1
|
Zhang J, Xie L, Cheng C, Liu Y, Zhang X, Wang H, Hu J, Yu H, Xu J. Hippocampal subfield volumes in mild cognitive impairment and alzheimer's disease: a systematic review and meta-analysis. Brain Imaging Behav 2023; 17:778-793. [PMID: 37768441 DOI: 10.1007/s11682-023-00804-3] [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] [Accepted: 09/10/2023] [Indexed: 09/29/2023]
Abstract
The hippocampus is a complex structure that consists of several subfields with distinct and specialized functions. Although numerous studies have been performed to explore hippocampal atrophy at the sub-regional level in mild cognitive impairment (MCI) and Alzheimer's disease (AD), the results have been inconsistent especially for whether and which subfields can be served as the most potential biomarkers in MCI and AD. Herein, we used a meta-analytic approach to synthesize the extant literatures on hippocampal subfields in MCI and AD through PubMed, Web of Science, and Embase (PROSPERO CRD42021257586). As a result, a total of twenty studies using Freesurfer 5 and Freesurfer 6 were included in this investigation. These studies revealed that at the sub-regional level, hippocampal subfield volume reductions in MCI and AD were not restricted to specific subfields, and subiculum and presubiculum had the largest z-scores across most comparisons. However, none of the subfield performed much better in discriminating MCI and HC, AD and MCI, AD and HC as compared to whole hippocampus volume. These results suggested that we should explore the changes in the hippocampal subfields in subtypes of MCI or even at an earlier stage, that is subjective cognitive impairment.
Collapse
Affiliation(s)
- Jinhuan Zhang
- The fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, 518033, China
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518033, China
| | - Linlin Xie
- The fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, 518033, China
- Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518033, China
| | - Changjiang Cheng
- The fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, 518033, China
| | - Yongfeng Liu
- Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518033, China
| | - Xiaodong Zhang
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Haoyu Wang
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Jingting Hu
- College of Creative Design, Shenzhen Technology University, Shenzhen, China
| | - Haibo Yu
- The fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, 518033, China.
- Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518033, China.
| | - Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| |
Collapse
|
2
|
Stanke KL, Larsen RJ, Rund L, Leyshon BJ, Louie AY, Steelman AJ. Automated identification of piglet brain tissue from MRI images using Region-based Convolutional Neural Networks. PLoS One 2023; 18:e0284951. [PMID: 37167205 PMCID: PMC10174584 DOI: 10.1371/journal.pone.0284951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 04/12/2023] [Indexed: 05/13/2023] Open
Abstract
Magnetic resonance imaging is an important tool for characterizing volumetric changes of the piglet brain during development. Typically, an early step of an imaging analysis pipeline is brain extraction, or skull stripping. Brain extractions are usually performed manually; however, this approach is time-intensive and can lead to variation between brain extractions when multiple raters are used. Automated brain extractions are important for reducing the time required for analyses and improving the uniformity of the extractions. Here we demonstrate the use of Mask R-CNN, a Region-based Convolutional Neural Network (R-CNN), for automated brain extractions of piglet brains. We validate our approach using Nested Cross-Validation on six sets of training/validation data drawn from 32 pigs. Visual inspection of the extractions shows acceptable accuracy, Dice coefficients are in the range of 0.95-0.97, and Hausdorff Distance values in the range of 4.1-8.3 voxels. These results demonstrate that R-CNNs provide a viable tool for skull stripping of piglet brains.
Collapse
Affiliation(s)
- Kayla L. Stanke
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America
| | - Ryan J. Larsen
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America
| | - Laurie Rund
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America
| | - Brian J. Leyshon
- Abbott Nutrition, Discovery Research, Columbus, Ohio, United States of America
| | - Allison Y. Louie
- Division of Nutritional Sciences, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America
| | - Andrew J. Steelman
- Department of Animal Sciences, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America
- Division of Nutritional Sciences, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America
- Neuroscience Program, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Champaign, Illinois, United States of America
| |
Collapse
|
3
|
Diagnostic Utility of Hippocampal Volumetric Data in a Memory Disorder Clinic Setting. Cogn Behav Neurol 2022; 35:66-75. [PMID: 35239600 DOI: 10.1097/wnn.0000000000000295] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 05/26/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND Hippocampal volumetric data are widely used in research but are rarely examined in clinical populations in regard to aiding diagnosis or correlating with objective memory test scores. OBJECTIVE To replicate and expand on the few prior clinical examinations of the utility of hippocampal volumetric data. We evaluated MRI volumetric data to determine (a) the degree of hippocampal loss across diagnostic groups compared with a cognitively intact group, (b) if total or lateralized hippocampal volumes predict diagnostic group membership, and (c) how total and lateralized volumes correlate with memory tests. METHOD We retrospectively examined hippocampal volumetric data and memory test scores for 294 individuals referred to a memory clinic. RESULTS Individuals with mild cognitive impairment or Alzheimer disease had smaller hippocampal volumes compared with cognitively intact individuals. The raw and normalized total and lateralized hippocampal volumes were essentially equal for predicting diagnostic group membership, and notably low hippocampal volumes evidenced greater specificity than sensitivity. All of the volumetric data correlated with the memory test scores, with the total and left hippocampal volumes accounting for the slightly more variance in the diagnostic groups. CONCLUSION The diagnostic groups exhibited hippocampal volume loss, which can be a potential biomarker for neurodegenerative disease in clinical practice. However, solely using hippocampal volumetric data to predict diagnostic group membership or memory test failure was not supported. While extreme hippocampal volume loss was rare in the cognitively intact group, the sensitivity of these volumetric data suggests a need for supplementation by other tools when making a diagnosis.
Collapse
|
4
|
Daviddi S, Pedale T, Serra L, Macrì S, Campolongo P, Santangelo V. Altered Hippocampal Resting-state Functional Connectivity in Highly Superior Autobiographical Memory. Neuroscience 2022; 480:1-8. [PMID: 34774712 DOI: 10.1016/j.neuroscience.2021.11.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/21/2021] [Accepted: 11/02/2021] [Indexed: 10/19/2022]
Abstract
Individuals with Highly Superior Autobiographical Memory (HSAM) provide the opportunity to investigate the neurobiological substrates of enhanced memory performance. While previous studies started to assess the neural correlates of memory retrieval in HSAM, here we assessed for the first time the intrinsic connectivity of a core memory region, the hippocampus, with the whole brain, in 8 HSAM subjects (HSAMs) and 21 controls during resting-state functional neuroimaging. We found in HSAMs vs. controls disrupted hippocampal resting-state functional connectivity (rsFC) with high-level control regions belonging to the saliency network (the anterior cingulate cortex and the left and right insulae), and to the ventral fronto-parietal attentional network (the temporo-parietal junction and the inferior frontal gyrus), also involved with salience detection. Conversely, HSAMs showed enhanced hippocampal rsFC with sensory regions along the fusiform gyrus and the inferior temporal cortex. This altered pattern of hippocampal rsFC might be interpreted as a reduced capability of HSAMs to discriminate and select salient information, with a subsequent increase in the probability to encode and consolidate sensory information irrespective of their task-relevancy. Ultimately, these findings provide evidence that HSAM might be paradoxically enabled by an altered hippocampal rsFC that bypasses regions involved with salience detection in favor of specialized sensory regions.
Collapse
Affiliation(s)
- Sarah Daviddi
- Department of Philosophy, Social Sciences & Education, University of Perugia, Piazza G. Ermini 1, 06123 Perugia, Italy
| | - Tiziana Pedale
- Neuroimaging Laboratory, Fondazione Santa Lucia, IRCCS, Via Ardeatina 306, 00179 Rome, Italy
| | - Laura Serra
- Neuroimaging Laboratory, Fondazione Santa Lucia, IRCCS, Via Ardeatina 306, 00179 Rome, Italy
| | - Simone Macrì
- Centre for Behavioral Sciences and Mental Health, Istituto Superiore di Sanità, Viale Regina Elena, 299 00161 Rome, Italy
| | - Patrizia Campolongo
- Department of Physiology and Pharmacology, Sapienza University of Rome, P.le A. Moro 5, 00185 Rome, Italy; CERC, Fondazione Santa Lucia, IRCCS, Via del Fosso di Fiorano 64, 00143 Rome, Italy
| | - Valerio Santangelo
- Department of Philosophy, Social Sciences & Education, University of Perugia, Piazza G. Ermini 1, 06123 Perugia, Italy; Neuroimaging Laboratory, Fondazione Santa Lucia, IRCCS, Via Ardeatina 306, 00179 Rome, Italy.
| |
Collapse
|
5
|
Dutta K, Roy S, Whitehead TD, Luo J, Jha AK, Li S, Quirk JD, Shoghi KI. Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary. Cancers (Basel) 2021; 13:cancers13153795. [PMID: 34359696 PMCID: PMC8345151 DOI: 10.3390/cancers13153795] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/22/2021] [Accepted: 07/22/2021] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Co-clinical trials are an emerging area of investigation in which a clinical trial is coupled with a corresponding preclinical trial to inform the corresponding clinical trial. The preclinical arm aids in assessing therapeutic efficacy, patient stratification, and designing optimal imaging strategies. There is much interest in harmonizing preclinical and clinical quantitative imaging pipelines. Radiomics is widely explored in clinical imaging to predict response to therapy. In preclinical imaging, high-throughput radiomic analysis is limited by manual delineation of tumor boundaries, which is labor intensive with poor reproducibility. Our proposed deep-learning-based system was trained to automatically segment tumors from multi-contrast MR images and extract radiomic features. The proposed method is highly reproducible with significant correlation in radiomic features. The deployment of this pipeline in the preclinical arm would provide high throughput and reproducible radiomic analysis. Abstract Preclinical magnetic resonance imaging (MRI) is a critical component in a co-clinical research pipeline. Importantly, segmentation of tumors in MRI is a necessary step in tumor phenotyping and assessment of response to therapy. However, manual segmentation is time-intensive and suffers from inter- and intra- observer variability and lack of reproducibility. This study aimed to develop an automated pipeline for accurate localization and delineation of TNBC PDX tumors from preclinical T1w and T2w MR images using a deep learning (DL) algorithm and to assess the sensitivity of radiomic features to tumor boundaries. We tested five network architectures including U-Net, dense U-Net, Res-Net, recurrent residual UNet (R2UNet), and dense R2U-Net (D-R2UNet), which were compared against manual delineation by experts. To mitigate bias among multiple experts, the simultaneous truth and performance level estimation (STAPLE) algorithm was applied to create consensus maps. Performance metrics (F1-Score, recall, precision, and AUC) were used to assess the performance of the networks. Multi-contrast D-R2UNet performed best with F1-score = 0.948; however, all networks scored within 1–3% of each other. Radiomic features extracted from D-R2UNet were highly corelated to STAPLE-derived features with 67.13% of T1w and 53.15% of T2w exhibiting correlation ρ ≥ 0.9 (p ≤ 0.05). D-R2UNet-extracted features exhibited better reproducibility relative to STAPLE with 86.71% of T1w and 69.93% of T2w features found to be highly reproducible (CCC ≥ 0.9, p ≤ 0.05). Finally, 39.16% T1w and 13.9% T2w features were identified as insensitive to tumor boundary perturbations (Spearman correlation (−0.4 ≤ ρ ≤ 0.4). We developed a highly reproducible DL algorithm to circumvent manual segmentation of T1w and T2w MR images and identified sensitivity of radiomic features to tumor boundaries.
Collapse
Affiliation(s)
- Kaushik Dutta
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; (K.D.); (S.R.); (T.D.W.); (A.K.J.); (J.D.Q.)
| | - Sudipta Roy
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; (K.D.); (S.R.); (T.D.W.); (A.K.J.); (J.D.Q.)
| | - Timothy Daniel Whitehead
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; (K.D.); (S.R.); (T.D.W.); (A.K.J.); (J.D.Q.)
| | - Jingqin Luo
- Department of Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA;
| | - Abhinav Kumar Jha
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; (K.D.); (S.R.); (T.D.W.); (A.K.J.); (J.D.Q.)
- Department of Biomedical Engineering McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Shunqiang Li
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA;
| | - James Dennis Quirk
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; (K.D.); (S.R.); (T.D.W.); (A.K.J.); (J.D.Q.)
| | - Kooresh Isaac Shoghi
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; (K.D.); (S.R.); (T.D.W.); (A.K.J.); (J.D.Q.)
- Department of Biomedical Engineering McKelvey School of Engineering, Washington University in St. Louis, St. Louis, MO 63110, USA
- Correspondence:
| |
Collapse
|
6
|
Wittens MMJ, Sima DM, Houbrechts R, Ribbens A, Niemantsverdriet E, Fransen E, Bastin C, Benoit F, Bergmans B, Bier JC, De Deyn PP, Deryck O, Hanseeuw B, Ivanoiu A, Lemper JC, Mormont E, Picard G, de la Rosa E, Salmon E, Segers K, Sieben A, Smeets D, Struyfs H, Thiery E, Tournoy J, Triau E, Vanbinst AM, Versijpt J, Bjerke M, Engelborghs S. Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer's Disease in a Clinical Setting: A REMEMBER Study. J Alzheimers Dis 2021; 83:623-639. [PMID: 34334402 PMCID: PMC8543261 DOI: 10.3233/jad-210450] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Magnetic resonance imaging (MRI) has become important in the diagnostic work-up of neurodegenerative diseases. icobrain dm, a CE-labeled and FDA-cleared automated brain volumetry software, has shown potential in differentiating cognitively healthy controls (HC) from Alzheimer’s disease (AD) dementia (ADD) patients in selected research cohorts. Objective: This study examines the diagnostic value of icobrain dm for AD in routine clinical practice, including a comparison to the widely used FreeSurfer software, and investigates if combined brain volumes contribute to establish an AD diagnosis. Methods: The study population included HC (n = 90), subjective cognitive decline (SCD, n = 93), mild cognitive impairment (MCI, n = 357), and ADD (n = 280) patients. Through automated volumetric analyses of global, cortical, and subcortical brain structures on clinical brain MRI T1w (n = 820) images from a retrospective, multi-center study (REMEMBER), icobrain dm’s (v.4.4.0) ability to differentiate disease stages via ROC analysis was compared to FreeSurfer (v.6.0). Stepwise backward regression models were constructed to investigate if combined brain volumes can differentiate between AD stages. Results: icobrain dm outperformed FreeSurfer in processing time (15–30 min versus 9–32 h), robustness (0 versus 67 failures), and diagnostic performance for whole brain, hippocampal volumes, and lateral ventricles between HC and ADD patients. Stepwise backward regression showed improved diagnostic accuracy for pairwise group differentiations, with highest performance obtained for distinguishing HC from ADD (AUC = 0.914; Specificity 83.0%; Sensitivity 86.3%). Conclusion: Automated volumetry has a diagnostic value for ADD diagnosis in routine clinical practice. Our findings indicate that combined brain volumes improve diagnostic accuracy, using real-world imaging data from a clinical setting.
Collapse
Affiliation(s)
- Mandy Melissa Jane Wittens
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | | | | | | | - Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium
| | - Erik Fransen
- StatUa Center for Statistics, University of Antwerp, Belgium
| | - Christine Bastin
- GIGA Cyclotron Research Centre in vivo Imaging, University of Liège, Liège, Belgium
| | - Florence Benoit
- Department of Geriatrics, Centre Hospitalier Universitaire (CHU) Brugmann, Brussels, Belgium
| | - Bruno Bergmans
- Department of Neurology and Center for Cognitive Disorders, Brugge, Belgium
| | | | - Peter Paul De Deyn
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA), Antwerp, Belgium
| | - Olivier Deryck
- Department of Neurology and Center for Cognitive Disorders, Brugge, Belgium
| | - Bernard Hanseeuw
- Department of Neurology, Cliniques Universitaires St Luc and Institute of Neuroscience, Université catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Adrian Ivanoiu
- Department of Neurology, Cliniques Universitaires St Luc and Institute of Neuroscience, Université catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Jean-Claude Lemper
- Department of Geriatrics, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel, Brussels, Belgium.,Silva medical Scheutbos, Molenbeek-Saint-Jean (Brussels), Belgium
| | - Eric Mormont
- UCLouvain, CHU UCL Namur, service de Neurologie, Yvoir, Belgium.,UCLouvain, Institute of NeuroScience, Louvain-la-Neuve, Belgium
| | - Gaëtane Picard
- Department of Neurology, Clinique Saint-Pierre, Ottignies, Belgium
| | | | - Eric Salmon
- GIGA Cyclotron Research Centre in vivo Imaging, University of Liège, Liège, Belgium.,Department of Neurology, Memory Clinic, Centre Hospitalier Universitaire (CHU) Liège, Liège, Belgium
| | - Kurt Segers
- Department of Neurology, Centre Hospitalier Universitaire (CHU) Brugmann, Brussels, Belgium
| | - Anne Sieben
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | | | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium
| | - Evert Thiery
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | - Jos Tournoy
- Geriatric Medicine and Memory Clinic, University Hospitals Leuven & Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | | | - Anne-Marie Vanbinst
- Department of Radiology, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Jan Versijpt
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium.,Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium.,Laboratory of Neurochemistry, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, University of Antwerp, Antwerp, Belgium.,Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium.,Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| |
Collapse
|
7
|
Miller BJ, Herzig KH, Jokelainen J, Karhu T, Keinänen-Kiukaanniemi S, Järvelin MR, Veijola J, Viinamäki H, Päivikki Tanskanen, Jääskeläinen E, Isohanni M, Timonen M. Inflammation, hippocampal volume, and cognition in schizophrenia: results from the Northern Finland Birth Cohort 1966. Eur Arch Psychiatry Clin Neurosci 2021; 271:609-622. [PMID: 32382794 DOI: 10.1007/s00406-020-01134-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 04/27/2020] [Indexed: 02/08/2023]
Abstract
Increased blood interleukin-6 (IL-6) levels are a replicated abnormality in schizophrenia, and may be associated with smaller hippocampal volumes and greater cognitive impairment. These findings have not been investigated in a population-based birth cohort. The general population Northern Finland Birth Cohort 1966 was followed until age 43. Subjects with schizophrenia were identified through the national Finnish Care Register. Blood IL-6 levels were measured in n = 82 subjects with schizophrenia and n = 5373 controls at age 31. Additionally, 31 patients with schizophrenia and 63 healthy controls underwent brain structural MRI at age 34, and cognitive testing at ages 34 and 43. Patients with schizophrenia had significantly higher median (interquartile range) blood IL-6 levels than controls (5.31, 0.85-17.20, versus 2.42, 0.54-9.36, p = 0.02) after controlling for potential confounding factors. In both schizophrenia and controls, higher blood IL-6 levels were predictors of smaller hippocampal volumes, but not cognitive performance at age 34. We found evidence for increased IL-6 levels in patients with midlife schizophrenia from a population-based birth cohort, and replicated associations between IL-6 levels and hippocampal volumes. Our results complement and extend the previous findings, providing additional evidence that IL-6 may play a role in the pathophysiology of schizophrenia and associated brain alterations.
Collapse
Affiliation(s)
- Brian J Miller
- Department of Psychiatry and Health Behavior, Augusta University, 997 Saint Sebastian Way, Augusta, GA, 30912, USA.
| | - Karl-Heinz Herzig
- Research Unit of Biomedicine, University of Oulu, Oulu, Finland.,Medical Research Center (MRC) and Oulu University Hospital, Oulu, Finland.,Department of Gastroenterology and Metabolism, Poznan University of Medical Sciences, Poznan, Poland
| | - Jari Jokelainen
- Medical Research Center (MRC) and Oulu University Hospital, Oulu, Finland.,Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Toni Karhu
- Research Unit of Biomedicine, University of Oulu, Oulu, Finland
| | - Sirkka Keinänen-Kiukaanniemi
- Medical Research Center (MRC) and Oulu University Hospital, Oulu, Finland.,Department of Gastroenterology and Metabolism, Poznan University of Medical Sciences, Poznan, Poland.,Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Marjo-Riitta Järvelin
- Medical Research Center (MRC) and Oulu University Hospital, Oulu, Finland.,Center for Life Course Health Research, University of Oulu, Oulu, Finland.,Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.,MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Juha Veijola
- Medical Research Center (MRC) and Oulu University Hospital, Oulu, Finland.,Department of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland
| | - Heimo Viinamäki
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK.,Psychiatry, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | | | - Erika Jääskeläinen
- Department of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland
| | - Matti Isohanni
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Markku Timonen
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| |
Collapse
|
8
|
van Oostveen WM, de Lange ECM. Imaging Techniques in Alzheimer's Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring. Int J Mol Sci 2021; 22:ijms22042110. [PMID: 33672696 PMCID: PMC7924338 DOI: 10.3390/ijms22042110] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting many individuals worldwide with no effective treatment to date. AD is characterized by the formation of senile plaques and neurofibrillary tangles, followed by neurodegeneration, which leads to cognitive decline and eventually death. INTRODUCTION In AD, pathological changes occur many years before disease onset. Since disease-modifying therapies may be the most beneficial in the early stages of AD, biomarkers for the early diagnosis and longitudinal monitoring of disease progression are essential. Multiple imaging techniques with associated biomarkers are used to identify and monitor AD. AIM In this review, we discuss the contemporary early diagnosis and longitudinal monitoring of AD with imaging techniques regarding their diagnostic utility, benefits and limitations. Additionally, novel techniques, applications and biomarkers for AD research are assessed. FINDINGS Reduced hippocampal volume is a biomarker for neurodegeneration, but atrophy is not an AD-specific measure. Hypometabolism in temporoparietal regions is seen as a biomarker for AD. However, glucose uptake reflects astrocyte function rather than neuronal function. Amyloid-β (Aβ) is the earliest hallmark of AD and can be measured with positron emission tomography (PET), but Aβ accumulation stagnates as disease progresses. Therefore, Aβ may not be a suitable biomarker for monitoring disease progression. The measurement of tau accumulation with PET radiotracers exhibited promising results in both early diagnosis and longitudinal monitoring, but large-scale validation of these radiotracers is required. The implementation of new processing techniques, applications of other imaging techniques and novel biomarkers can contribute to understanding AD and finding a cure. CONCLUSIONS Several biomarkers are proposed for the early diagnosis and longitudinal monitoring of AD with imaging techniques, but all these biomarkers have their limitations regarding specificity, reliability and sensitivity. Future perspectives. Future research should focus on expanding the employment of imaging techniques and identifying novel biomarkers that reflect AD pathology in the earliest stages.
Collapse
Affiliation(s)
- Wieke M. van Oostveen
- Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands;
| | - Elizabeth C. M. de Lange
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
- Correspondence: ; Tel.: +31-71-527-6330
| |
Collapse
|
9
|
Young PNE, Estarellas M, Coomans E, Srikrishna M, Beaumont H, Maass A, Venkataraman AV, Lissaman R, Jiménez D, Betts MJ, McGlinchey E, Berron D, O'Connor A, Fox NC, Pereira JB, Jagust W, Carter SF, Paterson RW, Schöll M. Imaging biomarkers in neurodegeneration: current and future practices. Alzheimers Res Ther 2020; 12:49. [PMID: 32340618 PMCID: PMC7187531 DOI: 10.1186/s13195-020-00612-7] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 04/01/2020] [Indexed: 12/12/2022]
Abstract
There is an increasing role for biological markers (biomarkers) in the understanding and diagnosis of neurodegenerative disorders. The application of imaging biomarkers specifically for the in vivo investigation of neurodegenerative disorders has increased substantially over the past decades and continues to provide further benefits both to the diagnosis and understanding of these diseases. This review forms part of a series of articles which stem from the University College London/University of Gothenburg course "Biomarkers in neurodegenerative diseases". In this review, we focus on neuroimaging, specifically positron emission tomography (PET) and magnetic resonance imaging (MRI), giving an overview of the current established practices clinically and in research as well as new techniques being developed. We will also discuss the use of machine learning (ML) techniques within these fields to provide additional insights to early diagnosis and multimodal analysis.
Collapse
Affiliation(s)
- Peter N E Young
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mar Estarellas
- Centre for Medical Image Computing (CMIC), Department of Computer Science & Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Emma Coomans
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Meera Srikrishna
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Helen Beaumont
- Neuroscience and Aphasia Research Unit, Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Ashwin V Venkataraman
- Division of Brain Sciences, Imperial College London, London, UK
- United Kingdom Dementia Research Institute, Imperial College London, London, UK
| | - Rikki Lissaman
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff, UK
| | - Daniel Jiménez
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
- Department of Neurological Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Matthew J Betts
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | | | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Antoinette O'Connor
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - William Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Stephen F Carter
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Wolfson Molecular Imaging Centre, Division of Neuroscience and Experimental Psychology, MAHSC, University of Manchester, Manchester, UK
| | - Ross W Paterson
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK.
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
| |
Collapse
|