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Ibanez A, Kringelbach ML, Deco G. A synergetic turn in cognitive neuroscience of brain diseases. Trends Cogn Sci 2024; 28:319-338. [PMID: 38246816 DOI: 10.1016/j.tics.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/15/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024]
Abstract
Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure-function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches.
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Affiliation(s)
- Agustin Ibanez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile; Global Brain Health Institute (GBHI), University California San Francisco (UCSF), San Francisco, CA, USA; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Department of Psychiatry, University of Oxford, Oxford, UK.
| | - Morten L Kringelbach
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain.
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Ma D, Stocks J, Rosen H, Kantarci K, Lockhart SN, Bateman JR, Craft S, Gurcan MN, Popuri K, Beg MF, Wang L. Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI. Front Neurosci 2024; 18:1331677. [PMID: 38384484 PMCID: PMC10879283 DOI: 10.3389/fnins.2024.1331677] [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: 11/01/2023] [Accepted: 01/08/2024] [Indexed: 02/23/2024] Open
Abstract
Background Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical, pathological, and genetic heterogeneity. Such heterogeneity hinders the identification of effective biomarkers, preventing effective targeted recruitment of participants in clinical trials for developing potential interventions and treatments. In the present study, we aim to automatically differentiate patients with three clinical phenotypes of FTD, behavioral-variant FTD (bvFTD), semantic variant PPA (svPPA), and nonfluent variant PPA (nfvPPA), based on their structural MRI by training a deep neural network (DNN). Methods Data from 277 FTD patients (173 bvFTD, 63 nfvPPA, and 41 svPPA) recruited from two multi-site neuroimaging datasets: the Frontotemporal Lobar Degeneration Neuroimaging Initiative and the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration databases. Raw T1-weighted MRI data were preprocessed and parcellated into patch-based ROIs, with cortical thickness and volume features extracted and harmonized to control the confounding effects of sex, age, total intracranial volume, cohort, and scanner difference. A multi-type parallel feature embedding framework was trained to classify three FTD subtypes with a weighted cross-entropy loss function used to account for unbalanced sample sizes. Feature visualization was achieved through post-hoc analysis using an integrated gradient approach. Results The proposed differential diagnosis framework achieved a mean balanced accuracy of 0.80 for bvFTD, 0.82 for nfvPPA, 0.89 for svPPA, and an overall balanced accuracy of 0.84. Feature importance maps showed more localized differential patterns among different FTD subtypes compared to groupwise statistical mapping. Conclusion In this study, we demonstrated the efficiency and effectiveness of using explainable deep-learning-based parallel feature embedding and visualization framework on MRI-derived multi-type structural patterns to differentiate three clinically defined subphenotypes of FTD: bvFTD, nfvPPA, and svPPA, which could help with the identification of at-risk populations for early and precise diagnosis for intervention planning.
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Affiliation(s)
- Da Ma
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Jane Stocks
- Department of Psychiatry and Behavioral Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Howard Rosen
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Samuel N. Lockhart
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - James R. Bateman
- Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Suzanne Craft
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Metin N. Gurcan
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Karteek Popuri
- Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Mirza Faisal Beg
- School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada
| | - Lei Wang
- Department of Psychiatry and Behavioral Health, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Psychiatry and Behavioral Health, Ohio State University Wexner Medical Center, Columbus, OH, United States
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Moguilner S, Whelan R, Adams H, Valcour V, Tagliazucchi E, Ibáñez A. Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples. EBioMedicine 2023; 90:104540. [PMID: 36972630 PMCID: PMC10066533 DOI: 10.1016/j.ebiom.2023.104540] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/02/2023] [Accepted: 03/10/2023] [Indexed: 03/28/2023] Open
Abstract
BACKGROUND Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease are difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, and non-harmonised pipelines. METHODS We implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied on raw (unpreprocessed) data from 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, and healthy controls; both male and female as self-reported by participants). We tested our results in demographically matched and unmatched samples to discard possible biases and performed multiple out-of-sample validations. FINDINGS Robust classification results across all groups were achieved from standardised 3T neuroimaging data from the Global North, which also generalised to standardised 3T neuroimaging data from Latin America. Moreover, DenseNet also generalised to non-standardised, routine 1.5T clinical images from Latin America. These generalisations were robust in samples with heterogenous MRI recordings and were not confounded by demographics (i.e., were robust in both matched and unmatched samples, and when incorporating demographic variables in a multifeatured model). Model interpretability analysis using occlusion sensitivity evidenced core pathophysiological regions for each disease (mainly the hippocampus in AD, and the insula in bvFTD) demonstrating biological specificity and plausibility. INTERPRETATION The generalisable approach outlined here could be used in the future to aid clinician decision-making in diverse samples. FUNDING The specific funding of this article is provided in the acknowledgements section.
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Affiliation(s)
- Sebastian Moguilner
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Robert Whelan
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Trinity College Institute of Neuroscience (TCIN), Trinity College Dublin, Dublin, Ireland
| | - Hieab Adams
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Victor Valcour
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland
| | - Enzo Tagliazucchi
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Department of Physics, University of Buenos Aires, Caba, Argentina
| | - Agustín Ibáñez
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), San Francisco, CA, USA; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Trinity College Institute of Neuroscience (TCIN), Trinity College Dublin, Dublin, Ireland.
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Sanz Perl Y, Fittipaldi S, Gonzalez Campo C, Moguilner S, Cruzat J, Fraile-Vazquez ME, Herzog R, Kringelbach ML, Deco G, Prado P, Ibanez A, Tagliazucchi E. Model-based whole-brain perturbational landscape of neurodegenerative diseases. eLife 2023; 12:e83970. [PMID: 36995213 PMCID: PMC10063230 DOI: 10.7554/elife.83970] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 03/15/2023] [Indexed: 03/31/2023] Open
Abstract
The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.
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Affiliation(s)
- Yonatan Sanz Perl
- Department of Physics, University of Buenos AiresBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
| | - Sol Fittipaldi
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
| | - Cecilia Gonzalez Campo
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
| | - Sebastián Moguilner
- Global Brain Health Institute, University of California, San FranciscoSan FranciscoUnited States
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
| | - Josephine Cruzat
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
| | | | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
| | - Morten L Kringelbach
- Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus UniversityÅrhusDenmark
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of MinhoBragaPortugal
- Centre for Eudaimonia and Human Flourishing, University of OxfordOxfordUnited Kingdom
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
- Department of Information and Communication Technologies, Universitat Pompeu FabraBarcelonaSpain
- Institució Catalana de la Recerca i Estudis Avancats (ICREA)BarcelonaSpain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- School of Psychological Sciences, Monash UniversityClaytonAustralia
| | - Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San SebastiánSantiagoChile
| | - Agustin Ibanez
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
- Global Brain Health Institute, University of California, San FranciscoSan FranciscoUnited States
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
- Trinity College Institute of Neuroscience (TCIN), Trinity College DublinDublinIreland
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos AiresBuenos AiresArgentina
- National Scientific and Technical Research Council (CONICET), CABABuenos AiresArgentina
- Cognitive Neuroscience Center (CNC), Universidad de San AndrésBuenos AiresArgentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo IbáñezSantiagoChile
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