverty to psychopathology.
• Inform targeted interventions to break cycles of adversity and poor mental health outcomes.

Data Access and Licensing

• The .fz files are shared under a Creative Commons Attribution-NonCommercial-ShareAlike License (CC BY-NC-SA).
• Access to the .sz and structural images requires submission of a Data Use Agreement (DUA) approved by the NDA’s Data Use Permission Group of the NIMH Data Archive.

Investigator

• Christopher Monk

  • Institution: University of Michigan

Collection ID: 2102

Project Overview

Psychotic spectrum disorders (PSD), including schizophrenia, schizoaffective disorder, and bipolar disorder with psychotic features, are often difficult to diagnose and treat. Despite traditional diagnostic categories, these disorders share overlapping symptoms—particularly cognitive control/executive functioning deficits, which arise from mesocortical and mesostriatal pathway dysregulation.

These cognitive deficits are strongly associated with poor social and occupational outcomes and remain largely refractory to existing treatments.

Research Objectives

• Primary Outcome: Identify neurophysiologically-based cluster metrics (circuit-level pathologies) that predict cognitive control deficits across PSD diagnoses.

• Secondary Outcome: Examine how these neurophysiological profiles correlate with real-world functioning and traditional clinical symptoms (e.g., disorganized thinking).

• Genetic Analyses:

  • Investigate the aggregation of SNPs in key neurotransmitter pathways (dopamine, glutamate, GABA) and in axonal guidance and synaptic long-term potentiation pathways.
  • Explore whether rare deletions (CNVs) mediate cognitive control deficits across multiple psychiatric illnesses.

Study Design

• Participants: 175 continuously recruited PSD patients.

• Assessments:

  • Extensive clinical and cognitive battery

  • Multimodal neuroimaging:

    • Functional MRI (evoked and resting state)
    • Diffusion Tensor Imaging (DTI) for white matter integrity and connectivity

  • Multisensory cognitive control task with real-world relevance

• Analytic Strategy:

  • Use univariate and multivariate indices of grey/white matter pathology within the cognitive control network (dorsal medial prefrontal cortex, lateral prefrontal cortex, caudate nucleus).
  • Apply K-means clustering to identify meaningful neurobiological subgroups.
  • Assess predictive validity of these clusters for cognitive control and everyday functioning using leave-one-out cross-validation.

Significance

By moving beyond traditional diagnostic frameworks and leveraging NIMH Research Domain Criteria (RDoC)-aligned multi-level analyses (genes, circuits, behaviors, paradigms), this project aims to:

• Develop a novel classification system based on neurophysiological and genetic biomarkers of impaired cognitive control.
• Inform more precise and effective treatments for refractory symptoms in PSD, improving mental health care outcomes.

Data Access and Licensing

• The .fz files are shared under a Creative Commons Attribution-NonCommercial-ShareAlike License (CC BY-NC-SA).
• Access to the .sz and structural images requires submission of a Data Use Agreement (DUA) approved by the NDA’s Data Use Permission Group of the NIMH Data Archive.

Investigator

• Andrew Robert Mayer

  • Institution: Mind Research Network

Collection ID: 2098

Project Overview

Schizophrenia Spectrum Disorders (SSDs), including schizophrenia, schizoaffective disorder, and schizophreniform disorder, are characterized by a continuum of social functioning impairments that often persist despite treatment.

This project, leveraging consistent pilot data across three sites, aims to comprehensively delineate the neurobiology of social cognitive (SCog) process impairment in SSDs using the Research Domain Criteria (RDoC) investigational framework.

Research Approach

The project will employ advanced structural and functional neuroimaging to investigate neural circuitry involved in SCog processes, moving from healthy controls to individuals with SSDs.

Key Neuroimaging and Analysis Methods:

1. Gray Matter Morphology:

   • Map cortical thickness and analyze network topology.

2. Diffusion Tensor Imaging (DTI):

   • Examine white matter circuits.

3. Functional MRI (fMRI):

   • Use both resting-state and task-based fMRI to measure circuit function.

4. Multivariate Neuroimaging Analysis:

   • Apply Partial Least Squares (PLS) to relate brain structure, function, and behavior across participants.

Matrix of Analysis:

• This RDoC-based approach will map SCog process constructs across the entire schizophrenia spectrum, from normal controls to those with SSDs.

Study Objectives

• Primary Goal:
  Identify abnormal brain-behavior relationships in social cognitive processes, starting at the circuit level.

• Long-Term Goal:
  Discover new therapeutic targets for the treatment of social impairments in SSDs by identifying the underlying neural circuitry and pathophysiology.

Data Access and Licensing

• The .fz files are shared under a Creative Commons Attribution-NonCommercial-ShareAlike License (CC BY-NC-SA).
• Access to the .sz and structural images requires submission of a Data Use Agreement (DUA) approved by the NDA’s Data Use Permission Group of the NIMH Data Archive.

Investigators

• Anil K Malhotra

• Aristotle Voineskos

• Robert W. Buchanan

  • Organization: The Feinstein Institute for Medical Research

Collection ID: 2021

Project Overview

Autism Spectrum Disorders (ASD) are known for their remarkable heterogeneity at both the phenotypic and genetic levels. Notably, ASD disproportionately affects males relative to females, yet the reasons for this disparity remain elusive due to limited phenotyping and small sample sizes in past studies.

This project seeks to bridge this gap by leveraging an interdisciplinary network to deeply phenotype and genotype a sex-balanced cohort, aiming to uncover sex-specific neurobiological and genetic factors in ASD.

Study Design

• Cohort:

  • ASD participants: 125
  • Typically developing (TD) controls: 125
  • Unaffected siblings also included

• Data Collected:

  • Behavioral phenotyping across multiple domains

  • Brain imaging:

    • Structural MRI (sMRI)
    • Diffusion Tensor Imaging (DTI)
    • Functional MRI (fMRI; task-based and resting-state)
    • Electroencephalography (EEG)

  • Genetic data:

    • Copy Number Variation (CNV)
    • Single Nucleotide Variation (SNV)
    • Parental genotyping for familial analyses

Research Objectives

1. Identify Sex Differences

   • In brain structure, function, connectivity, and temporal dynamics in ASD.

2. Genotype-Phenotype Correlations

   • Assess how CNVs and SNVs relate to brain structure and function in ASD versus TD.

3. Relate Brain and Genetic Differences to ASD Heterogeneity

   • Explore how these factors contribute to behavioral variability in ASD.

Advanced network-based methods (iWGCNA) will be employed to integrate and analyze these multimodal data across affected individuals, unaffected siblings, and controls.

Data Access and Licensing

• The .fz files are shared under a Creative Commons Attribution-NonCommercial-ShareAlike License (CC BY-NC-SA).
• Access to the .sz and structural images requires submission of a Data Use Agreement (DUA) approved by the NDA’s Data Use Permission Group of the NIMH Data Archive.

Investigator

• Kevin Pelphrey

  • Institution: University of Virginia