1
|
Vriend C, de Joode NT, Pouwels PJW, Liu F, Otaduy MCG, Pastorello B, Robertson FC, Ipser J, Lee S, Hezel DM, van Meter PE, Batistuzzo MC, Hoexter MQ, Sheshachala K, Narayanaswamy JC, Venkatasubramanian G, Lochner C, Miguel EC, Reddy YCJ, Shavitt RG, Stein DJ, Wall M, Simpson HB, van den Heuvel OA. Age of onset of obsessive-compulsive disorder differentially affects white matter microstructure. Mol Psychiatry 2024; 29:1033-1045. [PMID: 38228890 PMCID: PMC11176057 DOI: 10.1038/s41380-023-02390-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 12/04/2023] [Accepted: 12/15/2023] [Indexed: 01/18/2024]
Abstract
Previous diffusion MRI studies have reported mixed findings on white matter microstructure alterations in obsessive-compulsive disorder (OCD), likely due to variation in demographic and clinical characteristics, scanning methods, and underpowered samples. The OCD global study was created across five international sites to overcome these challenges by harmonizing data collection to identify consistent brain signatures of OCD that are reproducible and generalizable. Single-shell diffusion measures (e.g., fractional anisotropy), multi-shell Neurite Orientation Dispersion and Density Imaging (NODDI) and fixel-based measures, were extracted from skeletonized white matter tracts in 260 medication-free adults with OCD and 252 healthy controls. We additionally performed structural connectome analysis. We compared cases with controls and cases with early (<18) versus late (18+) OCD onset using mixed-model and Bayesian multilevel analysis. Compared with healthy controls, adult OCD individuals showed higher fiber density in the sagittal stratum (B[SE] = 0.10[0.05], P = 0.04) and credible evidence for higher fiber density in several other tracts. When comparing early (n = 145) and late-onset (n = 114) cases, converging evidence showed lower integrity of the posterior thalamic radiation -particularly radial diffusivity (B[SE] = 0.28[0.12], P = 0.03)-and lower global efficiency of the structural connectome (B[SE] = 15.3[6.6], P = 0.03) in late-onset cases. Post-hoc analyses indicated divergent direction of effects of the two OCD groups compared to healthy controls. Age of OCD onset differentially affects the integrity of thalamo-parietal/occipital tracts and the efficiency of the structural brain network. These results lend further support for the role of the thalamus and its afferent fibers and visual attentional processes in the pathophysiology of OCD.
Collapse
Affiliation(s)
- Chris Vriend
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, and Department of Anatomy and Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands.
- Compulsivity, Impulsivity and Attention, Amsterdam Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands.
| | - Niels T de Joode
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, and Department of Anatomy and Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands
- Compulsivity, Impulsivity and Attention, Amsterdam Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands
| | - Petra J W Pouwels
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands
- Brain Imaging, Amsterdam Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands
| | - Feng Liu
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Maria C G Otaduy
- LIM44, Hospital das Clinicas HCFMUSP, Instituto e Departamento de Radiologia da Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Bruno Pastorello
- LIM44, Hospital das Clinicas HCFMUSP, Instituto e Departamento de Radiologia da Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Frances C Robertson
- Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa
| | - Jonathan Ipser
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Seonjoo Lee
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Dianne M Hezel
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Page E van Meter
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Marcelo C Batistuzzo
- Obsessive-Compulsive Spectrum Disorders Program, LIM23, Hospital das Clinicas HCFMUSP, Instituto & Departamento de Psiquiatria da Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
- Department of Methods and Techniques in Psychology, Pontifical Catholic University, Sao Paulo, SP, Brazil
| | - Marcelo Q Hoexter
- LIM44, Hospital das Clinicas HCFMUSP, Instituto e Departamento de Radiologia da Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Karthik Sheshachala
- National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
| | | | | | - Christine Lochner
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Euripedes C Miguel
- Obsessive-Compulsive Spectrum Disorders Program, LIM23, Hospital das Clinicas HCFMUSP, Instituto & Departamento de Psiquiatria da Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Y C Janardhan Reddy
- National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
| | - Roseli G Shavitt
- Obsessive-Compulsive Spectrum Disorders Program, LIM23, Hospital das Clinicas HCFMUSP, Instituto & Departamento de Psiquiatria da Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Dan J Stein
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Melanie Wall
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Helen Blair Simpson
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Odile A van den Heuvel
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, and Department of Anatomy and Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands
- Compulsivity, Impulsivity and Attention, Amsterdam Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands
| |
Collapse
|
2
|
Leshin J, Carter MJ, Doyle CM, Lindquist KA. Language access differentially alters functional connectivity during emotion perception across cultures. Front Psychol 2024; 14:1084059. [PMID: 38425348 PMCID: PMC10901990 DOI: 10.3389/fpsyg.2023.1084059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 12/15/2023] [Indexed: 03/02/2024] Open
Abstract
Introduction It is often assumed that the ability to recognize the emotions of others is reflexive and automatic, driven only by observable facial muscle configurations. However, research suggests that accumulated emotion concept knowledge shapes the way people perceive the emotional meaning of others' facial muscle movements. Cultural upbringing can shape an individual's concept knowledge, such as expectations about which facial muscle configurations convey anger, disgust, or sadness. Additionally, growing evidence suggests that access to emotion category words, such as "anger," facilitates access to such emotion concept knowledge and in turn facilitates emotion perception. Methods To investigate the impact of cultural influence and emotion concept accessibility on emotion perception, participants from two cultural groups (Chinese and White Americans) completed a functional magnetic resonance imaging scanning session to assess functional connectivity between brain regions during emotion perception. Across four blocks, participants were primed with either English emotion category words ("anger," "disgust") or control text (XXXXXX) before viewing images of White American actors posing facial muscle configurations that are stereotypical of anger and disgust in the United States. Results We found that when primed with "disgust" versus control text prior to seeing disgusted facial expressions, Chinese participants showed a significant decrease in functional connectivity between a region associated with semantic retrieval (the inferior frontal gyrus) and regions associated with semantic processing, visual perception, and social cognition. Priming the word "anger" did not impact functional connectivity for Chinese participants relative to control text, and priming neither "disgust" nor "anger" impacted functional connectivity for White American participants. Discussion These findings provide preliminary evidence that emotion concept accessibility differentially impacts perception based on participants' cultural background.
Collapse
Affiliation(s)
- Joseph Leshin
- Department of Psychology and Neuroscience, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Maleah J. Carter
- Department of Psychology and Neuroscience, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Cameron M. Doyle
- Department of Psychology and Neuroscience, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Kristen A. Lindquist
- Department of Psychology and Neuroscience, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Biomedical Research Imaging Center, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| |
Collapse
|
3
|
Goeckner BD, Brett BL, Mayer AR, España LY, Banerjee A, Muftuler LT, Meier TB. Associations of prior concussion severity with brain microstructure using mean apparent propagator magnetic resonance imaging. Hum Brain Mapp 2024; 45:e26556. [PMID: 38158641 PMCID: PMC10789198 DOI: 10.1002/hbm.26556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 10/16/2023] [Accepted: 11/21/2023] [Indexed: 01/03/2024] Open
Abstract
Magnetic resonance imaging (MRI) diffusion studies have shown chronic microstructural tissue abnormalities in athletes with history of concussion, but with inconsistent findings. Concussions with post-traumatic amnesia (PTA) and/or loss of consciousness (LOC) have been connected to greater physiological injury. The novel mean apparent propagator (MAP) MRI is expected to be more sensitive to such tissue injury than the conventional diffusion tensor imaging. This study examined effects of prior concussion severity on microstructure with MAP-MRI. Collegiate-aged athletes (N = 111, 38 females; ≥6 months since most recent concussion, if present) completed semistructured interviews to determine the presence of prior concussion and associated injury characteristics, including PTA and LOC. MAP-MRI metrics (mean non-Gaussian diffusion [NG Mean], return-to-origin probability [RTOP], and mean square displacement [MSD]) were calculated from multi-shell diffusion data, then evaluated for associations with concussion severity through group comparisons in a primary model (athletes with/without prior concussion) and two secondary models (athletes with/without prior concussion with PTA and/or LOC, and athletes with/without prior concussion with LOC only). Bayesian multilevel modeling estimated models in regions of interest (ROI) in white matter and subcortical gray matter, separately. In gray matter, the primary model showed decreased NG Mean and RTOP in the bilateral pallidum and decreased NG Mean in the left putamen with prior concussion. In white matter, lower NG Mean with prior concussion was present in all ROI across all models and was further decreased with LOC. However, only prior concussion with LOC was associated with decreased RTOP and increased MSD across ROI. Exploratory analyses conducted separately in male and female athletes indicate associations in the primary model may differ by sex. Results suggest microstructural measures in gray matter are associated with a general history of concussion, while a severity-dependent association of prior concussion may exist in white matter.
Collapse
Affiliation(s)
- Bryna D. Goeckner
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Benjamin L. Brett
- Department of NeurosurgeryMedical College of WisconsinMilwaukeeWisconsinUSA
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Andrew R. Mayer
- The Mind Research Network/Lovelace Biomedical and Environmental Research InstituteAlbuquerqueNew MexicoUSA
- Departments of Neurology and PsychiatryUniversity of New Mexico School of MedicineAlbuquerqueNew MexicoUSA
- Department of PsychologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Lezlie Y. España
- Department of NeurosurgeryMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Anjishnu Banerjee
- Department of BiostatisticsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - L. Tugan Muftuler
- Department of NeurosurgeryMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Timothy B. Meier
- Department of NeurosurgeryMedical College of WisconsinMilwaukeeWisconsinUSA
- Department of Biomedical EngineeringMedical College of WisconsinMilwaukeeWisconsinUSA
- Department of Cell Biology, Neurobiology and AnatomyMedical College of WisconsinMilwaukeeWisconsinUSA
| |
Collapse
|
4
|
Purcell J, Wiley R, Won J, Callow D, Weiss L, Alfini A, Wei Y, Carson Smith J. Increased neural differentiation after a single session of aerobic exercise in older adults. Neurobiol Aging 2023; 132:67-84. [PMID: 37742442 DOI: 10.1016/j.neurobiolaging.2023.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 08/19/2023] [Accepted: 08/24/2023] [Indexed: 09/26/2023]
Abstract
Aging is associated with decreased cognitive function. One theory posits that this decline is in part due to multiple neural systems becoming dedifferentiated in older adults. Exercise is known to improve cognition in older adults, even after only a single session. We hypothesized that one mechanism of improvement is a redifferentiation of neural systems. We used a within-participant, cross-over design involving 2 sessions: either 30 minutes of aerobic exercise or 30 minutes of seated rest (n = 32; ages 55-81 years). Both functional Magnetic Resonance Imaging (fMRI) and Stroop performance were acquired soon after exercise and rest. We quantified neural differentiation via general heterogeneity regression. There were 3 prominent findings following the exercise. First, participants were better at reducing Stroop interference. Second, while there was greater neural differentiation within the hippocampal formation and cerebellum, there was lower neural differentiation within frontal cortices. Third, this greater neural differentiation in the cerebellum and temporal lobe was more pronounced in the older ages. These data suggest that exercise can induce greater neural differentiation in healthy aging.
Collapse
Affiliation(s)
- Jeremy Purcell
- Department of Kinesiology, University of Maryland, College Park, MD, USA; Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA.
| | - Robert Wiley
- Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, USA
| | - Junyeon Won
- Department of Kinesiology, University of Maryland, College Park, MD, USA; Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Dallas, Dallas, TX, USA
| | - Daniel Callow
- Department of Kinesiology, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - Lauren Weiss
- Department of Kinesiology, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - Alfonso Alfini
- National Center on Sleep Disorders Research, Division of Lung Diseases, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Yi Wei
- Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA
| | - J Carson Smith
- Department of Kinesiology, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA.
| |
Collapse
|
5
|
Liu TT, Fu JZ, Chai Y, Japee S, Chen G, Ungerleider LG, Merriam EP. Layer-specific, retinotopically-diffuse modulation in human visual cortex in response to viewing emotionally expressive faces. Nat Commun 2022; 13:6302. [PMID: 36273204 PMCID: PMC9588045 DOI: 10.1038/s41467-022-33580-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 09/22/2022] [Indexed: 12/25/2022] Open
Abstract
Viewing faces that are perceived as emotionally expressive evokes enhanced neural responses in multiple brain regions, a phenomenon thought to depend critically on the amygdala. This emotion-related modulation is evident even in primary visual cortex (V1), providing a potential neural substrate by which emotionally salient stimuli can affect perception. How does emotional valence information, computed in the amygdala, reach V1? Here we use high-resolution functional MRI to investigate the layer profile and retinotopic distribution of neural activity specific to emotional facial expressions. Across three experiments, human participants viewed centrally presented face stimuli varying in emotional expression and performed a gender judgment task. We found that facial valence sensitivity was evident only in superficial cortical layers and was not restricted to the retinotopic location of the stimuli, consistent with diffuse feedback-like projections from the amygdala. Together, our results provide a feedback mechanism by which the amygdala directly modulates activity at the earliest stage of visual processing.
Collapse
Affiliation(s)
- Tina T Liu
- Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, 20892, MD, USA.
| | - Jason Z Fu
- Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, 20892, MD, USA
| | - Yuhui Chai
- Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, 20892, MD, USA
| | - Shruti Japee
- Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, 20892, MD, USA
| | - Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, NIH, Bethesda, 20892, MD, USA
| | - Leslie G Ungerleider
- Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, 20892, MD, USA
| | - Elisha P Merriam
- Laboratory of Brain and Cognition, National Institute of Mental Health, NIH, Bethesda, 20892, MD, USA
| |
Collapse
|
6
|
Haller SP, Chen G, Kitt ER, Smith AR, Stoddard J, Abend R, Cardenas SI, Revzina O, Coppersmith D, Leibenluft E, Brotman MA, Pine DS, Pagliaccio D. Reliability of task-evoked neural activation during face-emotion paradigms: Effects of scanner and psychological processes. Hum Brain Mapp 2022; 43:2109-2120. [PMID: 35165974 PMCID: PMC8996353 DOI: 10.1002/hbm.25723] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/28/2021] [Accepted: 11/11/2021] [Indexed: 12/22/2022] Open
Abstract
Assessing and improving test-retest reliability is critical to efforts to address concerns about replicability of task-based functional magnetic resonance imaging. The current study uses two statistical approaches to examine how scanner and task-related factors influence reliability of neural response to face-emotion viewing. Forty healthy adult participants completed two face-emotion paradigms at up to three scanning sessions across two scanners of the same build over approximately 2 months. We examined reliability across the main task contrasts using Bayesian linear mixed-effects models performed voxel-wise across the brain. We also used a novel Bayesian hierarchical model across a predefined whole-brain parcellation scheme and subcortical anatomical regions. Scanner differences accounted for minimal variance in temporal signal-to-noise ratio and task contrast maps. Regions activated during task at the group level showed higher reliability relative to regions not activated significantly at the group level. Greater reliability was found for contrasts involving conditions with clearly distinct visual stimuli and associated cognitive demands (e.g., face vs. nonface discrimination) compared to conditions with more similar demands (e.g., angry vs. happy face discrimination). Voxel-wise reliability estimates tended to be higher than those based on predefined anatomical regions. This work informs attempts to improve reliability in the context of task activation patterns and specific task contrasts. Our study provides a new method to estimate reliability across a large number of regions of interest and can inform researchers' selection of task conditions and analytic contrasts.
Collapse
Affiliation(s)
- Simone P. Haller
- Emotion and Development BranchNational Institute of Mental Health, National Institutes of HealthBethesdaMarylandUSA
| | - Gang Chen
- Scientific and Statistical Computing CoreNational Institute of Mental Health, National Institutes of HealthBethesdaMarylandUSA
| | - Elizabeth R. Kitt
- Emotion and Development BranchNational Institute of Mental Health, National Institutes of HealthBethesdaMarylandUSA
| | - Ashley R. Smith
- Emotion and Development BranchNational Institute of Mental Health, National Institutes of HealthBethesdaMarylandUSA
| | - Joel Stoddard
- Pediatric Mental Health Institute, Children's Hospital Colorado, Department of Psychiatry & Neuroscience ProgramUniversity of Colorado, Anschutz Medical CampusAuroraColoradoUSA
| | - Rany Abend
- Emotion and Development BranchNational Institute of Mental Health, National Institutes of HealthBethesdaMarylandUSA
| | - Sofia I. Cardenas
- Emotion and Development BranchNational Institute of Mental Health, National Institutes of HealthBethesdaMarylandUSA
| | - Olga Revzina
- Emotion and Development BranchNational Institute of Mental Health, National Institutes of HealthBethesdaMarylandUSA
| | - Daniel Coppersmith
- Emotion and Development BranchNational Institute of Mental Health, National Institutes of HealthBethesdaMarylandUSA
| | - Ellen Leibenluft
- Emotion and Development BranchNational Institute of Mental Health, National Institutes of HealthBethesdaMarylandUSA
| | - Melissa A. Brotman
- Emotion and Development BranchNational Institute of Mental Health, National Institutes of HealthBethesdaMarylandUSA
| | - Daniel S. Pine
- Emotion and Development BranchNational Institute of Mental Health, National Institutes of HealthBethesdaMarylandUSA
| | - David Pagliaccio
- Division of Child and Adolescent Psychiatry, Department of Psychiatry Vagelos College of Physicians and SurgeonsNew York State Psychiatric Institute, Columbia UniversityNew YorkNew YorkUSA
| |
Collapse
|
7
|
Suarez GL, Burt SA, Gard AM, Burton J, Clark DA, Klump KL, Hyde LW. The impact of neighborhood disadvantage on amygdala reactivity: Pathways through neighborhood social processes. Dev Cogn Neurosci 2022; 54:101061. [PMID: 35042163 PMCID: PMC8777301 DOI: 10.1016/j.dcn.2022.101061] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 12/21/2021] [Accepted: 01/11/2022] [Indexed: 11/23/2022] Open
Abstract
Youth growing up in disadvantaged neighborhoods are more likely than their advantaged peers to face negative behavioral and mental health outcomes. Although studies have shown that adversity can undermine positive development via its impact on the developing brain, few studies have examined the association between neighborhood disadvantage and neural function, and no study has investigated potential social mechanisms within the neighborhood that might link neighborhood disadvantage to altered neural function. The current study evaluated the association between neighborhood disadvantage and amygdala reactivity during socioemotional face processing. We also assessed whether and which neighborhood-level social processes were related to amygdala reactivity, and whether these social processes mediated or moderated the association between neighborhood disadvantage and altered amygdala reactivity. We examined these aims in a registered report, using a sample of twins aged 7-19 years (N = 354 families, 708 twins) recruited from birth records with enrichment for neighborhood disadvantage. Twins completed a socioemotional face processing fMRI task and a sample of unrelated participants from the twins' neighborhoods were also recruited to serve as informants on neighborhood social processes. We found that neighborhood disadvantage was associated with greater right amygdala reactivity to threat, but only when neighborhood informants perceived norms in the neighborhood to be more permissive regarding general safety and management. The findings from this research add to the growing literature highlighting the influence of neighborhood disadvantage on amygdala function and the ways that supportive social processes may buffer the impact of adversity on brain function.
Collapse
Affiliation(s)
- Gabriela L Suarez
- Department of Psychology, The University of Michigan, Ann Arbor, MI 48109, USA
| | - S Alexandra Burt
- Department of Psychology, Michigan State University, East Lansing, MI 48824, USA
| | - Arianna M Gard
- Department of Psychology, Program in Neuroscience and Cognitive Neuroscience, The University of Maryland, College Park, MD 20742, USA
| | - Jared Burton
- Department of Psychology, The University of Michigan, Ann Arbor, MI 48109, USA
| | - D Angus Clark
- Department of Psychiatry, The University of Michigan, Ann Arbor, MI 48104, USA
| | - Kelly L Klump
- Department of Psychology, Michigan State University, East Lansing, MI 48824, USA
| | - Luke W Hyde
- Department of Psychology, The University of Michigan, Ann Arbor, MI 48109, USA; Survey Research Center at the Institute for Social Research, The University of Michigan, Ann Arbor, MI 48104, USA.
| |
Collapse
|
8
|
Hattori T, Reynolds R, Wiggs E, Horovitz SG, Lungu C, Chen G, Yasuda E, Hallett M. Neural correlates of working memory and compensation at different stages of cognitive impairment in Parkinson’s disease. NEUROIMAGE: CLINICAL 2022; 35:103100. [PMID: 35780660 PMCID: PMC9421432 DOI: 10.1016/j.nicl.2022.103100] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 05/09/2022] [Accepted: 06/23/2022] [Indexed: 11/22/2022] Open
Abstract
PD patients have two types of compensatory mechanisms for working memory (WM). First, hyperactivation for different WM load tasks depending on cognitive status. PD-CogNL has hyperactivation for moderate and heavy working memory load tasks. PD-MCI and PDD have hyperactivation for control and light working memory load tasks. Second, bilateral recruitment of WM-related areas improves WM performance.
Working memory (WM) impairment is one of the most frequent cognitive deficits in Parkinson's disease (PD). However, it is not known how neural activity is altered and compensatory responses eventually fail during progression. We aimed to elucidate neural correlates of WM and compensatory mechanisms in PD. Eighteen cognitively normal PD patients (PD-CogNL), 16 with PD with mild cognitive impairment (PD-MCI), 11 with PD with dementia (PDD), and 17 healthy controls (HCs) were evaluated. Subjects performed an n-back task. Functional MRI data were analyzed by event-related analysis for correct responses. Brain activations were evaluated by comparing them to fixation cross or 0-back task, and correlated with n-back task performance. When compared to fixation cross, PD-CogNL patients had more activation in WM areas than HCs for both the 2- and 3-back tasks. PD-MCI and PDD patients had more activation in WM areas than HCs for the 0- and 1-back task. 2-back task performance was correlated with brain activations (vs. 0-back task) in the bilateral dorsolateral prefrontal cortex and frontal eye field (FEF) and left rostral prefrontal cortex, caudate nucleus, inferior/superior parietal lobule (IPL/SPL), and anterior insular cortex as well as anterior cingulate cortex. 3-back task performance was correlated with brain activations (vs. 0-back task) in the left FEF, right caudate nucleus, and bilateral IPL/SPL. Additional activations on top of the 0-back task, rather than fixation cross, are the neural correlates of WM. Our results suggest PD patients have two types of compensatory mechanisms: (1) Hyperactivation for different WM load tasks depending on their cognitive status. PD-CogNL have hyperactivation for moderate and heavy working memory load tasks while maintaining normal working memory performance. In contrast, PD-MCI and PDD have hyperactivation for control task and light working memory load task, leaving less neural resources to further activate for more demanding tasks and resulting in impaired working memory performance. (2) Bilateral recruitment of WM-related areas, in particular the DLPFC, FEF, IPL/SPL and caudate nucleus, to improve WM performance.
Collapse
|
9
|
Masharipov R, Knyazeva I, Nikolaev Y, Korotkov A, Didur M, Cherednichenko D, Kireev M. Providing Evidence for the Null Hypothesis in Functional Magnetic Resonance Imaging Using Group-Level Bayesian Inference. Front Neuroinform 2021; 15:738342. [PMID: 34924989 PMCID: PMC8674455 DOI: 10.3389/fninf.2021.738342] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 11/05/2021] [Indexed: 11/23/2022] Open
Abstract
Classical null hypothesis significance testing is limited to the rejection of the point-null hypothesis; it does not allow the interpretation of non-significant results. This leads to a bias against the null hypothesis. Herein, we discuss statistical approaches to ‘null effect’ assessment focusing on the Bayesian parameter inference (BPI). Although Bayesian methods have been theoretically elaborated and implemented in common neuroimaging software packages, they are not widely used for ‘null effect’ assessment. BPI considers the posterior probability of finding the effect within or outside the region of practical equivalence to the null value. It can be used to find both ‘activated/deactivated’ and ‘not activated’ voxels or to indicate that the obtained data are not sufficient using a single decision rule. It also allows to evaluate the data as the sample size increases and decide to stop the experiment if the obtained data are sufficient to make a confident inference. To demonstrate the advantages of using BPI for fMRI data group analysis, we compare it with classical null hypothesis significance testing on empirical data. We also use simulated data to show how BPI performs under different effect sizes, noise levels, noise distributions and sample sizes. Finally, we consider the problem of defining the region of practical equivalence for BPI and discuss possible applications of BPI in fMRI studies. To facilitate ‘null effect’ assessment for fMRI practitioners, we provide Statistical Parametric Mapping 12 based toolbox for Bayesian inference.
Collapse
Affiliation(s)
- Ruslan Masharipov
- N. P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Saint Petersburg, Russia
| | - Irina Knyazeva
- N. P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Saint Petersburg, Russia
| | - Yaroslav Nikolaev
- N. P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Saint Petersburg, Russia
| | - Alexander Korotkov
- N. P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Saint Petersburg, Russia
| | - Michael Didur
- N. P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Saint Petersburg, Russia
| | - Denis Cherednichenko
- N. P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Saint Petersburg, Russia
| | - Maxim Kireev
- N. P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, Saint Petersburg, Russia
| |
Collapse
|
10
|
Chen G, Nash TA, Cole KM, Kohn PD, Wei SM, Gregory MD, Eisenberg DP, Cox RW, Berman KF, Shane Kippenhan J. Beyond linearity in neuroimaging: Capturing nonlinear relationships with application to longitudinal studies. Neuroimage 2021; 233:117891. [PMID: 33667672 PMCID: PMC8284193 DOI: 10.1016/j.neuroimage.2021.117891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 02/14/2021] [Accepted: 02/15/2021] [Indexed: 12/03/2022] Open
Abstract
The ubiquitous adoption of linearity for quantitative predictors in statistical modeling is likely attributable to its advantages of straightforward interpretation and computational feasibility. The linearity assumption may be a reasonable approximation especially when the variable is confined within a narrow range, but it can be problematic when the variable's effect is non-monotonic or complex. Furthermore, visualization and model assessment of a linear fit are usually omitted because of challenges at the whole brain level in neuroimaging. By adopting a principle of learning from the data in the presence of uncertainty to resolve the problematic aspects of conventional polynomial fitting, we introduce a flexible and adaptive approach of multilevel smoothing splines (MSS) to capture any nonlinearity of a quantitative predictor for population-level neuroimaging data analysis. With no prior knowledge regarding the underlying relationship other than a parsimonious assumption about the extent of smoothness (e.g., no sharp corners), we express the unknown relationship with a sufficient number of smoothing splines and use the data to adaptively determine the specifics of the nonlinearity. In addition to introducing the theoretical framework of MSS as an efficient approach with a counterbalance between flexibility and stability, we strive to (a) lay out the specific schemes for population-level nonlinear analyses that may involve task (e.g., contrasting conditions) and subject-grouping (e.g., patients vs controls) factors; (b) provide modeling accommodations to adaptively reveal, estimate and compare any nonlinear effects of a predictor across the brain, or to more accurately account for the effects (including nonlinear effects) of a quantitative confound; (c) offer the associated program 3dMSS to the neuroimaging community for whole-brain voxel-wise analysis as part of the AFNI suite; and (d) demonstrate the modeling approach and visualization processes with a longitudinal dataset of structural MRI scans.
Collapse
Affiliation(s)
- Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA.
| | - Tiffany A Nash
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Katherine M Cole
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA; Section on Behavioral Endocrinology, National Institute of Mental Health, USA
| | - Philip D Kohn
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Shau-Ming Wei
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA; Section on Behavioral Endocrinology, National Institute of Mental Health, USA
| | - Michael D Gregory
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Daniel P Eisenberg
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Robert W Cox
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Karen F Berman
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - J Shane Kippenhan
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| |
Collapse
|
11
|
Chen G, Padmala S, Chen Y, Taylor PA, Cox RW, Pessoa L. To pool or not to pool: Can we ignore cross-trial variability in FMRI? Neuroimage 2021; 225:117496. [PMID: 33181352 PMCID: PMC7861143 DOI: 10.1016/j.neuroimage.2020.117496] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 09/29/2020] [Accepted: 10/19/2020] [Indexed: 11/22/2022] Open
Abstract
In this work, we investigate the importance of explicitly accounting for cross-trial variability in neuroimaging data analysis. To attempt to obtain reliable estimates in a task-based experiment, each condition is usually repeated across many trials. The investigator may be interested in (a) condition-level effects, (b) trial-level effects, or (c) the association of trial-level effects with the corresponding behavior data. The typical strategy for condition-level modeling is to create one regressor per condition at the subject level with the underlying assumption that responses do not change across trials. In this methodology of complete pooling, all cross-trial variability is ignored and dismissed as random noise that is swept under the rug of model residuals. Unfortunately, this framework invalidates the generalizability from the confine of specific trials (e.g., particular faces) to the associated stimulus category ("face"), and may inflate the statistical evidence when the trial sample size is not large enough. Here we propose an adaptive and computationally tractable framework that meshes well with the current two-level pipeline and explicitly accounts for trial-by-trial variability. The trial-level effects are first estimated per subject through no pooling. To allow generalizing beyond the particular stimulus set employed, the cross-trial variability is modeled at the population level through partial pooling in a multilevel model, which permits accurate effect estimation and characterization. Alternatively, trial-level estimates can be used to investigate, for example, brain-behavior associations or correlations between brain regions. Furthermore, our approach allows appropriate accounting for serial correlation, handling outliers, adapting to data skew, and capturing nonlinear brain-behavior relationships. By applying a Bayesian multilevel model framework at the level of regions of interest to an experimental dataset, we show how multiple testing can be addressed and full results reported without arbitrary dichotomization. Our approach revealed important differences compared to the conventional method at the condition level, including how the latter can distort effect magnitude and precision. Notably, in some cases our approach led to increased statistical sensitivity. In summary, our proposed framework provides an effective strategy to capture trial-by-trial responses that should be of interest to a wide community of experimentalists.
Collapse
Affiliation(s)
- Gang Chen
- Scientific and Statistical Computing Core, NIMH, National Institutes of Health, USA.
| | - Srikanth Padmala
- Centre for Neuroscience, Indian Institute of Science, Bangalore, India
| | - Yi Chen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany; IKND, Universität Magdeburg, Germany
| | - Paul A Taylor
- Scientific and Statistical Computing Core, NIMH, National Institutes of Health, USA
| | - Robert W Cox
- Scientific and Statistical Computing Core, NIMH, National Institutes of Health, USA
| | - Luiz Pessoa
- Department of Psychology, Department of Electrical and Computer Engineering, Maryland Neuroimaging Center, University of Maryland, College Park, USA
| |
Collapse
|
12
|
Limbachia C, Morrow K, Khibovska A, Meyer C, Padmala S, Pessoa L. Controllability over stressor decreases responses in key threat-related brain areas. Commun Biol 2021; 4:42. [PMID: 33402686 PMCID: PMC7785729 DOI: 10.1038/s42003-020-01537-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 11/27/2020] [Indexed: 12/20/2022] Open
Abstract
Controllability over stressors has major impacts on brain and behavior. In humans, however, the effect of controllability on responses to stressors is poorly understood. Using functional magnetic resonance imaging (fMRI), we investigated how controllability altered responses to a shock-plus-sound stressor with a between-group yoked design, where participants in controllable and uncontrollable groups experienced matched stressor exposure. Employing Bayesian multilevel analysis at the level of regions of interest and voxels in the insula, and standard voxelwise analysis, we found that controllability decreased stressor-related responses across threat-related regions, notably in the bed nucleus of the stria terminalis and anterior insula. Posterior cingulate cortex, posterior insula, and possibly medial frontal gyrus showed increased responses during control over stressor. Our findings support the idea that the aversiveness of stressors is reduced when controllable, leading to decreased responses across key regions involved in anxiety-related processing, even at the level of the extended amygdala.
Collapse
Affiliation(s)
- Chirag Limbachia
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Kelly Morrow
- Department of Psychology, University of Maryland, College Park, MD, USA
- Neuroscience and Cognitive Sciences program, University of Maryland, College Park, MD, USA
| | - Anastasiia Khibovska
- Department of Psychology, University of Maryland, College Park, MD, USA
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| | - Christian Meyer
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA.
- Neuroscience and Cognitive Sciences program, University of Maryland, College Park, MD, USA.
- Maryland Neuroimaging Center, University of Maryland, College Park, MD, USA.
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA.
| |
Collapse
|
13
|
Chen G, Taylor PA, Qu X, Molfese PJ, Bandettini PA, Cox RW, Finn ES. Untangling the relatedness among correlations, part III: Inter-subject correlation analysis through Bayesian multilevel modeling for naturalistic scanning. Neuroimage 2020; 216:116474. [PMID: 31884057 PMCID: PMC7299750 DOI: 10.1016/j.neuroimage.2019.116474] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 12/06/2019] [Accepted: 12/17/2019] [Indexed: 01/21/2023] Open
Abstract
While inter-subject correlation (ISC) analysis is a powerful tool for naturalistic scanning data, drawing appropriate statistical inferences is difficult due to the daunting task of accounting for the intricate relatedness in data structure as well as handling the multiple testing issue. Although the linear mixed-effects (LME) modeling approach (Chen et al., 2017a) is capable of capturing the relatedness in the data and incorporating explanatory variables, there are a few challenging issues: 1) it is difficult to assign accurate degrees of freedom for each testing statistic, 2) multiple testing correction is potentially over-penalizing due to model inefficiency, and 3) thresholding necessitates arbitrary dichotomous decisions. Here we propose a Bayesian multilevel (BML) framework for ISC data analysis that integrates all regions of interest into one model. By loosely constraining the regions through a weakly informative prior, BML dissolves multiplicity through conservatively pooling the effect of each region toward the center and improves collective fitting and overall model performance. In addition to potentially achieving a higher inference efficiency, BML improves spatial specificity and easily allows the investigator to adopt a philosophy of full results reporting. A dataset of naturalistic scanning is utilized to illustrate the modeling approach with 268 parcels and to showcase the modeling capability, flexibility and advantages in results reporting. The associated program will be available as part of the AFNI suite for general use.
Collapse
Affiliation(s)
- Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA.
| | - Paul A Taylor
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Xianggui Qu
- Department of Mathematics and Statistics, Oakland University, USA
| | - Peter J Molfese
- Section on Functional Imaging Methods, National Institute of Mental Health, USA
| | - Peter A Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, USA
| | - Robert W Cox
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Emily S Finn
- Section on Functional Imaging Methods, National Institute of Mental Health, USA
| |
Collapse
|
14
|
Hubbard NA, Siless V, Frosch IR, Goncalves M, Lo N, Wang J, Bauer CCC, Conroy K, Cosby E, Hay A, Jones R, Pinaire M, Vaz De Souza F, Vergara G, Ghosh S, Henin A, Hirshfeld-Becker DR, Hofmann SG, Rosso IM, Auerbach RP, Pizzagalli DA, Yendiki A, Gabrieli JDE, Whitfield-Gabrieli S. Brain function and clinical characterization in the Boston adolescent neuroimaging of depression and anxiety study. Neuroimage Clin 2020; 27:102240. [PMID: 32361633 PMCID: PMC7199015 DOI: 10.1016/j.nicl.2020.102240] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 03/09/2020] [Accepted: 03/10/2020] [Indexed: 12/28/2022]
Abstract
We present a Human Connectome Project study tailored toward adolescent anxiety and depression. This study is one of the first studies of the Connectomes Related to Human Diseases initiative and is collecting structural, functional, and diffusion-weighted brain imaging data from up to 225 adolescents (ages 14-17 years), 150 of whom are expected to have a current diagnosis of an anxiety and/or depressive disorder. Comprehensive clinical and neuropsychological evaluations and longitudinal clinical data are also being collected. This article provides an overview of task functional magnetic resonance imaging (fMRI) protocols and preliminary findings (N = 140), as well as clinical and neuropsychological characterization of adolescents. Data collection is ongoing for an additional 85 adolescents, most of whom are expected to have a diagnosis of an anxiety and/or depressive disorder. Data from the first 140 adolescents are projected for public release through the National Institutes of Health Data Archive (NDA) with the timing of this manuscript. All other data will be made publicly-available through the NDA at regularly scheduled intervals. This article is intended to serve as an introduction to this project as well as a reference for those seeking to clinical, neurocognitive, and task fMRI data from this public resource.
Collapse
Affiliation(s)
- N A Hubbard
- Massachusetts Institute of Technology, Cambridge, MA, United States; University of Nebraska-Lincoln, Lincoln, NE, United States
| | - V Siless
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - I R Frosch
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - M Goncalves
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - N Lo
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - J Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - C C C Bauer
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - K Conroy
- Boston University, Boston, MA, United States
| | - E Cosby
- Harvard Medical School, Boston, MA, United States; McLean Hospital, Belmont, MA, United States
| | - A Hay
- Boston University, Boston, MA, United States
| | - R Jones
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - M Pinaire
- Boston University, Boston, MA, United States
| | - F Vaz De Souza
- Harvard Medical School, Boston, MA, United States; Massachusetts General Hospital, Boston, MA, United States
| | - G Vergara
- Harvard Medical School, Boston, MA, United States; McLean Hospital, Belmont, MA, United States
| | - S Ghosh
- Massachusetts Institute of Technology, Cambridge, MA, United States; Harvard Medical School, Boston, MA, United States
| | - A Henin
- Harvard Medical School, Boston, MA, United States; Massachusetts General Hospital, Boston, MA, United States
| | - D R Hirshfeld-Becker
- Harvard Medical School, Boston, MA, United States; Massachusetts General Hospital, Boston, MA, United States
| | - S G Hofmann
- Boston University, Boston, MA, United States
| | - I M Rosso
- Harvard Medical School, Boston, MA, United States; McLean Hospital, Belmont, MA, United States
| | - R P Auerbach
- Columbia University, New York, NY, United States
| | - D A Pizzagalli
- Harvard Medical School, Boston, MA, United States; McLean Hospital, Belmont, MA, United States
| | - A Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - J D E Gabrieli
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - S Whitfield-Gabrieli
- Massachusetts Institute of Technology, Cambridge, MA, United States; Northeastern University, Boston, MA, United States.
| |
Collapse
|