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Chai XJ, Tang L, Gabrieli JDE, Ofen N. From vision to memory: How scene-sensitive regions support episodic memory formation during child development. Dev Cogn Neurosci 2024; 65:101340. [PMID: 38218015 PMCID: PMC10825658 DOI: 10.1016/j.dcn.2024.101340] [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: 12/13/2022] [Revised: 12/21/2023] [Accepted: 01/02/2024] [Indexed: 01/15/2024] Open
Abstract
Previous brain imaging studies have identified three brain regions that selectively respond to visual scenes, the parahippocampal place area (PPA), the occipital place area (OPA), and the retrosplenial cortex (RSC). There is growing evidence that these visual scene-sensitive regions process different types of scene information and may have different developmental timelines in supporting scene perception. How these scene-sensitive regions support memory functions during child development is largely unknown. We investigated PPA, OPA and RSC activations associated with episodic memory formation in childhood (5-7 years of age) and young adulthood, using a subsequent scene memory paradigm and a functional localizer for scenes. PPA, OPA, and RSC subsequent memory activation and functional connectivity differed between children and adults. Subsequent memory effects were found in activations of all three scene regions in adults. In children, however, robust subsequent memory effects were only found in the PPA. Functional connectivity during successful encoding was significant among the three regions in adults, but not in children. PPA subsequently memory activations and PPA-RSC subsequent memory functional connectivity correlated with accuracy in adults, but not children. These age-related differences add new evidence linking protracted development of the scene-sensitive regions to the protracted development of episodic memory.
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Affiliation(s)
- Xiaoqian J Chai
- Department of Neurology and Neurosurgery, McGill University, USA.
| | - Lingfei Tang
- Department of Psychology and the Institute of Gerontology, Wayne State University, USA
| | - John DE Gabrieli
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Noa Ofen
- Department of Psychology and the Institute of Gerontology, Wayne State University, USA; Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA.
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Haines N, Sullivan-Toole H, Olino T. From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:822-831. [PMID: 36997406 PMCID: PMC10333448 DOI: 10.1016/j.bpsc.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/28/2022] [Accepted: 01/03/2023] [Indexed: 01/13/2023]
Abstract
Advances in computational statistics and corresponding shifts in funding initiatives over the past few decades have led to a proliferation of neuroscientific measures being developed in the context of mental health research. Although such measures have undoubtedly deepened our understanding of neural mechanisms underlying cognitive, affective, and behavioral processes associated with various mental health conditions, the clinical utility of such measures remains underwhelming. Recent commentaries point toward the poor reliability of neuroscientific measures to partially explain this lack of clinical translation. Here, we provide a concise theoretical overview of how unreliability impedes clinical translation of neuroscientific measures; discuss how various modeling principles, including those from hierarchical and structural equation modeling frameworks, can help to improve reliability; and demonstrate how to combine principles of hierarchical and structural modeling within the generative modeling framework to achieve more reliable, generalizable measures of brain-behavior relationships for use in mental health research.
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Affiliation(s)
- Nathaniel Haines
- Department of Data Science, Bayesian Beginnings LLC, Columbus, Ohio.
| | | | - Thomas Olino
- Department of Psychology, Temple University, Philadelphia, Pennsylvania
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Johnson EL, Yin Q, O'Hara NB, Tang L, Jeong JW, Asano E, Ofen N. Dissociable oscillatory theta signatures of memory formation in the developing brain. Curr Biol 2022; 32:1457-1469.e4. [PMID: 35172128 PMCID: PMC9007830 DOI: 10.1016/j.cub.2022.01.053] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/15/2021] [Accepted: 01/19/2022] [Indexed: 11/16/2022]
Abstract
Understanding complex human brain functions is critically informed by studying such functions during development. Here, we addressed a major gap in models of human memory by leveraging rare direct electrophysiological recordings from children and adolescents. Specifically, memory relies on interactions between the medial temporal lobe (MTL) and prefrontal cortex (PFC), and the maturation of these interactions is posited to play a key role in supporting memory development. To understand the nature of MTL-PFC interactions, we examined subdural recordings from MTL and PFC in 21 neurosurgical patients aged 5.9-20.5 years as they performed an established scene memory task. We determined signatures of memory formation by comparing the study of subsequently recognized to forgotten scenes in single trials. Results establish that MTL and PFC interact via two distinct theta mechanisms, an ∼3-Hz oscillation that supports amplitude coupling and slows down with age and an ∼7-Hz oscillation that supports phase coupling and speeds up with age. Slow and fast theta interactions immediately preceding scene onset further explained age-related differences in recognition performance. Last, with additional diffusion imaging data, we linked both functional mechanisms to the structural maturation of the cingulum tract. Our findings establish system-level dynamics of memory formation and suggest that MTL and PFC interact via increasingly dissociable mechanisms as memory improves across development.
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Affiliation(s)
- Elizabeth L Johnson
- Life-Span Cognitive Neuroscience Program, Institute of Gerontology and Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI 48202, USA; Departments of Medical Social Sciences and Pediatrics, Northwestern University, Chicago, IL 60611, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.
| | - Qin Yin
- Life-Span Cognitive Neuroscience Program, Institute of Gerontology and Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI 48202, USA; Department of Psychology, Wayne State University, Detroit, MI 48202, USA
| | - Nolan B O'Hara
- Translational Neuroscience Program, Wayne State University, Detroit, MI 48201, USA
| | - Lingfei Tang
- Life-Span Cognitive Neuroscience Program, Institute of Gerontology and Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI 48202, USA; Department of Psychology, Wayne State University, Detroit, MI 48202, USA
| | - Jeong-Won Jeong
- Translational Neuroscience Program, Wayne State University, Detroit, MI 48201, USA; Departments of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
| | - Eishi Asano
- Translational Neuroscience Program, Wayne State University, Detroit, MI 48201, USA; Departments of Pediatrics and Neurology, Children's Hospital of Michigan, Wayne State University, Detroit, MI 48201, USA
| | - Noa Ofen
- Life-Span Cognitive Neuroscience Program, Institute of Gerontology and Merrill Palmer Skillman Institute, Wayne State University, Detroit, MI 48202, USA; Department of Psychology, Wayne State University, Detroit, MI 48202, USA; Translational Neuroscience Program, Wayne State University, Detroit, MI 48201, USA.
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