1
|
Didier PR, Moore TM, Calkins ME, Prettyman G, Levinson T, Savage C, de Moraes Leme LFV, Kohler CG, Kable J, Satterthwaite T, Gur RC, Gur RE, Wolf DH. Evaluation of a new intrinsic and extrinsic motivation scale in youth with psychosis spectrum symptoms. Compr Psychiatry 2023; 127:152413. [PMID: 37696094 PMCID: PMC10644398 DOI: 10.1016/j.comppsych.2023.152413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/16/2023] [Accepted: 08/31/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Impairment in intrinsic motivation (IM), the drive to satisfy internal desires like mastery, may play a key role in disability in psychosis. However, we have limited knowledge regarding relative impairments in IM compared to extrinsic motivation (EM) or general motivation (GM), in part due to limitations in existing measures. METHODS Here we address this gap using a novel Trait Intrinsic and Extrinsic Motivation self-report scale in a sample of n = 243 participants including those with schizophrenia, psychosis-risk, and healthy controls. Each of the 7 IM and 6 EM items used a 7-point Likert scale assessing endorsement of dispositional statements. Bifactor analyses of these items yielded distinct IM, EM, and GM factor scores. Convergent and discriminant validity were examined in relation to General Causality Orientation Scale (GCOS-CP) and Quality of Life 3-item IM measure (QLS-IM). Utility was assessed in relation to psychosis-spectrum (PS) status and CAINS clinical amotivation. RESULTS IM and EM showed acceptable inter-item consistency (IM: α = 0.88; EM: α = 0.66); the bifactor model exhibited fit that varied from good to borderline to inadequate depending on the specific fit metric (SRMR = 0.038, CFI = 0.94, RMSEA = 0.106 ± 0.014). IM scores correlated with established IM measures: GCOS-CP Autonomy (rho = 0.38, p < 0.01) and QLS-IM (rho = 0.29, p < 0.01). Supporting discriminant validity, IM did not correlate with GCOS-CP Control (rho = -0.14, p > 0.05). Two-year stability in an available longitudinal subset (n = 35) was strong (IM: rho = 0.64, p < 0.01; EM: rho = 0.55, p < 0.01). Trait IM was lower in PS youth (t = 4.24, p < 0.01), and correlated with clinical amotivation (rho = -0.36, p < 0.01); EM did not show significant clinical associations. CONCLUSIONS These results demonstrate the clinical relevance of IM in psychosis risk. They also provide preliminary support for the reliability, validity and utility of this new Trait IM-EM scale, which addresses a measurement gap and can facilitate identification of neurobehavioral and clinical correlates of IM deficits.
Collapse
Affiliation(s)
- Paige R Didier
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychology, University of Maryland, College Park, MD 20742, USA.
| | - Tyler M Moore
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Monica E Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Greer Prettyman
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tess Levinson
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lynch School of Education and Human Development, Boston College, Chestnut Hill, MA 02467, USA
| | - Chloe Savage
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Christian G Kohler
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joseph Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
2
|
Wen J, Skampardoni I, Tian YE, Yang Z, Cui Y, Erus G, Hwang G, Varol E, Boquet-Pujadas A, Chand GB, Nasrallah I, Satterthwaite T, Shou H, Shen L, Toga AW, Zaleskey A, Davatzikos C. Neuroimaging-AI Endophenotypes of Brain Diseases in the General Population: Towards a Dimensional System of Vulnerability. medRxiv 2023:2023.08.16.23294179. [PMID: 37662256 PMCID: PMC10473785 DOI: 10.1101/2023.08.16.23294179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Disease heterogeneity poses a significant challenge for precision diagnostics in both clinical and sub-clinical stages. Recent work leveraging artificial intelligence (AI) has offered promise to dissect this heterogeneity by identifying complex intermediate phenotypes - herein called dimensional neuroimaging endophenotypes (DNEs) - which subtype various neurologic and neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer's disease (AD1-2)1, autism spectrum disorder (ASD1-3)2, late-life depression (LLD1-2)3, and schizophrenia (SCZ1-2)4, in the general population of 39,178 participants in the UK Biobank study. Phenome-wide associations revealed prominent associations between the nine DNEs and phenotypes related to the brain and other human organ systems. This phenotypic landscape aligns with the SNP-phenotype genome-wide associations, revealing 31 genomic loci associated with the nine DNEs (Bonferroni corrected P-value < 5×10-8/9). The DNEs exhibited significant genetic correlations, colocalization, and causal relationships with multiple human organ systems and chronic diseases. A causal effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72×1-4) was established from AD2, characterized by focal medial temporal lobe atrophy, to AD. The nine DNEs and their polygenic risk scores significantly improved the prediction accuracy for 14 systemic disease categories and mortality. These findings underscore the potential of the nine DNEs to identify individuals at a high risk of developing the four brain diseases during preclinical stages for precision diagnostics. All results are publicly available at: http://labs.loni.usc.edu/medicine/.
Collapse
Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ye Ella Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Erdem Varol
- Department of Computer Science and Engineering, New York University, New York, USA
| | | | - Ganesh B. Chand
- Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Ilya Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Theodore Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Andrew Zaleskey
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| |
Collapse
|
3
|
Radhakrishnan H, Zhao C, Sydnor VJ, Baller EB, Cook PA, Fair D, Giesbrecht B, Larsen B, Murtha K, Roalf DR, Rush-Goebel S, Shinohara R, Shou H, Tisdall MD, Vettel J, Grafton S, Cieslak M, Satterthwaite T. Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging. bioRxiv 2023:2023.02.22.529546. [PMID: 36865219 PMCID: PMC9980087 DOI: 10.1101/2023.02.22.529546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.
Collapse
Affiliation(s)
- Hamsanandini Radhakrishnan
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chenying Zhao
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Valerie J. Sydnor
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Erica B. Baller
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philip A. Cook
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Damien Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Barry Giesbrecht
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kristin Murtha
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sage Rush-Goebel
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell Shinohara
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jean Vettel
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
- U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA
| | - Scott Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore Satterthwaite
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
4
|
Sihag S, Massimo L, Burke SE, Phillips JS, Cook P, Duda JT, Gee JC, Satterthwaite T, Grossman M, Benatar M, McMillan CT. Structural Modularity is a Feature of C9orf72 Intrinsic Network Degeneration. Alzheimers Dement 2022. [DOI: 10.1002/alz.066119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | | | - Sarah E Burke
- Penn FTD Center, Perelman School of Medicine at the University of Pennsylvania Philadelphia PA USA
| | - Jeffrey S Phillips
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Philip Cook
- Penn Image Computing and Science Laboratory, University of Pennsylvania Philadelphia PA USA
| | | | - James C. Gee
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania Philadelphia PA USA
| | | | - Murray Grossman
- Penn FTD Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | | | | |
Collapse
|
5
|
Vogel JW, Strandberg O, Gaiteri C, Cieslak M, Covitz S, Wolk DA, Davatzikos C, Hansson O, Satterthwaite T. An autopsy‐validated, easily deployable MRI predictor of Alzheimer’s disease tau pathology. Alzheimers Dement 2022. [DOI: 10.1002/alz.065959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Jacob W Vogel
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania Philadelphia PA USA
- Department of Psychiatry, University of Pennsylvania Philadelphia PA USA
| | | | | | - Matthew Cieslak
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania Philadelphia PA USA
- Department of Psychiatry, University of Pennsylvania Philadelphia PA USA
| | - Sydney Covitz
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania Philadelphia PA USA
- Department of Psychiatry, University of Pennsylvania Philadelphia PA USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania School of Medicine Philadelphia PA USA
- Department of Pathology and Laboratory Medicine, Alzheimer’s Disease Center, Perelman School of Medicine, University of Pennsylvania Philadelphia PA USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania Philadelphia PA USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Philadelphia PA USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University Malmö Sweden
- Memory Clinic, Skåne University Hospital Malmö Sweden
| | - Theodore Satterthwaite
- Penn/CHOP Lifespan Brain Institute, University of Pennsylvania Philadelphia PA USA
- Department of Psychiatry, University of Pennsylvania Philadelphia PA USA
| |
Collapse
|
6
|
Ren B, Xia CH, Gehrman P, Barnett I, Satterthwaite T. Measuring Daily Activity Rhythms in Young Adults at Risk of Affective Instability Using Passively Collected Smartphone Data: Observational Study. JMIR Form Res 2022; 6:e33890. [PMID: 36103225 PMCID: PMC9520392 DOI: 10.2196/33890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 01/18/2022] [Accepted: 07/19/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Irregularities in circadian rhythms have been associated with adverse health outcomes. The regularity of rhythms can be quantified using passively collected smartphone data to provide clinically relevant biomarkers of routine. OBJECTIVE This study aims to develop a metric to quantify the regularity of activity rhythms and explore the relationship between routine and mood, as well as demographic covariates, in an outpatient psychiatric cohort. METHODS Passively sensed smartphone data from a cohort of 38 young adults from the Penn or Children's Hospital of Philadelphia Lifespan Brain Institute and Outpatient Psychiatry Clinic at the University of Pennsylvania were fitted with 2-state continuous-time hidden Markov models representing active and resting states. The regularity of routine was modeled as the hour-of-the-day random effects on the probability of state transition (ie, the association between the hour-of-the-day and state membership). A regularity score, Activity Rhythm Metric, was calculated from the continuous-time hidden Markov models and regressed on clinical and demographic covariates. RESULTS Regular activity rhythms were associated with longer sleep durations (P=.009), older age (P=.001), and mood (P=.049). CONCLUSIONS Passively sensed Activity Rhythm Metrics are an alternative to existing metrics but do not require burdensome survey-based assessments. Low-burden, passively sensed metrics based on smartphone data are promising and scalable alternatives to traditional measurements.
Collapse
Affiliation(s)
- Benny Ren
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Cedric Huchuan Xia
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Philip Gehrman
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
- Michael J Crescenz VA Medical Center, Philadelphia, PA, United States
| | - Ian Barnett
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Theodore Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| |
Collapse
|
7
|
Kimmey BA, McCall NM, Wooldridge LM, Satterthwaite T, Corder G. Engaging endogenous opioid circuits in pain affective processes. J Neurosci Res 2022; 100:66-98. [PMID: 33314372 PMCID: PMC8197770 DOI: 10.1002/jnr.24762] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/29/2020] [Accepted: 11/02/2020] [Indexed: 01/03/2023]
Abstract
The pervasive use of opioid compounds for pain relief is rooted in their utility as one of the most effective therapeutic strategies for providing analgesia. While the detrimental side effects of these compounds have significantly contributed to the current opioid epidemic, opioids still provide millions of patients with reprieve from the relentless and agonizing experience of pain. The human experience of pain has long recognized the perceived unpleasantness entangled with a unique sensation that is immediate and identifiable from the first-person subjective vantage point as "painful." From this phenomenological perspective, how is it that opioids interfere with pain perception? Evidence from human lesion, neuroimaging, and preclinical functional neuroanatomy approaches is sculpting the view that opioids predominately alleviate the affective or inferential appraisal of nociceptive neural information. Thus, opioids weaken pain-associated unpleasantness rather than modulate perceived sensory qualities. Here, we discuss the historical theories of pain to demonstrate how modern neuroscience is revisiting these ideas to deconstruct the brain mechanisms driving the emergence of aversive pain perceptions. We further detail how targeting opioidergic signaling within affective or emotional brain circuits remains a strong avenue for developing targeted pharmacological and gene-therapy analgesic treatments that might reduce the dependence on current clinical opioid options.
Collapse
Affiliation(s)
- Blake A. Kimmey
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, Department of Neuroscience, Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, Equal contributions
| | - Nora M. McCall
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, Department of Neuroscience, Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, Equal contributions
| | - Lisa M. Wooldridge
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, Department of Neuroscience, Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, Lifespan Informatics and Neuroimaging Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gregory Corder
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, Department of Neuroscience, Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
8
|
Axelrud LK, Simioni AR, Pine DS, Winkler AM, Pan PM, Sato JR, Zugman A, Parker N, Picon F, Jackowski A, Hoexter MQ, Barker G, Martinot JL, Martinot MLP, Satterthwaite T, Rohde LA, Milham M, Barker ED, Salum GA. Neuroimaging Association Scores: reliability and validity of aggregate measures of brain structural features linked to mental disorders in youth. Eur Child Adolesc Psychiatry 2021; 30:1895-1906. [PMID: 33030612 PMCID: PMC9077631 DOI: 10.1007/s00787-020-01653-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 09/21/2020] [Indexed: 10/23/2022]
Abstract
In genetics, aggregation of many loci with small effect sizes into a single score improved prediction. Nevertheless, studies applying easily replicable weighted scores to neuroimaging data are lacking. Our aim was to assess the reliability and validity of the Neuroimaging Association Score (NAS), which combines information from structural brain features previously linked to mental disorders. Participants were 726 youth (aged 6-14) from two cities in Brazil who underwent MRI and psychopathology assessment at baseline and 387 at 3-year follow-up. Results were replicated in two samples: IMAGEN (n = 1627) and the Healthy Brain Network (n = 843). NAS were derived by summing the product of each standardized brain feature by the effect size of the association of that brain feature with seven psychiatric disorders documented by previous meta-analyses. NAS were calculated for surface area, cortical thickness and subcortical volumes using T1-weighted scans. NAS reliability, temporal stability and psychopathology and cognition prediction were analyzed. NAS for surface area showed high internal consistency and 3-year stability and predicted general psychopathology and cognition with higher replicability than specific symptomatic domains for all samples. They also predicted general psychopathology with higher replicability than single structures alone, accounting for 1-3% of the variance, but without directionality. The NAS for cortical thickness and subcortical volumes showed lower internal consistency and less replicable associations with behavioural phenotypes. These findings indicate the NAS based on surface area might be replicable markers of general psychopathology, but these links are unlikely to be causal or clinically useful yet.
Collapse
Affiliation(s)
- Luiza Kvitko Axelrud
- Section On Negative Affect and Social Processes, Departamento de Psiquiatria e Medicina Legal, Hospital de Clínicas de Porto Alegre, Universidade Federal Do Rio Grande Do Sul, Ramiro Barcelos, 2350, Room 2202, Porto Alegre, 90035-003, Brazil.
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil.
| | - André Rafael Simioni
- Section On Negative Affect and Social Processes, Departamento de Psiquiatria e Medicina Legal, Hospital de Clínicas de Porto Alegre, Universidade Federal Do Rio Grande Do Sul, Ramiro Barcelos, 2350, Room 2202, Porto Alegre, 90035-003, Brazil
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil
| | - Daniel Samuel Pine
- National Institute of Mental Health Intramural Research Program, Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Anderson Marcelo Winkler
- National Institute of Mental Health Intramural Research Program, Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Pedro Mario Pan
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil
- Departamento de Psiquiatria, Universidade Federal de São Paulo, São Paulo, Brazil
| | - João Ricardo Sato
- Centro de Matemática, Computação E Cognição, Universidade Federal Do ABC, Santo André, Brazil
| | - André Zugman
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil
- Departamento de Psiquiatria, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Nadine Parker
- Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Felipe Picon
- Section On Negative Affect and Social Processes, Departamento de Psiquiatria e Medicina Legal, Hospital de Clínicas de Porto Alegre, Universidade Federal Do Rio Grande Do Sul, Ramiro Barcelos, 2350, Room 2202, Porto Alegre, 90035-003, Brazil
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil
| | - Andrea Jackowski
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil
- Departamento de Psiquiatria, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Marcelo Queiroz Hoexter
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil
- Departamento de Psiquiatria, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Gareth Barker
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Jean-Luc Martinot
- Institut National de La Santé Et de La Recherche Médicale, INSERM Unit 1000 "Neuroimaging and Psychiatry", University Paris Saclay, University Paris Descartes, Paris, France
| | - Marie Laure Paillère Martinot
- Institut National de La Santé Et de La Recherche Médicale, INSERM Unit 1000 "Neuroimaging and Psychiatry", University Paris Saclay, University Paris Descartes, Paris, France
| | - Theodore Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Luis Augusto Rohde
- Section On Negative Affect and Social Processes, Departamento de Psiquiatria e Medicina Legal, Hospital de Clínicas de Porto Alegre, Universidade Federal Do Rio Grande Do Sul, Ramiro Barcelos, 2350, Room 2202, Porto Alegre, 90035-003, Brazil
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil
| | | | - Edward Dylan Barker
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Giovanni Abrahão Salum
- Section On Negative Affect and Social Processes, Departamento de Psiquiatria e Medicina Legal, Hospital de Clínicas de Porto Alegre, Universidade Federal Do Rio Grande Do Sul, Ramiro Barcelos, 2350, Room 2202, Porto Alegre, 90035-003, Brazil
- National Institute of Developmental Psychiatry (INPD, CNPq), São Paulo, Brazil
| |
Collapse
|
9
|
Holleran L, Kelly S, Alloza C, Agartz I, Andreassen OA, Arango C, Banaj N, Calhoun V, Cannon D, Carr V, Corvin A, Glahn DC, Gur R, Hong E, Hoschl C, Howells FM, James A, Janssen J, Kochunov P, Lawrie SM, Liu J, Martinez C, McDonald C, Morris D, Mothersill D, Pantelis C, Piras F, Potkin S, Rasser PE, Roalf D, Rowland L, Satterthwaite T, Schall U, Spalletta G, Spaniel F, Stein DJ, Uhlmann A, Voineskos A, Zalesky A, van Erp TG, Turner JA, Deary IJ, Thompson PM, Jahanshad N, Donohoe G. The Relationship Between White Matter Microstructure and General Cognitive Ability in Patients With Schizophrenia and Healthy Participants in the ENIGMA Consortium. Am J Psychiatry 2020; 177:537-547. [PMID: 32212855 PMCID: PMC7938666 DOI: 10.1176/appi.ajp.2019.19030225] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
OBJECTIVE Schizophrenia has recently been associated with widespread white matter microstructural abnormalities, but the functional effects of these abnormalities remain unclear. Widespread heterogeneity of results from studies published to date preclude any definitive characterization of the relationship between white matter and cognitive performance in schizophrenia. Given the relevance of deficits in cognitive function to predicting social and functional outcomes in schizophrenia, the authors carried out a meta-analysis of available data through the ENIGMA Consortium, using a common analysis pipeline, to elucidate the relationship between white matter microstructure and a measure of general cognitive performance, IQ, in patients with schizophrenia and healthy participants. METHODS The meta-analysis included 760 patients with schizophrenia and 957 healthy participants from 11 participating ENIGMA Consortium sites. For each site, principal component analysis was used to calculate both a global fractional anisotropy component (gFA) and a fractional anisotropy component for six long association tracts (LA-gFA) previously associated with cognition. RESULTS Meta-analyses of regression results indicated that gFA accounted for a significant amount of variation in cognition in the full sample (effect size [Hedges' g]=0.27, CI=0.17-0.36), with similar effects sizes observed for both the patient (effect size=0.20, CI=0.05-0.35) and healthy participant groups (effect size=0.32, CI=0.18-0.45). Comparable patterns of association were also observed between LA-gFA and cognition for the full sample (effect size=0.28, CI=0.18-0.37), the patient group (effect size=0.23, CI=0.09-0.38), and the healthy participant group (effect size=0.31, CI=0.18-0.44). CONCLUSIONS This study provides robust evidence that cognitive ability is associated with global structural connectivity, with higher fractional anisotropy associated with higher IQ. This association was independent of diagnosis; while schizophrenia patients tended to have lower fractional anisotropy and lower IQ than healthy participants, the comparable size of effect in each group suggested a more general, rather than disease-specific, pattern of association.
Collapse
Affiliation(s)
- Laurena Holleran
- School of Psychology, Centre for Neuroimaging and Cognitive Genomics, National Centre for Biomedical Engineering Science and Galway Neuroscience Centre, National University of Ireland Galway, Galway
| | - Sinead Kelly
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey
| | - Clara Alloza
- Department of Psychiatry, University of Edinburgh, Edinburgh; Department of Child and Adolescent Psychiatry, Instituto de Investigación Sanitaria Gregorio Marañón, IiSGM, Hospital General Universitario Gregorio Marañón, School of Medicine, CIBERSAM, Universidad Complutense, Madrid
| | - Ingrid Agartz
- NORMENT, K.G. Jebsen Center for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo
| | - Ole A. Andreassen
- Department of Psychiatry, Ullevål University Hospital and Institute of Psychiatry, University of Oslo, Oslo
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Instituto de Investigación Sanitaria Gregorio Marañón, IiSGM, Hospital General Universitario Gregorio Marañón, School of Medicine, CIBERSAM, Universidad Complutense, Madrid
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome
| | - Vince Calhoun
- Mind Research Network and Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque
| | - Dara Cannon
- School of Psychology, Centre for Neuroimaging and Cognitive Genomics, National Centre for Biomedical Engineering Science and Galway Neuroscience Centre, National University of Ireland Galway, Galway
| | - Vaughan Carr
- Neuroscience Research Australia and School of Psychiatry, University of New South Wales, Sydney
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin
| | - David C. Glahn
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital and Department of Psychiatry, Yale University School of Medicine, New Haven, Conn
| | - Ruben Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia
| | - Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | - Cyril Hoschl
- National Institute of Mental Health, Klecany, Czech Republic
| | - Fleur M. Howells
- Department of Psychiatry and Mental Health, Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | | | - Joost Janssen
- Department of Child and Adolescent Psychiatry, Instituto de Investigación Sanitaria Gregorio Marañón, IiSGM, Hospital General Universitario Gregorio Marañón, School of Medicine, CIBERSAM, Universidad Complutense, Madrid
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | | | - Jingyu Liu
- Mind Research Network, Lovelace Biomedical and Environmental Research Institute, Albuquerque, N.Mex
| | - Covadonga Martinez
- Department of Child and Adolescent Psychiatry, Instituto de Investigación Sanitaria Gregorio Marañón, IiSGM, Hospital General Universitario Gregorio Marañón, School of Medicine, CIBERSAM, Universidad Complutense, Madrid
| | - Colm McDonald
- School of Psychology, Centre for Neuroimaging and Cognitive Genomics, National Centre for Biomedical Engineering Science and Galway Neuroscience Centre, National University of Ireland Galway, Galway
| | - Derek Morris
- School of Psychology, Centre for Neuroimaging and Cognitive Genomics, National Centre for Biomedical Engineering Science and Galway Neuroscience Centre, National University of Ireland Galway, Galway
| | - David Mothersill
- School of Psychology, Centre for Neuroimaging and Cognitive Genomics, National Centre for Biomedical Engineering Science and Galway Neuroscience Centre, National University of Ireland Galway, Galway
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome
| | - Steven Potkin
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine
| | - Paul E. Rasser
- Priority Centre for Brain and Mental Health Research, Priority Research Centre for Stroke and Brain Injury, University of Newcastle, Newcastle, Australia
| | - David Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia
| | - Laura Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore
| | | | - Ulrich Schall
- Priority Centre for Brain and Mental Health Research, University of Newcastle, Newcastle, Australia
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome; Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston
| | - Filip Spaniel
- National Institute of Mental Health, Klecany, Czech Republic
| | - Dan J. Stein
- Department of Psychiatry and Mental Health, Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Anne Uhlmann
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Aristotle Voineskos
- Kimel Family Translational Imaging-Genetics Research Laboratory, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Australia; Department of Biomedical Engineering and Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Australia
| | - Theo G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, and Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine
| | | | - Ian J. Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey
| | - Gary Donohoe
- School of Psychology, Centre for Neuroimaging and Cognitive Genomics, National Centre for Biomedical Engineering Science and Galway Neuroscience Centre, National University of Ireland Galway, Galway
| |
Collapse
|
10
|
Duprat R, Linn K, Satterthwaite T, Ciric R, Sheline Y, Platt M, Gold J, Kable J, Adams G, Kalamveetil-Meethal S, Dallstream A, Long H, Scully M, Shinohara R, Oathes D. Functional connectivity as a tool to individualize DLPFC targeting in TMS. Brain Stimul 2019. [DOI: 10.1016/j.brs.2018.12.742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
|
11
|
Zamzow J, Culnan E, Spiers M, Calkins M, Satterthwaite T, Ruparel K, Abrams D, Chiavacci R, Hakonarson H, Gur R. B-37 * The Relationship between Body Mass Index and Executive Function from Late Childhood through Adolescence. Arch Clin Neuropsychol 2014. [DOI: 10.1093/arclin/acu038.125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
12
|
Culnan E, Zamzow J, Spiers M, Calkins M, Satterthwaite T, Ruparel K, Abrams D, Chiavacci R, Hakonarson H, Gur R. B-36 * Relationships between Body Mass Index and Social Cognition among 8-19 Year-Olds. Arch Clin Neuropsychol 2014. [DOI: 10.1093/arclin/acu038.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|
13
|
Gur RE, Kaltman D, Melhem ER, Ruparel K, Prabhakaran K, Riley M, Yodh E, Hakonarson H, Satterthwaite T, Gur RC. Incidental findings in youths volunteering for brain MRI research. AJNR Am J Neuroradiol 2013; 34:2021-5. [PMID: 23811972 DOI: 10.3174/ajnr.a3525] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE MRIs are obtained in research in healthy and clinical populations, and incidental findings have been reported. Most studies have examined adults with variability in parameters of image acquisition and clinical measures available. We conducted a prospective study of youths and documented the frequency and concomitants of incidental findings. MATERIALS AND METHODS Youths (n = 1400) with an age range from 8-23 years were imaged on the same 3T scanner, with a standard acquisition protocol providing 1.0 mm(3) isotropic resolution of anatomic scans. All scans were reviewed by an experienced board-certified neuroradiologist and were categorized into 3 groups: 1) normal: no incidental findings; 2) coincidental: incidental finding(s) were noted, further reviewed with an experienced pediatric neuroradiologist, but were of no clinical significance; 3) incidental findings that on further review were considered to have potential clinical significance and participants were referred for appropriate clinical follow-up. RESULTS Overall, 148 incidental findings (10.6% of sample) were noted, and of these, 12 required clinical follow-up. Incidental findings were not related to age. However, whites had a higher incidence of pineal cysts, and males had a higher incidence of cavum septum pellucidum, which was associated with psychosis-related symptoms. CONCLUSIONS Incidental findings, moderated by race and sex, occur in approximately one-tenth of participants volunteering for pediatric research, with few requiring follow-up. The incidence supports a 2-tiered approach of neuroradiologic reading and clinical input to determine the potential significance of incidental findings detected on research MR imaging scans.
Collapse
Affiliation(s)
- R E Gur
- Brain Behavior Laboratory, Department of Psychiatry
| | | | | | | | | | | | | | | | | | | |
Collapse
|