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Gómez S, Rangel E, Mantilla D, Ortiz A, Camacho P, de la Rosa E, Seia J, Kirschke JS, Li Y, El Habib Daho M, Martínez F. APIS: a paired CT-MRI dataset for ischemic stroke segmentation - methods and challenges. Sci Rep 2024; 14:20543. [PMID: 39232010 PMCID: PMC11374904 DOI: 10.1038/s41598-024-71273-x] [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: 02/21/2024] [Accepted: 08/26/2024] [Indexed: 09/06/2024] Open
Abstract
Stroke, the second leading cause of mortality globally, predominantly results from ischemic conditions. Immediate attention and diagnosis, related to the characterization of brain lesions, play a crucial role in patient prognosis. Standard stroke protocols include an initial evaluation from a non-contrast CT to discriminate between hemorrhage and ischemia. However, non-contrast CTs lack sensitivity in detecting subtle ischemic changes in this phase. Alternatively, diffusion-weighted MRI studies provide enhanced capabilities, yet are constrained by limited availability and higher costs. Hence, we idealize new approaches that integrate ADC stroke lesion findings into CT, to enhance the analysis and accelerate stroke patient management. This study details a public challenge where scientists applied top computational strategies to delineate stroke lesions on CT scans, utilizing paired ADC information. Also, it constitutes the first effort to build a paired dataset with NCCT and ADC studies of acute ischemic stroke patients. Submitted algorithms were validated with respect to the references of two expert radiologists. The best achieved Dice score was 0.2 over a test study with 36 patient studies. Despite all the teams employing specialized deep learning tools, results reveal limitations of computational approaches to support the segmentation of small lesions with heterogeneous density.
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Affiliation(s)
- Santiago Gómez
- Biomedical Imaging, Vision, and Learning Laboratory (BIVL2ab), Universidad Industrial de Santander, Bucaramanga, Colombia
| | - Edgar Rangel
- Biomedical Imaging, Vision, and Learning Laboratory (BIVL2ab), Universidad Industrial de Santander, Bucaramanga, Colombia
| | | | | | | | - Ezequiel de la Rosa
- icometrix, Leuven, Belgium
- Department of Informatics, Technical University Munich, Munich, Germany
| | | | - Jan S Kirschke
- Department of Informatics, Technical University Munich, Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, University of Munich, Munich, Germany
| | - Yihao Li
- LaTIM UMR 1101, Inserm, Brest, France
- University of Western Brittany, Brest, France
| | | | - Fabio Martínez
- Biomedical Imaging, Vision, and Learning Laboratory (BIVL2ab), Universidad Industrial de Santander, Bucaramanga, Colombia.
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2
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Kang K, Zhang S, Xiao Y, Yu H, Zhang H. The effects of endogenous semantic variables during productive lexical retrieval: A Behavioral-Neural Dual Swinging Model (DSM). Neuroimage 2024; 298:120809. [PMID: 39187220 DOI: 10.1016/j.neuroimage.2024.120809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/30/2024] [Accepted: 08/22/2024] [Indexed: 08/28/2024] Open
Abstract
Conceptual preparation is the very initial step in language production. Endogenous semantic variables, reflecting the inherent semantic properties of concepts, could influence the productive lexical retrieval by modulating both conceptual activation and lexical selection. Yet, empirical understandings on this process and underlying mechanisms remain limited. Here, inspired by previous theoretical models and preliminary findings, we proposed a Behavioral-Neural Dual Swinging Model (DSM), revealing the swinging process between conceptual facilitation and lexical interference and extending to neural resource allocation during these processes. To further test the model, we examined the joint effect of semantic richness and semantic density on productive word retrieval both behaviorally and neurally, using a picture naming paradigm. Results nicely support the DSM by showing that the productive retrieval is driven by the swinging between semantic richness-induced conceptual facilitation primarily managed in semantic-related regions and semantic density-induced lexical interference managed in control-related regions. Moreover, the conceptual facilitation accumulated from semantic richness plays a decisive role, mitigating interference from competitors as well as the neural demands in control-related regions.
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Affiliation(s)
- Keyi Kang
- Centre for Cognitive and Brain Sciences, Taipa, Macau SAR, China; Department of Psychology, University of Macau, Taipa, Macau SAR, China
| | - Sifan Zhang
- Centre for Cognitive and Brain Sciences, Taipa, Macau SAR, China; Department of Psychology, University of Macau, Taipa, Macau SAR, China
| | - Yumeng Xiao
- Centre for Cognitive and Brain Sciences, Taipa, Macau SAR, China
| | - Hanxiang Yu
- Centre for Cognitive and Brain Sciences, Taipa, Macau SAR, China
| | - Haoyun Zhang
- Centre for Cognitive and Brain Sciences, Taipa, Macau SAR, China; Department of Psychology, University of Macau, Taipa, Macau SAR, China.
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3
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Sin ELL, Wong CHY, Chau BKH, Rauterberg M, Siu KWM, Shih YT. Understanding the Changes in Brain Activation When Viewing Products with Differences in Attractiveness. Neurol Int 2024; 16:918-932. [PMID: 39311342 PMCID: PMC11417844 DOI: 10.3390/neurolint16050069] [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: 07/03/2024] [Revised: 08/14/2024] [Accepted: 08/21/2024] [Indexed: 09/26/2024] Open
Abstract
Product design and attractiveness are pivotal factors that determine people's positive reactions when viewing a product and may eventually affect their purchasing choices. Comprehending how people assess product design is crucial. Various studies have explored the link between product attractiveness and consumer behavior, but these were predominantly behavioral studies that offered limited insight into the neural processes underlying perceptions of product attractiveness. Gaining a deeper understanding of these neural mechanisms is valuable, as it enables the formulation of more objective design guidelines based on brain activity, enhancing product appeal and, ultimately, spurring consumer purchases. In our study, we sought to (1) elucidate the neural network engaged when individuals evaluate highly attractive product images, (2) delineate the neural network activated during the evaluation of less attractive product images, and (3) contrast the differences in neural networks between evaluations of highly and less attractive images. We utilized fMRI to investigate the neural activation patterns elicited by viewing product images of varying attractiveness levels. The results indicated distinct neural activations in response to the two types of attractive images. Highly attractive product images elicited activity in the anterior cingulate cortex (ACC) and the occipital pole, whereas less attractive product images stimulated the insula and the inferior frontal gyrus (IFG). The findings of this project provide some of the first insights of its kind and valuable insights for future product design, suggesting that incorporating more positive and rewarding elements could enhance product appeal. This research elucidates the neural correlates of people's responses to product attractiveness, revealing that highly attractive designs activate reward-related brain regions, while less attractive designs engage areas associated with emotional processing. These findings offer a neuroscientific basis for further studies on developing design strategies that align with consumers' innate preferences, potentially transforming product design and marketing practices. By leveraging this knowledge, designers can craft products that not only meet functional needs but also resonate more deeply on an esthetic level, thereby enhancing consumer engagement and purchase likelihood.
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Affiliation(s)
- Emily L. L. Sin
- School of Design, The Hong Kong Polytechnic University, Hong Kong; (E.L.L.S.); (K.W.M.S.)
| | - Clive H. Y. Wong
- Department of Psychology, The Education University of Hong Kong, Hong Kong;
| | - Bolton K. H. Chau
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong;
- Mental Health Research Centre, The Hong Kong Polytechnic University, Hong Kong
- University Research Facility in Behavioral and Systems Neuroscience, The Hong Kong Polytechnic University, Hong Kong
| | - Matthias Rauterberg
- Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands;
| | - Kin Wai Michael Siu
- School of Design, The Hong Kong Polytechnic University, Hong Kong; (E.L.L.S.); (K.W.M.S.)
| | - Yi-Teng Shih
- School of Design, The Hong Kong Polytechnic University, Hong Kong; (E.L.L.S.); (K.W.M.S.)
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4
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Rasche SE, Beyh A, Paolini M, Zeki S. Neural correlates of the experience of ugliness. Eur J Neurosci 2024. [PMID: 39189356 DOI: 10.1111/ejn.16517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 08/06/2024] [Accepted: 08/09/2024] [Indexed: 08/28/2024]
Abstract
Previous studies have shown that the experience of beauty is dependent upon the co-activity of field A1 of the medial frontal cortex and sensory areas. This leaves us with the question of ugliness; are the same neural mechanisms involved in this experience, including neural activity patterns, or are different mechanisms at play? This question arises because ugliness, although often regarded as the opposite of beauty, could possibly be a distinct aesthetic category. Subjects were asked to rate faces according to how ugly they found them to be while their brain activity was measured with functional magnetic resonance imaging. There was moderate agreement in the experience of ugliness of faces among subjects. Univariate parametric analyses did not reveal any brain regions with increasing activity as the declared intensity of the experience of ugliness increased. In contrast, increasing activity appeared in the striatum and posterior medial orbitofrontal cortex with decreasing levels of ugliness. As with studies on facial beauty, representational similarity analysis revealed distinct neural activity patterns with the experience of facial ugliness in sensory areas relevant for face processing and in the medial orbitofrontal cortex. Thus, similar neural mechanisms appear to be involved in the experience of facial beauty and ugliness, the difference being the level and distribution of activity within the neural network. This suggests that ugliness and beauty are on the same aesthetic continuum.
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Affiliation(s)
- Samuel E Rasche
- Laboratory of Neurobiology, University College London, London, UK
| | - Ahmad Beyh
- Laboratory of Neurobiology, University College London, London, UK
| | - Marco Paolini
- Department of Radiology, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
| | - Semir Zeki
- Laboratory of Neurobiology, University College London, London, UK
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5
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Beyh A, Howells H, Giampiccolo D, Cancemi D, De Santiago Requejo F, Citro S, Keeble H, Lavrador JP, Bhangoo R, Ashkan K, Dell'Acqua F, Catani M, Vergani F. Connectivity defines the distinctive anatomy and function of the hand-knob area. Brain Commun 2024; 6:fcae261. [PMID: 39239149 PMCID: PMC11375856 DOI: 10.1093/braincomms/fcae261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 05/19/2024] [Accepted: 08/10/2024] [Indexed: 09/07/2024] Open
Abstract
Control of the hand muscles during fine digit movements requires a high level of sensorimotor integration, which relies on a complex network of cortical and subcortical hubs. The components of this network have been extensively studied in human and non-human primates, but discrepancies in the findings obtained from different mapping approaches are difficult to interpret. In this study, we defined the cortical and connectional components of the hand motor network in the same cohort of 20 healthy adults and 3 neurosurgical patients. We used multimodal structural magnetic resonance imaging (including T1-weighted imaging and diffusion tractography), as well as functional magnetic resonance imaging and navigated transcranial magnetic stimulation (nTMS). The motor map obtained from nTMS compared favourably with the one obtained from functional magnetic resonance imaging, both of which overlapped well within the 'hand-knob' region of the precentral gyrus and in an adjacent region of the postcentral gyrus. nTMS stimulation of the precentral and postcentral gyri led to motor-evoked potentials in the hand muscles in all participants, with more responses recorded from precentral stimulations. We also observed that precentral stimulations tended to produce motor-evoked potentials with shorter latencies and higher amplitudes than postcentral stimulations. Tractography showed that the region of maximum overlap between terminations of precentral-postcentral U-shaped association fibres and somatosensory projection tracts colocalizes with the functional motor maps. The relationships between the functional maps, and between them and the tract terminations, were replicated in the patient cohort. Three main conclusions can be drawn from our study. First, the hand-knob region is a reliable anatomical landmark for the functional localization of fine digit movements. Second, its distinctive shape is determined by the convergence of highly myelinated long projection fibres and short U-fibres. Third, the unique role of the hand-knob area is explained by its direct action on the spinal motoneurons and the access to high-order somatosensory information for the online control of fine movements. This network is more developed in the hand region compared to other body parts of the homunculus motor strip, and it may represent an important target for enhancing motor learning during early development.
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Affiliation(s)
- Ahmad Beyh
- NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ 08854, USA
| | - Henrietta Howells
- NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Davide Giampiccolo
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Department of Neurosurgery, Institute of Neurosciences, Cleveland Clinic London, London SW1X 7HY, UK
| | - Daniele Cancemi
- NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | | | | | - Hannah Keeble
- NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | | | - Ranjeev Bhangoo
- Neurosurgical Department, King's College Hospital, London SE5 9RS, UK
| | - Keyoumars Ashkan
- Neurosurgical Department, King's College Hospital, London SE5 9RS, UK
| | - Flavio Dell'Acqua
- NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | | | - Francesco Vergani
- Neurosurgical Department, King's College Hospital, London SE5 9RS, UK
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Li JS, Tun SM, Ficek-Tani B, Xu W, Wang S, Horien CL, Toyonaga T, Nuli SS, Zeiss CJ, Powers AR, Zhao Y, Mormino EC, Fredericks CA. Medial amygdalar tau is associated with mood symptoms in preclinical Alzheimer's disease. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00200-3. [PMID: 39059466 DOI: 10.1016/j.bpsc.2024.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/01/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND While the amygdala receives early tau deposition in Alzheimer's disease (AD) and is involved in social and emotional processing, the relationship between amygdalar tau and early neuropsychiatric symptoms in AD is unknown. We sought to determine whether focal tau binding in the amygdala and abnormal amygdalar connectivity were detectable in a preclinical AD cohort and identify relationships between these and self-reported mood symptoms. METHODS We examined n=598 individuals (n=347 amyloid-positive (58% female), n=251 amyloid-negative (62% female); subset into tau PET and fMRI cohorts) from the A4 Study. In the tau PET cohort, we used amygdalar segmentations to examine representative nuclei from three functional divisions of the amygdala. We analyzed between-group differences in division-specific tau binding in the amygdala in preclinical AD. We conducted seed-based functional connectivity analyses from each division in the fMRI cohort. Finally, we conducted exploratory post-hoc correlation analyses between neuroimaging biomarkers of interest and anxiety and depression scores. RESULTS Amyloid-positive individuals demonstrated increased tau binding in medial and lateral amygdala, and tau binding in these regions was associated with mood symptoms. Across amygdalar divisions, amyloid-positive individuals had relatively higher regional connectivity from amygdala to other temporal regions, insula, and orbitofrontal cortex, but medial amygdala to retrosplenial cortex was lower. Medial amygdala to retrosplenial connectivity was negatively associated with anxiety symptoms, as was retrosplenial tau. CONCLUSIONS Our findings suggest that preclinical tau deposition in the amygdala and associated changes in functional connectivity may relate to early mood symptoms in AD.
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Affiliation(s)
- Joyce S Li
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Samantha M Tun
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | | | - Wanwan Xu
- Department of Biostatistics, Yale School of Medicine, New Haven, CT
| | - Selena Wang
- Department of Biostatistics, Yale School of Medicine, New Haven, CT
| | | | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | | | - Caroline J Zeiss
- Department of Comparative Medicine, Yale School of Medicine, New Haven, CT
| | - Albert R Powers
- Department of Psychiatry, Yale School of Medicine, New Haven, CT
| | - Yize Zhao
- Department of Biostatistics, Yale School of Medicine, New Haven, CT
| | - Elizabeth C Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
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7
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Manelis A, Lima Santos JP, Suss SJ, Holland CL, Perry CA, Hickey RW, Collins MW, Kontos AP, Versace A. Working Memory Recovery in Adolescents with Concussion: Longitudinal fMRI Study. J Clin Med 2024; 13:3585. [PMID: 38930114 PMCID: PMC11204632 DOI: 10.3390/jcm13123585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/11/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024] Open
Abstract
Background: Understanding the behavioral and neural underpinnings of the post-concussion recovery of working memory function is critically important for improving clinical outcomes and adequately planning return-to-activity decisions. Previous studies provided inconsistent results due to small sample sizes and the use of a mixed population of participants who were at different post-injury time points. We aimed to examine working memory recovery during the first 6 months post-concussion in adolescents. Methods: We used functional magnetic resonance imaging (fMRI) to scan 45 concussed adolescents [CONCs] at baseline (<10 days post-concussion) and at 6 months post-concussion. Healthy control adolescents [HCs; n = 32] without a history of concussion were scanned once. During the scans, participants performed one-back and two-back working memory tasks with letters as the stimuli and angry, happy, neutral, and sad faces as distractors. Results: All affected adolescents were asymptomatic and cleared to return to activity 6 months after concussion. Working memory recovery was associated with faster and more accurate responses at 6 months vs. baseline (p-values < 0.05). It was also characterized by significant difficulty-related activation increases in the left inferior frontal gyrus (LIFG) and the left orbitofrontal cortex (LOFC) at 6 months vs. baseline. Although the activation differences between one-back and two-back were comparable between HCs and CONCs at 6 months, HCs had more pronounced activation in the LIFG than concussed adolescents. Conclusions: Post-concussion recovery is associated with significant performance improvements in speed and accuracy, as well as the normalization of brain responses in the LIFG and LOFC during the n-back task. The observed patterns of LOFC activation might reflect compensatory strategies to distribute neural processing and reduce neural fatigue post-concussion.
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Affiliation(s)
- Anna Manelis
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA; (J.P.L.S.); (S.J.S.)
| | - João P. Lima Santos
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA; (J.P.L.S.); (S.J.S.)
| | - Stephen J. Suss
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA; (J.P.L.S.); (S.J.S.)
| | - Cynthia L. Holland
- Department of Orthopaedic Surgery/UPMC Sports Medicine Concussion Program, University of Pittsburgh, Pittsburgh, PA 15213, USA; (C.L.H.); (C.A.P.); (M.W.C.); (A.P.K.)
| | - Courtney A. Perry
- Department of Orthopaedic Surgery/UPMC Sports Medicine Concussion Program, University of Pittsburgh, Pittsburgh, PA 15213, USA; (C.L.H.); (C.A.P.); (M.W.C.); (A.P.K.)
| | - Robert W. Hickey
- Department of Pediatric Emergency Medicine, Children’s Hospital of Pittsburgh, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA 15224, USA;
| | - Michael W. Collins
- Department of Orthopaedic Surgery/UPMC Sports Medicine Concussion Program, University of Pittsburgh, Pittsburgh, PA 15213, USA; (C.L.H.); (C.A.P.); (M.W.C.); (A.P.K.)
| | - Anthony P. Kontos
- Department of Orthopaedic Surgery/UPMC Sports Medicine Concussion Program, University of Pittsburgh, Pittsburgh, PA 15213, USA; (C.L.H.); (C.A.P.); (M.W.C.); (A.P.K.)
| | - Amelia Versace
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA; (J.P.L.S.); (S.J.S.)
- Department of Radiology, Magnetic Resonance Research Center, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, PA 15213, USA
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8
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Li JS, Tun SM, Ficek-Tani B, Xu W, Wang S, Horien CL, Toyonaga T, Nuli SS, Zeiss CJ, Powers AR, Zhao Y, Mormino EC, Fredericks CA. Medial amygdalar tau is associated with anxiety symptoms in preclinical Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.03.597160. [PMID: 38895308 PMCID: PMC11185761 DOI: 10.1101/2024.06.03.597160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
BACKGROUND While the amygdala receives early tau deposition in Alzheimer's disease (AD) and is involved in social and emotional processing, the relationship between amygdalar tau and early neuropsychiatric symptoms in AD is unknown. We sought to determine whether focal tau binding in the amygdala and abnormal amygdalar connectivity were detectable in a preclinical AD cohort and identify relationships between these and self-reported mood symptoms. METHODS We examined n=598 individuals (n=347 amyloid-positive (58% female), n=251 amyloid-negative (62% female); subset into tau PET and fMRI cohorts) from the A4 Study. In our tau PET cohort, we used amygdalar segmentations to examine representative nuclei from three functional divisions of the amygdala. We analyzed between-group differences in division-specific tau binding in the amygdala in preclinical AD. We conducted seed-based functional connectivity analyses from each division in the fMRI cohort. Finally, we conducted exploratory post-hoc correlation analyses between neuroimaging biomarkers of interest and anxiety and depression scores. RESULTS Amyloid-positive individuals demonstrated increased tau binding in medial and lateral amygdala (F(4,442)=14.61, p=0.00045; F(4,442)=5.83, p=0.024, respectively). Across amygdalar divisions, amyloid-positive individuals had relatively increased regional connectivity from amygdala to other temporal regions, insula, and orbitofrontal cortex. There was an interaction by amyloid group between tau binding in the medial and lateral amygdala and anxiety. Medial amygdala to retrosplenial connectivity negatively correlated with anxiety symptoms (rs=-0.103, p=0.015). CONCLUSIONS Our findings suggest that preclinical tau deposition in the amygdala may result in meaningful changes in functional connectivity which may predispose patients to mood symptoms.
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Affiliation(s)
- Joyce S Li
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | - Samantha M Tun
- Department of Neurology, Yale School of Medicine, New Haven, CT
| | | | - Wanwan Xu
- Department of Biostatistics, Yale School of Medicine, New Haven, CT
| | - Selena Wang
- Department of Biostatistics, Yale School of Medicine, New Haven, CT
| | | | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | | | - Caroline J Zeiss
- Department of Comparative Medicine, Yale School of Medicine, New Haven, CT
| | - Albert R Powers
- Department of Psychiatry, Yale School of Medicine, New Haven, CT
| | - Yize Zhao
- Department of Biostatistics, Yale School of Medicine, New Haven, CT
| | - Elizabeth C Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
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Valverde S, Coll L, Valencia L, Clèrigues A, Oliver A, Vilanova JC, Ramió-Torrentà L, Rovira À, Lladó X. Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm. J Magn Reson Imaging 2024; 59:1991-2000. [PMID: 34137113 DOI: 10.1002/jmri.27776] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 05/31/2021] [Accepted: 06/01/2021] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Manual brain extraction from magnetic resonance (MR) images is time-consuming and prone to intra- and inter-rater variability. Several automated approaches have been developed to alleviate these constraints, including deep learning pipelines. However, these methods tend to reduce their performance in unseen magnetic resonance imaging (MRI) scanner vendors and different imaging protocols. PURPOSE To present and evaluate for clinical use PARIETAL, a pre-trained deep learning brain extraction method. We compare its reproducibility in a scan/rescan analysis and its robustness among scanners of different manufacturers. STUDY TYPE Retrospective. POPULATION Twenty-one subjects (12 women) with age range 22-48 years acquired using three different MRI scanner machines including scan/rescan in each of them. FIELD STRENGTH/SEQUENCE T1-weighted images acquired in a 3-T Siemens with magnetization prepared rapid gradient-echo sequence and two 1.5 T scanners, Philips and GE, with spin-echo and spoiled gradient-recalled (SPGR) sequences, respectively. ASSESSMENT Analysis of the intracranial cavity volumes obtained for each subject on the three different scanners and the scan/rescan acquisitions. STATISTICAL TESTS Parametric permutation tests of the differences in volumes to rank and statistically evaluate the performance of PARIETAL compared to state-of-the-art methods. RESULTS The mean absolute intracranial volume differences obtained by PARIETAL in the scan/rescan analysis were 1.88 mL, 3.91 mL, and 4.71 mL for Siemens, GE, and Philips scanners, respectively. PARIETAL was the best-ranked method on Siemens and GE scanners, while decreasing to Rank 2 on the Philips images. Intracranial differences for the same subject between scanners were 5.46 mL, 27.16 mL, and 30.44 mL for GE/Philips, Siemens/Philips, and Siemens/GE comparison, respectively. The permutation tests revealed that PARIETAL was always in Rank 1, obtaining the most similar volumetric results between scanners. DATA CONCLUSION PARIETAL accurately segments the brain and it generalizes to images acquired at different sites without the need of training or fine-tuning it again. PARIETAL is publicly available. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Sergi Valverde
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Llucia Coll
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Liliana Valencia
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Albert Clèrigues
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Arnau Oliver
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
- REEM, Red Española de Esclerosis Múltiple
| | | | - Lluís Ramió-Torrentà
- REEM, Red Española de Esclerosis Múltiple
- Multiple Sclerosis and Neuroimmunology Unit, Neurology Department, Dr. Josep Trueta University Hospital, Institut d'Investigació Biomèdica, Girona, Spain
- Medical Sciences Department, University of Girona, Girona, Spain
| | - Àlex Rovira
- Magnetic Resonance Unit, Department of Radiology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Xavier Lladó
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
- REEM, Red Española de Esclerosis Múltiple
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10
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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson SD, Schweser F, Shmueli K, Spincemaille P, Straub S, van Zijl P, Wang Y. Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group. Magn Reson Med 2024; 91:1834-1862. [PMID: 38247051 PMCID: PMC10950544 DOI: 10.1002/mrm.30006] [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: 07/11/2023] [Revised: 10/31/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.
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Affiliation(s)
- Berkin Bilgic
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy
| | - Kwok-Shing Chan
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Carlos Milovic
- School of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Simon Daniel Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Centre of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, New York, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, New York, USA
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pascal Spincemaille
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, Florida, USA
| | - Peter van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, New York, USA
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11
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Beyh A, Rasche SE, Leff A, Ffytche D, Zeki S. A clinico-anatomical dissection of the magnocellular and parvocellular pathways in a patient with the Riddoch syndrome. Brain Struct Funct 2024; 229:937-946. [PMID: 38492041 PMCID: PMC11004049 DOI: 10.1007/s00429-024-02774-8] [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: 11/29/2023] [Accepted: 02/06/2024] [Indexed: 03/18/2024]
Abstract
KEY MESSAGE The Riddoch syndrome is thought to be caused by damage to the primary visual cortex (V1), usually following a vascular event. This study shows that damage to the anatomical input to V1, i.e., the optic radiations, can result in selective visual deficits that mimic the Riddoch syndrome. The results also highlight the differential susceptibility of the magnocellular and parvocellular visual systems to injury. Overall, this study offers new insights that will improve our understanding of the impact of brain injury and neurosurgery on the visual pathways. The Riddoch syndrome, characterised by the ability to perceive, consciously, moving visual stimuli but not static ones, has been associated with lesions of primary visual cortex (V1). We present here the case of patient YL who, after a tumour resection surgery that spared his V1, nevertheless showed symptoms of the Riddoch syndrome. Based on our testing, we postulated that the magnocellular (M) and parvocellular (P) inputs to his V1 may be differentially affected. In a first experiment, YL was presented with static and moving checkerboards in his blind field while undergoing multimodal magnetic resonance imaging (MRI), including structural, functional, and diffusion, acquired at 3 T. In a second experiment, we assessed YL's neural responses to M and P visual stimuli using psychophysics and high-resolution fMRI acquired at 7 T. YL's optic radiations were partially damaged but not severed. We found extensive activity in his visual cortex for moving, but not static, visual stimuli, while our psychophysical tests revealed that only low-spatial frequency moving checkerboards were perceived. High-resolution fMRI revealed strong responses in YL's V1 to M stimuli and very weak ones to P stimuli, indicating a functional P lesion affecting V1. In addition, YL frequently reported seeing moving stimuli and discriminating their direction of motion in the absence of visual stimulation, suggesting that he was experiencing visual hallucinations. Overall, this study highlights the possibility of a selective loss of P inputs to V1 resulting in the Riddoch syndrome and in hallucinations of visual motion.
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Affiliation(s)
- Ahmad Beyh
- Laboratory of Neurobiology, University College London, London, UK
| | - Samuel E Rasche
- Laboratory of Neurobiology, University College London, London, UK
| | - Alexander Leff
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Dominic Ffytche
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Semir Zeki
- Laboratory of Neurobiology, University College London, London, UK.
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12
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Rachmadi MF, Byra M, Skibbe H. A new family of instance-level loss functions for improving instance-level segmentation and detection of white matter hyperintensities in routine clinical brain MRI. Comput Biol Med 2024; 174:108414. [PMID: 38599072 DOI: 10.1016/j.compbiomed.2024.108414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/16/2024] [Accepted: 04/03/2024] [Indexed: 04/12/2024]
Abstract
In this study, we introduce "instance loss functions", a new family of loss functions designed to enhance the training of neural networks in the instance-level segmentation and detection of objects in biomedical image data, particularly those of varied numbers and sizes. Intended to be utilized conjointly with traditional loss functions, these proposed functions, prioritize object instances over pixel-by-pixel comparisons. The specific functions, the instance segmentation loss (Linstance), the instance center loss (Lcenter), the false instance rate loss (Lfalse), and the instance proximity loss (Lproximity), serve distinct purposes. Specifically, Linstance improves instance-wise segmentation quality, Lcenter enhances segmentation quality of small instances, Lfalse minimizes the rate of false and missed detections across varied instance sizes, and Lproximity improves detection quality by pulling predicted instances towards the ground truth instances. Through the task of segmenting white matter hyperintensities (WMH) in brain MRI, we benchmarked our proposed instance loss functions, both individually and in combination via an ensemble inference models approach, against traditional pixel-level loss functions. Data were sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the WMH Segmentation Challenge datasets, which exhibit significant variation in WMH instance sizes. Empirical evaluations demonstrate that combining two instance-level loss functions through ensemble inference models outperforms models using other loss function on both the ADNI and WMH Segmentation Challenge datasets for the segmentation and detection of WMH instances. Further, applying these functions to the segmentation of nuclei in histopathology images demonstrated their effectiveness and generalizability beyond WMH, improving performance even in contexts with less severe instance imbalance.
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Affiliation(s)
- Muhammad Febrian Rachmadi
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako-shi, Japan; Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia.
| | - Michal Byra
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako-shi, Japan; Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Henrik Skibbe
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako-shi, Japan
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13
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Rodriguez RX, Noble S, Camp CC, Scheinost D. Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.08.588578. [PMID: 38645002 PMCID: PMC11030410 DOI: 10.1101/2024.04.08.588578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
High-amplitude co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity 1-5 . Further, they resemble task activation patterns and are well-studied 3,5-10 . However, little research has characterized the remaining majority of the resting-state signal. In this work, we introduced caricaturing-a method to project resting-state data to a subspace orthogonal to a manifold of co-activation patterns estimated from the task fMRI data. Projecting to this subspace removes linear combinations of these co-activation patterns from the resting-state data to create Caricatured connectomes. We used rich task data from the Human Connectome Project (HCP) 11 and the UCLA Consortium for Neuropsychiatric Phenomics 12 to construct a manifold of task co-activation patterns. Caricatured connectomes were created by projecting resting-state data from the HCP and the Yale Test-Retest 13 datasets away from this manifold. Like caricatures, these connectomes emphasized individual differences by reducing between-individual similarity and increasing individual identification 14 . They also improved predictive modeling of brain-phenotype associations. As caricaturing removes group-relevant task variance, it is an initial attempt to remove task-like co-activations from rest. Therefore, our results suggest that there is a useful signal beyond the dominating co-activations that drive resting-state functional connectivity, which may better characterize the brain's intrinsic functional architecture.
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14
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Abuwarda H, Trainer A, Horien C, Shen X, Ju S, Constable RT, Fredericks C. Whole-brain functional connectivity predicts groupwise and sex-specific tau PET in preclincal Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.02.587791. [PMID: 38617320 PMCID: PMC11014551 DOI: 10.1101/2024.04.02.587791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Preclinical Alzheimer's disease, characterized by the initial accumulation of amyloid and tau pathologies without symptoms, presents a critical opportunity for early intervention. Yet, the interplay between these pathological markers and the functional connectome during this window remains understudied. We therefore set out to elucidate the relationship between the functional connectome and amyloid and tau, as assessed by PET imaging, in individuals with preclinical AD using connectome-based predictive modeling (CPM). We found that functional connectivity predicts tau PET, outperforming amyloid PET models. These models were predominantly governed by linear relationships between functional connectivity and tau. Tau models demonstrated a stronger correlation to global connectivity than underlying tau PET. Furthermore, we identify sex-based differences in the ability to predict regional tau, without any underlying differences in tau PET or global connectivity. Taken together, these results suggest tau is more closely coupled to functional connectivity than amyloid in preclinical disease, and that multimodal predictive modeling approaches stand to identify unique relationships that any one modality may be insufficient to discern.
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15
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Evans JW, Graves MC, Nugent AC, Zarate CA. Hippocampal volume changes after (R,S)-ketamine administration in patients with major depressive disorder and healthy volunteers. Sci Rep 2024; 14:4538. [PMID: 38402253 PMCID: PMC10894199 DOI: 10.1038/s41598-024-54370-9] [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: 07/11/2023] [Accepted: 02/12/2024] [Indexed: 02/26/2024] Open
Abstract
The hippocampus and amygdala have been implicated in the pathophysiology and treatment of major depressive disorder (MDD). Preclinical models suggest that stress-related changes in these regions can be reversed by antidepressants, including ketamine. Clinical studies have identified reduced volumes in MDD that are thought to be potentiated by early life stress and worsened by repeated depressive episodes. This study used 3T and 7T structural magnetic resonance imaging data to examine longitudinal changes in hippocampal and amygdalar subfield volumes associated with ketamine treatment. Data were drawn from a previous double-blind, placebo-controlled, crossover trial of healthy volunteers (HVs) unmedicated individuals with treatment-resistant depression (TRD) (3T: 18 HV, 26 TRD, 7T: 17 HV, 30 TRD) who were scanned at baseline and twice following either a 40 min IV ketamine (0.5 mg/kg) or saline infusion (acute: 1-2 days, interim: 9-10 days post infusion). No baseline differences were noted between the two groups. At 10 days post-infusion, a slight increase was observed between ketamine and placebo scans in whole left amygdalar volume in individuals with TRD. No other differences were found between individuals with TRD and HVs at either field strength. These findings shed light on the timing of ketamine's effects on cortical structures.
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Affiliation(s)
- Jennifer W Evans
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, 10 Center Dr., Bldg 10, Rm 7-3335, Bethesda, MD, 20814, USA.
| | - Morgan C Graves
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, 10 Center Dr., Bldg 10, Rm 7-3335, Bethesda, MD, 20814, USA
| | - Allison C Nugent
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, 10 Center Dr., Bldg 10, Rm 7-3335, Bethesda, MD, 20814, USA
- MEG Core, NIMH, Bethesda, MD, USA
| | - Carlos A Zarate
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, 10 Center Dr., Bldg 10, Rm 7-3335, Bethesda, MD, 20814, USA
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16
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Naval-Baudin P, Pons-Escoda A, Camins À, Arroyo P, Viveros M, Castell J, Cos M, Martínez-Yélamos A, Martínez-Yélamos S, Majós C. Deeply 3D-T1-TFE hypointense voxels are characteristic of phase-rim lesions in multiple sclerosis. Eur Radiol 2024; 34:1337-1345. [PMID: 37278854 PMCID: PMC10853299 DOI: 10.1007/s00330-023-09784-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 04/29/2023] [Accepted: 05/16/2023] [Indexed: 06/07/2023]
Abstract
OBJECTIVES The development of new drugs for the treatment of progressive multiple sclerosis (MS) highlights the need for new prognostic biomarkers. Phase-rim lesions (PRLs) have been proposed as markers of progressive disease but are difficult to identify and quantify. Previous studies have identified T1-hypointensity in PRLs. The aim of this study was to compare the intensity profiles of PRLs and non-PRL white-matter lesions (nPR-WMLs) on three-dimensional T1-weighted turbo field echo (3DT1TFE) MRI. We then evaluated the performance of a derived metric as a surrogate for PRLs as potential markers for risk of disease progression. METHODS This study enrolled a cohort of relapsing-remitting (n = 10) and secondary progressive MS (n = 10) patients for whom 3 T MRI was available. PRLs and nPR-WMLs were segmented, and voxel-wise normalized T1-intensity histograms were analyzed. The lesions were divided equally into training and test datasets, and the fifth-percentile (p5)-normalized T1-intensity of each lesion was compared between groups and used for classification prediction. RESULTS Voxel-wise histogram analysis showed a unimodal histogram for nPR-WMLs and a bimodal histogram for PRLs with a large peak in the hypointense limit. Lesion-wise analysis included 1075 nPR-WMLs and 39 PRLs. The p5 intensity of PRLs was significantly lower than that of nPR-WMLs. The T1 intensity-based PRL classifier had a sensitivity of 0.526 and specificity of 0.959. CONCLUSIONS Profound hypointensity on 3DT1TFE MRI is characteristic of PRLs and rare in other white-matter lesions. Given the widespread availability of T1-weighted imaging, this feature might serve as a surrogate biomarker for smoldering inflammation. CLINICAL RELEVANCE STATEMENT Quantitative analysis of 3DT1TFE may detect deeply hypointense voxels in multiple sclerosis lesions, which are highly specific to PRLs. This could serve as a specific indicator of smoldering inflammation in MS, aiding in early detection of disease progression. KEY POINTS • Phase-rim lesions (PRLs) in multiple sclerosis present a characteristic T1-hypointensity on 3DT1TFE MRI. • Intensity-normalized 3DT1TFE can be used to systematically identify and quantify these deeply hypointense foci. • Deep T1-hypointensity may act as an easily detectable, surrogate marker for PRLs.
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Affiliation(s)
- Pablo Naval-Baudin
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain.
- Institut de Diagnòstic Per La Imatge (IDI), L'Hospitalet de Llobregat, Centre Bellvige, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain.
- Bellvitge Biomedical Research Institute (IDIBELL), Universitat de Barcelona (UB), L'Hospitalet de Llobregat, 08907, Barcelona, Spain.
- Departament de Ciències Clíniques, Facultat de Medicina I Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain.
| | - Albert Pons-Escoda
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Institut de Diagnòstic Per La Imatge (IDI), L'Hospitalet de Llobregat, Centre Bellvige, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Bellvitge Biomedical Research Institute (IDIBELL), Universitat de Barcelona (UB), L'Hospitalet de Llobregat, 08907, Barcelona, Spain
- Departament de Ciències Clíniques, Facultat de Medicina I Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain
| | - Àngels Camins
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Institut de Diagnòstic Per La Imatge (IDI), L'Hospitalet de Llobregat, Centre Bellvige, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Bellvitge Biomedical Research Institute (IDIBELL), Universitat de Barcelona (UB), L'Hospitalet de Llobregat, 08907, Barcelona, Spain
| | - Pablo Arroyo
- Departament de Ciències Clíniques, Facultat de Medicina I Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain
| | - Mildred Viveros
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Institut de Diagnòstic Per La Imatge (IDI), L'Hospitalet de Llobregat, Centre Bellvige, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
| | - Josep Castell
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
| | - Mònica Cos
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Institut de Diagnòstic Per La Imatge (IDI), L'Hospitalet de Llobregat, Centre Bellvige, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
| | - Antonio Martínez-Yélamos
- Bellvitge Biomedical Research Institute (IDIBELL), Universitat de Barcelona (UB), L'Hospitalet de Llobregat, 08907, Barcelona, Spain
- Departament de Ciències Clíniques, Facultat de Medicina I Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain
- Multiple Sclerosis Unit, Department of Neurology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
| | - Sergio Martínez-Yélamos
- Bellvitge Biomedical Research Institute (IDIBELL), Universitat de Barcelona (UB), L'Hospitalet de Llobregat, 08907, Barcelona, Spain
- Departament de Ciències Clíniques, Facultat de Medicina I Ciències de La Salut, Universitat de Barcelona (UB), Carrer de Casanova 143, 08036, Barcelona, Spain
- Multiple Sclerosis Unit, Department of Neurology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
| | - Carles Majós
- Neuroradiology Section, Department of Radiology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Institut de Diagnòstic Per La Imatge (IDI), L'Hospitalet de Llobregat, Centre Bellvige, Carrer de Feixa Llarga SN, 08907, Barcelona, Spain
- Bellvitge Biomedical Research Institute (IDIBELL), Universitat de Barcelona (UB), L'Hospitalet de Llobregat, 08907, Barcelona, Spain
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17
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Falck RS, Hsu CL, Best JR, Boa Sorte Silva NC, Hall PA, Li LC, Liu-Ambrose T. Cross-sectional and longitudinal neural predictors of physical activity and sedentary behaviour from a 6-month randomized controlled trial. Sci Rep 2024; 14:919. [PMID: 38195673 PMCID: PMC10776740 DOI: 10.1038/s41598-023-48715-z] [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: 04/18/2023] [Accepted: 11/29/2023] [Indexed: 01/11/2024] Open
Abstract
A sedentary lifestyle offers immediate gratification, but at the expense of long-term health. It is thus critical to understand how the brain evaluates immediate rewards and long-term health effects in the context of deciding whether to engage in moderate-to-vigorous physical activity (MVPA) or sedentary behaviour (SB). In this secondary analysis of a 6-month randomized controlled trial to increase MVPA and reduce SB among community-dwelling adults, we explored how neural activity during an executive control task was associated with MVPA and SB levels. At baseline, a subset of participants (n = 26/61) underwent task-based functional magnetic resonance imaging (fMRI) to examine neural activity underlying executive control using the Now/Later task. MVPA and SB were measured objectively using the Sensewear Mini at baseline, and 2, 4, and 6 months follow-up. We then examined the associations of baseline neural activation underlying executive control with: (1) baseline MVPA or SB; and (2) changes in MVPA and SB over 6 months. Our results determined that there is a complex neurocognitive system associated with MVPA levels, while SB appears to lack any neurocognitive control. In other words, MVPA appears to require neurocognitive effort, while SB may be the default behavioural pattern in adults.
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Affiliation(s)
- Ryan Stanley Falck
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, BC, Canada
- Centre for Aging SMART at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Chun Liang Hsu
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
| | - John R Best
- Gerontology Research Centre, Simon Fraser University, Vancouver, BC, Canada
| | - Narlon Cassio Boa Sorte Silva
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, BC, Canada
- Centre for Aging SMART at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Peter A Hall
- School of Kinesiology, The University of Waterloo, Waterloo, ON, Canada
| | - Linda C Li
- Department of Physical Therapy, Faculty of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Teresa Liu-Ambrose
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, BC, Canada.
- Centre for Aging SMART at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, BC, Canada.
- Aging, Mobility, and Cognitive Neuroscience Lab, Department of Physical Therapy, Vancouver Coastal Health Research Institute, Faculty of Medicine, University of British Columbia, 212-177 Wesbrook Mall, Vancouver, BC, V6T 1Z3, Canada.
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18
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Rempe M, Mentzel F, Pomykala KL, Haubold J, Nensa F, Kroeninger K, Egger J, Kleesiek J. k-strip: A novel segmentation algorithm in k-space for the application of skull stripping. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107912. [PMID: 37981454 DOI: 10.1016/j.cmpb.2023.107912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/30/2023] [Accepted: 11/02/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND AND OBJECTIVE We present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich complex valued k-space. METHODS Using four datasets from different institutions with a total of around 200,000 MRI slices, we show that our network can perform skull-stripping on the raw data of MRIs while preserving the phase information which no other skull stripping algorithm is able to work with. For two of the datasets, skull stripping performed by HD-BET (Brain Extraction Tool) in the image domain is used as the ground truth, whereas the third and fourth dataset comes with per-hand annotated brain segmentations. RESULTS All four datasets were very similar to the ground truth (DICE scores of 92 %-99 % and Hausdorff distances of under 5.5 pixel). Results on slices above the eye-region reach DICE scores of up to 99 %, whereas the accuracy drops in regions around the eyes and below, with partially blurred output. The output of k-Strip often has smoothed edges at the demarcation to the skull. Binary masks are created with an appropriate threshold. CONCLUSION With this proof-of-concept study, we were able to show the feasibility of working in the k-space frequency domain, preserving phase information, with consistent results. Besides preserving valuable information for further diagnostics, this approach makes an immediate anonymization of patient data possible, already before being transformed into the image domain. Future research should be dedicated to discovering additional ways the k-space can be used for innovative image analysis and further workflows.
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Affiliation(s)
- Moritz Rempe
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany; Otto-Hahn-Straße 4a, Department of Physics of the Technical University Dortmund, Dortmund 44227, Germany
| | - Florian Mentzel
- Otto-Hahn-Straße 4a, Department of Physics of the Technical University Dortmund, Dortmund 44227, Germany
| | - Kelsey L Pomykala
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany
| | - Johannes Haubold
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany
| | - Felix Nensa
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany
| | - Kevin Kroeninger
- Otto-Hahn-Straße 4a, Department of Physics of the Technical University Dortmund, Dortmund 44227, Germany
| | - Jan Egger
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany; The Computer Algorithms for Medicine Laboratory, Graz, Austria; The Institute of Computer Graphics and Vision, Inffeldgasse 16, Graz University of Technology, Graz 8010, Austria; Cancer Research Center Cologne Essen (CCCE), Hufelandstraße 55, University Medicine Essen, Essen 45147, Germany
| | - Jens Kleesiek
- The Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, Essen 45131, Germany; Cancer Research Center Cologne Essen (CCCE), Hufelandstraße 55, University Medicine Essen, Essen 45147, Germany; Partner Site Essen, Hufelandstraße 55, German Cancer Consortium (DKTK), Essen 45147, Germany.
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Antons S, Yip SW, Lacadie CM, Dadashkarimi J, Scheinost D, Brand M, Potenza MN. Connectome-based prediction of craving in gambling disorder and cocaine use disorder. DIALOGUES IN CLINICAL NEUROSCIENCE 2023; 25:33-42. [PMID: 37190759 PMCID: PMC10190201 DOI: 10.1080/19585969.2023.2208586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 04/24/2023] [Indexed: 05/17/2023]
Abstract
INTRODUCTION Craving, involving intense and urgent desires to engage in specific behaviours, is a feature of addictions. Multiple studies implicate regions of salience/limbic networks and basal ganglia, fronto-parietal, medial frontal regions in craving in addictions. However, prior studies have not identified common neural networks that reliably predict craving across substance and behavioural addictions. METHODS Functional magnetic resonance imaging during an audiovisual cue-reactivity task and connectome-based predictive modelling (CPM), a data-driven method for generating brain-behavioural models, were used to study individuals with cocaine-use disorder and gambling disorder. Functions of nodes and networks relevant to craving were identified and interpreted based on meta-analytic data. RESULTS Craving was predicted by neural connectivity across disorders. The highest degree nodes were mostly located in the prefrontal cortex. Overall, the prediction model included complex networks including motor/sensory, fronto-parietal, and default-mode networks. The decoding revealed high functional associations with components of memory, valence ratings, physiological responses, and finger movement/motor imagery. CONCLUSIONS Craving could be predicted across substance and behavioural addictions. The model may reflect general neural mechanisms of craving despite specificities of individual disorders. Prefrontal regions associated with working memory and autobiographical memory seem important in predicting craving. For further validation, the model should be tested in diverse samples and contexts.
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Affiliation(s)
- Stephanie Antons
- General Psychology: Cognition and Center for Behavioral Addiction Research (CeBAR), University of Duisburg-Essen, Duisburg, Germany
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, Essen, Germany
| | - Sarah W. Yip
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
| | - Cheryl M. Lacadie
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | | | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Matthias Brand
- General Psychology: Cognition and Center for Behavioral Addiction Research (CeBAR), University of Duisburg-Essen, Duisburg, Germany
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, Essen, Germany
| | - Marc N. Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Child Study Center, Yale University School of Medicine, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Council on Problem Gambling, Wethersfield, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
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20
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Kang K, Xiao Y, Yu H, Diaz MT, Zhang H. Multilingual Language Diversity Protects Native Language Production under Different Control Demands. Brain Sci 2023; 13:1587. [PMID: 38002547 PMCID: PMC10670415 DOI: 10.3390/brainsci13111587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/06/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023] Open
Abstract
The use of multiple languages has been found to influence individuals' cognitive abilities. Although some studies have also investigated the effect of multilingualism on non-native language proficiency, fewer studies have focused on how multilingual experience affects native language production. This study investigated the effect of multilingualism on native language production, specifically examining control demands through a semantic Go/No-Go picture naming task. The multilingual experience was quantified using language entropy, which measures the uncertainty and diversity of language use. Control demands were achieved by manipulating the proportion of Go (i.e., naming) trials in different conditions. Results showed that as control demands increased, multilingual individuals exhibited poorer behavioral performance and greater brain activation throughout the brain. Moreover, more diverse language use was associated with higher accuracy in naming and more interconnected brain networks with greater involvement of domain-general neural resources and less domain-specific neural resources. Notably, the varied and balanced use of multiple languages enabled multilingual individuals to respond more efficiently to increased task demands during native language production.
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Affiliation(s)
- Keyi Kang
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
- Department of Psychology, University of Macau, Taipa, Macau SAR, China
| | - Yumeng Xiao
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Hanxiang Yu
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Michele T. Diaz
- Department of Psychology, The Pennsylvania State University, State College, PA 16801, USA
| | - Haoyun Zhang
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
- Department of Psychology, University of Macau, Taipa, Macau SAR, China
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21
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Beyh A, Rasche SE, Leff A, Ffytche D, Zeki S. Neural patterns of conscious visual awareness in the Riddoch syndrome. J Neurol 2023; 270:5360-5371. [PMID: 37429978 PMCID: PMC10576735 DOI: 10.1007/s00415-023-11861-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 07/12/2023]
Abstract
The Riddoch syndrome is one in which patients blinded by lesions to their primary visual cortex can consciously perceive visual motion in their blind field, an ability that correlates with activity in motion area V5. Our assessment of the characteristics of this syndrome in patient ST, using multimodal MRI, showed that: 1. ST's V5 is intact, receives direct subcortical input, and decodable neural patterns emerge in it only during the conscious perception of visual motion; 2. moving stimuli activate medial visual areas but, unless associated with decodable V5 activity, they remain unperceived; 3. ST's high confidence ratings when discriminating motion at chance levels, is associated with inferior frontal gyrus activity. Finally, we report that ST's Riddoch Syndrome results in hallucinatory motion with hippocampal activity as a correlate. Our results shed new light on perceptual experiences associated with this syndrome and on the neural determinants of conscious visual experience.
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Affiliation(s)
- Ahmad Beyh
- Laboratory of Neurobiology, Department of Cell and Developmental Biology, University College London, London, UK
| | - Samuel E Rasche
- Laboratory of Neurobiology, Department of Cell and Developmental Biology, University College London, London, UK
| | - Alexander Leff
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Dominic Ffytche
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Semir Zeki
- Laboratory of Neurobiology, Department of Cell and Developmental Biology, University College London, London, UK.
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22
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Song SE, Krishnamurthy LC, Rodriguez AD, Han JH, Crosson BA, Krishnamurthy V. Methodologies for task-fMRI based prognostic biomarkers in response to aphasia treatment. Behav Brain Res 2023; 452:114575. [PMID: 37423319 DOI: 10.1016/j.bbr.2023.114575] [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: 11/16/2022] [Revised: 06/14/2023] [Accepted: 07/06/2023] [Indexed: 07/11/2023]
Abstract
With the diversity in aphasia coupled with diminished gains at the chronic phase, it is imperative to deliver effective rehabilitation plans. Treatment outcomes have therefore been predicted using lesion-to-symptom mapping, but this method lacks holistic functional information about the language-network. This study, therefore, aims to develop whole-brain task-fMRI multivariate analysis to neurobiologically inspect lesion impacts on the language-network and predict behavioral outcomes in persons with aphasia (PWA) undergoing language therapy. In 14 chronic PWA, semantic fluency task-fMRI and behavioral measures were collected to develop prediction methodologies for post-treatment outcomes. Then, a recently developed imaging-based multivariate method to predict behavior (i.e., LESYMAP) was optimized to intake whole-brain task-fMRI data, and systematically tested for reliability with mass univariate methods. We also accounted for lesion size in both methods. Results showed that both mass univariate and multivariate methods identified unique biomarkers for semantic fluency improvements from baseline to 2-weeks post-treatment. Additionally, both methods demonstrated reliable spatial overlap in task-specific areas including the right middle frontal gyrus when identifying biomarkers of language discourse. Thus whole-brain task-fMRI multivariate analysis has the potential to identify functionally meaningful prognostic biomarkers even for relatively small sample sizes. In sum, our task-fMRI based multivariate approach holistically estimates post-treatment response for both word and sentence production and may serve as a complementary tool to mass univariate analysis in developing brain-behavior relationships for improved personalization of aphasia rehabilitation regimens.
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Affiliation(s)
- Serena E Song
- Center for Visual and Neurocognitive Rehabilitation, Atlanta Veterans Affairs Medical Center, 1670 Clairmont Rd., Decatur, GA 30033, United States; Department of Neuroscience and Behavioral Biology, Emory University, 201 Dowman Dr., Atlanta, GA 30322, United States
| | - Lisa C Krishnamurthy
- Center for Visual and Neurocognitive Rehabilitation, Atlanta Veterans Affairs Medical Center, 1670 Clairmont Rd., Decatur, GA 30033, United States; Joint GSU, Georgia Tech, and Emory Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, United States; Department of Physics and Astronomy, Georgia State University, Atlanta, GA 30302, United States; Department of Radiology and Imaging Sciences, Emory University, 201 Dowman Dr., Atlanta, GA 30322, United States
| | - Amy D Rodriguez
- Center for Visual and Neurocognitive Rehabilitation, Atlanta Veterans Affairs Medical Center, 1670 Clairmont Rd., Decatur, GA 30033, United States; Department of Neurology, Emory University, 201 Dowman Dr., Atlanta, GA 30322, United States
| | - Joo H Han
- Center for Visual and Neurocognitive Rehabilitation, Atlanta Veterans Affairs Medical Center, 1670 Clairmont Rd., Decatur, GA 30033, United States; Department of Physics and Astronomy, Georgia State University, Atlanta, GA 30302, United States
| | - Bruce A Crosson
- Center for Visual and Neurocognitive Rehabilitation, Atlanta Veterans Affairs Medical Center, 1670 Clairmont Rd., Decatur, GA 30033, United States; Department of Neurology, Emory University, 201 Dowman Dr., Atlanta, GA 30322, United States
| | - Venkatagiri Krishnamurthy
- Center for Visual and Neurocognitive Rehabilitation, Atlanta Veterans Affairs Medical Center, 1670 Clairmont Rd., Decatur, GA 30033, United States; Department of Neurology, Emory University, 201 Dowman Dr., Atlanta, GA 30322, United States; Department of Medicine, Division of Geriatrics and Gerontology, Emory University, 201 Dowman Dr., Atlanta, GA 30322, United States.
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23
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Cushing CA, Peng Y, Anderson Z, Young KS, Bookheimer SY, Zinbarg RE, Nusslock R, Craske MG. Broadening the scope: Multiple functional connectivity networks underlying threat and safety signaling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.16.553609. [PMID: 37645883 PMCID: PMC10462158 DOI: 10.1101/2023.08.16.553609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Introduction Threat learning and extinction processes are thought to be foundational to anxiety and fear-related disorders. However, the study of these processes in the human brain has largely focused on a priori regions of interest, owing partly to the ease of translating between these regions in human and non-human animals. Moving beyond analyzing focal regions of interest to whole-brain dynamics during threat learning is essential for understanding the neuropathology of fear-related disorders in humans. Methods 223 participants completed a 2-day Pavlovian threat conditioning paradigm while undergoing fMRI. Participants completed threat acquisition and extinction. Extinction recall was assessed 48 hours later. Using a data-driven group independent component analysis (ICA), we examined large-scale functional connectivity networks during each phase of threat conditioning. Connectivity networks were tested to see how they responded to conditional stimuli during early and late phases of threat acquisition and extinction and during early trials of extinction recall. Results A network overlapping with the default mode network involving hippocampus, vmPFC, and posterior cingulate was implicated in threat acquisition and extinction. Another network overlapping with the salience network involving dACC, mPFC, and inferior frontal gyrus was implicated in threat acquisition and extinction recall. Other networks overlapping with parts of the salience, somatomotor, visual, and fronto-parietal networks were involved in the acquisition or extinction of learned threat responses. Conclusions These findings help confirm previous investigations of specific brain regions in a model-free fashion and introduce new findings of spatially independent networks during threat and safety learning. Rather than being a single process in a core network of regions, threat learning involves multiple brain networks operating in parallel coordinating different functions at different timescales. Understanding the nature and interplay of these dynamics will be critical for comprehensive understanding of the multiple processes that may be at play in the neuropathology of anxiety and fear-related disorders.
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Gkiatis K, Garganis K, Karanasiou I, Chatzisotiriou A, Zountsas B, Kondylidis N, Matsopoulos GK. Independent component analysis: a reliable alternative to general linear model for task-based fMRI. Front Psychiatry 2023; 14:1214067. [PMID: 37663605 PMCID: PMC10468574 DOI: 10.3389/fpsyt.2023.1214067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/17/2023] [Indexed: 09/05/2023] Open
Abstract
Background Functional magnetic resonance imaging (fMRI) is a valuable tool for the presurgical evaluation of patients undergoing neurosurgeries. Although many pre-processing steps have been modified according to advances in recent years, statistical analysis has remained largely the same since the first days of fMRI. In this study, we examined the ability of Independent Component Analysis (ICA) to separate the activation of a language task in fMRI, and we compared it with the results of the General Lineal Model (GLM). Methods Sixty patients undergoing evaluation for brain surgery due to various brain lesions and/or epilepsy and 20 control subjects completed an fMRI language mapping protocol that included three tasks, resulting in 259 fMRI scans. Depending on brain lesion characteristics, patients were allocated to (1) static/chronic not-expanding lesions (Group 1) and (2) progressive/expanding lesions (Group 2). GLM and ICA statistical maps were evaluated by fMRI experts to assess the performance of each technique. Results In the control group, ICA and GLM maps were similar without any superiority of either technique. In Group 1 and Group 2, ICA performed statistically better than GLM, with a p-value of < 0.01801 and < 0.0237, respectively. This indicated that ICA performs as well as GLM when the subjects are able to cooperate well (less movement, good task performance), but ICA could outperform GLM in the patient groups. When both techniques were combined, 240 out of 259 scans produced reliable results, showing that the sensitivity of task-based fMRI can be increased when both techniques are integrated with the clinical setup. Conclusion ICA may be slightly more advantageous, compared to GLM, in patients with brain lesions, across the range of pathologies included in our population and independent of symptoms chronicity. Our findings suggest that GLM analysis may be more susceptible to brain activity perturbations induced by a variety of lesions or scanner-induced artifacts due to motion or other factors. In our research, we demonstrated that ICA is able to provide fMRI results that can be used in surgery, taking into account patient and task-wise aspects that differ from those when fMRI is used in research.
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Affiliation(s)
- Kostakis Gkiatis
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
- Epilepsy Monitoring Department, St. Luke's Hospital, Thessaloniki, Greece
| | - Kyriakos Garganis
- Epilepsy Monitoring Department, St. Luke's Hospital, Thessaloniki, Greece
| | - Irene Karanasiou
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
- Department of Mathematic and Engineering Sciences, Hellenic Military Academy, Athens, Greece
| | - Athanasios Chatzisotiriou
- Department of Neurosurgery, St. Luke's Hospital, Thessaloniki, Greece
- Department of Physiology, Medical School Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Basilios Zountsas
- Epilepsy Monitoring Department, St. Luke's Hospital, Thessaloniki, Greece
- Department of Neurosurgery, St. Luke's Hospital, Thessaloniki, Greece
| | | | - George K. Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
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25
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Burrows M, Kotoula V, Dipasquale O, Stringaris A, Mehta MA. Ketamine-induced changes in resting state connectivity, 2 h after the drug administration in patients with remitted depression. J Psychopharmacol 2023; 37:784-794. [PMID: 37491833 DOI: 10.1177/02698811231189432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
BACKGROUND Resting state connectivity studies link ketamine's antidepressant effects with normalisation of the brain connectivity changes that are observed in depression. These changes, however, usually co-occur with improvement in depressive symptoms, making it difficult to attribute these changes to ketamine's effects per se. AIMS Our aim is to examine the effects of ketamine in brain connectivity, 2 h after its administration in a cohort of volunteers with remitted depression. Any significant changes observed in this study could provide insight of ketamine's antidepressant mechanism as they are not accompanied by symptom changes. METHODS In total, 35 participants with remitted depression (21 females, mean age = 28.5 years) participated in a double-blind, placebo-controlled study of ketamine (0.5 mg/kg) or saline. Resting state scans were acquired approximately 2 h after the ketamine infusion. Brain connectivity was examined using a seed-based approach (ventral striatum, amygdala, hippocampus, posterior cingulate cortex and subgenual anterior cingulate cortex (sgACC)) and a brain network analysis (independent component analysis). RESULTS Decreased connectivity between the sgACC and the amygdala was observed approximately 2 h after the ketamine infusion, compared to placebo (pFWE < 0.05). The executive network presented with altered connectivity with different cortical and subcortical regions. Within the network, the left hippocampus and right amygdala had decreased connectivity (pFWE < 0.05). CONCLUSIONS Our findings support a model whereby ketamine would change the connectivity of brain areas and networks that are important for cognitive processing and emotional regulation. These changes could also be an indirect indicator of the plasticity changes induced by the drug.
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Affiliation(s)
- Matthew Burrows
- Centre for Neuroimaging Sciences, IoPPN, King's College London, London, UK
| | - Vasileia Kotoula
- Experimental Therapeutics and Pathophysiology Branch, NIMH, Bethesda, MA, USA
| | - Ottavia Dipasquale
- Centre for Neuroimaging Sciences, IoPPN, King's College London, London, UK
| | - Argyris Stringaris
- Division of Psychiatry and Department of Clinical, Educational & Health Psychology, UCL, London, UK
- First Department of Psychiatry, National and Kapodistrian University of Athens, Athens, Greece
| | - Mitul A Mehta
- Centre for Neuroimaging Sciences, IoPPN, King's College London, London, UK
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Bilgic B, Costagli M, Chan KS, Duyn J, Langkammer C, Lee J, Li X, Liu C, Marques JP, Milovic C, Robinson S, Schweser F, Shmueli K, Spincemaille P, Straub S, van Zijl P, Wang Y. Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. ARXIV 2023:arXiv:2307.02306v1. [PMID: 37461418 PMCID: PMC10350101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting.
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Affiliation(s)
- Berkin Bilgic
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris, Pisa, Italy
| | - Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Jeff Duyn
- Advanced MRI Section, NINDS, National Institutes of Health, Bethesda, MD, United States
| | | | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Xu Li
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Carlos Milovic
- School of Electrical Engineering (EIE), Pontificia Universidad Catolica de Valparaiso, Valparaiso, Chile
| | - Simon Robinson
- High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Austria
| | - Ferdinand Schweser
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, NY, USA
- Center for Biomedical Imaging, Clinical and Translational Science Institute at the University at Buffalo, Buffalo, NY, United States
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Pascal Spincemaille
- MRI Research Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Sina Straub
- Department of Radiology, Mayo Clinic, Jacksonville, FL, United States
| | - Peter van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Yi Wang
- MRI Research Institute, Departments of Radiology and Biomedical Engineering, Cornell University, New York, NY, United States
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Oh J, Crockett RA, Hsu CL, Dao E, Tam R, Liu-Ambrose T. Resistance Training Maintains White Matter and Physical Function in Older Women with Cerebral Small Vessel Disease: An Exploratory Analysis of a Randomized Controlled Trial. J Alzheimers Dis Rep 2023; 7:627-639. [PMID: 37483319 PMCID: PMC10357123 DOI: 10.3233/adr-220113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 05/17/2023] [Indexed: 07/25/2023] Open
Abstract
Background As the aging population grows, there is an increasing need to develop accessible interventions against risk factors for cognitive impairment and dementia, such as cerebral small vessel disease (CSVD). The progression of white matter hyperintensities (WMHs), a key hallmark of CSVD, can be slowed by resistance training (RT). We hypothesize RT preserves white matter integrity and that this preservation is associated with improved cognitive and physical function. Objective To determine if RT preserves regional white matter integrity and if any changes are associated with cognitive and physical outcomes. Methods Using magnetic resonance imaging data from a 12-month randomized controlled trial, we compared the effects of a twice-weekly 60-minute RT intervention versus active control on T1-weighted over T2-weighted ratio (T1w/T2w; a non-invasive proxy measure of white matter integrity) in a subset of study participants (N = 21 females, mean age = 69.7 years). We also examined the association between changes in T1w/T2w with two key outcomes of the parent study: (1) selective attention and conflict resolution, and (2) peak muscle power. Results Compared with an active control group, RT increased T1w/T2w in the external capsule (p = 0.024) and posterior thalamic radiations (p = 0.013) to a greater degree. Increased T1w/T2w in the external capsule was associated with an increase in peak muscle power (p = 0.043) in the RT group. Conclusion By maintaining white matter integrity, RT may be a promising intervention to counteract the pathological changes that accompany CSVD, while improving functional outcomes such as muscle power.
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Affiliation(s)
- Jean Oh
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, Canada
| | - Rachel A. Crockett
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, Canada
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, Canada
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada
- Centre for SMART Aging at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, Vancouver, Canada
| | - Chun-Liang Hsu
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, Canada
- Department of Rehabilitation Sciences, Hong Kong Polytechnic University, Hung Hom, Hong Kong
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, Canada
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada
- Centre for SMART Aging at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, Vancouver, Canada
| | - Elizabeth Dao
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
- Centre for SMART Aging at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, Vancouver, Canada
| | - Roger Tam
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
- Centre for SMART Aging at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, Vancouver, Canada
| | - Teresa Liu-Ambrose
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, Canada
- Djavad Mowafaghian Centre for Brain Health, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, Canada
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada
- Centre for SMART Aging at Vancouver Coastal Health, Vancouver Coastal Health Research Institute, Vancouver, Canada
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Horien C, Greene AS, Shen X, Fortes D, Brennan-Wydra E, Banarjee C, Foster R, Donthireddy V, Butler M, Powell K, Vernetti A, Mandino F, O’Connor D, Lake EMR, McPartland JC, Volkmar FR, Chun M, Chawarska K, Rosenberg MD, Scheinost D, Constable RT. A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth. Cereb Cortex 2023; 33:6320-6334. [PMID: 36573438 PMCID: PMC10183743 DOI: 10.1093/cercor/bhac506] [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: 03/09/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 12/29/2022] Open
Abstract
Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate functional connectome-based markers of attention. In dataset 1, we use connectome-based predictive modeling and observe successful prediction of performance on an in-scan sustained attention task in a sample of youth, including participants with a neurodiverse condition. The predictions are not driven by confounds, such as head motion. In dataset 2, we find that the attention network model defined in dataset 1 generalizes to predict in-scan attention in a separate sample of neurotypical participants performing the same attention task. In datasets 3-5, we use connectome-based identification and longitudinal scans to probe the stability of the attention network across months to years in individual participants. Our results help elucidate the brain correlates of attentional state in youth and support the further development of predictive dimensional models of other clinically relevant phenotypes.
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- MD-PhD Program, Yale School of Medicine, New Haven, CT, United States
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- MD-PhD Program, Yale School of Medicine, New Haven, CT, United States
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Diogo Fortes
- Yale Child Study Center, New Haven, CT, United States
| | | | | | - Rachel Foster
- Yale Child Study Center, New Haven, CT, United States
| | | | | | - Kelly Powell
- Yale Child Study Center, New Haven, CT, United States
| | | | - Francesca Mandino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - David O’Connor
- Department of Biomedical Engineering, Yale University, New Haven, CT, United States
| | - Evelyn M R Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - James C McPartland
- Yale Child Study Center, New Haven, CT, United States
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Fred R Volkmar
- Yale Child Study Center, New Haven, CT, United States
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Marvin Chun
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Katarzyna Chawarska
- Yale Child Study Center, New Haven, CT, United States
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, United States
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, IL, United States
- Neuroscience Institute, University of Chicago, Chicago, IL, United States
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
- Yale Child Study Center, New Haven, CT, United States
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, United States
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Ficek-Tani B, Horien C, Ju S, Xu W, Li N, Lacadie C, Shen X, Scheinost D, Constable T, Fredericks C. Sex differences in default mode network connectivity in healthy aging adults. Cereb Cortex 2023; 33:6139-6151. [PMID: 36563018 PMCID: PMC10183749 DOI: 10.1093/cercor/bhac491] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/18/2022] [Accepted: 11/20/2022] [Indexed: 12/24/2022] Open
Abstract
Women show an increased lifetime risk of Alzheimer's disease (AD) compared with men. Characteristic brain connectivity changes, particularly within the default mode network (DMN), have been associated with both symptomatic and preclinical AD, but the impact of sex on DMN function throughout aging is poorly understood. We investigated sex differences in DMN connectivity over the lifespan in 595 cognitively healthy participants from the Human Connectome Project-Aging cohort. We used the intrinsic connectivity distribution (a robust voxel-based metric of functional connectivity) and a seed connectivity approach to determine sex differences within the DMN and between the DMN and whole brain. Compared with men, women demonstrated higher connectivity with age in posterior DMN nodes and lower connectivity in the medial prefrontal cortex. Differences were most prominent in the decades surrounding menopause. Seed-based analysis revealed higher connectivity in women from the posterior cingulate to angular gyrus, which correlated with neuropsychological measures of declarative memory, and hippocampus. Taken together, we show significant sex differences in DMN subnetworks over the lifespan, including patterns in aging women that resemble changes previously seen in preclinical AD. These findings highlight the importance of considering sex in neuroimaging studies of aging and neurodegeneration.
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Affiliation(s)
- Bronte Ficek-Tani
- Department of Neurology, Yale School of Medicine, New Haven, CT 06520, United States
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06520, United States
| | - Suyeon Ju
- Department of Neurology, Yale School of Medicine, New Haven, CT 06520, United States
| | - Wanwan Xu
- Department of Biostatistics, Yale School of Medicine, New Haven, CT 06520, United States
| | - Nancy Li
- Department of Neurology, Yale School of Medicine, New Haven, CT 06520, United States
| | - Cheryl Lacadie
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, United States
| | - Carolyn Fredericks
- Department of Neurology, Yale School of Medicine, New Haven, CT 06520, United States
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Ju S, Horien C, Shen X, Abuwarda H, Trainer A, Constable RT, Fredericks CA. Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance. FRONTIERS IN DEMENTIA 2023; 2:1126016. [PMID: 39082002 PMCID: PMC11285565 DOI: 10.3389/frdem.2023.1126016] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/28/2023] [Indexed: 08/02/2024]
Abstract
Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women's elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (n = 579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ = 0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between- visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women's elevated risk of AD.
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Affiliation(s)
- Suyeon Ju
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Hamid Abuwarda
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - Anne Trainer
- Department of Neurology, Yale School of Medicine, New Haven, CT, United States
| | - R. Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
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Hsu CL, Manor B, Travison T, Pascual-Leone A, Lipsitz LA. Sensorimotor and Frontoparietal Network Connectivity Are Associated With Subsequent Maintenance of Gait Speed and Episodic Memory in Older Adults. J Gerontol A Biol Sci Med Sci 2023; 78:521-526. [PMID: 36124711 PMCID: PMC9977250 DOI: 10.1093/gerona/glac193] [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: 03/17/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Slow gait is predictive of functional impairments in older adults, while concomitant slow gait and cognitive complaints are associated with a greater risk for cognitive decline and dementia. However, functional neural correlates for gait speed maintenance are unclear. As the sensorimotor network (SMN) and frontoparietal network (FPN) are integral components of these functions, this study investigated differences in SMN and FPN in older adults with/without gait speed decline over 24 months; and whether these networks were associated with the maintenance of cognitive function. METHODS We included 42 community-dwelling older adults aged >70 years from the MOBILIZE Boston Study. Resting-state fMRI was performed at the study baseline. Participant characteristics, gait speed, Mini-Mental State Examination, and Hopkins Verbal Learning Test (HVLT) were assessed at baseline and at 24-month follow-up. Decliners were identified as individuals with >0.05 meters/second decline in gait speed from baseline to 24 months. Of the 26 decliners and 16 maintainers, decliners exhibited a significant decline in delayed-recall performance on the HVLT over 24 months. RESULTS Controlling for baseline age and multiple comparisons, contrary to initial hypothesis, maintainers exhibited lower baseline primary motor and premotor connectivity (p = .01) within the SMN, and greater baseline ventral visual-supramarginal gyrus connectivity within the FPN (p = .02) compared to decliners. Lower primary motor-premotor connectivity was correlated with maintenance of delayed-recall performance on the HVLT (p = .04). CONCLUSION These findings demonstrated a potential compensatory mechanism involved in the link between the decline in gait speed and episodic memory, whereby baseline connectivity of the SMN and FPN may underlie subsequent maintenance of gait speed and cognitive function in old age.
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Affiliation(s)
- Chun Liang Hsu
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Brad Manor
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Thomas Travison
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Alvaro Pascual-Leone
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Lewis A Lipsitz
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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32
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Rasche SE, Beyh A, Paolini M, Zeki S. The neural determinants of abstract beauty. Eur J Neurosci 2023; 57:633-645. [PMID: 36633957 DOI: 10.1111/ejn.15912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 01/03/2023] [Indexed: 01/13/2023]
Abstract
We have enquired into the neural activity which correlates with the experience of beauty aroused by abstract paintings consisting of arbitrary assemblies of lines and colours. During the brain imaging experiments, subjects rated abstract paintings according to aesthetic appeal. There was low agreement on the aesthetic classification of these paintings among participants. Univariate analyses revealed higher activity with higher declared aesthetic appeal in both the visual areas and the medial frontal cortex. Additionally, representational similarity analysis (RSA) revealed that the experience of beauty correlated with decodable patterns of activity in visual areas. These results are broadly similar to those obtained in previous studies on facial beauty. With abstract art, it was the involvement of visual areas implicated in the processing of lines and colours while with faces it was of visual areas implicated in the processing of faces. Both categories of aesthetic experience correlated with increased activity in medial frontal cortex. We conclude that the sensory areas participate in the selection of stimuli according to aesthetic appeal and that it is the co-operative activity between the sensory areas and the medial frontal cortex that is the basis for the experience of abstract visual beauty. Further, this co-operation is enabled by "experience dependent" functional connections, in the sense that currently the existence and high specificity of these connections can only be demonstrated during certain experiences.
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Affiliation(s)
- Samuel E Rasche
- Laboratory of Neurobiology, Division of Cell & Developmental Biology, University College London, London, UK
| | - Ahmad Beyh
- Laboratory of Neurobiology, Division of Cell & Developmental Biology, University College London, London, UK
| | - Marco Paolini
- Department of Radiology, University Hospital, Ludwig-Maximilians-Universität, Munich, Germany
| | - Semir Zeki
- Laboratory of Neurobiology, Division of Cell & Developmental Biology, University College London, London, UK
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33
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Zhang J, Treyer V, Sun J, Zhang C, Gietl A, Hock C, Razansky D, Nitsch RM, Ni R. Automatic analysis of skull thickness, scalp-to-cortex distance and association with age and sex in cognitively normal elderly. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.19.524484. [PMID: 36711717 PMCID: PMC9882276 DOI: 10.1101/2023.01.19.524484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Personalized neurostimulation has been a potential treatment for many brain diseases, which requires insights into brain/skull geometry. Here, we developed an open source efficient pipeline BrainCalculator for automatically computing the skull thickness map, scalp-to-cortex distance (SCD), and brain volume based on T 1 -weighted magnetic resonance imaging (MRI) data. We examined the influence of age and sex cross-sectionally in 407 cognitively normal older adults (71.9±8.0 years, 60.2% female) from the ADNI. We demonstrated the compatibility of our pipeline with commonly used preprocessing packages and found that BrainSuite Skullfinder was better suited for such automatic analysis compared to FSL Brain Extraction Tool 2 and SPM12- based unified segmentation using ground truth. We found that the sphenoid bone and temporal bone were thinnest among the skull regions in both females and males. There was no increase in regional minimum skull thickness with age except in the female sphenoid bone. No sex difference in minimum skull thickness or SCD was observed. Positive correlations between age and SCD were observed, faster in females (0.307%/y) than males (0.216%/y) in temporal SCD. A negative correlation was observed between age and whole brain volume computed based on brain surface (females -1.031%/y, males -0.998%/y). In conclusion, we developed an automatic pipeline for MR-based skull thickness map, SCD, and brain volume analysis and demonstrated the sex-dependent association between minimum regional skull thickness, SCD and brain volume with age. This pipeline might be useful for personalized neurostimulation planning.
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Affiliation(s)
- Junhao Zhang
- Institute for Regenerative Medicine, University of Zurich, 8952 Zurich, Switzerland
- Institute for Biomedical Engineering, ETH Zurich & University of Zurich, 8093 Zurich, Switzerland
| | - Valerie Treyer
- Institute for Regenerative Medicine, University of Zurich, 8952 Zurich, Switzerland
- Department of Nuclear Medicine, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Junfeng Sun
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chencheng Zhang
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China
| | - Anton Gietl
- Institute for Regenerative Medicine, University of Zurich, 8952 Zurich, Switzerland
| | - Christoph Hock
- Institute for Regenerative Medicine, University of Zurich, 8952 Zurich, Switzerland
- Neurimmune, Schlieren, Switzerland
| | - Daniel Razansky
- Institute for Biomedical Engineering, ETH Zurich & University of Zurich, 8093 Zurich, Switzerland
| | - Roger M Nitsch
- Institute for Regenerative Medicine, University of Zurich, 8952 Zurich, Switzerland
- Neurimmune, Schlieren, Switzerland
| | - Ruiqing Ni
- Institute for Regenerative Medicine, University of Zurich, 8952 Zurich, Switzerland
- Institute for Biomedical Engineering, ETH Zurich & University of Zurich, 8093 Zurich, Switzerland
- Zentrum für Neurowissenschaften Zurich, Zurich, Switzerland
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Praveenkumar S, Kalaiselvi T, Somasundaram K. Methods of Brain Extraction from Magnetic Resonance Images of Human Head: A Review. Crit Rev Biomed Eng 2023; 51:1-40. [PMID: 37581349 DOI: 10.1615/critrevbiomedeng.2023047606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
Medical images are providing vital information to aid physicians in diagnosing a disease afflicting the organ of a human body. Magnetic resonance imaging is an important imaging modality in capturing the soft tissues of the brain. Segmenting and extracting the brain is essential in studying the structure and pathological condition of brain. There are several methods that are developed for this purpose. Researchers in brain extraction or segmentation need to know the current status of the work that have been done. Such an information is also important for improving the existing method to get more accurate results or to reduce the complexity of the algorithm. In this paper we review the classical methods and convolutional neural network-based deep learning brain extraction methods.
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Affiliation(s)
| | - T Kalaiselvi
- Department of Computer Science and Applications, Gandhigram Rural Institute, Gandhigram 624302, Tamil Nadu, India
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Rodriguez RX, Noble S, Tejavibulya L, Scheinost D. Leveraging edge-centric networks complements existing network-level inference for functional connectomes. Neuroimage 2022; 264:119742. [PMID: 36368501 PMCID: PMC9838718 DOI: 10.1016/j.neuroimage.2022.119742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 11/04/2022] [Accepted: 11/07/2022] [Indexed: 11/09/2022] Open
Abstract
The human connectome is modular with distinct brain regions clustering together to form large-scale communities, or networks. This concept has recently been leveraged in novel inferencing procedures by averaging the edge-level statistics within networks to induce more powerful inferencing at the network level. However, these networks are constructed based on the similarity between pairs of nodes. Emerging work has described novel edge-centric networks, which instead use the similarity between pairs of edges to construct networks. In this work, we use these edge-centric networks in a network-level inferencing procedure and compare this novel method to traditional inferential procedures and the network-level procedure using node-centric networks. We use data from the Human Connectome Project, the Healthy Brain Network, and the Philadelphia Neurodevelopmental Cohort and use a resampling technique with various sample sizes (n=40, 80, 120) to probe the power and specificity of each method. Across datasets and sample sizes, using the edge-centric networks outperforms using node-centric networks for inference as well as edge-level FDR correction and NBS. Additionally, the edge-centric networks were found to be more consistent in clustering effect sizes of similar values as compared to node-centric networks, although node-centric networks often had a lower average within-network effect size variability. Together, these findings suggest that using edge-centric networks for network-level inference can procure relatively powerful results while remaining similarly accurate to the underlying edge-level effects across networks, complementing previous inferential methods.
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Affiliation(s)
- Raimundo X. Rodriguez
- Interdepartmental Neuroscience Program, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA,Corresponding author. (R.X. Rodriguez)
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA,Department of Radiology and Biomedical Imaging, Yale School of Medicine, 330 Cedar Street, New Haven, CT 06520, USA,Department of Biomedical Engineering, Yale School of Engineering and Applied Science, 17 Hillhouse Avenue, New Haven, CT 06511, USA,Department of Statistics and Data Science, Yale University, 24 Hillhouse Avenue, New Haven, CT 06511, USA,Child Study Center, Yale School of Medicine, 230 South Frontage Road, New Haven, CT 06519, USA,Wu Tsai Institute, Yale University, 100 College Street, New Haven, CT 06510, USA
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36
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Hsu CL, Manor B, Iloputaife I, Oddsson LIE, Lipsitz L. Six month lower-leg mechanical tactile sensory stimulation alters functional network connectivity associated with improved gait in older adults with peripheral neuropathy – A pilot study. Front Aging Neurosci 2022; 14:1027242. [PMID: 36408098 PMCID: PMC9669982 DOI: 10.3389/fnagi.2022.1027242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Foot sole somatosensory impairment associated with peripheral neuropathy (PN) is prevalent and a strong independent risk factor for gait disturbance and falls in older adults. Walkasins, a lower-limb sensory prosthesis, has been shown to improve gait and mobility in people with PN by providing afferent input related to foot sole pressure distributions via lower-leg mechanical tactile stimulation. Given that gait and mobility are regulated by sensorimotor and cognitive brain networks, it is plausible improvements in gait and mobility from wearing the Walkasins may be associated with elicited neuroplastic changes in the brain. As such, this study aimed to examine changes in brain network connectivity after 26 weeks of daily use of the prosthesis among individuals with diagnosed PN and balance problems. In this exploratory investigation, assessments of participant characteristics, Functional Gait Assessment (FGA), and resting-state functional magnetic resonance imaging were completed at study baseline and 26 weeks follow-up. We found that among those who have completed the study (N = 8; mean age 73.7 years) we observed a five-point improvement in FGA performance as well as significant changes in network connectivity over the 26 weeks that were correlated with improved FGA performance. Specifically, greater improvement in FGA score over 26 weeks was associated with increased connectivity within the Default Mode Network (DMN; p < 0.01), the Somatosensory Network (SMN; p < 0.01), and the Frontoparietal Network (FPN; p < 0.01). FGA improvement was also correlated with increased connectivity between the DMN and the FPN (p < 0.01), and decreased connectivity between the SMN and both the FPN (p < 0.01) and cerebellum (p < 0.01). These findings suggest that 26 weeks of daily use of the Walkasins device may provide beneficial neural modulatory changes in brain network connectivity via the sensory replacement stimulation that are relevant to gait improvements among older adults with PN.
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Affiliation(s)
- Chun Liang Hsu
- Hebrew SeniorLife, Hinda and Arthur Marcus Institute for Aging Research, Roslindale, MA, United States
- Harvard Medical School, Boston, MA, United States
- *Correspondence: Chun Liang Hsu,
| | - Brad Manor
- Hebrew SeniorLife, Hinda and Arthur Marcus Institute for Aging Research, Roslindale, MA, United States
- Harvard Medical School, Boston, MA, United States
- Division of Gerontology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Ikechkwu Iloputaife
- Hebrew SeniorLife, Hinda and Arthur Marcus Institute for Aging Research, Roslindale, MA, United States
| | - Lars I. E. Oddsson
- Division of Rehabilitation Science, Department of Rehabilitation Medicine, Medical School, University of Minnesota, Minneapolis, MN, United States
- RxFunction Inc., Eden Prairie, MN, United States
| | - Lewis Lipsitz
- Hebrew SeniorLife, Hinda and Arthur Marcus Institute for Aging Research, Roslindale, MA, United States
- Harvard Medical School, Boston, MA, United States
- Division of Gerontology, Beth Israel Deaconess Medical Center, Boston, MA, United States
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Hemond CC, Baek J, Ionete C, Reich DS. Paramagnetic rim lesions are associated with pathogenic CSF profiles and worse clinical status in multiple sclerosis: A retrospective cross-sectional study. Mult Scler 2022; 28:2046-2056. [PMID: 35748669 PMCID: PMC9588517 DOI: 10.1177/13524585221102921] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Paramagnetic rims have been observed as a feature of some multiple sclerosis (MS) lesions on susceptibility-sensitive magnetic resonance imaging (MRI) and indicate compartmentalized inflammation. OBJECTIVE To investigate clinical, MRI, and intrathecal (cerebrospinal fluid, CSF) associations of paramagnetic rim lesions (PRLs) using 3T MRI in MS. METHODS This is a retrospective, cross-sectional analysis. All patients underwent 3T MRI using a T2*-weighted sequence with susceptibility postprocessing (susceptibility-weighted angiography (SWAN) protocol, GE). SWAN-derived filtered-phase maps and corresponding T2-FLAIR images were manually reviewed to determine PRL. Descriptive statistics, t-tests, and regression determined demographic, clinical, MRI, and CSF associations with PRL. RESULTS A total of 147 MS patients were included; 79 of whom had available CSF. Forty-three percent had at least one PRL. PRL status (presence/absence) did not vary by sex or Expanded Disability Status Scale (EDSS) but was associated with younger age, shorter disease duration, worse disease severity, high-efficacy therapy use, and poorer dexterity, as well as lower age-adjusted brain volumes and cognitive processing speeds. PRL status was moreover associated with blood-brain barrier disruption as determined by pathologically elevated albumin quotient. Sensitivity analyses remained supportive of these findings. CONCLUSION PRLs, an emerging noninvasive biomarker of chronic neuroinflammation, are confirmed to be associated with greater disease severity and newly shown to be preliminarily associated with blood-brain barrier disruption.
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Affiliation(s)
- Christopher C. Hemond
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, Massachusetts
| | - Jonggyu Baek
- Department of Population and Quantitative Health, University of Massachusetts Chan Medical School, Worcester, Massachusetts
| | - Carolina Ionete
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, Massachusetts
| | - Daniel S. Reich
- Translational Neuroradiology Section, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
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38
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Saat P, Nogovitsyn N, Hassan MY, Ganaie MA, Souza R, Hemmati H. A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation. Front Neuroinform 2022; 16:919779. [PMID: 36213544 PMCID: PMC9538795 DOI: 10.3389/fninf.2022.919779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/29/2022] [Indexed: 01/18/2023] Open
Abstract
Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques for this task. Nevertheless, the segmentations produced by machine learning models often degrade in the presence of expected domain shifts between the test and train sets data distributions. These domain shifts are expected due to several factors, such as scanner hardware and software differences, technology updates, and differences in MRI acquisition parameters. Domain adaptation (DA) methods can make machine learning models more resilient to these domain shifts. This paper proposes a benchmark for investigating DA techniques for brain MR image segmentation using data collected across sites with scanners from different vendors (Philips, Siemens, and General Electric). Our work provides labeled data, publicly available source code for a set of baseline and DA models, and a benchmark for assessing different brain MR image segmentation techniques. We applied the proposed benchmark to evaluate two segmentation tasks: skull-stripping; and white-matter, gray-matter, and cerebrospinal fluid segmentation, but the benchmark can be extended to other brain structures. Our main findings during the development of this benchmark are that there is not a single DA technique that consistently outperforms others, and hyperparameter tuning and computational times for these methods still pose a challenge before broader adoption of these methods in the clinical practice.
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Affiliation(s)
- Parisa Saat
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Nikita Nogovitsyn
- Centre for Depression and Suicide Studies, St. Michael's Hospital, Toronto, ON, Canada
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Muhammad Yusuf Hassan
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Electrical Engineering, Indian Institute of Technology, Gandhinagar, Gujarat, India
| | - Muhammad Athar Ganaie
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Chemical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India
| | - Roberto Souza
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Hadi Hemmati
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, ON, Canada
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Hsu CL, Falck RS, Backhouse D, Chan P, Dao E, Ten Brinke LF, Manor B, Liu-Ambrose T. Objective Sleep Quality and the Underlying Functional Neural Correlates Among Older Adults with Possible Mild Cognitive Impairment. J Alzheimers Dis 2022; 89:1473-1482. [PMID: 36057822 DOI: 10.3233/jad-220457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Poor sleep quality is common among older individuals with mild cognitive impairment (MCI) and may be a consequence of functional alterations in the brain; yet few studies have investigated the underlying neural correlates of actigraphy-measured sleep quality in this cohort. OBJECTIVE The objective of this study was to examine the relationship between brain networks and sleep quality measured by actigraphy. METHODS In this cross-sectional analysis, sleep efficiency and sleep fragmentation were estimated using Motionwatch8 (MW8) over a period of 14 days in 36 community-dwelling older adults with possible MCI aged 65-85 years. All 36 participants underwent resting-state functional magnetic resonance imaging (fMRI) scanning. Independent associations between network connectivity and MW8 measures of sleep quality were determined using general linear modeling via FSL. Networks examined included the somatosensory network (SMN), frontoparietal network (FPN), and default mode network (DMN). RESULTS Across the 36 participants (mean age 71.8 years; SD = 5.2 years), mean Montreal Cognitive Assessment score was 22.5 (SD = 2.7) and Mini-Mental State Examination score was 28.3 (SD = 1.5). Mean sleep efficiency and fragmentation index was 80.1% (SD = 10.0) and 31.8 (SD = 10.4) respectively. Higher sleep fragmentation was significantly correlated with increased connectivity between the SMN and insula, the SMN and posterior cingulate, as well as FPN and primary motor area (FDR-corrected, p < 0.004). CONCLUSION Functional connectivity between brain regions involved in attentional and somatosensory processes may be associated with disrupted sleep in older adults with MCI.
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Affiliation(s)
- Chun Liang Hsu
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Roslindale, MA, USA.,Harvard Medical School, Harvard University, Boston, MA, USA.,Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Ryan S Falck
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Daniel Backhouse
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Patrick Chan
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Elizabeth Dao
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Lisanne F Ten Brinke
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Brad Manor
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Roslindale, MA, USA.,Harvard Medical School, Harvard University, Boston, MA, USA
| | - Teresa Liu-Ambrose
- Aging, Mobility, and Cognitive Health Laboratory, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
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40
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Lee K, Horien C, O’Connor D, Garand-Sheridan B, Tokoglu F, Scheinost D, Lake EM, Constable RT. Arousal impacts distributed hubs modulating the integration of brain functional connectivity. Neuroimage 2022; 258:119364. [PMID: 35690257 PMCID: PMC9341222 DOI: 10.1016/j.neuroimage.2022.119364] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.
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Affiliation(s)
- Kangjoo Lee
- Department of Radiology and Bioimaging Sciences, Yale University School of Medicine, New Haven, CT 06520, United States.
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale University
School of Medicine, New Haven, CT 06520, United States
| | - David O’Connor
- Department of Biomedical Engineering, Yale University, New
Haven, CT 06520, United States
| | | | - Fuyuze Tokoglu
- Department of Radiology and Bioimaging Sciences, Yale
University School of Medicine, New Haven, CT 06520, United States
| | - Dustin Scheinost
- Department of Radiology and Bioimaging Sciences, Yale
University School of Medicine, New Haven, CT 06520, United States,Department of Biomedical Engineering, Yale University, New
Haven, CT 06520, United States,The Child Study Center, Yale University School of Medicine,
New Haven, CT 06520, United States,Department of Statistics and Data Science, Yale University,
New Haven, CT 06511, United States
| | - Evelyn M.R. Lake
- Department of Radiology and Bioimaging Sciences, Yale
University School of Medicine, New Haven, CT 06520, United States
| | - R. Todd Constable
- Department of Radiology and Bioimaging Sciences, Yale
University School of Medicine, New Haven, CT 06520, United States,Department of Biomedical Engineering, Yale University, New
Haven, CT 06520, United States,Department of Neurosurgery, Yale University School of
Medicine, New Haven, CT 06520, United States
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41
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Greene AS, Shen X, Noble S, Horien C, Hahn CA, Arora J, Tokoglu F, Spann MN, Carrión CI, Barron DS, Sanacora G, Srihari VH, Woods SW, Scheinost D, Constable RT. Brain-phenotype models fail for individuals who defy sample stereotypes. Nature 2022; 609:109-118. [PMID: 36002572 PMCID: PMC9433326 DOI: 10.1038/s41586-022-05118-w] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 07/15/2022] [Indexed: 01/19/2023]
Abstract
Individual differences in brain functional organization track a range of traits, symptoms and behaviours1-12. So far, work modelling linear brain-phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain-phenotype relationships. To this end, here we related brain activity to phenotype using predictive models-trained and tested on independent data to ensure generalizability15-and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18-20 on the interpretation and utility of resulting brain-phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.
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Affiliation(s)
- Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA.
- MD-PhD program, Yale School of Medicine, New Haven, CT, USA.
| | - Xilin Shen
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- MD-PhD program, Yale School of Medicine, New Haven, CT, USA
| | - C Alice Hahn
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Jagriti Arora
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Fuyuze Tokoglu
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Marisa N Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Carmen I Carrión
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Daniel S Barron
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gerard Sanacora
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Vinod H Srihari
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Scott W Woods
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA.
- Depatment of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
- Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT, USA.
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA.
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Jacobs SM, Versteeg E, van der Kolk AG, Visser LNC, Oliveira ÍAF, van Maren E, Klomp DWJ, Siero JCW. Image quality and subject experience of quiet T1-weighted 7-T brain imaging using a silent gradient coil. Eur Radiol Exp 2022; 6:36. [PMID: 36042139 PMCID: PMC9428090 DOI: 10.1186/s41747-022-00293-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 06/16/2022] [Indexed: 11/17/2022] Open
Abstract
Objectives Acoustic noise in magnetic resonance imaging (MRI) negatively impacts patients. We assessed a silent gradient coil switched at 20 kHz combined with a T1-weighted magnetisation prepared rapid gradient-echo (MPRAGE) sequence at 7 T. Methods Five healthy subjects (21–29 years; three females) without previous 7-T MRI experience underwent both a quiet MPRAGE (Q-MPRAGE) and conventional MPRAGE (C-MPRAGE) sequence twice. Image quality was assessed quantitatively, and qualitatively by two neuroradiologists. Sound level was measured objectively and rated subjectively on a 0 to 10 scale by all subjects immediately following each sequence and after the whole examination (delayed). All subjects also reported comfort level, overall experience and willingness to undergo the sequence again. Results Compared to C-MPRAGE, Q-MPRAGE showed higher signal-to-noise ratio (10%; p = 0.012) and lower contrast-to-noise ratio (20%; p < 0.001) as well as acceptable to good image quality. Q-MPRAGE produced 27 dB lower sound level (76 versus 103 dB). Subjects reported lower sound level for Q-MPRAGE both immediate (4.4 ± 1.4 versus 6.4 ± 1.3; p = 0.007) and delayed (4.6 ± 1.4 versus 6.3 ± 1.3; p = 0.005), while they rated comfort level (7.4 ± 1.0 versus 6.1 ± 1.7; p = 0.016) and overall experience (7.6 ± 1.0 versus 6.0 ± 0.9; p = 0.005) higher. Willingness to undergo the sequence again was also higher, however not significantly (8.1 ± 1.0 versus 7.2 ± 1.3; p = 0.066). Conclusion Q-MPRAGE using a silent gradient coil reduced sound level by 27 dB compared to C-MPRAGE at 7 T while featuring acceptable-to-good image quality and a quieter and more pleasant subject experience.
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Affiliation(s)
- Sarah M Jacobs
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Edwin Versteeg
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Anja G van der Kolk
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, the Netherlands.,Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Leonie N C Visser
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
| | - Ícaro A F Oliveira
- Spinoza Centre for Neuroimaging Amsterdam, Amsterdam, the Netherlands.,Experimental and Applied Psychology, VU University, Amsterdam, the Netherlands
| | - Emiel van Maren
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Dennis W J Klomp
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jeroen C W Siero
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, the Netherlands.,Spinoza Centre for Neuroimaging Amsterdam, Amsterdam, the Netherlands
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Huisman SI, van der Boog ATJ, Cialdella F, Verhoeff JJC, David S. Quantifying the post-radiation accelerated brain aging rate in glioma patients with deep learning. Radiother Oncol 2022; 175:18-25. [PMID: 35963398 DOI: 10.1016/j.radonc.2022.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 07/12/2022] [Accepted: 08/01/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND AND PURPOSE Changes of healthy appearing brain tissue after radiotherapy (RT) have been previously observed. Patients undergoing RT may have a higher risk of cognitive decline, leading to a reduced quality of life. The experienced tissue atrophy is similar to the effects of normal aging in healthy individuals. We propose a new way to quantify tissue changes after cranial RT as accelerated brain aging using the BrainAGE framework. MATERIALS AND METHODS BrainAGE was applied to longitudinal MRI scans of 32 glioma patients. Utilizing a pre-trained deep learning model, brain age is estimated for all patients' pre-radiotherapy planning and follow-up MRI scans to acquire a quantification of the changes occurring in the brain over time. Saliency maps were extracted from the model to spatially identify which areas of the brain the deep learning model weighs highest for predicting age. The predicted ages from the deep learning model were used in a linear mixed effects model to quantify aging of patients after RT. RESULTS The linear mixed effects model resulted in an accelerated aging rate of 2.78 years/year, a significant increase over a normal aging rate of 1 (p < 0.05, confidence interval = 2.54-3.02). Furthermore, the saliency maps showed numerous anatomically well-defined areas, e.g.: Heschl's gyrus among others, determined by the model as important for brain age prediction. CONCLUSION We found that patients undergoing RT are affected by significant post-radiation accelerated aging, with several anatomically well-defined areas contributing to this aging. The estimated brain age could provide a method for quantifying quality of life post-radiotherapy.
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Affiliation(s)
- Selena I Huisman
- Department of Radiation Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands.
| | | | - Fia Cialdella
- Department of Radiation Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands; Department of Medical Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands.
| | - Joost J C Verhoeff
- Department of Radiation Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands.
| | - Szabolcs David
- Department of Radiation Oncology, UMC Utrecht, 3584 CX Utrecht, The Netherlands.
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Falck RS, Hsu CL, Silva NCBS, Li LC, Best JR, Liu-Ambrose T. The independent associations of physical activity and sleep with neural activity during an inhibitory task: cross-sectional results from the MONITOR-OA study. J Sleep Res 2022; 31:e13692. [PMID: 35821379 DOI: 10.1111/jsr.13692] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 06/18/2022] [Accepted: 06/22/2022] [Indexed: 11/29/2022]
Abstract
Sleep and physical activity (PA) are important for the maintenance of executive functions. Whether these lifestyle factors independently contribute to associated neural correlates of executive functions is unknown. We therefore investigated the independent associations of PA and sleep with neural activity during executive performance using task-based functional magnetic resonance imaging (fMRI). Baseline data from a subset of participants (n = 29) enrolled in a randomised trial were used for this cross-sectional analysis. We measured PA, sleep duration and efficiency for 7 days using the SenseWear Mini and examined neural activity underlying response inhibition using the Go/NoGo executive performance task. Brain activation patterns during the NoGo condition were contrasted to activation patterns during the Go condition (i.e., NoGo-Go). We constructed two separate models (controlling for age, sex, and education) to examine the independent associations of (i) PA and sleep duration; and (ii) PA and sleep efficiency with brain activation. Significant clusters were corrected for multiple comparisons (p < 0.05) to determine region-specific activation patterns. The mean (SD) participant age was 61 (9) years, and 79% were female. PA was independently associated with greater task-related blood-oxygen-level dependent (BOLD) signal activity in the left cingulate gyrus; longer sleep duration was independently associated with greater BOLD signal activity in the left putamen. Higher sleep efficiency was independently associated with increased BOLD signal activity in the left hippocampus. PA, sleep duration, and efficiency are each independently associated with greater neural activity underlying response inhibition, which further illustrates that PA and sleep are each uniquely important for brain health.
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Affiliation(s)
- Ryan Stanley Falck
- Aging, Mobility, and Cognitive Neuroscience Laboratory, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, University of British Columbia, Vancouver, British Columbia, Canada.,Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Chun Liang Hsu
- Aging, Mobility, and Cognitive Neuroscience Laboratory, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.,Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada.,Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA
| | - Narlon Cassio Boa Sorte Silva
- Aging, Mobility, and Cognitive Neuroscience Laboratory, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, University of British Columbia, Vancouver, British Columbia, Canada.,Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
| | - Linda C Li
- Arthritis Research Canada, University of British Columbia, Vancouver, British Columbia, Canada
| | - John R Best
- Gerontology Research Centre, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Teresa Liu-Ambrose
- Aging, Mobility, and Cognitive Neuroscience Laboratory, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada.,Djavad Mowafaghian Centre for Brain Health, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.,Centre for Hip Health and Mobility, University of British Columbia, Vancouver, British Columbia, Canada.,Vancouver Coastal Health Research Institute, Vancouver, British Columbia, Canada
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45
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Diaz MT, Zhang H, Cosgrove AL, Gertel VH, Troutman SBW, Karimi H. Neural sensitivity to semantic neighbors is stable across the adult lifespan. Neuropsychologia 2022; 171:108237. [PMID: 35413304 PMCID: PMC10022434 DOI: 10.1016/j.neuropsychologia.2022.108237] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 03/04/2022] [Accepted: 04/06/2022] [Indexed: 11/30/2022]
Abstract
As we age, language reflects patterns of both stability and change. On the one hand, vocabulary and semantic abilities are largely stable across the adult lifespan, yet lexical retrieval is often slower and less successful (i.e., slower picture naming times, increased tip of the tongue incidents). Although the behavioral bases of these effects have been well established, less is known about the brain regions that support these age-related differences. We used functional Magnetic Resonance Imaging (fMRI) to examine the neural basis of picture naming. Specifically, we were interested in whether older adults would be equally sensitive to semantic characteristics, specifically the number of semantic near neighbors. Near neighbors, defined here as items with a high degree of semantic feature overlap, were of interest as these are thought to elicit competition among potential candidates and increase naming difficulty. Consistent with prior reports, pictures with more semantic near neighbors were named more slowly and less accurately for all adults. Additionally, this interference for naming times was larger as age increased, starting around 30 years old. In contrast to the age-related behavioral slowing, the neural basis of these effects was stable across adulthood. Across all adults, a number of language-relevant regions including left posterior middle temporal gyrus and left inferior frontal gyrus, pars triangularis were sensitive to the number of near neighbors. Our results suggest that although middle-aged and older adults' picture naming is more slowed by increased semantic competition, the brain regions supporting semantic processes remain stable across the adult lifespan.
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Affiliation(s)
- Michele T Diaz
- Department of Psychology, The Pennsylvania State University, USA; Social, Life, and Engineering Sciences Imaging Center, The Pennsylvania State University, USA.
| | - Haoyun Zhang
- Social, Life, and Engineering Sciences Imaging Center, The Pennsylvania State University, USA
| | | | | | | | - Hossein Karimi
- Department of Psychology, The Pennsylvania State University, USA
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46
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Weiler M, Casseb RF, de Campos BM, Crone JS, Lutkenhoff ES, Vespa PM, Monti MM. Evaluating denoising strategies in resting-state functional magnetic resonance in traumatic brain injury (EpiBioS4Rx). Hum Brain Mapp 2022; 43:4640-4649. [PMID: 35723510 PMCID: PMC9491287 DOI: 10.1002/hbm.25979] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 05/17/2022] [Accepted: 05/29/2022] [Indexed: 11/11/2022] Open
Abstract
Resting-state functional MRI is increasingly used in the clinical setting and is now included in some diagnostic guidelines for severe brain injury patients. However, to ensure high-quality data, one should mitigate fMRI-related noise typical of this population. Therefore, we aimed to evaluate the ability of different preprocessing strategies to mitigate noise-related signal (i.e., in-scanner movement and physiological noise) in functional connectivity (FC) of traumatic brain injury (TBI) patients. We applied nine commonly used denoising strategies, combined into 17 pipelines, to 88 TBI patients from the Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy clinical trial. Pipelines were evaluated by three quality control (QC) metrics across three exclusion regimes based on the participant's head movement profile. While no pipeline eliminated noise effects on FC, some pipelines exhibited relatively high effectiveness depending on the exclusion regime. Once high-motion participants were excluded, the choice of denoising pipeline becomes secondary - although this strategy leads to substantial data loss. Pipelines combining spike regression with physiological regressors were the best performers, whereas pipelines that used automated data-driven methods performed comparatively worse. In this study, we report the first large-scale evaluation of denoising pipelines aimed at reducing noise-related FC in a clinical population known to be highly susceptible to in-scanner motion and significant anatomical abnormalities. If resting-state functional magnetic resonance is to be a successful clinical technique, it is crucial that procedures mitigating the effect of noise be systematically evaluated in the most challenging populations, such as TBI datasets.
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Affiliation(s)
- Marina Weiler
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA
| | - Raphael F Casseb
- Neuroimaging Laboratory, University of Campinas, Campinas, São Paulo, Brazil
| | - Brunno M de Campos
- Neuroimaging Laboratory, University of Campinas, Campinas, São Paulo, Brazil
| | - Julia S Crone
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA
| | - Evan S Lutkenhoff
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA
| | - Paul M Vespa
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Martin M Monti
- Department of Psychology, University of California Los Angeles, Los Angeles, California, USA.,Department of Neurosurgery, Brain Injury Research Center, University of California Los Angeles, Los Angeles, California, USA
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Gkiatis K, Garganis K, Benjamin CF, Karanasiou I, Kondylidis N, Harushukuri J, Matsopoulos GK. Standardization of presurgical language fMRI in Greek population: Mapping of six critical regions. Brain Behav 2022; 12:e2609. [PMID: 35587046 PMCID: PMC9226851 DOI: 10.1002/brb3.2609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 04/04/2022] [Accepted: 04/12/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Mapping the language system has been crucial in presurgical evaluation especially when the area to be resected is near relevant eloquent cortex. Functional magnetic resonance imaging (fMRI) proved to be a noninvasive alternative of Wada test that can account not only for language lateralization but also for localization when appropriate tasks and MRI sequences are being used. The tasks utilized during the fMRI acquisition are playing a crucial role as to which areas will be activated. Recent studies demonstrated that key language regions exist outside the classical model of "Wernicke-Lichtheim-Geschwind," but sensitive tasks must take place in order to be revealed. On top of that, the tasks should be in mother tongue for appropriate language mapping to be possible. METHODS For that reason, in this study, we adopted an English protocol that can reveal six language critical regions even in clinical setups and we translated it into Greek to prove its efficacy in Greek population. Twenty healthy right-handed volunteers were recruited and performed the fMRI acquisition in a standardized manner. RESULTS Results demonstrated that all six language critical regions were activated in all subjects as well as the group mean map. Furthermore, activations were found in the thalamus, the caudate, and the contralateral cerebellum. CONCLUSION In this study, we standardized an fMRI protocol in Greek and proved that it can reliably activate six language critical regions. We have validated its efficacy for presurgical language mapping in Greek patients capable to be adopted in clinical setup.
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Affiliation(s)
- Kostakis Gkiatis
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.,Epilepsy Monitoring Unit, St. Luke's Hospital, Thessaloniki, Greece
| | | | - Christopher F Benjamin
- Department of Neurology, Comprehensive Epilepsy Center, Yale School of Medicine, New Haven, Connecticut, USA.,Department of Neurosurgery, Yale School of Medicine, New Haven, Connecticut, USA
| | - Irene Karanasiou
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | | | - Jean Harushukuri
- Epilepsy Monitoring Unit, St. Luke's Hospital, Thessaloniki, Greece
| | - George K Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
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Lutkenhoff ES, Nigri A, Rossi Sebastiano D, Sattin D, Visani E, Rosazza C, D'Incerti L, Bruzzone MG, Franceschetti S, Leonardi M, Ferraro S, Monti MM. EEG Power spectra and subcortical pathology in chronic disorders of consciousness. Psychol Med 2022; 52:1491-1500. [PMID: 32962777 DOI: 10.1017/s003329172000330x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Despite a growing understanding of disorders of consciousness following severe brain injury, the association between long-term impairment of consciousness, spontaneous brain oscillations, and underlying subcortical damage, and the ability of such information to aid patient diagnosis, remains incomplete. METHODS Cross-sectional observational sample of 116 patients with a disorder of consciousness secondary to brain injury, collected prospectively at a tertiary center between 2011 and 2013. Multimodal analyses relating clinical measures of impairment, electroencephalographic measures of spontaneous brain activity, and magnetic resonance imaging data of subcortical atrophy were conducted in 2018. RESULTS In the final analyzed sample of 61 patients, systematic associations were found between electroencephalographic power spectra and subcortical damage. Specifically, the ratio of beta-to-delta relative power was negatively associated with greater atrophy in regions of the bilateral thalamus and globus pallidus (both left > right) previously shown to be preferentially atrophied in chronic disorders of consciousness. Power spectrum total density was also negatively associated with widespread atrophy in regions of the left globus pallidus, right caudate, and in the brainstem. Furthermore, we showed that the combination of demographics, encephalographic, and imaging data in an analytic framework can be employed to aid behavioral diagnosis. CONCLUSIONS These results ground, for the first time, electroencephalographic presentation detected with routine clinical techniques in the underlying brain pathology of disorders of consciousness and demonstrate how multimodal combination of clinical, electroencephalographic, and imaging data can be employed in potentially mitigating the high rates of misdiagnosis typical of this patient cohort.
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Affiliation(s)
- Evan S Lutkenhoff
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
- Brain Injury Research Center (BIRC), Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Anna Nigri
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Davide Rossi Sebastiano
- Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Davide Sattin
- Neurology, Public Health, Disability Unit and Coma Research Centre, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Elisa Visani
- Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Cristina Rosazza
- Scientific Direction, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Ludovico D'Incerti
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Maria Grazia Bruzzone
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Silvana Franceschetti
- Department of Neurophysiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Matilde Leonardi
- Neurology, Public Health, Disability Unit and Coma Research Centre, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Stefania Ferraro
- Department of Neuroradiology, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
- School of Life Science and Technology, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China: On the behalf of the Coma Research Center, Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Milan, Italy
| | - Martin M Monti
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA
- Brain Injury Research Center (BIRC), Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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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.
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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
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50
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Tejavibulya L, Peterson H, Greene A, Gao S, Rolison M, Noble S, Scheinost D. Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity. Neuroimage 2022; 252:119040. [PMID: 35272202 PMCID: PMC9013515 DOI: 10.1016/j.neuroimage.2022.119040] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 11/15/2022] Open
Abstract
Handedness influences differences in lateralization of language areas as well as dominance of motor and somatosensory cortices. However, differences in whole-brain functional connectivity (i.e., functional connectomes) due to handedness have been relatively understudied beyond pre-specified networks of interest. Here, we compared functional connectomes of left- and right-handed individuals at the whole brain level. We explored differences in functional connectivity of previously established regions of interest, and showed differences between primarily left- and primarily right-handed individuals in the motor, somatosensory, and language areas using functional connectivity. We then proceeded to investigate these differences in the whole brain and found that the functional connectivity of left- and right-handed individuals are not specific to networks of interest, but extend across every region of the brain. In particular, we found that connections between and within the cerebellum show distinct patterns of connectivity. To put these effects into context, we show that the effect sizes associated with handedness differences account for a similar amount of individual differences in the connectome as sex differences. Together these results shed light on regions of the brain beyond those traditionally explored that contribute to differences in the functional organization of left- and right-handed individuals and underscore that handedness effects are neurobiologically meaningful in addition to being statistically significant.
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Affiliation(s)
- Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA.
| | - Hannah Peterson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Abigail Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; MD PhD Program, Yale School of Medicine, New Haven, CT, USA
| | - Siyuan Gao
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Max Rolison
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA
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