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Degutis JK, Chaimow D, Haenelt D, Assem M, Duncan J, Haynes JD, Weiskopf N, Lorenz R. Dynamic layer-specific processing in the prefrontal cortex during working memory. Commun Biol 2024; 7:1140. [PMID: 39277694 PMCID: PMC11401931 DOI: 10.1038/s42003-024-06780-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 08/26/2024] [Indexed: 09/17/2024] Open
Abstract
The dorsolateral prefrontal cortex (dlPFC) is reliably engaged in working memory (WM) and comprises different cytoarchitectonic layers, yet their functional role in human WM is unclear. Here, participants completed a delayed-match-to-sample task while undergoing functional magnetic resonance imaging (fMRI) at ultra-high resolution. We examine layer-specific activity to manipulations in WM load and motor response. Superficial layers exhibit a preferential response to WM load during the delay and retrieval periods of a WM task, indicating a lamina-specific activation of the frontoparietal network. Multivariate patterns encoding WM load in the superficial layer dynamically change across the three periods of the task. Last, superficial and deep layers are non-differentially involved in the motor response, challenging earlier findings of a preferential deep layer activation. Taken together, our results provide new insights into the functional laminar circuitry of the dlPFC during WM and support a dynamic account of dlPFC coding.
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Affiliation(s)
- Jonas Karolis Degutis
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.
- Bernstein Center for Computational Neuroscience Berlin and Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, corporate member of the Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
- Max Planck School of Cognition, Leipzig, Germany.
| | - Denis Chaimow
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Daniel Haenelt
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Moataz Assem
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - John-Dylan Haynes
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin and Berlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, corporate member of the Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Max Planck School of Cognition, Leipzig, Germany
- Research Training Group "Extrospection" and Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Research Cluster of Excellence "Science of Intelligence", Technische Universität Berlin, Berlin, Germany
- Collaborative Research Center "Volition and Cognitive Control", Technische Universität Dresden, Dresden, Germany
| | - Nikolaus Weiskopf
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Romy Lorenz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
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Yun SD, Küppers F, Shah NJ. Submillimeter fMRI Acquisition Techniques for Detection of Laminar and Columnar Level Brain Activation. J Magn Reson Imaging 2024; 59:747-766. [PMID: 37589385 DOI: 10.1002/jmri.28911] [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: 01/27/2023] [Revised: 07/07/2023] [Accepted: 07/07/2023] [Indexed: 08/18/2023] Open
Abstract
Since the first demonstration in the early 1990s, functional MRI (fMRI) has emerged as one of the most powerful, noninvasive neuroimaging tools to probe brain functions. Subsequently, fMRI techniques have advanced remarkably, enabling the acquisition of functional signals with a submillimeter voxel size. This innovation has opened the possibility of investigating subcortical neural activities with respect to the cortical depths or cortical columns. For this purpose, numerous previous works have endeavored to design suitable functional contrast mechanisms and dedicated imaging techniques. Depending on the choice of the functional contrast, functional signals can be detected with high sensitivity or with improved spatial specificity to the actual activation site, and the pertaining issues have been discussed in a number of earlier works. This review paper primarily aims to provide an overview of the subcortical fMRI techniques that allow the acquisition of functional signals with a submillimeter resolution. Here, the advantages and disadvantages of the imaging techniques will be described and compared. We also summarize supplementary imaging techniques that assist in the analysis of the subcortical brain activation for more accurate mapping with reduced geometric deformation. This review suggests that there is no single universally accepted method as the gold standard for subcortical fMRI. Instead, the functional contrast and the corresponding readout imaging technique should be carefully determined depending on the purpose of the study. Due to the technical limitations of current fMRI techniques, most subcortical fMRI studies have only targeted partial brain regions. As a future prospect, the spatiotemporal resolution of fMRI will be pushed to satisfy the community's need for a deeper understanding of whole-brain functions and the underlying connectivity in order to achieve the ultimate goal of a time-resolved and layer-specific spatial scale. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Seong Dae Yun
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
| | - Fabian Küppers
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
- RWTH Aachen University, Aachen, Germany
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Jülich, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Jülich, Germany
- JARA - BRAIN - Translational Medicine, Aachen, Germany
- Department of Neurology, RWTH Aachen University, Aachen, Germany
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Kay K. The risk of bias in denoising methods: Examples from neuroimaging. PLoS One 2022; 17:e0270895. [PMID: 35776751 PMCID: PMC9249232 DOI: 10.1371/journal.pone.0270895] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 06/01/2022] [Indexed: 11/25/2022] Open
Abstract
Experimental datasets are growing rapidly in size, scope, and detail, but the value of these datasets is limited by unwanted measurement noise. It is therefore tempting to apply analysis techniques that attempt to reduce noise and enhance signals of interest. In this paper, we draw attention to the possibility that denoising methods may introduce bias and lead to incorrect scientific inferences. To present our case, we first review the basic statistical concepts of bias and variance. Denoising techniques typically reduce variance observed across repeated measurements, but this can come at the expense of introducing bias to the average expected outcome. We then conduct three simple simulations that provide concrete examples of how bias may manifest in everyday situations. These simulations reveal several findings that may be surprising and counterintuitive: (i) different methods can be equally effective at reducing variance but some incur bias while others do not, (ii) identifying methods that better recover ground truth does not guarantee the absence of bias, (iii) bias can arise even if one has specific knowledge of properties of the signal of interest. We suggest that researchers should consider and possibly quantify bias before deploying denoising methods on important research data.
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Affiliation(s)
- Kendrick Kay
- Department of Radiology, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, United States of America
- * E-mail:
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Kong Y, Li QB, Yuan ZH, Jiang XF, Zhang GQ, Cheng N, Dang N. Multimodal Neuroimaging in Rett Syndrome With MECP2 Mutation. Front Neurol 2022; 13:838206. [PMID: 35280272 PMCID: PMC8904872 DOI: 10.3389/fneur.2022.838206] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/24/2022] [Indexed: 01/11/2023] Open
Abstract
Rett syndrome (RTT) is a rare neurodevelopmental disorder characterized by severe cognitive, social, and physical impairments resulting from de novo mutations in the X-chromosomal methyl-CpG binding protein gene 2 (MECP2). While there is still no cure for RTT, exploring up-to date neurofunctional diagnostic markers, discovering new potential therapeutic targets, and searching for novel drug efficacy evaluation indicators are fundamental. Multiple neuroimaging studies on brain structure and function have been carried out in RTT-linked gene mutation carriers to unravel disease-specific imaging features and explore genotype-phenotype associations. Here, we reviewed the neuroimaging literature on this disorder. MRI morphologic studies have shown global atrophy of gray matter (GM) and white matter (WM) and regional variations in brain maturation. Diffusion tensor imaging (DTI) studies have demonstrated reduced fractional anisotropy (FA) in left peripheral WM areas, left major WM tracts, and cingulum bilaterally, and WM microstructural/network topology changes have been further found to be correlated with behavioral abnormalities in RTT. Cerebral blood perfusion imaging studies using single-photon emission CT (SPECT) or PET have evidenced a decreased global cerebral blood flow (CBF), particularly in prefrontal and temporoparietal areas, while magnetic resonance spectroscopy (MRS) and PET studies have contributed to unraveling metabolic alterations in patients with RTT. The results obtained from the available reports confirm that multimodal neuroimaging can provide new insights into a complex interplay between genes, neurotransmitter pathway abnormalities, disease-related behaviors, and clinical severity. However, common limitations related to the available studies include small sample sizes and hypothesis-based and region-specific approaches. We, therefore, conclude that this field is still in its early development phase and that multimodal/multisequence studies with improved post-processing technologies as well as combined PET–MRI approaches are urgently needed to further explore RTT brain alterations.
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Affiliation(s)
- Yu Kong
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
- *Correspondence: Yu Kong
| | - Qiu-bo Li
- Department of Pediatrics, Affiliated Hospital of Jining Medical University, Jining, China
| | - Zhao-hong Yuan
- Department of Pediatric Rehabilitation, Affiliated Hospital of Jining Medical University, Jining, China
| | - Xiu-fang Jiang
- Department of Pediatric Rehabilitation, Affiliated Hospital of Jining Medical University, Jining, China
| | - Gu-qing Zhang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
- Gu-qing Zhang
| | - Nan Cheng
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
| | - Na Dang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, China
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