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Liaw YS, Augustine GJ. The claustrum and consciousness: An update. Int J Clin Health Psychol 2023; 23:100405. [PMID: 37701759 PMCID: PMC10493512 DOI: 10.1016/j.ijchp.2023.100405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 08/10/2023] [Indexed: 09/14/2023] Open
Abstract
The seminal paper of Crick and Koch (2005) proposed that the claustrum, an enigmatic and thin grey matter structure that lies beside the insular cortex, may be involved in the processing of consciousness. As a result, this otherwise obscure structure has received ever-increasing interest in the search for neural correlates of consciousness. Here we review theories of consciousness and discuss the possible relationship between the claustrum and consciousness. We review relevant experimental evidence collected since the Crick and Koch (2005) paper and consider whether these findings support or contradict their hypothesis. We also explore how future experimental work can be designed to clarify how consciousness emerges from neural activity and to understand the role of the claustrum in consciousness.
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Affiliation(s)
- Yin Siang Liaw
- Neuroscience & Mental Health Program, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - George J. Augustine
- Neuroscience & Mental Health Program, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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Rau A, Schröter N, Rijntjes M, Bamberg F, Jost WH, Zaitsev M, Weiller C, Rau S, Urbach H, Reisert M, Russe MF. Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy. Eur Radiol 2023; 33:7160-7167. [PMID: 37121929 PMCID: PMC10511621 DOI: 10.1007/s00330-023-09665-2] [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: 12/05/2022] [Revised: 03/05/2023] [Accepted: 03/10/2023] [Indexed: 05/02/2023]
Abstract
OBJECTIVES The precise segmentation of atrophic structures remains challenging in neurodegenerative diseases. We determined the performance of a Deep Neural Patchwork (DNP) in comparison to established segmentation algorithms regarding the ability to delineate the putamen in multiple system atrophy (MSA), Parkinson's disease (PD), and healthy controls. METHODS We retrospectively included patients with MSA and PD as well as healthy controls. A DNP was trained on manual segmentations of the putamen as ground truth. For this, the cohort was randomly split into a training (N = 131) and test set (N = 120). The DNP's performance was compared with putaminal segmentations as derived by Automatic Anatomic Labelling, Freesurfer and Fastsurfer. For validation, we assessed the diagnostic accuracy of the resulting segmentations in the delineation of MSA vs. PD and healthy controls. RESULTS A total of 251 subjects (61 patients with MSA, 158 patients with PD, and 32 healthy controls; mean age of 61.5 ± 8.8 years) were included. Compared to the dice-coefficient of the DNP (0.96), we noted significantly weaker performance for AAL3 (0.72; p < .001), Freesurfer (0.82; p < .001), and Fastsurfer (0.84, p < .001). This was corroborated by the superior diagnostic performance of MSA vs. PD and HC of the DNP (AUC 0.93) versus the AUC of 0.88 for AAL3 (p = 0.02), 0.86 for Freesurfer (p = 0.048), and 0.85 for Fastsurfer (p = 0.04). CONCLUSION By utilization of a DNP, accurate segmentations of the putamen can be obtained even if substantial atrophy is present. This allows for more precise extraction of imaging parameters or shape features from the putamen in relevant patient cohorts. CLINICAL RELEVANCE STATEMENT Deep learning-based segmentation of the putamen was superior to currently available algorithms and is beneficial for the diagnosis of multiple system atrophy. KEY POINTS • A Deep Neural Patchwork precisely delineates the putamen and performs equal to human labeling in multiple system atrophy, even when pronounced putaminal volume loss is present. • The Deep Neural Patchwork-based segmentation was more capable to differentiate between multiple system atrophy and Parkinson's disease than the AAL3 atlas, Freesurfer, or Fastsurfer.
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Affiliation(s)
- Alexander Rau
- Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Nils Schröter
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michel Rijntjes
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | - Maxim Zaitsev
- Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Cornelius Weiller
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Stephan Rau
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marco Reisert
- Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, University of Freiburg, Freiburg, Germany
- Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Maximilian F Russe
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Kikuchi H, Jitsuishi T, Hirono S, Yamaguchi A, Iwadate Y. 2D and 3D structures of the whole-brain, directly visible from 100-micron slice 7TMRI images. INTERDISCIPLINARY NEUROSURGERY 2023. [DOI: 10.1016/j.inat.2023.101755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
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Neubauer A, Menegaux A, Wendt J, Li HB, Schmitz-Koep B, Ruzok T, Thalhammer M, Schinz D, Bartmann P, Wolke D, Priller J, Zimmer C, Rueckert D, Hedderich DM, Sorg C. Aberrant claustrum structure in preterm-born neonates: an MRI study. Neuroimage Clin 2023; 37:103286. [PMID: 36516730 PMCID: PMC9755238 DOI: 10.1016/j.nicl.2022.103286] [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: 09/09/2022] [Revised: 11/18/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022]
Abstract
The human claustrum is a gray matter structure in the white matter between insula and striatum. Previous analysis found altered claustrum microstructure in very preterm-born adults associated with lower cognitive performance. As the claustrum development is related to hypoxia-ischemia sensitive transient cell populations being at-risk in premature birth, we hypothesized that claustrum structure is already altered in preterm-born neonates. We studied anatomical and diffusion-weighted MRIs of 83 preterm- and 83 term-born neonates at term-equivalent age. Additionally, claustrum development was analyzed both in a spectrum of 377 term-born neonates and longitudinally in 53 preterm-born subjects. Data was provided by the developing Human Connectome Project. Claustrum development showed increasing volume, increasing fractional anisotropy (FA), and decreasing mean diffusivity (MD) around term both across term- and preterm-born neonates. Relative to term-born ones, preterm-born neonates had (i) increased absolute and relative claustrum volumes, both indicating increased cellular and/or extracellular matter and being in contrast to other subcortical gray matter regions of decreased volumes such as thalamus; (ii) lower claustrum FA and higher claustrum MD, pointing at increased extracellular matrix and impaired axonal integrity; and (iii) aberrant covariance between claustrum FA and MD, respectively, and that of distributed gray matter regions, hinting at relatively altered claustrum microstructure. Results together demonstrate specifically aberrant claustrum structure in preterm-born neonates, suggesting altered claustrum development in prematurity, potentially relevant for later cognitive performance.
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Affiliation(s)
- Antonia Neubauer
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Germany; School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Germany.
| | - Aurore Menegaux
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Germany; School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Germany
| | - Jil Wendt
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Germany; School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Germany
| | - Hongwei Bran Li
- Department of Informatics, Technical University of Munich, Germany; Department of Quantitative Biomedicine, University of Zurich, Switzerland
| | - Benita Schmitz-Koep
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Germany; School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Germany
| | - Tobias Ruzok
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Germany; School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Germany
| | - Melissa Thalhammer
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Germany; School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Germany
| | - David Schinz
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Germany; School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Germany
| | - Peter Bartmann
- Department of Neonatology and Pediatric Intensive Care, University Hospital Bonn, Germany
| | - Dieter Wolke
- Department of Psychology, University of Warwick, Coventry, UK; Warwick Medical School, University of Warwick, Coventry, UK
| | - Josef Priller
- Department of Psychiatry and Psychotherapy, Klinikum Rechts der Isar, Technical University of Munich, Germany; Neuropsychiatry, Charité - Universitätsmedizin Berlin and DZNE, Berlin, Germany; University of Edinburgh and UK DRI, Edinburgh, UK
| | - Claus Zimmer
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Germany; School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Germany
| | - Daniel Rueckert
- School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Germany; Department of Informatics, Technical University of Munich, Germany; Department of Computing, Imperial College London, UK
| | - Dennis M Hedderich
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Germany; School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Germany
| | - Christian Sorg
- Department of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Germany; School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Germany; Department of Psychiatry and Psychotherapy, Klinikum Rechts der Isar, Technical University of Munich, Germany
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Avberšek LK, Repovš G. Deep learning in neuroimaging data analysis: Applications, challenges, and solutions. FRONTIERS IN NEUROIMAGING 2022; 1:981642. [PMID: 37555142 PMCID: PMC10406264 DOI: 10.3389/fnimg.2022.981642] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 10/10/2022] [Indexed: 08/10/2023]
Abstract
Methods for the analysis of neuroimaging data have advanced significantly since the beginning of neuroscience as a scientific discipline. Today, sophisticated statistical procedures allow us to examine complex multivariate patterns, however most of them are still constrained by assuming inherent linearity of neural processes. Here, we discuss a group of machine learning methods, called deep learning, which have drawn much attention in and outside the field of neuroscience in recent years and hold the potential to surpass the mentioned limitations. Firstly, we describe and explain the essential concepts in deep learning: the structure and the computational operations that allow deep models to learn. After that, we move to the most common applications of deep learning in neuroimaging data analysis: prediction of outcome, interpretation of internal representations, generation of synthetic data and segmentation. In the next section we present issues that deep learning poses, which concerns multidimensionality and multimodality of data, overfitting and computational cost, and propose possible solutions. Lastly, we discuss the current reach of DL usage in all the common applications in neuroimaging data analysis, where we consider the promise of multimodality, capability of processing raw data, and advanced visualization strategies. We identify research gaps, such as focusing on a limited number of criterion variables and the lack of a well-defined strategy for choosing architecture and hyperparameters. Furthermore, we talk about the possibility of conducting research with constructs that have been ignored so far or/and moving toward frameworks, such as RDoC, the potential of transfer learning and generation of synthetic data.
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Affiliation(s)
- Lev Kiar Avberšek
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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Deep learning with multiresolution handcrafted features for brain MRI segmentation. Artif Intell Med 2022; 131:102365. [DOI: 10.1016/j.artmed.2022.102365] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 06/28/2022] [Accepted: 07/09/2022] [Indexed: 12/26/2022]
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Rickert CA, Lieleg O. Machine learning approaches for biomolecular, biophysical, and biomaterials research. BIOPHYSICS REVIEWS 2022; 3:021306. [PMID: 38505413 PMCID: PMC10914139 DOI: 10.1063/5.0082179] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/12/2022] [Indexed: 03/21/2024]
Abstract
A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.
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Efficient Claustrum Segmentation in T2-weighted Neonatal Brain MRI Using Transfer Learning from Adult Scans. Clin Neuroradiol 2022; 32:665-676. [PMID: 35072752 PMCID: PMC9424135 DOI: 10.1007/s00062-021-01137-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 12/25/2021] [Indexed: 11/03/2022]
Abstract
Abstract
Purpose
Intrauterine claustrum and subplate neuron development have been suggested to overlap. As premature birth typically impairs subplate neuron development, neonatal claustrum might indicate a specific prematurity impact; however, claustrum identification usually relies on expert knowledge due to its intricate structure. We established automated claustrum segmentation in newborns.
Methods
We applied a deep learning-based algorithm for segmenting the claustrum in 558 T2-weighted neonatal brain MRI of the developing Human Connectome Project (dHCP) with transfer learning from claustrum segmentation in T1-weighted scans of adults. The model was trained and evaluated on 30 manual bilateral claustrum annotations in neonates.
Results
With only 20 annotated scans, the model yielded median volumetric similarity, robust Hausdorff distance and Dice score of 95.9%, 1.12 mm and 80.0%, respectively, representing an excellent agreement between the automatic and manual segmentations. In comparison with interrater reliability, the model achieved significantly superior volumetric similarity (p = 0.047) and Dice score (p < 0.005) indicating stable high-quality performance. Furthermore, the effectiveness of the transfer learning technique was demonstrated in comparison with nontransfer learning. The model can achieve satisfactory segmentation with only 12 annotated scans. Finally, the model’s applicability was verified on 528 scans and revealed reliable segmentations in 97.4%.
Conclusion
The developed fast and accurate automated segmentation has great potential in large-scale study cohorts and to facilitate MRI-based connectome research of the neonatal claustrum. The easy to use models and codes are made publicly available.
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