1
|
Amaral L, Thomas P, Amedi A, Striem-Amit E. Longitudinal stability of individual brain plasticity patterns in blindness. Proc Natl Acad Sci U S A 2024; 121:e2320251121. [PMID: 39078671 PMCID: PMC11317565 DOI: 10.1073/pnas.2320251121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 05/24/2024] [Indexed: 07/31/2024] Open
Abstract
The primary visual cortex (V1) in blindness is engaged in a wide spectrum of tasks and sensory modalities, including audition, touch, language, and memory. This widespread involvement raises questions regarding the constancy of its role and whether it might exhibit flexibility in its function over time, connecting to diverse network functions specific to task demands. This would suggest that reorganized V1 assumes a role like multiple-demand system regions. Alternatively, varying patterns of plasticity in blind V1 may be attributed to individual factors, with different blind individuals recruiting V1 preferentially for different functions. In support of this, we recently showed that V1 functional connectivity (FC) varies greatly across blind individuals. But do these represent stable individual patterns of plasticity, or are they driven more by instantaneous changes, like a multiple-demand system now inhabiting V1? Here, we tested whether individual FC patterns from the V1 of blind individuals are stable over time. We show that over two years, FC from the V1 is unique and highly stable in a small sample of repeatedly sampled congenitally blind individuals. Further, using multivoxel pattern analysis, we demonstrate that the unique reorganization patterns of these individuals allow decoding of participant identity. Together with recent evidence for substantial individual differences in V1 connectivity, this indicates that there may be a consistent role for V1 in blindness, which may differ for each individual. Further, it suggests that the variability in visual reorganization in blindness across individuals could be used to seek stable neuromarkers for sight rehabilitation and assistive approaches.
Collapse
Affiliation(s)
- Lénia Amaral
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC20057
| | - Peyton Thomas
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC20057
| | - Amir Amedi
- Ivcher School of Psychology, The Institute for Brain, Mind and Technology, Reichman University, Herzliya4610101, Israel
- The Ruth & Meir Rosenthal Brain Imaging Center, Reichman University, Herzliya4610101, Israel
| | - Ella Striem-Amit
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC20057
| |
Collapse
|
2
|
Hu F, Lucas A, Chen AA, Coleman K, Horng H, Ng RWS, Tustison NJ, Davis KA, Shou H, Li M, Shinohara RT. DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data. Hum Brain Mapp 2024; 45:e26708. [PMID: 39056477 PMCID: PMC11273293 DOI: 10.1002/hbm.26708] [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: 02/26/2024] [Revised: 04/19/2024] [Accepted: 04/25/2024] [Indexed: 07/28/2024] Open
Abstract
Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.
Collapse
Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alfredo Lucas
- Center for Neuroengineering and Therapeutics, Department of EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Andrew A. Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kyle Coleman
- Statistical Center for Single‐Cell and Spatial GenomicsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Raymond W. S. Ng
- Perelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Nicholas J. Tustison
- Department of Radiology and Medical ImagingUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Kathryn A. Davis
- Center for Neuroengineering and Therapeutics, Department of EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and Analytics (CBICA)Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Mingyao Li
- Statistical Center for Single‐Cell and Spatial GenomicsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and Analytics (CBICA)Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | | |
Collapse
|
3
|
Hajj‐Boutros G, Sonjak V, Faust A, Balram S, Lagacé J, St‐Martin P, Divsalar DN, Sadeghian F, Liu‐Ambrose T, Blaber AP, Dionne IJ, Duchesne S, Kontulainen S, Theou O, Morais JA. Myths and Methodologies: Understanding the health impact of head down bedrest for the benefit of older adults and astronauts. Study protocol of the Canadian Bedrest Study. Exp Physiol 2024; 109:812-827. [PMID: 38372420 PMCID: PMC11061626 DOI: 10.1113/ep091473] [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: 08/18/2023] [Accepted: 01/23/2024] [Indexed: 02/20/2024]
Abstract
Weightlessness during spaceflight can harm various bodily systems, including bone density, muscle mass, strength and cognitive functions. Exercise appears to somewhat counteract these effects. A terrestrial model for this is head-down bedrest (HDBR), simulating gravity loss. This mirrors challenges faced by older adults in extended bedrest and space environments. The first Canadian study, backed by the Canadian Space Agency, Canadian Institutes of Health Research, and Canadian Frailty Network, aims to explore these issues. The study seeks to: (1) scrutinize the impact of 14-day HDBR on physiological, psychological and neurocognitive systems, and (2) assess the benefits of exercise during HDBR. Eight teams developed distinct protocols, harmonized in three videoconferences, at the McGill University Health Center. Over 26 days, 23 participants aged 55-65 underwent baseline measurements, 14 days of -6° HDBR, and 7 days of recovery. Half did prescribed exercise thrice daily combining resistance and endurance exercise for a total duration of 1 h. Assessments included demographics, cardiorespiratory fitness, bone health, body composition, quality of life, mental health, cognition, muscle health and biomarkers. This study has yielded some published outcomes, with more forthcoming. Findings will enrich our comprehension of HDBR effects, guiding future strategies for astronaut well-being and aiding bedrest-bound older adults. By outlining evidence-based interventions, this research supports both space travellers and those enduring prolonged bedrest.
Collapse
Affiliation(s)
- Guy Hajj‐Boutros
- Research Institute of McGill University Health CentreMcGill UniversityMontréalQuebecCanada
| | - Vita Sonjak
- Research Institute of McGill University Health CentreMcGill UniversityMontréalQuebecCanada
| | - Andréa Faust
- Research Institute of McGill University Health CentreMcGill UniversityMontréalQuebecCanada
| | - Sharmila Balram
- Research Institute of McGill University Health CentreMcGill UniversityMontréalQuebecCanada
| | - Jean‐Christophe Lagacé
- Faculté des Sciences de l'activité physique, Centre de recherche sur le VieillissementUniversité de SherbrookeSherbrookeQuebecCanada
| | - Philippe St‐Martin
- Faculté des Sciences de l'activité physique, Centre de recherche sur le VieillissementUniversité de SherbrookeSherbrookeQuebecCanada
| | - Donya Naz Divsalar
- Department of Biomedical Physiology and KinesiologySimon Fraser UniversityGreater VancouverBritish ColumbiaCanada
| | - Farshid Sadeghian
- Department of Biomedical Physiology and KinesiologySimon Fraser UniversityGreater VancouverBritish ColumbiaCanada
| | - Teresa Liu‐Ambrose
- Aging, Mobility and Cognitive Neuroscience Laboratory, Department of Physical Therapy, Faculty of MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Health Research InstituteVancouverBritish ColumbiaCanada
- Centre for Hip Health and MobilityVancouver Coastal Health Research InstituteVancouverBritish ColumbiaCanada
| | - Andrew P. Blaber
- Department of Biomedical Physiology and KinesiologySimon Fraser UniversityGreater VancouverBritish ColumbiaCanada
| | - Isabelle J. Dionne
- Faculté des Sciences de l'activité physique, Centre de recherche sur le VieillissementUniversité de SherbrookeSherbrookeQuebecCanada
| | - Simon Duchesne
- Department of Radiology and Nuclear MedicineUniversité LavalQuebec CityQuebecCanada
- CERVO Brain Research CenterQuebec CityQuebecCanada
| | - Saija Kontulainen
- College of KinesiologyUniversity of SaskatchewanSaskatoonSaskatchewanCanada
| | - Olga Theou
- Physiotherapy and Geriatric MedicineDalhousie UniversityHalifaxNova ScotiaCanada
| | - José A. Morais
- Division of Geriatric Medicine, McGill University Health CentreMcGill UniversityMontréalQuebecCanada
| |
Collapse
|
4
|
Rubio JM, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, Horga G, Sarpal DK, Argyelan M, Gallego J, Cholewa J, Barber A, Kane JM, Malhotra AK. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. Mol Psychiatry 2024; 29:929-938. [PMID: 38177349 PMCID: PMC11176002 DOI: 10.1038/s41380-023-02381-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 12/06/2023] [Accepted: 12/13/2023] [Indexed: 01/06/2024]
Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n = 101) from healthy controls (n = 51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n = 97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC = 75.4%, 95% CI = 67.0-83.3%; in non-affective psychosis AUC = 80.5%, 95% CI = 72.1-88.0%, and in affective psychosis AUC = 58.7%, 95% CI = 44.2-72.0%). Test-retest reliability ranged between ICC = 0.48 (95% CI = 0.35-0.59) and ICC = 0.22 (95% CI = 0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC = 0.51 (95% CI = 0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 min, diagnostic classification of the FSA increased from AUC = 71.7% (95% CI = 63.1-80.3%) to 75.4% (95% CI = 67.0-83.3%) and phase encoding direction reliability from ICC = 0.29 (95% CI = 0.14-0.43) to ICC = 0.51 (95% CI = 0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
Collapse
Affiliation(s)
- Jose M Rubio
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA.
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA.
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA.
| | - Todd Lencz
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Hengyi Cao
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Nina Kraguljac
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Elvisha Dhamala
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, 8057, Zurich, Switzerland
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Miklos Argyelan
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Juan Gallego
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - John Cholewa
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Anita Barber
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - John M Kane
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| | - Anil K Malhotra
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA
| |
Collapse
|
5
|
Amaral L, Thomas P, Amedi A, Striem-Amit E. Longitudinal stability of individual brain plasticity patterns in blindness. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.01.565196. [PMID: 37986779 PMCID: PMC10659359 DOI: 10.1101/2023.11.01.565196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The primary visual cortex (V1) in individuals born blind is engaged in a wide spectrum of tasks and sensory modalities, including audition, touch, language, and memory. This widespread involvement raises questions regarding the constancy of its role and whether it might exhibit flexibility in its function over time, connecting to diverse network functions in response to task-specific demands. This would suggest that reorganized V1 takes on a role similar to cognitive multiple-demand system regions. Alternatively, it is possible that the varying patterns of plasticity observed in the blind V1 can be attributed to individual factors, whereby different blind individuals recruit V1 for different functions, highlighting the immense idiosyncrasy of plasticity. In support of this second account, we have recently shown that V1 functional connectivity varies greatly across blind individuals. But do these represent stable individual patterns of plasticity or merely instantaneous changes, for a multiple-demand system now inhabiting V1? Here we tested if individual connectivity patterns from the visual cortex of blind individuals are stable over time. We show that over two years, fMRI functional connectivity from the primary visual cortex is unique and highly stable in a small sample of repeatedly sampled congenitally blind individuals. Further, using multivoxel pattern analysis, we demonstrate that the unique reorganization patterns of these individuals allow decoding of participant identity. Together with recent evidence for substantial individual differences in visual cortex connectivity, this indicates there may be a consistent role for the visual cortex in blindness, which may differ for each individual. Further, it suggests that the variability in visual reorganization in blindness across individuals could be used to seek stable neuromarkers for sight rehabilitation and assistive approaches.
Collapse
Affiliation(s)
- Lénia Amaral
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057, USA
| | - Peyton Thomas
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057, USA
| | - Amir Amedi
- Ivcher School of Psychology, The Institute for Brain, Mind and Technology, Reichman University, Herzliya, Israel
- The Ruth & Meir Rosenthal Brain Imaging Center, Reichman University, Herzliya, Israel
| | - Ella Striem-Amit
- Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057, USA
| |
Collapse
|
6
|
Wang D, Fan Q, Xiao X, He H, Yang Y, Li Y. Structural Fingerprinting of the Frontal Aslant Tract: Predicting Cognitive Control Capacity and Obsessive-Compulsive Symptoms. J Neurosci 2023; 43:7016-7027. [PMID: 37696666 PMCID: PMC10586535 DOI: 10.1523/jneurosci.0628-23.2023] [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: 04/04/2023] [Revised: 06/29/2023] [Accepted: 08/18/2023] [Indexed: 09/13/2023] Open
Abstract
White matter of the human brain is influenced by common genetic variations and shaped by neural activity-dependent experiences. Variations in microstructure of cerebral white matter across individuals and even across fiber tracts might underlie differences in cognitive capacity and vulnerabilities to mental disorders. The frontoparietal and cingulo-opercular networks of the brain constitute the central system supporting cognitive functions, and functional connectivity of these networks has been used to distinguish individuals known as "functional fingerprinting." The frontal aslant tract (FAT) that passes through the two networks has been implicated in executive functions. However, whether FAT can be used as a "structural fingerprint" to distinguish individuals and predict an individual's cognitive function and dysfunction is unknown. Here we investigated the fingerprinting property of FAT microstructural profiles using three independent diffusion MRI datasets with repeated scans on human participants including both females and males. We found that diffusion and geometric profiles of FAT can be used to distinguish individuals with a high accuracy. Next, we demonstrated that fractional anisotropy in different FAT segments predicted distinct cognitive functions, including working memory, inhibitory control, and relational reasoning. Finally, we assessed the contribution of altered FAT microstructural profiles to cognitive dysfunction in unmedicated patients with obsessive-compulsive disorders. We found that the altered microstructure in FAT was associated with the severity of obsessive-compulsive symptoms. Collectively, our findings suggest that the microstructural profiles of FAT can identify individuals with a high accuracy and may serve as an imaging marker for predicting an individual's cognitive capacity and disease severity.SIGNIFICANCE STATEMENT The frontoparietal network and cingulo-opercular network of the brain constitute a dual-network architecture for human cognitive functions, and functional connectivity of these two networks can be used as a "functional fingerprint" to distinguish individuals. However, the structural underpinnings of these networks subserving individual heterogeneities in their functional connectivity and cognitive ability remain unknown. We show here that the frontal aslant tract (FAT) that passes through the two networks distinguishes individuals with a high accuracy. Further, we demonstrate that the diffusion profiles of FAT predict distinct cognitive functions in healthy subjects and are associated with the clinical symptoms in patients with obsessive-compulsive disorders. Our findings suggest that the FAT may serve as a unique structural fingerprint underlying individual cognitive capability.
Collapse
Affiliation(s)
- Danni Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland 21224
| | - Qing Fan
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, People's Republic of China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, People's Republic of China
| | - Xiang Xiao
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland 21224
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou 310027, People's Republic of China
- School of Physics, Zhejiang University, Hangzhou 310027, People's Republic of China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland 21224
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
| |
Collapse
|
7
|
Mieling M, Göttlich M, Yousuf M, Bunzeck N. Basal forebrain activity predicts functional degeneration in the entorhinal cortex in Alzheimer's disease. Brain Commun 2023; 5:fcad262. [PMID: 37901036 PMCID: PMC10608112 DOI: 10.1093/braincomms/fcad262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 08/23/2023] [Accepted: 10/07/2023] [Indexed: 10/31/2023] Open
Abstract
Recent models of Alzheimer's disease suggest the nucleus basalis of Meynert (NbM) as an early origin of structural degeneration followed by the entorhinal cortex (EC). However, the functional properties of NbM and EC regarding amyloid-β and hyperphosphorylated tau remain unclear. We analysed resting-state functional fMRI data with CSF assays from the Alzheimer's Disease Neuroimaging Initiative (n = 71) at baseline and 2 years later. At baseline, local activity, as quantified by fractional amplitude of low-frequency fluctuations, differentiated between normal and abnormal CSF groups in the NbM but not EC. Further, NbM activity linearly decreased as a function of CSF ratio, resembling the disease status. Finally, NbM activity predicted the annual percentage signal change in EC, but not the reverse, independent from CSF ratio. Our findings give novel insights into the pathogenesis of Alzheimer's disease by showing that local activity in NbM is affected by proteinopathology and predicts functional degeneration within the EC.
Collapse
Affiliation(s)
- Marthe Mieling
- Department of Psychology, University of Lübeck, Lübeck 23562, Germany
| | - Martin Göttlich
- Department of Neurology, University of Lübeck, Lübeck 23562, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck 23562, Germany
| | - Mushfa Yousuf
- Department of Psychology, University of Lübeck, Lübeck 23562, Germany
| | - Nico Bunzeck
- Department of Psychology, University of Lübeck, Lübeck 23562, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck 23562, Germany
| |
Collapse
|
8
|
Artiles O, Al Masry Z, Saeed F. Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data. Neuroinformatics 2023; 21:651-668. [PMID: 37581850 DOI: 10.1007/s12021-023-09639-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2023] [Indexed: 08/16/2023]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the functional connectivity of the human brain. While rs-fMRI multi-site data can help to understand the inner working of the brain, the data acquisition and processing of this data has many challenges. One of the challenges is the variability of the data associated with different acquisitions sites, and different MRI machines vendors. Other factors such as population heterogeneity among different sites, with variables such as age and gender of the subjects, must also be considered. Given that most of the machine-learning models are developed using these rs-fMRI multi-site data sets, the intrinsic confounding effects can adversely affect the generalizability and reliability of these computational methods, as well as the imposition of upper limits on the classification scores. This work aims to identify the phenotypic and imaging variables producing the confounding effects, as well as to control these effects. Our goal is to maximize the classification scores obtained from the machine learning analysis of the Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI multi-site data. To achieve this goal, we propose novel methods of stratification to produce homogeneous sub-samples of the 17 ABIDE sites, as well as the generation of new features from the static functional connectivity values, using multiple linear regression models, ComBat harmonization models, and normalization methods. The main results obtained with our statistical models and methods are an accuracy of 76.4%, sensitivity of 82.9%, and specificity of 77.0%, which are 8.8%, 20.5%, and 7.5% above the baseline classification scores obtained from the machine learning analysis of the static functional connectivity computed from the ABIDE rs-fMRI multi-site data.
Collapse
Affiliation(s)
- Oswaldo Artiles
- Knight Foundation School of Computing and Information Sciences, Florida International University, 11200 SW 8th Street CASE 354, Miami, Florida, 33199, USA
| | - Zeina Al Masry
- SUPMICROTECH, CNRS, institut FEMTO-ST, 24 rue Alain Savary, Besançon, F-25000, France
| | - Fahad Saeed
- Knight Foundation School of Computing and Information Sciences, Florida International University, 11200 SW 8th Street CASE 354, Miami, Florida, 33199, USA.
| |
Collapse
|
9
|
Rubio J, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, Horga G, Sarpal D, Argyelan M, Gallego J, Cholewa J, Barber A, Kane J, Maholtra A. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. RESEARCH SQUARE 2023:rs.3.rs-3185688. [PMID: 37609149 PMCID: PMC10441472 DOI: 10.21203/rs.3.rs-3185688/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n=101) from healthy controls (n=51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n=97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC=75.4%, 95%CI=67.0%-83.3%; in non-affective psychosis AUC=80.5%, 95%CI=72.1-88.0%, and in affective psychosis AUC=58.7%, 95%CI=44.2-72.0%). Test-retest reliability ranged between ICC=0.48 (95%CI=0.35-0.59) and ICC=0.22 (95%CI=0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC=0.51 (95%CI=0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 minutes, diagnostic classification of the FSA increased from AUC=71.7% (95%CI=63.1%-80.3%) to 75.4% (95%CI=67.0%-83.3%) and phase encoding direction reliability from ICC=0.29 (95%CI=0.14-0.43) to ICC=0.51 (95%CI=0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
Collapse
Affiliation(s)
- Jose Rubio
- Institute of Behavioral Science, Feinstein Institutes of Medical Research, Northwell Health
| | - Todd Lencz
- Zucker School of Medicine at Hofstra/Northwell
| | - Hengyi Cao
- The Feinstein Institute for Medical Research
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
10
|
Rubio JM, Lencz T, Cao H, Kraguljac N, Dhamala E, Homan P, Horga G, Sarpal DK, Argyelan M, Gallego J, Cholewa J, Barber A, Kane J, Malhotra A. Replication of a neuroimaging biomarker for striatal dysfunction in psychosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.17.23292779. [PMID: 37503088 PMCID: PMC10371185 DOI: 10.1101/2023.07.17.23292779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n=101) from healthy controls (n=51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n=97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC=75.4%, 95%CI=67.0%-83.3%; in non-affective psychosis AUC=80.5%, 95%CI=72.1-88.0%, and in affective psychosis AUC=58.7%, 95%CI=44.2-72.0%). Test-retest reliability ranged between ICC=0.48 (95%CI=0.35-0.59) and ICC=0.22 (95%CI=0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC=0.51 (95%CI=0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 minutes, diagnostic classification of the FSA increased from AUC=71.7% (95%CI=63.1%-80.3%) to 75.4% (95%CI=67.0%-83.3%) and phase encoding direction reliability from ICC=0.29 (95%CI=0.14-0.43) to ICC=0.51 (95%CI=0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.
Collapse
Affiliation(s)
- Jose M Rubio
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Todd Lencz
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Hengyi Cao
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Nina Kraguljac
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, Ohio
| | - Elvisha Dhamala
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, 8032, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, 8057, Zurich, Switzerland
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, and New York State Psychiatric Institute, New York, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Miklos Argyelan
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Juan Gallego
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - John Cholewa
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Anita Barber
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - John Kane
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| | - Anil Malhotra
- Donald and Barbara Zucker School of Medicine at Hofstra University - Northwell Health, New York, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, New York, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, USA
| |
Collapse
|
11
|
Hidalgo-Lopez E, Noachtar I, Pletzer B. Hormonal contraceptive exposure relates to changes in resting state functional connectivity of anterior cingulate cortex and amygdala. Front Endocrinol (Lausanne) 2023; 14:1131995. [PMID: 37522123 PMCID: PMC10374315 DOI: 10.3389/fendo.2023.1131995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 06/09/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction Hormonal contraceptives (HCs), nowadays one of the most used contraceptive methods, downregulate endogenous ovarian hormones, which have multiple plastic effects in the adult brain. HCs usually contain a synthetic estrogen, ethinyl-estradiol, and a synthetic progestin, which can be classified as androgenic or anti-androgenic, depending on their interaction with androgen receptors. Both the anterior cingulate cortex (ACC) and the amygdala express steroid receptors and have shown differential functionality depending on the hormonal status of the participant and the use of HC. In this work, we investigated for the first time the relationship between ACC and amygdala resting state functional connectivity (rs-FC) and HC use duration, while controlling for progestin androgenicity. Methods A total of 231 healthy young women participated in five different magnetic resonance imaging studies and were included in the final analysis. The relation between HC use duration and (i) gray matter volume, (ii) fractional amplitude of low-frequency fluctuations, and (iii) seed-based connectivity during resting state in the amygdalae and ACC was investigated in this large sample of women. Results In general, rs-FC of the amygdalae with frontal areas, and between the ACC and temporoparietal areas, decreased the longer the HC exposure and independently of the progestin's androgenicity. The type of HC's progestin did show a differential effect in the gray matter volume of left ACC and the connectivity between bilateral ACC and the right inferior frontal gyrus.
Collapse
Affiliation(s)
- Esmeralda Hidalgo-Lopez
- Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
- Department of Psychology, University of Salzburg, Salzburg, Austria
| | - Isabel Noachtar
- Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
- Department of Psychology, University of Salzburg, Salzburg, Austria
| | - Belinda Pletzer
- Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
- Department of Psychology, University of Salzburg, Salzburg, Austria
| |
Collapse
|
12
|
Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
Collapse
Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| |
Collapse
|
13
|
Hu F, Lucas A, Chen AA, Coleman K, Horng H, Ng RW, Tustison NJ, Davis KA, Shou H, Li M, Shinohara RT. DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.24.537396. [PMID: 37163042 PMCID: PMC10168207 DOI: 10.1101/2023.04.24.537396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Neuroimaging data from multiple batches (i.e. acquisition sites, scanner manufacturer, datasets, etc.) are increasingly necessary to gain new insights into the human brain. However, multi-batch data, as well as extracted radiomic features, exhibit pronounced technical artifacts across batches. These batch effects introduce confounding into the data and can obscure biological effects of interest, decreasing the generalizability and reproducibility of findings. This is especially true when multi-batch data is used alongside complex downstream analysis models, such as machine learning methods. Image harmonization methods seeking to remove these batch effects are important for mitigating these issues; however, significant multivariate batch effects remain in the data following harmonization by current state-of-the-art statistical and deep learning methods. We present DeepCombat, a deep learning harmonization method based on a conditional variational autoencoder architecture and the ComBat harmonization model. DeepCombat learns and removes subject-level batch effects by accounting for the multivariate relationships between features. Additionally, DeepComBat relaxes a number of strong assumptions commonly made by previous deep learning harmonization methods and is empirically robust across a wide range of hyperparameter choices. We apply this method to neuroimaging data from a large cognitive-aging cohort and find that DeepCombat outperforms existing methods, as assessed by a battery of machine learning methods, in removing scanner effects from cortical thickness measurements while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically-motivated deep learning harmonization methods.
Collapse
Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania
| | - Alfredo Lucas
- Center for Neuroengineering and Therapeutics, Department of Engineering, University of Pennsylvania
| | - Andrew A. Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania
| | - Kyle Coleman
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania
| | | | | | - Kathryn A. Davis
- Center for Neuroengineering and Therapeutics, Department of Engineering, University of Pennsylvania
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine
| | | |
Collapse
|
14
|
Mieling M, Göttlich M, Yousuf M, Bunzeck N. Basal forebrain activity predicts functional degeneration in the entorhinal cortex and decreases with Alzheimer's Disease progression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.28.534523. [PMID: 37034733 PMCID: PMC10081194 DOI: 10.1101/2023.03.28.534523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
BACKGROUND AND OBJECTIVES Recent models of Alzheimer's Disease (AD) suggest the nucleus basalis of Meynert (NbM) as the origin of structural degeneration followed by the entorhinal cortex (EC). However, the functional properties of NbM and EC regarding amyloid-β and hyperphosphorylated tau remain unclear. METHODS We analyzed resting-state (rs)fMRI data with CSF assays from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n=71) at baseline and two years later. RESULTS At baseline, local activity, as quantified by fractional amplitude of low-frequency fluctuations (fALFF), differentiated between normal and abnormal CSF groups in the NbM but not EC. Further, NbM activity linearly decreased as a function of CSF ratio, resembling the disease status. Finally, NbM activity predicted the annual percentage signal change in EC, but not the reverse, independent from CSF ratio. DISCUSSION Our findings give novel insights into the pathogenesis of AD by showing that local activity in NbM is affected by proteinopathology and predicts functional degeneration within the EC.
Collapse
Affiliation(s)
- Marthe Mieling
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Martin Göttlich
- Department of Neurology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Mushfa Yousuf
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Nico Bunzeck
- Department of Psychology, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | | |
Collapse
|
15
|
Hranilovich JA, Legget KT, Dodd KC, Wylie KP, Tregellas JR. Functional magnetic resonance imaging of headache: Issues, best-practices, and new directions, a narrative review. Headache 2023; 63:309-321. [PMID: 36942411 PMCID: PMC10089616 DOI: 10.1111/head.14487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/26/2022] [Accepted: 01/20/2023] [Indexed: 03/23/2023]
Abstract
OBJECTIVE To ensure readers are informed consumers of functional magnetic resonance imaging (fMRI) research in headache, to outline ongoing challenges in this area of research, and to describe potential considerations when asked to collaborate on fMRI research in headache, as well as to suggest future directions for improvement in the field. BACKGROUND Functional MRI has played a key role in understanding headache pathophysiology, and mapping networks involved with headache-related brain activity have the potential to identify intervention targets. Some investigators have also begun to explore its use for diagnosis. METHODS/RESULTS The manuscript is a narrative review of the current best practices in fMRI in headache research, including guidelines on transparency and reproducibility. It also contains an outline of the fundamentals of MRI theory, task-related study design, resting-state functional connectivity, relevant statistics and power analysis, image preprocessing, and other considerations essential to the field. CONCLUSION Best practices to increase reproducibility include methods transparency, eliminating error, using a priori hypotheses and power calculations, using standardized instruments and diagnostic criteria, and developing large-scale, publicly available datasets.
Collapse
Affiliation(s)
- Jennifer A Hranilovich
- Division of Child Neurology, Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Kristina T Legget
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, Colorado, USA
- Research Service, Rocky Mountain Regional VA Medical Center, Aurora, Colorado, USA
| | - Keith C Dodd
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Korey P Wylie
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Jason R Tregellas
- Department of Psychiatry, University of Colorado School of Medicine, Aurora, Colorado, USA
- Research Service, Rocky Mountain Regional VA Medical Center, Aurora, Colorado, USA
| |
Collapse
|
16
|
De Rosa AP, Esposito F, Valsasina P, d'Ambrosio A, Bisecco A, Rocca MA, Tommasin S, Marzi C, De Stefano N, Battaglini M, Pantano P, Cirillo M, Tedeschi G, Filippi M, Gallo A. Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative. J Neurol 2023; 270:1047-1066. [PMID: 36350401 PMCID: PMC9886598 DOI: 10.1007/s00415-022-11479-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/11/2022]
Abstract
The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.
Collapse
Affiliation(s)
- Alessandro Pasquale De Rosa
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy.
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Alessandro d'Ambrosio
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Alvino Bisecco
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Silvia Tommasin
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università, 30, 00185, Rome, Italy
| | - Chiara Marzi
- Institute of Applied Physics "Nello Cararra" (IFAC), National Research Council (CNR), Via Madonna del Piano, 10, Sesto Fiorentino, 50019, Florence, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università, 30, 00185, Rome, Italy
| | - Mario Cirillo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Gioacchino Tedeschi
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Antonio Gallo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| |
Collapse
|
17
|
Cahart M, O'Daly O, Giampietro V, Timmers M, Streffer J, Einstein S, Zelaya F, Dell'Acqua F, Williams SCR. Comparing the test-retest reliability of resting-state functional magnetic resonance imaging metrics across single band and multiband acquisitions in the context of healthy aging. Hum Brain Mapp 2022; 44:1901-1912. [PMID: 36546653 PMCID: PMC9980889 DOI: 10.1002/hbm.26180] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 11/17/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
The identification of meaningful functional magnetic resonance imaging (fMRI) biomarkers requires measures that reliably capture brain performance across different subjects and over multiple scanning sessions. Recent developments in fMRI acquisition, such as the introduction of multiband (MB) protocols and in-plane acceleration, allow for increased scanning speed and improved temporal resolution. However, they may also lead to reduced temporal signal to noise ratio and increased signal leakage between simultaneously excited slices. These methods have been adopted in several scanning modalities including diffusion weighted imaging and fMRI. To our knowledge, no study has formally compared the reliability of the same resting-state fMRI (rs-fMRI) metrics (amplitude of low-frequency fluctuations; seed-to-voxel and region of interest [ROI]-to-ROI connectivity) across conventional single-band fMRI and different MB acquisitions, with and without in-plane acceleration, across three sessions. In this study, 24 healthy older adults were scanned over three visits, on weeks 0, 1, and 4, and, on each occasion, underwent a conventional single band rs-fMRI scan and three different rs-fMRI scans with MB factors 4 and 6, with and without in-plane acceleration. Across all three rs-fMRI metrics, the reliability scores were highest with MB factor 4 with no in-plane acceleration for cortical areas and with conventional single band for subcortical areas. Recommendations for future research studies are discussed.
Collapse
Affiliation(s)
- Marie‐Stephanie Cahart
- Neuroimaging DepartmentInstitute of Psychiatry, Psychology and Neuroscience, Kings College LondonLondonUK
| | - Owen O'Daly
- Neuroimaging DepartmentInstitute of Psychiatry, Psychology and Neuroscience, Kings College LondonLondonUK
| | - Vincent Giampietro
- Neuroimaging DepartmentInstitute of Psychiatry, Psychology and Neuroscience, Kings College LondonLondonUK
| | - Maarten Timmers
- Division of Janssen Pharmaceutica NVJanssen Research and DevelopmentBeerseBelgium
| | - Johannes Streffer
- AC Immune SALausanneSwitzerland
- Reference Center for Biological Markers of Dementia (BIODEM)University of AntwerpAntwerpBelgium
| | | | - Fernando Zelaya
- Neuroimaging DepartmentInstitute of Psychiatry, Psychology and Neuroscience, Kings College LondonLondonUK
| | - Flavio Dell'Acqua
- Natbrainlab; Forensic and Neurodevelopmental Sciences DepartmentInstitute of Psychiatry, Psychology and Neuroscience, Kings College LondonLondonUK
| | - Steven C. R. Williams
- Neuroimaging DepartmentInstitute of Psychiatry, Psychology and Neuroscience, Kings College LondonLondonUK
| |
Collapse
|
18
|
Michon KJ, Khammash D, Simmonite M, Hamlin AM, Polk TA. Person-specific and precision neuroimaging: Current methods and future directions. Neuroimage 2022; 263:119589. [PMID: 36030062 DOI: 10.1016/j.neuroimage.2022.119589] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/13/2022] [Accepted: 08/23/2022] [Indexed: 10/31/2022] Open
Abstract
Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.
Collapse
Affiliation(s)
| | - Dalia Khammash
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Molly Simmonite
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Abbey M Hamlin
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Thad A Polk
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
19
|
Mills-Finnerty C, Frangos E, Allen K, Komisaruk B, Wise N. Functional Magnetic Resonance Imaging Studies in Sexual Medicine: A Primer. J Sex Med 2022; 19:1073-1089. [DOI: 10.1016/j.jsxm.2022.03.217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 01/27/2022] [Accepted: 03/04/2022] [Indexed: 11/17/2022]
|
20
|
De Stefano N, Battaglini M, Pareto D, Cortese R, Zhang J, Oesingmann N, Prados F, Rocca MA, Valsasina P, Vrenken H, Gandini Wheeler-Kingshott CAM, Filippi M, Barkhof F, Rovira À. MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies. Neuroimage Clin 2022; 34:102972. [PMID: 35245791 PMCID: PMC8892169 DOI: 10.1016/j.nicl.2022.102972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/24/2022]
Abstract
Sharing data from cooperative studies is essential to develop new biomarkers in MS. Differences in MRI acquisition, analysis, storage represent a substantial constraint. We review the state of the art and developments in the harmonization of MRI. We provide recommendations to harmonize large MRI datasets in the MS field.
There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.
Collapse
Affiliation(s)
- Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Jian Zhang
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | | | - Ferran Prados
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Hugo Vrenken
- Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy; Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| |
Collapse
|
21
|
Nigri A, Ferraro S, Gandini Wheeler-Kingshott CAM, Tosetti M, Redolfi A, Forloni G, D'Angelo E, Aquino D, Biagi L, Bosco P, Carne I, De Francesco S, Demichelis G, Gianeri R, Lagana MM, Micotti E, Napolitano A, Palesi F, Pirastru A, Savini G, Alberici E, Amato C, Arrigoni F, Baglio F, Bozzali M, Castellano A, Cavaliere C, Contarino VE, Ferrazzi G, Gaudino S, Marino S, Manzo V, Pavone L, Politi LS, Roccatagliata L, Rognone E, Rossi A, Tonon C, Lodi R, Tagliavini F, Bruzzone MG. Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN-Neuroimaging Network. Front Neurol 2022; 13:855125. [PMID: 35493836 PMCID: PMC9047871 DOI: 10.3389/fneur.2022.855125] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures.
Collapse
Affiliation(s)
- Anna Nigri
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Stefania Ferraro
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Michela Tosetti
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Gianluigi Forloni
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Egidio D'Angelo
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Domenico Aquino
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Laura Biagi
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Paolo Bosco
- Medical Physics and MR Lab, Fondazione IRCCS Stella Maris, Pisa, Italy
| | - Irene Carne
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Greta Demichelis
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Ruben Gianeri
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Edoardo Micotti
- Laboratory of Biology of Neurodegenerative Disorders, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Antonio Napolitano
- Medical Physics, IRCCS Istituto Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Fulvia Palesi
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | - Giovanni Savini
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Elisa Alberici
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Carmelo Amato
- Unit of Neuroradiology, Oasi Research Institute-IRCCS, Troina, Italy
| | - Filippo Arrigoni
- Neuroimaging Unit, Scientific Institute, IRCCS E. Medea, Bosisio Parini, Italy
| | | | - Marco Bozzali
- Neuroimaging Laboratory, Santa Lucia Foundation, IRCCS, Rome, Italy
| | | | | | - Valeria Elisa Contarino
- Unità di Neuroradiologia, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Simona Gaudino
- Istituto di Radiologia, UOC Radiologia e Neuroradiologia, IRCCS Fondazione Policlinico Universitario Agostino Gemelli, Rome, Italy
| | - Silvia Marino
- IRCCS Centro Neurolesi “Bonino-Pulejo”, Messina, Italy
| | - Vittorio Manzo
- Department of Radiology, Istituto Auxologico Italiano, IRCCS, Milan, Italy
| | | | - Letterio S. Politi
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Luca Roccatagliata
- Neuroradiologia IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Dipartimento di Scienze della Salute Università di Genova, Genoa, Italy
| | - Elisa Rognone
- Unità di Neuroradiologia, IRCCS Mondino Foundation, Pavia, Italy
| | - Andrea Rossi
- Dipartimento di Scienze della Salute Università di Genova, Genoa, Italy
- UO Neuroradiologia, IRCCS Istituto Giannina Gaslini, Genoa, Italy
| | - Caterina Tonon
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Raffaele Lodi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Fabrizio Tagliavini
- Scientific Direction, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Maria Grazia Bruzzone
- U.O. Neuroradiologia, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | |
Collapse
|
22
|
Dennis EL, Baron D, Bartnik‐Olson B, Caeyenberghs K, Esopenko C, Hillary FG, Kenney K, Koerte IK, Lin AP, Mayer AR, Mondello S, Olsen A, Thompson PM, Tate DF, Wilde EA. ENIGMA brain injury: Framework, challenges, and opportunities. Hum Brain Mapp 2022; 43:149-166. [PMID: 32476212 PMCID: PMC8675432 DOI: 10.1002/hbm.25046] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 04/23/2020] [Accepted: 05/03/2020] [Indexed: 12/19/2022] Open
Abstract
Traumatic brain injury (TBI) is a major cause of disability worldwide, but the heterogeneous nature of TBI with respect to injury severity and health comorbidities make patient outcome difficult to predict. Injury severity accounts for only some of this variance, and a wide range of preinjury, injury-related, and postinjury factors may influence outcome, such as sex, socioeconomic status, injury mechanism, and social support. Neuroimaging research in this area has generally been limited by insufficient sample sizes. Additionally, development of reliable biomarkers of mild TBI or repeated subconcussive impacts has been slow, likely due, in part, to subtle effects of injury and the aforementioned variability. The ENIGMA Consortium has established a framework for global collaboration that has resulted in the largest-ever neuroimaging studies of multiple psychiatric and neurological disorders. Here we describe the organization, recent progress, and future goals of the Brain Injury working group.
Collapse
Affiliation(s)
- Emily L. Dennis
- Department of NeurologyUniversity of Utah School of MedicineSalt Lake CityUtahUSA
- George E. Wahlen Veterans Affairs Medical CenterSalt Lake CityUtahUSA
- Imaging Genetics CenterStevens Neuroimaging & Informatics Institute, Keck School of Medicine of USCMarina del ReyCaliforniaUSA
| | - David Baron
- Western University of Health SciencesPomonaCaliforniaUSA
| | - Brenda Bartnik‐Olson
- Department of RadiologyLoma Linda University Medical CenterLoma LindaCaliforniaUSA
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of PsychologyDeakin UniversityBurwoodVictoriaAustralia
| | - Carrie Esopenko
- Department of Rehabilitation and Movement SciencesRutgers Biomedical Health SciencesNewarkNew JerseyUSA
| | - Frank G. Hillary
- Department of PsychologyPennsylvania State UniversityUniversity ParkPennsylvaniaUSA
- Social Life and Engineering Sciences Imaging CenterUniversity ParkPennsylvaniaUSA
| | - Kimbra Kenney
- Department of NeurologyUniformed Services University of the Health SciencesBethesdaMarylandUSA
- National Intrepid Center of ExcellenceWalter Reed National Military Medical CenterBethesdaMarylandUSA
| | - Inga K. Koerte
- Psychiatry Neuroimaging LaboratoryBrigham and Women's HospitalBostonMassachusettsUSA
- Department of Child and Adolescent Psychiatry, Psychosomatics and PsychotherapyLudwig‐Maximilians‐UniversitätMunichGermany
| | - Alexander P. Lin
- Center for Clinical SpectroscopyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Andrew R. Mayer
- Mind Research NetworkAlbuquerqueNew MexicoUSA
- Department of Neurology and PsychiatryUniversity of New Mexico School of MedicineAlbuquerqueNew MexicoUSA
| | - Stefania Mondello
- Department of Biomedical and Dental Sciences and Morphofunctional ImagingUniversity of MessinaMessinaItaly
| | - Alexander Olsen
- Department of PsychologyNorwegian University of Science and TechnologyTrondheimNorway
- Department of Physical Medicine and RehabilitationSt. Olavs Hospital, Trondheim University HospitalTrondheimNorway
| | - Paul M. Thompson
- Imaging Genetics CenterStevens Neuroimaging & Informatics Institute, Keck School of Medicine of USCMarina del ReyCaliforniaUSA
- Department of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and OphthalmologyUniversity of Southern California (USC)Los AngelesCaliforniaUSA
| | - David F. Tate
- Department of NeurologyUniversity of Utah School of MedicineSalt Lake CityUtahUSA
- George E. Wahlen Veterans Affairs Medical CenterSalt Lake CityUtahUSA
| | - Elisabeth A. Wilde
- Department of NeurologyUniversity of Utah School of MedicineSalt Lake CityUtahUSA
- George E. Wahlen Veterans Affairs Medical CenterSalt Lake CityUtahUSA
| |
Collapse
|
23
|
Reardon AM, Li K, Hu XP. Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning. Front Comput Neurosci 2021; 15:762781. [PMID: 34924984 PMCID: PMC8674307 DOI: 10.3389/fncom.2021.762781] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 11/04/2021] [Indexed: 11/18/2022] Open
Abstract
Background: Multi-site functional MRI (fMRI) databases are becoming increasingly prevalent in the study of neurodevelopmental and psychiatric disorders. However, multi-site databases are known to introduce site effects that may confound neurobiological and measures such as functional connectivity (FC). Although studies have been conducted to mitigate site effects, these methods often result in reduced effect size in FC comparisons between controls and patients. Methods: We present a site-wise de-meaning (SWD) strategy in multi-site FC analysis and compare its performance with two common site-effect mitigation methods, i.e., generalized linear model (GLM) and Combining Batches (ComBat) Harmonization. For SWD, after FC was calculated and Fisher z-transformed, the site-wise FC mean was removed from each subject before group-level statistical analysis. The above methods were tested on two multi-site psychiatric consortiums [Autism Brain Imaging Data Exchange (ABIDE) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP)]. Preservation of consistent FC alterations in patients were evaluated for each method through the effect sizes (Hedge’s g) of patients vs. controls. Results: For the B-SNIP dataset, SWD improved the effect size between schizophrenic and control subjects by 4.5–7.9%, while GLM and ComBat decreased the effect size by 22.5–42.6%. For the ABIDE dataset, SWD improved the effect size between autistic and control subjects by 2.9–5.3%, while GLM and ComBat decreased the effect size by up to 11.4%. Conclusion: Compared to the original data and commonly used methods, the SWD method demonstrated superior performance in preserving the effect size in FC features associated with disorders.
Collapse
Affiliation(s)
- Alexandra M Reardon
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
| | - Kaiming Li
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
| | - Xiaoping P Hu
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States.,Center for Advanced Neuroimaging, University of California, Riverside, Riverside, CA, United States
| |
Collapse
|
24
|
Shao X, Sun S, Li J, Kong W, Zhu J, Li X, Hu B. Analysis of Functional Brain Network in MDD Based on Improved Empirical Mode Decomposition With Resting State EEG Data. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1546-1556. [PMID: 34166194 DOI: 10.1109/tnsre.2021.3092140] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
At present, most brain functional studies are based on traditional frequency bands to explore the abnormal functional connections and topological organization of patients with depression. However, they ignore the characteristic relationship of electroencephalogram (EEG) signals in the time domain. Therefore, this paper proposes a network decomposition model based on Improved Empirical Mode Decomposition (EMD), it is suitable for time-frequency analysis of brain functional network. On the one hand, it solves the problem of mode mixing on original EMD method, especially on high-density EEG data. On the other hand, by building brain function networks on different intrinsic mode function (IMF), we can perform time-frequency analysis of brain function connections. It provides a new insight for brain function connectivity analysis of major depressive disorder (MDD). Experimental results found that the IMFs waveform decomposed by Improved EMD was more stable and the difference between IMFs was obvious, it indicated that the mode mixing can be effectively solved. Besides, the analysis of the brain network, we found that the changes in MDD functional connectivity on different IMFs, it may be related to the pathological changes for MDD. More statistical results on three network metrics proved that there were significant differences between MDD and normal controls (NC) group. In addition, the aberrant brain network structure of MDDs was also confirmed in the hubs characteristic. These findings may provide potential biomarkers for the clinical diagnosis of MDD patients.
Collapse
|
25
|
Noble S, Scheinost D, Constable RT. A guide to the measurement and interpretation of fMRI test-retest reliability. Curr Opin Behav Sci 2021; 40:27-32. [PMID: 33585666 PMCID: PMC7875178 DOI: 10.1016/j.cobeha.2020.12.012] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The test-retest reliability of functional neuroimaging data has recently been a topic of much discussion. Despite early conflicting reports, converging reports now suggest that test-retest reliability is poor for standard univariate measures-namely, voxel- and region-level task-based activation and edge-level functional connectivity. To better understand the implications of these recent studies requires understanding the nuances of test-retest reliability as commonly measured by the intraclass correlation coefficient (ICC). Here we provide a guide to the measurement and interpretation of test-retest reliability in functional neuroimaging and review major findings in the literature. We highlight the importance of making choices that improve reliability so long as they do not diminish validity, pointing to the potential of multivariate approaches that improve both. Finally, we discuss the implications of recent reports of low test-retest reliability in the context of ongoing work in the field.
Collapse
Affiliation(s)
- Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine
- Department of Statistics and Data Science, Yale University
- Child Study Center, Yale School of Medicine
- Department of Biomedical Engineering, Yale School of Medicine
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine
- Child Study Center, Yale School of Medicine
- Department of Biomedical Engineering, Yale School of Medicine
- Department of Neurosurgery, Yale School of Medicine
| |
Collapse
|
26
|
Merhar SL, Jiang W, Parikh NA, Yin W, Zhou Z, Tkach JA, Wang L, Kline-Fath BM, He L, Braimah A, Vannest J, Lin W. Effects of prenatal opioid exposure on functional networks in infancy. Dev Cogn Neurosci 2021; 51:100996. [PMID: 34388637 PMCID: PMC8363826 DOI: 10.1016/j.dcn.2021.100996] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 07/22/2021] [Accepted: 07/29/2021] [Indexed: 11/20/2022] Open
Abstract
Prenatal opioid exposure has been linked to altered neurodevelopment and visual problems such as strabismus and nystagmus. The neural substrate underlying these alterations is unclear. Resting-state functional connectivity MRI (rsfMRI) is an advanced and well-established technique to evaluate brain networks. Few studies have examined the effects of prenatal opioid exposure on resting-state network connectivity in infancy. In this pilot study, we characterized network connectivity in opioid-exposed infants (n = 19) and controls (n = 20) between 4–8 weeks of age using both a whole-brain connectomic approach and a seed-based approach. Prenatal opioid exposure was associated with differences in distribution of betweenness centrality and connection length, with positive connections unique to each group significantly longer than common connections. The unique connections in the opioid-exposed group were more often inter-network connections while unique connections in controls and connections common to both groups were more often intra-network. The opioid-exposed group had smaller network volumes particularly in the primary visual network, but similar network strength as controls. Network topologies as determined by dice similarity index were different between groups, particularly in visual and executive control networks. These results may provide insight into the neural basis for the developmental and visual problems associated with prenatal opioid exposure.
Collapse
Affiliation(s)
- Stephanie L Merhar
- Perinatal Institute, Division of Neonatology, Cincinnati Children's Hospital and University of Cincinnati, Department of Pediatrics, Cincinnati, OH, USA.
| | - Weixiong Jiang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nehal A Parikh
- Perinatal Institute, Division of Neonatology, Cincinnati Children's Hospital and University of Cincinnati, Department of Pediatrics, Cincinnati, OH, USA
| | - Weiyan Yin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhen Zhou
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jean A Tkach
- Imaging Research Center, Cincinnati Children's Hospital, Cincinnati, OH, USA; Department of Radiology, Cincinnati Children's Hospital, Cincinnati, OH, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Beth M Kline-Fath
- Imaging Research Center, Cincinnati Children's Hospital, Cincinnati, OH, USA; Department of Radiology, Cincinnati Children's Hospital, Cincinnati, OH, USA
| | - Lili He
- Perinatal Institute, Division of Neonatology, Cincinnati Children's Hospital and University of Cincinnati, Department of Pediatrics, Cincinnati, OH, USA
| | - Adebayo Braimah
- Imaging Research Center, Cincinnati Children's Hospital, Cincinnati, OH, USA
| | - Jennifer Vannest
- Department of Communication Sciences and Disorders, College of Allied Health Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| |
Collapse
|
27
|
O'Connor D, Lake EMR, Scheinost D, Constable RT. Resample aggregating improves the generalizability of connectome predictive modeling. Neuroimage 2021; 236:118044. [PMID: 33848621 PMCID: PMC8282199 DOI: 10.1016/j.neuroimage.2021.118044] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 04/04/2021] [Accepted: 04/05/2021] [Indexed: 11/25/2022] Open
Abstract
It is a longstanding goal of neuroimaging to produce reliable, generalizable models of brain behavior relationships. More recently, data driven predictive models have become popular. However, overfitting is a common problem with statistical models, which impedes model generalization. Cross validation (CV) is often used to estimate expected model performance within sample. Yet, the best way to generate brain behavior models, and apply them out-of-sample, on an unseen dataset, is unclear. As a solution, this study proposes an ensemble learning method, in this case resample aggregating, encompassing both model parameter estimation and feature selection. Here we investigate the use of resampled aggregated models when used to estimate fluid intelligence (fIQ) from fMRI based functional connectivity (FC) data. We take advantage of two large openly available datasets, the Human Connectome Project (HCP), and the Philadelphia Neurodevelopmental Cohort (PNC). We generate aggregated and non-aggregated models of fIQ in the HCP, using the Connectome Prediction Modelling (CPM) framework. Over various test-train splits, these models are evaluated in sample, on left-out HCP data, and out-of-sample, on PNC data. We find that a resample aggregated model performs best both within- and out-of-sample. We also find that feature selection can vary substantially within-sample. More robust feature selection methods, as detailed here, are needed to improve cross sample performance of CPM based brain behavior models.
Collapse
Affiliation(s)
- David O'Connor
- Department of Biomedical Engineering, Yale University, United States.
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, United States
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, United States; Department of Radiology and Biomedical Imaging, Yale School of Medicine, United States; Deparment of Statistics & Data Science, Yale University, United States; Child Study Center, Yale School of Medicine, United States
| | - R Todd Constable
- Department of Biomedical Engineering, Yale University, United States; Department of Radiology and Biomedical Imaging, Yale School of Medicine, United States; Department of Neurosurgery, Yale School of Medicine, United States
| |
Collapse
|
28
|
Wirsich J, Jorge J, Iannotti GR, Shamshiri EA, Grouiller F, Abreu R, Lazeyras F, Giraud AL, Gruetter R, Sadaghiani S, Vulliémoz S. The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T. Neuroimage 2021; 231:117864. [PMID: 33592241 DOI: 10.1016/j.neuroimage.2021.117864] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 01/21/2021] [Accepted: 02/09/2021] [Indexed: 01/01/2023] Open
Abstract
Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r ≈ 0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-β frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship. This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.
Collapse
Affiliation(s)
- Jonathan Wirsich
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland.
| | - João Jorge
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Systems Division, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Giannina Rita Iannotti
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
| | - Elhum A Shamshiri
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
| | - Frédéric Grouiller
- Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
| | - Rodolfo Abreu
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico - Universidade de Lisboa, Lisbon, Portugal; Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - François Lazeyras
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Anne-Lise Giraud
- Department of Neuroscience, University of Geneva, Geneva, Switzerland
| | - Rolf Gruetter
- Laboratory for Functional and Metabolic Imaging, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Department of Radiology, University of Lausanne, Lausanne, Switzerland
| | - Sepideh Sadaghiani
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Psychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine of Geneva, University of Geneva, Geneva, Switzerland
| |
Collapse
|
29
|
Abstract
OBJECTIVE To describe the neuroimaging and other methods for assessing vascular contributions to neurodegeneration in the Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) study, a Canadian multi-center, prospective longitudinal cohort study, including reliability and feasibility in the first 200 participants. METHODS COMPASS-ND includes persons with Alzheimer's disease (AD; n = 150), Parkinson's disease (PD) and Lewy body dementias (LBDs) (200), mixed dementia (200), mild cognitive impairment (MCI; 400), subcortical ischemic vascular MCI (V-MCI; 200), subjective cognitive impairment (SCI; 300), and cognitively intact elderly controls (660). Magnetic resonance imaging (MRI) was acquired according to the validated Canadian Dementia Imaging Protocol and visually reviewed by either of two experienced readers blinded to clinical characteristics. Other relevant assessments include history of vascular disease and risk factors, blood pressure, height and weight, cholesterol, glucose, and hemoglobin A1c. RESULTS Analyzable data were obtained in 197/200 of whom 18 of whom were clinically diagnosed with V-MCI or mixed dementia. The overall prevalence of infarcts was 24.9%, microbleeds was 24.6%, and high white matter hyperintensity (WMH) was 31.0%. MRI evidence of a potential vascular contribution to neurodegeneration was seen in 12.9%-40.0% of participants clinically diagnosed with another condition such as AD. Inter-rater reliability was good to excellent. CONCLUSION COMPASS-ND will be a useful platform to study vascular brain injury and its association with risk factors, biomarkers, and cognitive and functional decline across multiple age-related neurodegenerative diseases. Initial findings show that MRI-defined vascular brain injury is common in all cognitive syndromes and is under-recognized clinically.
Collapse
|
30
|
Menara T, Lisi G, Pasqualetti F, Cortese A. Brain network dynamics fingerprints are resilient to data heterogeneity. J Neural Eng 2020; 18:026004. [PMID: 33361552 DOI: 10.1088/1741-2552/abd684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
CONTEXT Large multi-site neuroimaging datasets have significantly advanced our quest to understand brain-behavior relationships and to develop biomarkers of psychiatric and neurodegenerative disorders. Yet, such data collections come at a cost, as the inevitable differences across samples may lead to biased or erroneous conclusions. OBJECTIVE We aim to validate the estimation of individual brain network dynamics fingerprints and appraise sources of variability in large resting-state functional magnetic resonance imaging (rs-fMRI) datasets by providing a novel point of view based on data-driven dynamical models. APPROACH Previous work has investigated this critical issue in terms of effects on static measures, such as functional connectivity and brain parcellations. Here, we utilize dynamical models (Hidden Markov models - HMM) to examine how diverse scanning factors in multi-site fMRI recordings affect our ability to infer the brain's spatiotemporal wandering between large-scale networks of activity. Specifically, we leverage a stable HMM trained on the Human Connectome Project (homogeneous) dataset, which we then apply to an heterogeneous dataset of traveling subjects scanned under a multitude of conditions. MAIN RESULTS Building upon this premise, we first replicate previous work on the emergence of non-random sequences of brain states. We next highlight how these time-varying brain activity patterns are robust subject-specific fingerprints. Finally, we suggest these fingerprints may be used to assess which scanning factors induce high variability in the data. SIGNIFICANCE These results demonstrate that we can i) use large scale dataset to train models that can be then used to interrogate subject-specific data, ii) recover the unique trajectories of brain activity changes in each individual, but also iii) urge caution as our ability to infer such patterns is affected by how, where and when we do so.
Collapse
Affiliation(s)
- Tommaso Menara
- Bourns College of Engineering, University of California Riverside, 900 University Ave, Riverside, California, 92521, UNITED STATES
| | - Giuseppe Lisi
- Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555, JAPAN
| | - Fabio Pasqualetti
- Bourns College of Engineering, University of California Riverside, 900 University Ave, Riverside, California, 92521, UNITED STATES
| | - Aurelio Cortese
- Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, JAPAN
| |
Collapse
|
31
|
Badhwar A, Collin-Verreault Y, Lussier D, Sharmarke H, Orban P, Urchs S, Chouinard I, Vogel J, Potvin O, Duchesne S, Bellec P. A dataset of long-term consistency values of resting-state fMRI connectivity maps in a single individual derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol. Data Brief 2020; 31:105699. [PMID: 32518809 PMCID: PMC7270189 DOI: 10.1016/j.dib.2020.105699] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 05/01/2020] [Accepted: 05/06/2020] [Indexed: 11/25/2022] Open
Abstract
The impact of multisite acquisition on resting-state functional MRI (rsfMRI) connectivity has recently gained attention. We provide consistency values (Pearson's correlation) between rsfMRI connectivity maps of an adult volunteer (Csub) scanned 25 times over 3.5 years at 13 sites using the Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). This dataset was generated as part of the following article: Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors [1]. Acquired on three 3T scanner vendors (GE, Siemens and Philips), the Csub dataset is part of an ongoing effort to monitor the quality and comparability of MRI data collected across the Canadian Consortium on Neurodegeneration in Aging (CCNA) imaging network. The participant was scanned 25 times in the above-mentioned article: multiple times at six sites over a period of 2.5 years, and once at the remaining seven sites. Since then the participant was scanned an additional 45 times, allowing us to extend the dataset to 70 rsfMRI scans over a period of >4 years. In addition, we provide intra- and inter-subject consistency values of rsfMRI connectivity maps derived from 26 adult participants belonging to the publicly released Hangzhou Normal University dataset (HNU1). All HNU1 participants underwent 10 rsfMRI scans over one month on a single 3T scanner (GE). Connectivity maps of seven canonical networks were generated for each scan in the two datasets (Csub and HNU1). All consistency values, along with the scripts used to preprocess the rsfMRI data and generate connectivity maps and pairwise consistency values, have been made available on two public repositories, Github and Zenodo. We have also made available four Jupyter notebooks that use the provided consistency values to (a) generate interactive graphical summaries – 1 notebook, (b) perform statistical analyses - 2 notebooks, and (c) perform data-driven cluster analysis for the recovery of subject identity (i.e. rsfMRI fingerprinting) – 1 notebook. In addition, we provide two interactive dashboards that allow visualization of individual connectivity maps from the two datasets. Finally, we also provide minimally preprocessed rsfMRI data in Brain Imaging Data Standard (BIDS) format on all 70 scans in the extended dataset.
Collapse
Affiliation(s)
- AmanPreet Badhwar
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada.,Université de Montréal, Montréal, Canada
| | - Yannik Collin-Verreault
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada
| | - Desiree Lussier
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada
| | - Hanad Sharmarke
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada
| | - Pierre Orban
- Université de Montréal, Montréal, Canada.,Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Montréal, Canada
| | - Sebastian Urchs
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada.,McGill University, Montréal, Canada
| | | | | | - Olivier Potvin
- Centre CERVO, Quebec City Mental Health Institute, Quebec, Canada
| | - Simon Duchesne
- Centre CERVO, Quebec City Mental Health Institute, Quebec, Canada.,Department of Radiology, Faculty of Medicine, Université Laval, Quebec, Canada
| | - Pierre Bellec
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal (CRIUGM), Montréal, Canada.,Université de Montréal, Montréal, Canada
| |
Collapse
|
32
|
Duchesne S, Dieumegarde L, Chouinard I, Farokhian F, Badhwar A, Bellec P, Tétreault P, Descoteaux M, Boré A, Houde JC, Beaulieu C, Potvin O. Structural and functional multi-platform MRI series of a single human volunteer over more than fifteen years. Sci Data 2019; 6:245. [PMID: 31672977 PMCID: PMC6823440 DOI: 10.1038/s41597-019-0262-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 09/06/2019] [Indexed: 11/16/2022] Open
Abstract
We present MRI data from a single human volunteer consisting in over 599 multi-contrast MR images (T1-weighted, T2-weighted, proton density, fluid-attenuated inversion recovery, T2* gradient-echo, diffusion, susceptibility-weighted, arterial-spin labelled, and resting state BOLD functional connectivity imaging) acquired in over 73 sessions on 36 different scanners (13 models, three manufacturers) over the course of 15+ years (cf. Data records). Data included planned data collection acquired within the Consortium pour l'identification précoce de la maladie Alzheimer - Québec (CIMA-Q) and Canadian Consortium on Neurodegeneration in Aging (CCNA) studies, as well as opportunistic data collection from various protocols. These multiple within- and between-centre scans over a substantial time course of a single, cognitively healthy volunteer can be useful to answer a number of methodological questions of interest to the community.
Collapse
Affiliation(s)
- Simon Duchesne
- Department of Radiology, Université Laval, Québec, Canada.
- CERVO Brain Research Centre, Institut universitaire de santé mentale de Québec, Québec, Canada.
| | - Louis Dieumegarde
- CERVO Brain Research Centre, Institut universitaire de santé mentale de Québec, Québec, Canada
| | - Isabelle Chouinard
- CERVO Brain Research Centre, Institut universitaire de santé mentale de Québec, Québec, Canada
| | - Farnaz Farokhian
- CERVO Brain Research Centre, Institut universitaire de santé mentale de Québec, Québec, Canada
| | - Amanpreet Badhwar
- Centre de recherche de l'Institut universitaire en gériatrie de Montréal, Québec, Canada
| | - Pierre Bellec
- Centre de recherche de l'Institut universitaire en gériatrie de Montréal, Québec, Canada
| | - Pascal Tétreault
- Department of Biomedical Engineering, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, Canada
| | - Arnaud Boré
- Centre de recherche de l'Institut universitaire en gériatrie de Montréal, Québec, Canada
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, Canada
| | - Jean-Christophe Houde
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, Canada
| | - Christian Beaulieu
- Department of Biomedical Engineering, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
| | - Olivier Potvin
- CERVO Brain Research Centre, Institut universitaire de santé mentale de Québec, Québec, Canada
| |
Collapse
|