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Ghofrani-Jahromi M, Poudel GR, Razi A, Abeyasinghe PM, Paulsen JS, Tabrizi SJ, Saha S, Georgiou-Karistianis N. Prognostic enrichment for early-stage Huntington's disease: An explainable machine learning approach for clinical trial. Neuroimage Clin 2024; 43:103650. [PMID: 39142216 DOI: 10.1016/j.nicl.2024.103650] [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: 04/18/2024] [Revised: 07/11/2024] [Accepted: 07/31/2024] [Indexed: 08/16/2024]
Abstract
BACKGROUND In Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process. OBJECTIVES To improve stratification of Huntington's disease individuals for clinical trials. METHODS We used data from 451 gene positive individuals with Huntington's disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement. RESULTS The predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm3/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %). CONCLUSIONS This study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.
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Affiliation(s)
| | - Govinda R Poudel
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne VIC3000, Australia
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Clayton VIC3800, Australia
| | - Pubu M Abeyasinghe
- Turner Institute for Brain and Mental Health, Monash University, Clayton VIC3800, Australia
| | - Jane S Paulsen
- Department of Neurology, University of Wisconsin-Madison, 1685 Highland Avenue, Madison, WI, USA
| | - Sarah J Tabrizi
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, UK Dementia Research Institute, Department of Neurodegenerative Diseases, University College London, London, UK
| | - Susmita Saha
- Turner Institute for Brain and Mental Health, Monash University, Clayton VIC3800, Australia
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Parent JH, Cassady K, Jagust WJ, Berry AS. Pathological and neurochemical correlates of locus coeruleus functional network activity. Biol Psychol 2024; 192:108847. [PMID: 39038634 DOI: 10.1016/j.biopsycho.2024.108847] [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: 03/19/2024] [Revised: 06/25/2024] [Accepted: 07/15/2024] [Indexed: 07/24/2024]
Abstract
The locus coeruleus (LC) produces the neuromodulators norepinephrine and dopamine, and projects widely to subcortical and cortical brain regions. The LC has been a focus of neuroimaging biomarker development for the early detection of Alzheimer's disease (AD) since it was identified as one of the earliest brain regions to develop tau pathology. Our recent research established the use of positron emission tomography (PET) to measure LC catecholamine synthesis capacity in cognitively unimpaired older adults. We extend this work by investigating the possible influence of pathology and LC neurochemical function on LC network activity using functional magnetic resonance imaging (fMRI). In separate sessions, participants underwent PET imaging to measure LC catecholamine synthesis capacity ([18F]Fluoro-m-tyrosine), tau pathology ([18F]Flortaucipir), and amyloid-β pathology ([11C]Pittsburgh compound B), and fMRI imaging to measure LC functional network activity at rest. Consistent with a growing body of research in aging and preclinical AD, we find that higher functional network activity is associated with higher tau burden in individuals at risk of developing AD (amyloid-β positive). Critically, relationships between higher LC network activity and higher pathology (amyloid-β and tau) were moderated by LC catecholamine synthesis capacity. High levels of LC catecholamine synthesis capacity reduced relationships between higher network activity and pathology. Broadly, these findings support the view that individual differences in functional network activity are shaped by interactions between pathology and neuromodulator function, and point to catecholamine systems as potential therapeutic targets.
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Affiliation(s)
- Jourdan H Parent
- Department of Psychology, Brandeis University, Waltham, MA 02453, USA.
| | - Kaitlin Cassady
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA; Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA; Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Anne S Berry
- Department of Psychology, Brandeis University, Waltham, MA 02453, USA; Volen Center for Complex Systems, Brandeis University, Waltham, MA 02453, USA
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Öz G, Cocozza S, Henry PG, Lenglet C, Deistung A, Faber J, Schwarz AJ, Timmann D, Van Dijk KRA, Harding IH. MR Imaging in Ataxias: Consensus Recommendations by the Ataxia Global Initiative Working Group on MRI Biomarkers. CEREBELLUM (LONDON, ENGLAND) 2024; 23:931-945. [PMID: 37280482 PMCID: PMC11102392 DOI: 10.1007/s12311-023-01572-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/18/2023] [Indexed: 06/08/2023]
Abstract
With many viable strategies in the therapeutic pipeline, upcoming clinical trials in hereditary and sporadic degenerative ataxias will benefit from non-invasive MRI biomarkers for patient stratification and the evaluation of therapies. The MRI Biomarkers Working Group of the Ataxia Global Initiative therefore devised guidelines to facilitate harmonized MRI data acquisition in clinical research and trials in ataxias. Recommendations are provided for a basic structural MRI protocol that can be used for clinical care and for an advanced multi-modal MRI protocol relevant for research and trial settings. The advanced protocol consists of modalities with demonstrated utility for tracking brain changes in degenerative ataxias and includes structural MRI, magnetic resonance spectroscopy, diffusion MRI, quantitative susceptibility mapping, and resting-state functional MRI. Acceptable ranges of acquisition parameters are provided to accommodate diverse scanner hardware in research and clinical contexts while maintaining a minimum standard of data quality. Important technical considerations in setting up an advanced multi-modal protocol are outlined, including the order of pulse sequences, and example software packages commonly used for data analysis are provided. Outcome measures most relevant for ataxias are highlighted with use cases from recent ataxia literature. Finally, to facilitate access to the recommendations by the ataxia clinical and research community, examples of datasets collected with the recommended parameters are provided and platform-specific protocols are shared via the Open Science Framework.
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Affiliation(s)
- Gülin Öz
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, 2021 Sixth Street Southeast, Minneapolis, MN, 55455, USA.
| | - Sirio Cocozza
- UNINA Department of Advanced Biomedical Sciences, University of Naples Federico II , Naples, Italy
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, 2021 Sixth Street Southeast, Minneapolis, MN, 55455, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, 2021 Sixth Street Southeast, Minneapolis, MN, 55455, USA
| | - Andreas Deistung
- Department for Radiation Medicine, University Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), Halle (Saale), Germany
| | - Jennifer Faber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University Hospital Bonn, Bonn, Germany
| | | | - Dagmar Timmann
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (C-TNBS), Essen University Hospital, University of Duisburg-Essen, Essen, Germany
| | - Koene R A Van Dijk
- Digital Sciences and Translational Imaging, Early Clinical Development, Pfizer, Inc., Cambridge, MA, USA
| | - Ian H Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
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Vigil IM, Sylvia M. Transforming Neurology Care Delivery Through a Population Health Data Strategy. Neurol Clin Pract 2024; 14:e200248. [PMID: 38585437 PMCID: PMC10996910 DOI: 10.1212/cpj.0000000000200248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 11/08/2023] [Indexed: 04/09/2024]
Abstract
Background With more than 30% of global data originating from health care, deriving usable insights that improve health requires population health analytics. In neurology, data-driven approaches have grown in significance because of digital health records and advanced analytics. A vital aspect of this evolution is adopting a population health data strategy (PHDS). Recent Findings Crafting a tailored PHDS for neurology involves cataloging data points and measures spanning demographics, clinical history, genetics, and social determinants. Neurologic outcomes include mortality rates, functional and cognitive abilities, and imaging results. A robust strategy relies on interoperability, advanced analytics, and transparent AI algorithms. Summary Neurology is embracing data-driven health care. The PHDS synthesizes diverse patient data to provide personalized care. It includes a wide range of outcome measures to address neurologic complexities. Advanced analytics and collaboration among neurologists, data scientists, and business leaders uncover hidden patterns and promote outcome-driven medicine in the 21st century.
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Affiliation(s)
- Ines M Vigil
- Clarify Health Solutions (IMV); and Medical University of South Carolina College of Nursing (MS)
| | - Martha Sylvia
- Clarify Health Solutions (IMV); and Medical University of South Carolina College of Nursing (MS)
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Wang J, Chen T, Xie J, Zhao S, Jiang Y, Zhang H, Zhu W. A bibliometric analysis of international publication trends in brain atrophy research (2008-2023). Front Neurol 2024; 15:1348778. [PMID: 38356880 PMCID: PMC10864491 DOI: 10.3389/fneur.2024.1348778] [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: 12/03/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024] Open
Abstract
Background Brain atrophy is a type of neurological and psychiatric disorder characterized by a decrease in brain tissue volume and weight for various reasons and can have a serious impact on the quality of life of patients. Although there are many studies on brain atrophy, there is a lack of relevant bibliometric studies. Therefore, this study aims to provide a visual analysis of global trends in brain atrophy research over the past 16 years. Methods CiteSpace and VOSviewer were used to visually analyze publication output, scientific collaborations, cocitations, publishing journals, and keywords to determine the current status and future trends of brain atrophy research. Materials published from 2008 to 2023 were collected from the Web of Science Core Collection (WoSCC) database. This study placed no restrictions on the types of literature and focused on English language publications. Results A total of 3,371 publications were included in the analysis. From 2008 to 2023, the number of publications increased annually. In terms of national and academic institutions, universities in the United States and University College London rank first in publication out. Barkhof Frederik and Zivadinov Robert are the most prolific researchers in this field. The publication with the highest cocitation strength is "Deep gray matter volume loss drives disability worsening in multiple sclerosis." Keyword clustering analysis showed that "Alzheimer's disease" and "multiple sclerosis" are current popular topics. The analysis of emergent words indicates that "cerebral small vessel disease," "neurodegeneration," and "cortex/gray matter volume" may become hot research topics in the coming years. Conclusion This study analyses papers on brain atrophy from the past 16 years, providing a new perspective for research in this field. In the past 16 years, research on brain atrophy has received increasing attention. The quality of articles in this field is generally high. Extensive national cooperation already exists. The statistical results indicate that a stable core author group in the field of brain atrophy has almost formed.
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Affiliation(s)
- Juwei Wang
- Zhejiang Chinese Medical University, Department of Graduate College, Hangzhou, China
| | - Tingting Chen
- Zhejiang Chinese Medical University, Department of Graduate College, Hangzhou, China
| | - Jiayi Xie
- Zhejiang Chinese Medical University, Department of Graduate College, Hangzhou, China
| | - Sheng Zhao
- Zhejiang Chinese Medical University, Department of Graduate College, Hangzhou, China
| | - Yue Jiang
- Department of Acupuncture, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huihe Zhang
- Department of Neurology, Wenzhou Hospital of Traditional Chinese Medicine, Wenzhou, China
| | - Wenzong Zhu
- Department of Neurology, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Zhejiang Chinese Medical University, Wenzhou, China
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Ten Kate M, Barkhof F, Schwarz AJ. Consistency between Treatment Effects on Clinical and Brain Atrophy Outcomes in Alzheimer's Disease Trials. J Prev Alzheimers Dis 2024; 11:38-47. [PMID: 38230715 PMCID: PMC10994869 DOI: 10.14283/jpad.2023.92] [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: 04/17/2023] [Accepted: 06/17/2023] [Indexed: 01/18/2024]
Abstract
BACKGROUND Longitudinal changes in volumetric MRI outcome measures have been shown to correlate well with longitudinal changes in clinical instruments and have been widely used as biomarker outcomes in clinical trials for Alzheimer's disease (AD). While instances of discordant findings have been noted in some trials, especially the recent amyloid-removing therapies, the overall relationship between treatment effects on brain atrophy and clinical outcomes, and how it might depend on treatment target or mechanism, clinical instrument or imaging variable is not yet clear. OBJECTIVE To systematically assess the consistency and therapeutic class-dependence of treatment effects on clinical outcomes and on brain atrophy in published reports of clinical trials conducted in mild cognitive impairment (MCI) and/or AD. DESIGN Quantitative review of the published literature. The consistency of treatment effects on clinical and brain atrophy outcomes was assessed in terms of statistical agreement with hypothesized equal magnitude effects (e.g., 30% slowing of both) and nominal directional concordance, as a function of therapeutic class. SETTING Interventional randomized clinical trials. PARTICIPANTS MCI or AD trial participants. INTERVENTION Treatments included were those that involved ingestion or injection of a putatively active substance into the body, encompassing both pharmacological and controlled dietary interventions. MEASUREMENTS Each trial included in the analysis reported at least one of the required clinical outcomes (ADAS-Cog, CDR-SB or MMSE) and at least one of the required imaging outcomes (whole brain, ventricular or hippocampal volume). RESULTS Data from 35 trials, comprising 185 pairwise comparisons, were included. Overall, the 95% confidence bounds overlapped with the line of identity for 150/185 (81%) of the imaging-clinical variable pairs. The greatest proportion of outliers was found in trials of anti-amyloid antibodies that have been shown to dramatically reduce the level of PET-detectable amyloid plaques, for which only 13/33 (39%) of observations overlapped the identity line. A Deming regression calculated using all data points yielded a slope of 0.54, whereas if data points from the amyloid remover class were excluded, the Deming regression line had a slope of 0.92. Directional discordance of treatment effects was also most pronounced for the amyloid-removing class, and for comparisons involving ventricular volume. CONCLUSION Our results provide a frame of reference for the interpretation of clinical and brain atrophy results from future clinical trials and highlight the importance of mechanism of action in the interpretation of imaging results.
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Affiliation(s)
- M Ten Kate
- Adam J Schwarz, PhD, Takeda Pharmaceuticals, Ltd., 40 Landsdowne St., Cambridge MA 02139, USA Tel: (+1) 317 282 3557,
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Hobbs NZ, Papoutsi M, Delva A, Kinnunen KM, Nakajima M, Van Laere K, Vandenberghe W, Herath P, Scahill RI. Neuroimaging to Facilitate Clinical Trials in Huntington's Disease: Current Opinion from the EHDN Imaging Working Group. J Huntingtons Dis 2024; 13:163-199. [PMID: 38788082 PMCID: PMC11307036 DOI: 10.3233/jhd-240016] [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] [Accepted: 04/22/2024] [Indexed: 05/26/2024]
Abstract
Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.
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Affiliation(s)
- Nicola Z. Hobbs
- HD Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Marina Papoutsi
- HD Research Centre, UCL Institute of Neurology, UCL, London, UK
- IXICO plc, London, UK
| | - Aline Delva
- Department of Neurosciences, KU Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Belgium
| | | | | | - Koen Van Laere
- Department of Imaging and Pathology, Nuclear Medicine and Molecular Imaging, KU Leuven, Belgium
- Division of Nuclear Medicine, University Hospitals Leuven, Belgium
| | - Wim Vandenberghe
- Department of Neurosciences, KU Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Belgium
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Cheng Y, Zhang XD, Chen C, He LF, Li FF, Lu ZN, Man WQ, Zhao YJ, Chang ZX, Wu Y, Shen W, Fan LZ, Xu JH. Dynamic evolution of brain structural patterns in liver transplantation recipients: a longitudinal study based on 3D convolutional neuronal network model. Eur Radiol 2023; 33:6134-6144. [PMID: 37014408 DOI: 10.1007/s00330-023-09604-1] [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: 09/17/2022] [Revised: 02/19/2023] [Accepted: 02/24/2023] [Indexed: 04/05/2023]
Abstract
OBJECTIVES To evaluate the dynamic evolution process of overall brain health in liver transplantation (LT) recipients, we employed a deep learning-based neuroanatomic biomarker to measure longitudinal changes of brain structural patterns before and 1, 3, and 6 months after surgery. METHODS Because of the ability to capture patterns across all voxels from a brain scan, the brain age prediction method was adopted. We constructed a 3D-CNN model through T1-weighted MRI of 3609 healthy individuals from 8 public datasets and further applied it to a local dataset of 60 LT recipients and 134 controls. The predicted age difference (PAD) was calculated to estimate brain changes before and after LT, and the network occlusion sensitivity analysis was used to determine the importance of each network in age prediction. RESULTS The PAD of patients with cirrhosis increased markedly at baseline (+ 5.74 years) and continued to increase within one month after LT (+ 9.18 years). After that, the brain age began to decrease gradually, but it was still higher than the chronological age. The PAD values of the OHE subgroup were higher than those of the no-OHE, and the discrepancy was more obvious at 1-month post-LT. High-level cognition-related networks were more important in predicting the brain age of patients with cirrhosis at baseline, while the importance of primary sensory networks increased temporarily within 6-month post-LT. CONCLUSIONS The brain structural patterns of LT recipients showed inverted U-shaped dynamic change in the early stage after transplantation, and the change in primary sensory networks may be the main contributor. KEY POINTS • The recipients' brain structural pattern showed an inverted U-shaped dynamic change after LT. • The patients' brain aging aggravated within 1 month after surgery, and the subset of patients with a history of OHE was particularly affected. • The change of primary sensory networks is the main contributor to the change in brain structural patterns.
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Affiliation(s)
- Yue Cheng
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
| | - Xiao-Dong Zhang
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
| | - Cheng Chen
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Ling-Fei He
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Fang-Fei Li
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Zi-Ning Lu
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Wei-Qi Man
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Yu-Jiao Zhao
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | | | - Ying Wu
- School of Statistics and Data Science, Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin, Nankai University, Tianjin, China
| | - Wen Shen
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Ling-Zhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jun-Hai Xu
- College of Intelligence and Computing, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China.
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Zhu H, Li T, Zhao B. Statistical Learning Methods for Neuroimaging Data Analysis with Applications. Annu Rev Biomed Data Sci 2023; 6:73-104. [PMID: 37127052 DOI: 10.1146/annurev-biodatasci-020722-100353] [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] [Indexed: 05/03/2023]
Abstract
The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.
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Affiliation(s)
- Hongtu Zhu
- Department of Biostatistics, Department of Statistics, Department of Genetics, and Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA;
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Tengfei Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Hall M, Dobson F, Klyne DM, Zheng CJ, Lima YL, Egorova-Brumley N. Neurobiology of osteoarthritis: a systematic review and activation likelihood estimation meta-analysis. Sci Rep 2023; 13:12442. [PMID: 37528135 PMCID: PMC10394087 DOI: 10.1038/s41598-023-39245-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 07/21/2023] [Indexed: 08/03/2023] Open
Abstract
Osteoarthritis (OA) affects 240 million people worldwide. Neuroimaging has been increasingly used to investigate brain changes in OA, however, there is considerable heterogeneity in reported results. The goal of this systematic review and meta-analysis was to synthesise existing literature and identify consistent brain alterations in OA. Six databases were searched from inception up to June, 2022. Full-texts of original human studies were included if they had: (i) neuroimaging data by site of OA (e.g. hand, knee, hip); (ii) data in healthy controls (HC); (iii) > 10 participants. Activation likelihood estimation (ALE) was conducted using GingerALE software on studies that reported peak activation coordinates and sample size. Our search strategy identified 6250 articles. Twenty-eight studies fulfilled the eligibility criteria, of which 18 were included in the meta-analysis. There were no significant differences in brain structure or function between OA and healthy control contrasts. In exploratory analysis, the right insula was associated with OA vs healthy controls, with less activity, connectivity and brain volume in OA. This region was implicated in both knee and hip OA, with an additional cluster in the medial prefrontal cortex observed only in the contrast between healthy controls and the hip OA subgroup, suggesting a possible distinction between the neural correlates of OA subtypes. Despite the limitations associated with heterogeneity and poor study quality, this synthesis identified neurobiological outcomes associated with OA, providing insight for future research. PROSPERO registration number: CRD42021238735.
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Affiliation(s)
- Michelle Hall
- Centre for Health, Exercise and Sports Medicine, Department of Physiotherapy, School of Health Sciences, The University of Melbourne, Melbourne, VIC, Australia.
- Centre for Arthritis Research, School of Medicine, University College Dublin, Dublin, Ireland.
| | - Fiona Dobson
- Centre for Health, Exercise and Sports Medicine, Department of Physiotherapy, School of Health Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - David Murray Klyne
- School of Health and Rehabilitation Sciences, University of Queensland, Brisbane, QLD, Australia
| | - Carmen Jiamin Zheng
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Yuri Lopes Lima
- School of Health Science and Social Work, Griffith University, Gold Coast, QLD, Australia
| | - Natalia Egorova-Brumley
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, 3010, Australia.
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Yen C, Lin CL, Chiang MC. Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders. Life (Basel) 2023; 13:1472. [PMID: 37511847 PMCID: PMC10381462 DOI: 10.3390/life13071472] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/12/2023] [Accepted: 06/27/2023] [Indexed: 07/30/2023] Open
Abstract
Neuroimaging has revolutionized our understanding of brain function and has become an essential tool for researchers studying neurological disorders. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two widely used neuroimaging techniques to review changes in brain activity. fMRI is a noninvasive technique that uses magnetic fields and radio waves to produce detailed brain images. An EEG is a noninvasive technique that records the brain's electrical activity through electrodes placed on the scalp. This review overviews recent developments in noninvasive functional neuroimaging methods, including fMRI and EEG. Recent advances in fMRI technology, its application to studying brain function, and the impact of neuroimaging techniques on neuroscience research are discussed. Advances in EEG technology and its applications to analyzing brain function and neural oscillations are also highlighted. In addition, advanced courses in neuroimaging, such as diffusion tensor imaging (DTI) and transcranial electrical stimulation (TES), are described, along with their role in studying brain connectivity, white matter tracts, and potential treatments for schizophrenia and chronic pain. Application. The review concludes by examining neuroimaging studies of neurodevelopmental and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease (AD), and Parkinson's disease (PD). We also described the role of transcranial direct current stimulation (tDCS) in ASD, ADHD, AD, and PD. Neuroimaging techniques have significantly advanced our understanding of brain function and provided essential insights into neurological disorders. However, further research into noninvasive treatments such as EEG, MRI, and TES is necessary to continue to develop new diagnostic and therapeutic strategies for neurological disorders.
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Affiliation(s)
- Chiahui Yen
- Department of International Business, Ming Chuan University, Taipei 111, Taiwan
| | - Chia-Li Lin
- Department of International Business, Ming Chuan University, Taipei 111, Taiwan
| | - Ming-Chang Chiang
- Department of Life Science, College of Science and Engineering, Fu Jen Catholic University, New Taipei City 242, Taiwan
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Izmailova ES, Maguire RP, McCarthy TJ, Müller MLTM, Murphy P, Stephenson D. Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies. Clin Transl Sci 2023; 16:383-397. [PMID: 36382716 PMCID: PMC10014695 DOI: 10.1111/cts.13461] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/27/2022] [Accepted: 11/03/2022] [Indexed: 11/17/2022] Open
Abstract
The US Food and Drug Administration (FDA) has publicly recognized the importance of improving drug development efficiency, deeming translational biomarkers a top priority. The use of imaging biomarkers has been associated with increased rates of drug approvals. An appropriate level of validation provides a pragmatic way to choose and implement these biomarkers. Standardizing imaging modality selection, data acquisition protocols, and image analysis (in ways that are agnostic to equipment and algorithms) have been key to imaging biomarker deployment. The best known examples come from studies done via precompetitive collaboration efforts, which enable input from multiple stakeholders and data sharing. Digital health technologies (DHTs) provide an opportunity to measure meaningful aspects of patient health, including patient function, for extended periods of time outside of the hospital walls, with objective, sensor-based measures. We identified the areas where learnings from the imaging biomarker field can accelerate the adoption and widespread use of DHTs to develop novel treatments. As with imaging, technical validation parameters and performance acceptance thresholds need to be established. Approaches amenable to multiple hardware options and data processing algorithms can be enabled by sharing DHT data and by cross-validating algorithms. Data standardization and creation of shared databases will be vital. Pre-competitive consortia (public-private partnerships and professional societies that bring together all stakeholders, including patient organizations, industry, academic experts, and regulators) will advance the regulatory maturity of DHTs in clinical trials.
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Georgiou-Karistianis N, Corben LA, Reetz K, Adanyeguh IM, Corti M, Deelchand DK, Delatycki MB, Dogan I, Evans R, Farmer J, França MC, Gaetz W, Harding IH, Harris KS, Hersch S, Joules R, Joers JJ, Krishnan ML, Lax M, Lock EF, Lynch D, Mareci T, Muthuhetti Gamage S, Pandolfo M, Papoutsi M, Rezende TJR, Roberts TPL, Rosenberg JT, Romanzetti S, Schulz JB, Schilling T, Schwarz AJ, Subramony S, Yao B, Zicha S, Lenglet C, Henry PG. A natural history study to track brain and spinal cord changes in individuals with Friedreich's ataxia: TRACK-FA study protocol. PLoS One 2022; 17:e0269649. [PMID: 36410013 PMCID: PMC9678384 DOI: 10.1371/journal.pone.0269649] [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: 01/09/2022] [Accepted: 05/25/2022] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION Drug development for neurodegenerative diseases such as Friedreich's ataxia (FRDA) is limited by a lack of validated, sensitive biomarkers of pharmacodynamic response in affected tissue and disease progression. Studies employing neuroimaging measures to track FRDA have thus far been limited by their small sample sizes and limited follow up. TRACK-FA, a longitudinal, multi-site, and multi-modal neuroimaging natural history study, aims to address these shortcomings by enabling better understanding of underlying pathology and identifying sensitive, clinical trial ready, neuroimaging biomarkers for FRDA. METHODS 200 individuals with FRDA and 104 control participants will be recruited across seven international study sites. Inclusion criteria for participants with genetically confirmed FRDA involves, age of disease onset ≤ 25 years, Friedreich's Ataxia Rating Scale (FARS) functional staging score of ≤ 5, and a total modified FARS (mFARS) score of ≤ 65 upon enrolment. The control cohort is matched to the FRDA cohort for age, sex, handedness, and years of education. Participants will be evaluated at three study visits over two years. Each visit comprises of a harmonized multimodal Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) scan of the brain and spinal cord; clinical, cognitive, mood and speech assessments and collection of a blood sample. Primary outcome measures, informed by previous neuroimaging studies, include measures of: spinal cord and brain morphometry, spinal cord and brain microstructure (measured using diffusion MRI), brain iron accumulation (using Quantitative Susceptibility Mapping) and spinal cord biochemistry (using MRS). Secondary and exploratory outcome measures include clinical, cognitive assessments and blood biomarkers. DISCUSSION Prioritising immediate areas of need, TRACK-FA aims to deliver a set of sensitive, clinical trial-ready neuroimaging biomarkers to accelerate drug discovery efforts and better understand disease trajectory. Once validated, these potential pharmacodynamic biomarkers can be used to measure the efficacy of new therapeutics in forestalling disease progression. CLINICAL TRIAL REGISTRATION ClinicalTrails.gov Identifier: NCT04349514.
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Affiliation(s)
- Nellie Georgiou-Karistianis
- School of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia
| | - Louise A. Corben
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children’s Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Kathrin Reetz
- Department of Neurology, RWTH Aachen University, Aachen, Germany
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Isaac M. Adanyeguh
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Manuela Corti
- Powell Gene Therapy Centre, University of Florida, Gainesville, Florida, United States of America
| | - Dinesh K. Deelchand
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Martin B. Delatycki
- School of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children’s Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - Imis Dogan
- Department of Neurology, RWTH Aachen University, Aachen, Germany
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Rebecca Evans
- Takeda Pharmaceutical Company Ltd, Cambridge, Massachusetts, United States of America
| | - Jennifer Farmer
- Friedreich’s Ataxia Research Alliance (FARA), Downingtown, Pennsylvania, United States of America
| | - Marcondes C. França
- Department of Neurology, University of Campinas, Campinas, Sao Paulo, Brazil
| | - William Gaetz
- Department of Radiology, Lurie Family Foundations MEG Imaging Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Ian H. Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia
| | - Karen S. Harris
- School of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia
| | - Steven Hersch
- Neurology Business Group, Eisai Inc., Nutley, New Jersey, United States of America
| | | | - James J. Joers
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Michelle L. Krishnan
- Translational Medicine, Novartis Institutes for Biomedical Research, Cambridge, MA, United States of America
| | | | - Eric F. Lock
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States of America
| | - David Lynch
- Department of Neurology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Thomas Mareci
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, United States of America
| | - Sahan Muthuhetti Gamage
- School of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, Australia
| | - Massimo Pandolfo
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | | | | | - Timothy P. L. Roberts
- Department of Radiology, Lurie Family Foundations MEG Imaging Center, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, United States of America
| | - Jens T. Rosenberg
- McKnight Brain Institute, Department of Neurology, University of Florida, Gainesville, Florida, United States of America
| | - Sandro Romanzetti
- Department of Neurology, RWTH Aachen University, Aachen, Germany
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Jörg B. Schulz
- Department of Neurology, RWTH Aachen University, Aachen, Germany
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Traci Schilling
- PTC Therapeutics, Inc, South Plainfield, New Jersey, United States of America
| | - Adam J. Schwarz
- Takeda Pharmaceutical Company Ltd, Cambridge, Massachusetts, United States of America
| | - Sub Subramony
- McKnight Brain Institute, Department of Neurology, University of Florida, Gainesville, Florida, United States of America
| | - Bert Yao
- PTC Therapeutics, Inc, South Plainfield, New Jersey, United States of America
| | - Stephen Zicha
- Takeda Pharmaceutical Company Ltd, Cambridge, Massachusetts, United States of America
| | - Christophe Lenglet
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, Minnesota, United States of America
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Pemberton HG, Collij LE, Heeman F, Bollack A, Shekari M, Salvadó G, Alves IL, Garcia DV, Battle M, Buckley C, Stephens AW, Bullich S, Garibotto V, Barkhof F, Gispert JD, Farrar G. Quantification of amyloid PET for future clinical use: a state-of-the-art review. Eur J Nucl Med Mol Imaging 2022; 49:3508-3528. [PMID: 35389071 PMCID: PMC9308604 DOI: 10.1007/s00259-022-05784-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 03/25/2022] [Indexed: 12/15/2022]
Abstract
Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.
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Affiliation(s)
- Hugh G Pemberton
- GE Healthcare, Amersham, UK.
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Fiona Heeman
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ariane Bollack
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
| | - Mahnaz Shekari
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Gemma Salvadó
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Isadora Lopes Alves
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Brain Research Center, Amsterdam, The Netherlands
| | - David Vallez Garcia
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mark Battle
- GE Healthcare, Amersham, UK
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | | | | | | | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, University Hospitals of Geneva, Geneva, Switzerland
- NIMTLab, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam Neurocience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina, Madrid, Spain
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15
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Lu H, Chan SSM, Ma S, Lin C, Mok VCT, Shi L, Wang D, Mak AD, Lam LCW. Clinical and radiomic features for predicting the treatment response of repetitive transcranial magnetic stimulation in major neurocognitive disorder: Results from a randomized controlled trial. Hum Brain Mapp 2022; 43:5579-5592. [PMID: 35912517 PMCID: PMC9704797 DOI: 10.1002/hbm.26032] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 06/15/2022] [Accepted: 07/18/2022] [Indexed: 01/15/2023] Open
Abstract
Image-guided repetitive transcranial magnetic stimulation (rTMS) has shown clinical effectiveness in senior adults with co-occurring depression and cognitive impairment, yet the imaging markers for predicting the treatment response are less investigated. In this clinical trial, we examined the efficacy and sustainability of 10 Hz rTMS for the treatment of depression and cognitive impairment in major neurocognitive disorder (NCD) patients and tested the predictive values of imaging-informed radiomic features in response to rTMS treatment. Fifty-five major NCD patients with depression were randomly assigned to receive a 3-week rTMS treatment of either active 10 Hz rTMS (n = 27) or sham rTMS (n = 28). Left dorsolateral prefrontal cortex (DLPFC) was the predefined treatment target. Based on individual structural magnetic resonance imaging scans, surface-based analysis was conducted to quantitatively measure the baseline radiomic features of left DLPFC. Severity of depression, global cognition and the serum brain-derived neurotrophic factor (BDNF) level were evaluated at baseline, 3-, 6- and 12-week follow-ups. Logistic regression analysis revealed that advanced age, higher baseline cognition and randomized group were associated with the remission of depression. Increased cortical thickness and gyrification in left DLPFC were the significant predictors of clinical remission and cognitive enhancement. A 3-week course of 10 Hz rTMS is an effective adjuvant treatment for rapid ameliorating depressive symptoms and enhancing cognitive function. Pre-treatment radiomic features of the stimulation target can predict the response to rTMS treatment in major NCD. Cortical thickness and folding of treatment target may serve as imaging markers to detect the responders. ChiCTR-IOR-16008191, registered on March 30, 2016.
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Affiliation(s)
- Hanna Lu
- Department of PsychiatryThe Chinese University of Hong KongHong Kong SARChina,The Affiliated Brain Hospital of Guangzhou Medical UniversityGuangzhouChina
| | - Sandra Sau Man Chan
- Department of PsychiatryThe Chinese University of Hong KongHong Kong SARChina
| | - Sukling Ma
- Department of PsychiatryThe Chinese University of Hong KongHong Kong SARChina
| | - Cuichan Lin
- Department of PsychiatryThe Chinese University of Hong KongHong Kong SARChina
| | - Vincent Chung Tong Mok
- Department of Medicine and TherapeuticsThe Chinese University of Hong KongHong Kong SARChina
| | - Lin Shi
- Department of Imaging and Interventional RadiologyThe Chinese University of Hong KongHong Kong SARChina
| | - Defeng Wang
- Department of Imaging and Interventional RadiologyThe Chinese University of Hong KongHong Kong SARChina
| | - Arthur Dun‐Ping Mak
- Department of PsychiatryThe Chinese University of Hong KongHong Kong SARChina
| | - Linda Chiu Wa Lam
- Department of PsychiatryThe Chinese University of Hong KongHong Kong SARChina
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16
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Varrone A, Bundgaard C, Bang-Andersen B. PET as a Translational Tool in Drug Development for Neuroscience Compounds. Clin Pharmacol Ther 2022; 111:774-785. [PMID: 35201613 PMCID: PMC9305164 DOI: 10.1002/cpt.2548] [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: 12/15/2021] [Accepted: 01/29/2022] [Indexed: 11/05/2022]
Abstract
In central nervous system drug discovery programs, early development of new chemical entities (NCEs) requires a multidisciplinary strategy and a translational approach to obtain proof of distribution, proof of occupancy, and proof of function in specific brain circuits. Positron emission tomography (PET) provides a way to assess in vivo the brain distribution of NCEs and their binding to the target of interest, provided that radiolabeling of the NCE is possible or that a suitable radioligand is available. PET is therefore a key tool for early phases of drug discovery programs. This review will summarize the main applications of PET in early drug development and discuss the usefulness of PET microdosing studies performed with direct labelling of the NCE and PET occupancy studies. The purpose of this review is also to propose an alignment of the nomenclatures used by drug metabolism and pharmacokinetic scientists and PET imaging scientists to indicate key pharmacokinetic parameters and to provide guidance in the performance and interpretation of PET studies.
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Affiliation(s)
- Andrea Varrone
- Translational Biomarkers and Imaging, H. Lundbeck A/S, Copenhagen, Denmark
| | | | - Benny Bang-Andersen
- Translational Biomarkers and Imaging, H. Lundbeck A/S, Copenhagen, Denmark.,Medicinal Chemistry & Translational DMPK, H. Lundbeck A/S, Copenhagen, Denmark
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17
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Harding IH, Ward PGD. OUP accepted manuscript. Brain Commun 2022; 4:fcac007. [PMID: 35178517 PMCID: PMC8846579 DOI: 10.1093/braincomms/fcac007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/09/2021] [Accepted: 02/03/2022] [Indexed: 11/14/2022] Open
Affiliation(s)
- Ian H. Harding
- Correspondence to: Ian H. Harding, Department of Neuroscience & Monash Biomedical Imaging, Monash University, VIC 3800, Australia E-mail:
| | - Phillip G. D. Ward
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Monash University, Melbourne, Australia
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18
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Kinnunen KM, Mullin AP, Pustina D, Turner EC, Burton J, Gordon MF, Scahill RI, Gantman EC, Noble S, Romero K, Georgiou-Karistianis N, Schwarz AJ. Recommendations to Optimize the Use of Volumetric MRI in Huntington's Disease Clinical Trials. Front Neurol 2021; 12:712565. [PMID: 34744964 PMCID: PMC8569234 DOI: 10.3389/fneur.2021.712565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 09/21/2021] [Indexed: 12/12/2022] Open
Abstract
Volumetric magnetic resonance imaging (vMRI) has been widely studied in Huntington's disease (HD) and is commonly used to assess treatment effects on brain atrophy in interventional trials. Global and regional trajectories of brain atrophy in HD, with early involvement of striatal regions, are becoming increasingly understood. However, there remains heterogeneity in the methods used and a lack of widely-accessible multisite, longitudinal, normative datasets in HD. Consensus for standardized practices for data acquisition, analysis, sharing, and reporting will strengthen the interpretation of vMRI results and facilitate their adoption as part of a pathobiological disease staging system. The Huntington's Disease Regulatory Science Consortium (HD-RSC) currently comprises 37 member organizations and is dedicated to building a regulatory science strategy to expedite the approval of HD therapeutics. Here, we propose four recommendations to address vMRI standardization in HD research: (1) a checklist of standardized practices for the use of vMRI in clinical research and for reporting results; (2) targeted research projects to evaluate advanced vMRI methodologies in HD; (3) the definition of standard MRI-based anatomical boundaries for key brain structures in HD, plus the creation of a standard reference dataset to benchmark vMRI data analysis methods; and (4) broad access to raw images and derived data from both observational studies and interventional trials, coded to protect participant identity. In concert, these recommendations will enable a better understanding of disease progression and increase confidence in the use of vMRI for drug development.
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Affiliation(s)
| | - Ariana P Mullin
- Critical Path Institute, Tucson, AZ, United States.,Wave Life Sciences, Ltd., Cambridge, MA, United States
| | - Dorian Pustina
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | | | | | - Mark F Gordon
- Teva Pharmaceuticals, West Chester, PA, United States
| | - Rachael I Scahill
- Huntington's Disease Research Centre, UCL Institute of Neurology, London, United Kingdom
| | - Emily C Gantman
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | - Simon Noble
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | - Klaus Romero
- Critical Path Institute, Tucson, AZ, United States
| | - Nellie Georgiou-Karistianis
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - Adam J Schwarz
- Takeda Pharmaceuticals, Ltd., Cambridge, MA, United States
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19
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Risacher SL, Saykin AJ. Neuroimaging Advances in Neurologic and Neurodegenerative Diseases. Neurotherapeutics 2021; 18:659-660. [PMID: 34410634 PMCID: PMC8423931 DOI: 10.1007/s13311-021-01105-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/06/2021] [Indexed: 01/01/2023] Open
Affiliation(s)
- Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
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