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Pinto J, Comprido C, Moreira V, Maccarone MT, Cogoni C, Faustino R, Pignatelli D, Cera N. The Complex Role Played by the Default Mode Network during Sexual Stimulation: A Cluster-Based fMRI Meta-Analysis. Behav Sci (Basel) 2024; 14:570. [PMID: 39062393 PMCID: PMC11273531 DOI: 10.3390/bs14070570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/14/2024] [Accepted: 07/02/2024] [Indexed: 07/28/2024] Open
Abstract
The default mode network (DMN) is a complex network that plays a significant and active role during naturalistic stimulation. Previous studies that have used naturalistic stimuli, such as real-life stories or silent or sonorous films, have found that the information processing involved a complex hierarchical set of brain regions, including the DMN nodes. The DMN is not involved in low-level features and is only associated with high-level content-related incoming information. The human sexual experience involves a complex set of processes related to both external context and inner processes. Since the DMN plays an active role in the integration of naturalistic stimuli and aesthetic perception with beliefs, thoughts, and episodic autobiographical memories, we aimed at quantifying the involvement of the nodes of the DMN during visual sexual stimulation. After a systematic search in the principal electronic databases, we selected 83 fMRI studies, and an ALE meta-analysis was calculated. We performed conjunction analyses to assess differences in the DMN related to stimulus modalities, sex differences, and sexual orientation. The results show that sexual stimulation alters the topography of the DMN and highlights the DMN's active role in the integration of sexual stimuli with sexual schemas and beliefs.
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Affiliation(s)
- Joana Pinto
- Faculty of Psychology and Education Sciences, University of Porto, 4200-135 Porto, Portugal (C.C.)
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - Camila Comprido
- Faculty of Psychology and Education Sciences, University of Porto, 4200-135 Porto, Portugal (C.C.)
| | - Vanessa Moreira
- Faculty of Psychology and Education Sciences, University of Porto, 4200-135 Porto, Portugal (C.C.)
| | | | - Carlotta Cogoni
- Instituto de Biofísica e Engenharia Biomédica, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal;
| | - Ricardo Faustino
- Research Unit in Medical Imaging and Radiotherapy, Cross I&D Lisbon Research Center, Escola Superior de Saúde da Cruz Vermelha Portuguesa, 1300-125 Lisbon, Portugal;
| | - Duarte Pignatelli
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
- Department of Endocrinology, Centro Hospitalar Universitário de São João, 4200-319 Porto, Portugal
| | - Nicoletta Cera
- Faculty of Psychology and Education Sciences, University of Porto, 4200-135 Porto, Portugal (C.C.)
- Research Unit in Medical Imaging and Radiotherapy, Cross I&D Lisbon Research Center, Escola Superior de Saúde da Cruz Vermelha Portuguesa, 1300-125 Lisbon, Portugal;
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Costa T, Ferraro M, Manuello J, Camasio A, Nani A, Mancuso L, Cauda F, Fox PT, Liloia D. Activation Likelihood Estimation Neuroimaging Meta-Analysis: a Powerful Tool for Emotion Research. Psychol Res Behav Manag 2024; 17:2331-2345. [PMID: 38882233 PMCID: PMC11179639 DOI: 10.2147/prbm.s453035] [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: 01/23/2024] [Accepted: 05/31/2024] [Indexed: 06/18/2024] Open
Abstract
Over the past two decades, functional magnetic resonance imaging (fMRI) has become the primary tool for exploring neural correlates of emotion. To enhance the reliability of results in understanding the complex nature of emotional experiences, researchers combine findings from multiple fMRI studies using coordinate-based meta-analysis (CBMA). As one of the most widely employed CBMA methods worldwide, activation likelihood estimation (ALE) is of great importance in affective neuroscience and neuropsychology. This comprehensive review provides an introductory guide for implementing the ALE method in emotion research, outlining the experimental steps involved. By presenting a case study about the emotion of disgust, with regard to both its core and social processing, we offer insightful commentary as to how ALE can enable researchers to produce consistent results and, consequently, fruitfully investigate the neural mechanisms underpinning emotions, facilitating further progress in this field.
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Affiliation(s)
- Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Mario Ferraro
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
- Department of Physics, University of Turin, Turin, Italy
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Alessia Camasio
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
- Department of Physics, University of Turin, Turin, Italy
| | - Andrea Nani
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Lorenzo Mancuso
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
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Bulut T, Hagoort P. Contributions of the left and right thalami to language: A meta-analytic approach. Brain Struct Funct 2024:10.1007/s00429-024-02795-3. [PMID: 38625556 DOI: 10.1007/s00429-024-02795-3] [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: 09/23/2023] [Accepted: 03/25/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Despite a pervasive cortico-centric view in cognitive neuroscience, subcortical structures including the thalamus have been shown to be increasingly involved in higher cognitive functions. Previous structural and functional imaging studies demonstrated cortico-thalamo-cortical loops which may support various cognitive functions including language. However, large-scale functional connectivity of the thalamus during language tasks has not been examined before. METHODS The present study employed meta-analytic connectivity modeling to identify language-related coactivation patterns of the left and right thalami. The left and right thalami were used as regions of interest to search the BrainMap functional database for neuroimaging experiments with healthy participants reporting language-related activations in each region of interest. Activation likelihood estimation analyses were then carried out on the foci extracted from the identified studies to estimate functional convergence for each thalamus. A functional decoding analysis based on the same database was conducted to characterize thalamic contributions to different language functions. RESULTS The results revealed bilateral frontotemporal and bilateral subcortical (basal ganglia) coactivation patterns for both the left and right thalami, and also right cerebellar coactivations for the left thalamus, during language processing. In light of previous empirical studies and theoretical frameworks, the present connectivity and functional decoding findings suggest that cortico-subcortical-cerebellar-cortical loops modulate and fine-tune information transfer within the bilateral frontotemporal cortices during language processing, especially during production and semantic operations, but also other language (e.g., syntax, phonology) and cognitive operations (e.g., attention, cognitive control). CONCLUSION The current findings show that the language-relevant network extends beyond the classical left perisylvian cortices and spans bilateral cortical, bilateral subcortical (bilateral thalamus, bilateral basal ganglia) and right cerebellar regions.
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Affiliation(s)
- Talat Bulut
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
- Department of Speech and Language Therapy, School of Health Sciences, Istanbul Medipol University, Istanbul, Turkey.
| | - Peter Hagoort
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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El Ouardi L, Yeou M, Faroqi-Shah Y. Neural correlates of pronoun processing: An activation likelihood estimation meta-analysis. BRAIN AND LANGUAGE 2023; 246:105347. [PMID: 37847932 PMCID: PMC11305457 DOI: 10.1016/j.bandl.2023.105347] [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: 05/23/2023] [Revised: 08/30/2023] [Accepted: 10/10/2023] [Indexed: 10/19/2023]
Abstract
Pronouns are unique linguistic devices that allow for the expression of referential relationships. Despite their communicative utility, the neural correlates of the operations involved in reference assignment and/or resolution, are not well-understood. The present study synthesized the neuroimaging literature on pronoun processing to test extant theories of pronoun comprehension. Following the PRISMA guidelines and thebest-practice recommendations for neuroimaging meta-analyses, a systematic literature search and record assessment were performed. As a result, 16 fMRI studies were included in the meta-analysis, and were coded in Scribe 3.6 for inclusion in the BrainMap database. The activation coordinates for the contrasts of interest were transformed into Talairach space and submitted to an Activation Likelihood Estimation (ALE) meta-analysis in GingerALE 3.0.1. The results indicated that pronoun processing had functional convergence in the left posterior middle and superior temporal gyri, potentially reflecting the retrieval, prediction and integration roles of these areas for pronoun processing.
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Affiliation(s)
- Loubna El Ouardi
- Department of Hearing and Speech Sciences, University of Maryland, College Park, Maryland, United States; Applied Language and Culture Studies Laboratory, Chouaib Doukkali University, El Jadida, Morocco.
| | - Mohamed Yeou
- Applied Language and Culture Studies Laboratory, Chouaib Doukkali University, El Jadida, Morocco
| | - Yasmeen Faroqi-Shah
- Department of Hearing and Speech Sciences, University of Maryland, College Park, Maryland, United States
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Coughlin B, Muñoz W, Kfir Y, Young MJ, Meszéna D, Jamali M, Caprara I, Hardstone R, Khanna A, Mustroph ML, Trautmann EM, Windolf C, Varol E, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Mark Richardson R, Williams ZM, Cash SS, Paulk AC. Modified Neuropixels probes for recording human neurophysiology in the operating room. Nat Protoc 2023; 18:2927-2953. [PMID: 37697108 DOI: 10.1038/s41596-023-00871-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 06/08/2023] [Indexed: 09/13/2023]
Abstract
Neuropixels are silicon-based electrophysiology-recording probes with high channel count and recording-site density. These probes offer a turnkey platform for measuring neural activity with single-cell resolution and at a scale that is beyond the capabilities of current clinically approved devices. Our team demonstrated the first-in-human use of these probes during resection surgery for epilepsy or tumors and deep brain stimulation electrode placement in patients with Parkinson's disease. Here, we provide a better understanding of the capabilities and challenges of using Neuropixels as a research tool to study human neurophysiology, with the hope that this information may inform future efforts toward regulatory approval of Neuropixels probes as research devices. In perioperative procedures, the major concerns are the initial sterility of the device, maintaining a sterile field during surgery, having multiple referencing and grounding schemes available to de-noise recordings (if necessary), protecting the silicon probe from accidental contact before insertion and obtaining high-quality action potential and local field potential recordings. The research team ensures that the device is fully operational while coordinating with the surgical team to remove sources of electrical noise that could otherwise substantially affect the signals recorded by the sensitive hardware. Prior preparation using the equipment and training in human clinical research and working in operating rooms maximize effective communication within and between the teams, ensuring high recording quality and minimizing the time added to the surgery. The perioperative procedure requires ~4 h, and the entire protocol requires multiple weeks.
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Affiliation(s)
- Brian Coughlin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - William Muñoz
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Yoav Kfir
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Michael J Young
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Domokos Meszéna
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Mohsen Jamali
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Irene Caprara
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Richard Hardstone
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Arjun Khanna
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Martina L Mustroph
- Department of Neurosurgery, Harvard Medical School and Brigham & Women's Hospital, Boston, MA, USA
| | - Eric M Trautmann
- Department of Neuroscience, Columbia University Medical Center, New York, NY, USA
- Zuckerman Institute, Columbia University, New York, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York, NY, USA
| | - Charlie Windolf
- Department of Statistics, Zuckerman Institute, Columbia University, New York, NY, USA
| | - Erdem Varol
- Department of Statistics, Zuckerman Institute, Columbia University, New York, NY, USA
- Department of Computer Science and Engineering, Zuckerman Institute, Columbia University, New York, NY, USA
| | - Dan J Soper
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Sergey D Stavisky
- Department of Neurological Surgery, University of California Davis, Davis, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
| | | | | | - Krishna V Shenoy
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - R Mark Richardson
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA.
| | - Sydney S Cash
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
| | - Angelique C Paulk
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
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Soper DJ, Reich D, Ross A, Salami P, Cash SS, Basu I, Peled N, Paulk AC. Modular pipeline for reconstruction and localization of implanted intracranial ECoG and sEEG electrodes. PLoS One 2023; 18:e0287921. [PMID: 37418486 PMCID: PMC10328232 DOI: 10.1371/journal.pone.0287921] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 06/15/2023] [Indexed: 07/09/2023] Open
Abstract
Implantation of electrodes in the brain has been used as a clinical tool for decades to stimulate and record brain activity. As this method increasingly becomes the standard of care for several disorders and diseases, there is a growing need to quickly and accurately localize the electrodes once they are placed within the brain. We share here a protocol pipeline for localizing electrodes implanted in the brain, which we have applied to more than 260 patients, that is accessible to multiple skill levels and modular in execution. This pipeline uses multiple software packages to prioritize flexibility by permitting multiple different parallel outputs while minimizing the number of steps for each output. These outputs include co-registered imaging, electrode coordinates, 2D and 3D visualizations of the implants, automatic surface and volumetric localizations of the brain regions per electrode, and anonymization and data sharing tools. We demonstrate here some of the pipeline's visualizations and automatic localization algorithms which we have applied to determine appropriate stimulation targets, to conduct seizure dynamics analysis, and to localize neural activity from cognitive tasks in previous studies. Further, the output facilitates the extraction of information such as the probability of grey matter intersection or the nearest anatomic structure per electrode contact across all data sets that go through the pipeline. We expect that this pipeline will be a useful framework for researchers and clinicians alike to localize implanted electrodes in the human brain.
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Affiliation(s)
- Daniel J. Soper
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Neurology, Harvard Medical School, Boston, MA, United States of America
| | - Dustine Reich
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Alex Ross
- Department of Neurosurgery, University of Cincinnati College of Medicine, Cincinnati, OH, United States of America
| | - Pariya Salami
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Neurology, Harvard Medical School, Boston, MA, United States of America
| | - Sydney S. Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Neurology, Harvard Medical School, Boston, MA, United States of America
| | - Ishita Basu
- Department of Neurosurgery, University of Cincinnati College of Medicine, Cincinnati, OH, United States of America
| | - Noam Peled
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Angelique C. Paulk
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Neurology, Harvard Medical School, Boston, MA, United States of America
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7
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Paré S, Bleau M, Dricot L, Ptito M, Kupers R. Brain structural changes in blindness: a systematic review and an anatomical likelihood estimation (ALE) meta-analysis. Neurosci Biobehav Rev 2023; 150:105165. [PMID: 37054803 DOI: 10.1016/j.neubiorev.2023.105165] [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: 09/22/2022] [Revised: 03/23/2023] [Accepted: 04/09/2023] [Indexed: 04/15/2023]
Abstract
In recent decades, numerous structural brain imaging studies investigated purported morphometric changes in early (EB) and late onset blindness (LB). The results of these studies have not yielded very consistent results, neither with respect to the type, nor to the anatomical locations of the brain morphometric alterations. To better characterize the effects of blindness on brain morphometry, we performed a systematic review and an Anatomical-Likelihood-Estimation (ALE) coordinate-based-meta-analysis of 65 eligible studies on brain structural changes in EB and LB, including 890 EB, 466 LB and 1257 sighted controls. Results revealed atrophic changes throughout the whole extent of the retino-geniculo-striate system in both EB and LB, whereas changes in areas beyond the occipital lobe occurred in EB only. We discuss the nature of some of the contradictory findings with respect to the used brain imaging methodologies and characteristics of the blind populations such as the onset, duration and cause of blindness. Future studies should aim for much larger sample sizes, eventually by merging data from different brain imaging centers using the same imaging sequences, opt for multimodal structural brain imaging, and go beyond a purely structural approach by combining functional with structural connectivity network analyses.
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Affiliation(s)
- Samuel Paré
- School of Optometry, University of Montreal, Montreal, Qc, Canada
| | - Maxime Bleau
- School of Optometry, University of Montreal, Montreal, Qc, Canada
| | - Laurence Dricot
- Institute of NeuroScience (IoNS), Université catholique de Louvain (UCLouvain), Bruxelles, Belgium
| | - Maurice Ptito
- School of Optometry, University of Montreal, Montreal, Qc, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Qc, Canada; Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
| | - Ron Kupers
- School of Optometry, University of Montreal, Montreal, Qc, Canada; Institute of NeuroScience (IoNS), Université catholique de Louvain (UCLouvain), Bruxelles, Belgium; Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark.
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8
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A deep learning-based framework for automatic detection of drug resistance in tuberculosis patients. EGYPTIAN INFORMATICS JOURNAL 2023. [DOI: 10.1016/j.eij.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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9
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Luo J, Yi P, Liang M, Zhang S, Tao Q, Li N, Zhang H, Wen J, Xue X, Fan C, Li X. Distinct brain activity alterations of treatment for bipolar disorders with psychotherapy and drug therapy: activation likelihood estimation meta-analysis. Psychol Med 2023; 53:625-637. [PMID: 36722029 DOI: 10.1017/s0033291722003889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUNDS Many studies suggest that both psychotherapy and drug therapy are effective in the treatment of bipolar disorders (BDs). However, the pathophysiology of both types of intervention has not been established definitively. METHODS An activation likelihood estimation meta-analysis was performed to identify the distinct brain activity alterations between psychotherapy and drug therapy for the treatment of BDs. Articles were identified by searching databases including PubMed, Embase, Cochrane Library, and Web of Science databases. Eligible studies on BDs were published up until 10 June 2021. RESULTS 21 studies were included and we conducted a meta-analysis for different therapies and imaging tasks. After receiving psychotherapy, BD patients showed increased activation in the inferior frontal gyrus (IFG) and superior temporal gyrus. While after taking drug therapy, BD patients displayed increased activation in the anterior cingulate cortex, medial frontal gyrus, IFG, and decreased activation in the posterior cingulate cortex. The regions of brain activity changes caused by psychotherapy were mostly focused on the frontal areas, while drug therapy mainly impacted on the limbic areas. Different type of tasks also affected brain regions which were activated. CONCLUSIONS Our comprehensive meta-analysis indicates that these two treatments might have effect on BD in their own therapeutic modes. Psychotherapy might have a top-down effect, while drug therapy might have a bottom-up effect. This study may contribute to differential diagnosis of BDs and would be helpful to finding more accurate neuroimaging biomarkers for BD treatment.
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Affiliation(s)
- Jingyi Luo
- Centre for Translational Medicine, Second Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Pengcheng Yi
- Department of Clinical Psychology, the Third People's Hospital of Xiangshan County, Ningbo, China
| | - Meng Liang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Shuyu Zhang
- School of Psychology, the Australian National University, Canberra, Australia
| | - Qian Tao
- Department of Psychology, School of Basic Medicine, Jinan University, Guangzhou, China
| | - Ni Li
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Han Zhang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Jialin Wen
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Xinrong Xue
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
| | - Chuan Fan
- Department of Psychiatry, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Xiaoming Li
- Centre for Translational Medicine, Second Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, China
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10
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Zhang L, Liu H, Ren Z, Wang X, Meng X, Wei X, Yang J. Coronal T2-weighted imaging improves the measurement accuracy of the subarachnoid space in infants: A descriptive study. J Clin Transl Res 2022; 8:532-539. [PMID: 36518203 PMCID: PMC9741930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND The subarachnoid space width (SASw) is part of crucial neuroimaging criteria for the diagnosis of subarachnoid space enlargement in infants. In addition to indicating the presence of these diseases, SASw can be used to assess their severity. Therefore, it is important to be able to measure the SASw accurately. AIM This study aimed to compare the accuracy of measurements made from axial and coronal T2-weighted imaging (T2WI) and to establish a consentaneous measurement scheme of SASw in infants. METHODS A total of 63 infants (31 males and 32 females) aged 4 days to 24 months were enrolled in this study. The supratentorial subarachnoid space volume (SASv) and corrected SASv (cSASv) were used as the gold standard reference. The SASw (including interhemispheric width and bilateral frontal craniocortical width) was measured on axial and coronal T2WI. The intra- and inter-observer reproducibility and agreement of the SASw were assessed by the intraclass correlation coefficient (ICC) and Bland-Altman analysis. A paired t-test was used to compare SASw measured on axial and coronal images. The accuracy of SASw measurements made from axial and coronal T2WI was evaluated by the relationships between the SASw and supratentorial SASv and between the SASw and supratentorial cSASv, and the relationships were examined by multivariate linear regression. RESULTS The intra- and inter-observer ICC values of the three SASw measurements were greater on coronal T2WI than on axial T2WI. Bland-Altman analysis confirmed that the SASw values measured on coronal T2WI had better intra- and inter-observer agreement than axial T2WI. According to the multivariate linear regression results, model 4 (the SASw measured in coronal T2WI) was the best predictor of supratentorial cSASv (R2 = 0.755). CONCLUSIONS The SASw measured on coronal T2WI was more repeatable and accurate than axial T2WI and was more representative of the actual cerebrospinal fluid accumulation in the supratentorial subarachnoid space. RELEVANCE FOR PATIENTS The SASw has been found to be a simple and essential substitution for supratentorial SASv, which can be measured on both axial T2WI passing through the bodies of the bilateral ventricles and coronal T2WI at the level of the foramen of Monro. The SASw measured on coronal T2WI was more beneficial to the diagnosis and severity assessment of subarachnoid space enlargement in infants.
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Affiliation(s)
- Lei Zhang
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, P.R. China
- Department of Radiology, Baoji Hi-Tech Hospital, Baoji 721013, Shaanxi, P.R. China
| | - Heng Liu
- Medical Imaging Center of Guizhou Province, Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Zunyi 563003, Guizhou, P.R. China
| | - Zhuanqin Ren
- Department of Radiology, Baoji Center Hospital, Baoji 721008, Shaanxi, P.R. China
| | - Xiaohu Wang
- Department of Radiology, Baoji Center Hospital, Baoji 721008, Shaanxi, P.R. China
| | - Xiaoli Meng
- Department of Radiology, Baoji Center Hospital, Baoji 721008, Shaanxi, P.R. China
| | - Xiaocheng Wei
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, P.R. China
| | - Jian Yang
- Department of Radiology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi, P.R. China
- Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710054, Shaanxi, P.R. China
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11
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Cera N, Monteiro J, Esposito R, Di Francesco G, Cordes D, Caldwell JZK, Cieri F. Neural correlates of psychodynamic and non-psychodynamic therapies in different clinical populations through fMRI: A meta-analysis and systematic review. Front Hum Neurosci 2022; 16:1029256. [PMID: 36644207 PMCID: PMC9832372 DOI: 10.3389/fnhum.2022.1029256] [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: 08/27/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022] Open
Abstract
Background The COVID-19 pandemic has exacerbated the ongoing crisis in psychiatric and psychological care, contributing to what we have identified as a new psychological and psychiatric pandemic. Psychotherapy is an effective method for easing the psychological suffering experienced also by the various impacts of COVID-19. This treatment can be examined from a neurological perspective, through the application of brain imaging techniques. Specifically, the meta-analysis of imaging studies can aid in expanding researchers' understanding of the many beneficial applications of psychotherapy. Objectives We examined the functional brain changes accompanying different mental disorders with functional Magnetic Resonance Imaging (fMRI), through a meta-analysis, and systematic review in order to better understand the general neural mechanism involved in psychotherapy and the potential neural difference between psychodynamic and non-psychodynamic approaches. Data sources The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were employed for our systematic review and meta-analysis. We conducted a computer-based literature search, following the Population, Intervention, Comparison and Outcomes (PICO) approach, to retrieve all published articles in English regarding the above-described topics from PubMed (MEDLINE), Scopus, and Web of Science. Study eligibility criteria participants and interventions We combined terms related to psychotherapy and fMRI: ("psychotherapy" [All Fields] OR "psychotherapy" [MeSH Terms] OR "psychotherapy" [All Fields] OR "psychotherapies" [All Fields] OR "psychotherapy s" [All Fields]) AND ("magnetic resonance imaging" [MeSH Terms]) OR ("magnetic"[All Fields] AND "resonance"[All Fields] AND "imaging"[All Fields]) OR ("magnetic resonance imaging"[All Fields] OR "fmri"[All Fields]). We considered (1) whole brain fMRI studies; (2) studies in which participants have been involved in a clinical trial with psychotherapy sessions, with pre/post fMRI; (3) fMRI results presented in coordinate-based (x, y, and z) in MNI or Talairach space; (4) presence of neuropsychiatric patients. The exclusion criteria were: (1) systematic review or meta-analysis; (2) behavioral study; (3) single-case MRI or fMRI study; and (4) other imaging techniques (i.e., PET, SPECT) or EEG. Results After duplicates removal and assessment of the content of each published study, we included 38 sources. The map including all studies that assessed longitudinal differences in brain activity showed two homogeneous clusters in the left inferior frontal gyrus, and caudally involving the anterior insular cortex (p < 0.0001, corr.). Similarly, studies that assessed psychotherapy-related longitudinal changes using emotional or cognitive tasks (TASK map) showed a left-sided homogeneity in the anterior insula (p < 0.000) extending to Broca's area of the inferior frontal gyrus (p < 0.0001) and the superior frontal gyrus (p < 0.0001). Studies that applied psychodynamic psychotherapy showed Family-Wise Error (FWE) cluster-corrected (p < 0.05) homogeneity values in the right superior and inferior frontal gyri, with a small cluster in the putamen. No FWE-corrected homogeneity foci were observed for Mindful- based and cognitive behavioral therapy psychotherapy. In both pre- and post-therapy results, studies showed two bilateral clusters in the dorsal anterior insulae (p = 0.00001 and p = 0.00003, respectively) and involvement of the medial superior frontal gyrus (p = 0.0002). Limitations Subjective experiences, such as an individual's response to therapy, are intrinsically challenging to quantify as objective, factual realities. Brain changes observed both pre- and post-therapy could be related to other factors, not necessary to the specific treatment received. Therapeutic modalities and study designs are generally heterogeneous. Differences exist in sample characteristics, such as the specificity of the disorder and number and duration of sessions. Moreover, the sample size is relatively small, particularly due to the paucity of studies in this field and the little contribution of PDT. Conclusions and implications of key findings All psychological interventions seem to influence the brain from a functional point of view, showing their efficacy from a neurological perspective. Frontal, prefrontal regions, insular cortex, superior and inferior frontal gyrus, and putamen seem involved in these neural changes, with the psychodynamic more linked to the latter three regions.
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Affiliation(s)
- Nicoletta Cera
- Faculty of Psychology and Education Sciences, University of Porto, Porto, Portugal
- CIBIT-Coimbra Institute for Biomedical Imaging and Translational Research, Coimbra, Portugal
| | - Jessica Monteiro
- Faculty of Psychology and Education Sciences, University of Porto, Porto, Portugal
| | - Roberto Esposito
- Department of Radiology, Area Vasta 1/ASUR Marche, Pesaro, Italy
| | | | - Dietmar Cordes
- Department of Neurology, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
- Department of Brain Health, University of Nevada, Las Vegas, NV, United States
- Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, United States
| | - Jessica Z. K. Caldwell
- Department of Neurology, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
| | - Filippo Cieri
- Department of Neurology, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
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Cao H, Lin F, Ke B, Song J, Xue Y, Fang X, Zeng E. Alterations of amplitude of low-frequency fluctuations and fractional amplitude of low-frequency fluctuations in end-stage renal disease on maintenance dialysis: An activation likelihood estimation meta-analysis. Front Hum Neurosci 2022; 16:1040553. [PMID: 36530199 PMCID: PMC9751321 DOI: 10.3389/fnhum.2022.1040553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 11/16/2022] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Cognitive impairment (CI) is a common complication of end-stage renal disease (ESRD). Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have identified abnormal spontaneous low-frequency brain activity in ESRD dialysis patients. However, these studies have reported inconsistent results. So far, no meta-analyses on this topic have been published. This meta-analysis aimed to identify the more consistently vulnerable brain regions in ESRD patients at rest and to reveal its possible neuropathophysiological mechanisms. METHODS We systematically searched PubMed, Cochrane Library, Web of Science, Medline, and EMBASE databases up to July 20, 2022 based on the amplitude of low-frequency fluctuation (ALFF) or fractional amplitude of low-frequency fluctuation (fALFF). Brain regions with abnormal spontaneous neural activity in ESRD compared to healthy controls (HCs) from previous studies were integrated and analyzed using an activation likelihood estimation (ALE) method. Jackknife sensitivity analysis was carried out to assess the reproducibility of the results. RESULTS In total, 11 studies (380 patients and 351 HCs) were included in the final analysis. According to the results of the meta-analysis, compared with HCs, ESRD patients had decreased ALFF/fALFF in the right precuneus, right cuneus, and left superior temporal gyrus (STG), while no brain regions with increased brain activity were identified. Jackknife sensitivity analysis showed that our results were highly reliable. CONCLUSION Compared to HCs, ESRD dialysis patients exhibit significant abnormalities in spontaneous neural activity associated with CI, occurring primarily in the default mode network, visual recognition network (VRN), and executive control network (ECN). This contributes to the understanding of its pathophysiological mechanisms. SYSTEMATIC REVIEW REGISTRATION [https://www.crd.york.ac.uk/prospero/], identifier [CRD42022348694].
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Affiliation(s)
- Huiling Cao
- Department of Nephrology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Feng Lin
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Ben Ke
- Department of Nephrology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Jianling Song
- Department of Nephrology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yuting Xue
- Department of Nephrology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xiangdong Fang
- Department of Nephrology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Erming Zeng
- Department of Neurosurgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
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Lim J, Wang PT, Shaw SJ, Gong H, Armacost M, Liu CY, Do AH, Heydari P, Nenadic Z. Artifact propagation in subdural cortical electrostimulation: Characterization and modeling. Front Neurosci 2022; 16:1021097. [PMID: 36312030 PMCID: PMC9596776 DOI: 10.3389/fnins.2022.1021097] [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: 08/17/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
Cortical stimulation via electrocorticography (ECoG) may be an effective method for inducing artificial sensation in bi-directional brain-computer interfaces (BD-BCIs). However, strong electrical artifacts caused by electrostimulation may significantly degrade or obscure neural information. A detailed understanding of stimulation artifact propagation through relevant tissues may improve existing artifact suppression techniques or inspire the development of novel artifact mitigation strategies. Our work thus seeks to comprehensively characterize and model the propagation of artifacts in subdural ECoG stimulation. To this end, we collected and analyzed data from eloquent cortex mapping procedures of four subjects with epilepsy who were implanted with subdural ECoG electrodes. From this data, we observed that artifacts exhibited phase-locking and ratcheting characteristics in the time domain across all subjects. In the frequency domain, stimulation caused broadband power increases, as well as power bursts at the fundamental stimulation frequency and its super-harmonics. The spatial distribution of artifacts followed the potential distribution of an electric dipole with a median goodness-of-fit of R2 = 0.80 across all subjects and stimulation channels. Artifacts as large as ±1,100 μV appeared anywhere from 4.43 to 38.34 mm from the stimulation channel. These temporal, spectral and spatial characteristics can be utilized to improve existing artifact suppression techniques, inspire new strategies for artifact mitigation, and aid in the development of novel cortical stimulation protocols. Taken together, these findings deepen our understanding of cortical electrostimulation and provide critical design specifications for future BD-BCI systems.
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Affiliation(s)
- Jeffrey Lim
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- *Correspondence: Jeffrey Lim
| | - Po T. Wang
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Susan J. Shaw
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States
| | - Hui Gong
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States
| | - Michelle Armacost
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States
| | - Charles Y. Liu
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States
| | - An H. Do
- Department of Neurology, University of California, Irvine, Irvine, CA, United States
| | - Payam Heydari
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Zoran Nenadic
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
- Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, United States
- Zoran Nenadic
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Hamdy E, Galeel AA, Ramadan I, Gaber D, Mustafa H, Mekky J. Iron deposition in multiple sclerosis: overall load or distribution alteration? Eur Radiol Exp 2022; 6:49. [PMID: 36074209 PMCID: PMC9458829 DOI: 10.1186/s41747-022-00279-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/14/2022] [Indexed: 11/14/2022] Open
Abstract
Background Though abnormal iron deposition has been reported in specific brain regions in multiple sclerosis (MS), no data exist about whether the overall quantity of iron in the brain is altered or not. We aimed to determine whether the noted aberrant iron deposition in MS brains was a problem of overall load or regional distribution in a cohort of MS patients. Methods An experienced neuroradiologist, a radiology software engineer, and four neurologists analysed data from quantitative susceptibility maps reconstructed from 3-T magnetic resonance brain images of 30 MS patients and 15 age- and sex-matched healthy controls. Global brain iron load was calculated, and the regional iron concentrations were assessed in 1,000 regions of interest placed in MS lesions in different locations, normal appearing white matter, thalami, and basal ganglia. Results Global brain iron load was comparable between patients and controls after adjustment for volume (p = 0.660), whereas the regional iron concentrations were significantly different in patients than in control (p ≤ 0.031). There was no significant correlation between global iron load and clinical parameters, whereas regional iron concentrations correlated with patients’ age, disease duration, and disability grade (p ≤ 0.039). Conclusions The aberrant iron deposition noted in MS seems to be a problem of regional distribution rather than an altered global brain iron load. Supplementary Information The online version contains supplementary material available at 10.1186/s41747-022-00279-9.
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Affiliation(s)
- Eman Hamdy
- Department of Neurology, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
| | - Aya Abdel Galeel
- Department of Radiology, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Ismail Ramadan
- Department of Neurology, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | - Dina Gaber
- Department of Neurology, Faculty of Medicine, Alexandria University, Alexandria, Egypt
| | | | - Jaidaa Mekky
- Department of Neurology, Faculty of Medicine, Alexandria University, Alexandria, Egypt
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15
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Tan E, Merchant K, Kn BP, Cs A, Zhao JJ, Saffari SE, Tan PH, Tang PH. CT-based morphologic and radiomics features for the classification of MYCN gene amplification status in pediatric neuroblastoma. Childs Nerv Syst 2022; 38:1487-1495. [PMID: 35460355 DOI: 10.1007/s00381-022-05534-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/13/2022] [Indexed: 11/24/2022]
Abstract
PURPOSE MYCN onco-gene amplification in neuroblastoma confers patients to the high-risk disease category for which prognosis is poor and more aggressive multimodal treatment is indicated. This retrospective study leverages machine learning techniques to develop a computed tomography (CT)-based model incorporating semantic and non-semantic features for non-invasive prediction of MYCN amplification status in pediatric neuroblastoma. METHODS From 2009 to 2020, 54 pediatric patients treated for neuroblastoma at a specialized children's hospital with pre-treatment contrast-enhanced CT and MYCN status were identified (training cohort, n = 44; testing cohort, n = 10). Six morphologic features and 107 quantitative gray-level texture radiomics features extracted from manually drawn volume-of-interest were analyzed. Following feature selection and class balancing, the final predictive model was developed with eXtreme Gradient Boosting (XGBoost) algorithm. Accumulated local effects (ALE) plots were used to explore main effects of the predictive features. Tumor texture maps were also generated for visualization of radiomics features. RESULTS One morphologic and 2 radiomics features were selected for model building. The XGBoost model from the training cohort yielded an area under the receiver operating characteristics curve (AUC-ROC) of 0.930 (95% CI, 0.85-1.00), optimized F1-score of 0.878, and Matthews correlation coefficient (MCC) of 0.773. Evaluation on the testing cohort returned AUC-ROC of 0.880 (95% CI, 0.64-1.00), optimized F1-score of 0.933, and MCC of 0.764. ALE plots and texture maps showed higher "GreyLevelNonUniformity" values, lower "Strength" values, and higher number of image-defined risk factors contribute to higher predicted probability of MYCN amplification. CONCLUSION The machine learning model reliably classified MYCN amplification in pediatric neuroblastoma and shows potential as a surrogate imaging biomarker.
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Affiliation(s)
- Eelin Tan
- Department of Diagnostic & Interventional Imaging, KK Womens' and Childrens' Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore.
| | - Khurshid Merchant
- Department of Pathology and Laboratory Medicine, KK Womens' and Childrens' Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore
| | - Bhanu Prakash Kn
- Bioinformatics Institute, A*Star, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore
| | - Arvind Cs
- Bioinformatics Institute, A*Star, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore
| | - Joseph J Zhao
- Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Dr, Singapore, 117597, Singapore
| | - Seyed Ehsan Saffari
- Center for Quantitative Medicine, Duke-NUS Graduate Medical School, 8 College Rd, Singapore, 169857, Singapore
| | - Poh Hwa Tan
- Department of Diagnostic & Interventional Imaging, KK Womens' and Childrens' Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore
| | - Phua Hwee Tang
- Department of Diagnostic & Interventional Imaging, KK Womens' and Childrens' Hospital, 100 Bukit Timah Rd, Singapore, 229899, Singapore
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16
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Bulut T. Meta-analytic connectivity modeling of the left and right inferior frontal gyri. Cortex 2022; 155:107-131. [DOI: 10.1016/j.cortex.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/21/2022] [Accepted: 07/15/2022] [Indexed: 11/03/2022]
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Visualization of Resected Area in Endonasal Endoscopic Approach versus Transcranial Approach for Skull Base Meningiomas by Voxel-Based-Lesion Mapping. Brain Sci 2022; 12:brainsci12070875. [PMID: 35884682 PMCID: PMC9313245 DOI: 10.3390/brainsci12070875] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/28/2022] [Indexed: 11/17/2022] Open
Abstract
Background: We aimed to evaluate the resected area of endonasal endoscopic approach (EEA) and transcranial approach (TCA) for skull base meningiomas (SBMs) using voxel-based-lesion mapping and visualized the appropriate tumor location in each approach. Methods: We retrospectively examined 182 patients with SBMs who underwent tumor resection in our hospital between 2014 and 2019. Pre- and post-operative SBMs were manually delineated on MRI to create the voxels-of-interest (VOIpre and VOIpost) and were registered onto the normalized brain (normalized VOIpre and normalized VOIpost). The resected map was created by subtracting normalized VOIpost from the normalized VOIpre divided by the number of cases. The resected maps of TCA and EEA were compared by subtracting them. Results: Twenty patients underwent EEA and 135 patients underwent TCA. The tumor resected map demonstrated that the resected area of EEA frequently accumulated on the central skull base, while that of TCA accumulated near the central skull base. The border of both approaches matched the circle that connects neural foramens at the skull base. Conclusions: The resected area of SBMs by EEA and TCA was well visualized by voxel-based-lesion mapping. The circle connecting the neural foramens was the border of EEA and TCA.
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Mancuso L, Cavuoti-Cabanillas S, Liloia D, Manuello J, Buzi G, Cauda F, Costa T. Tasks activating the default mode network map multiple functional systems. Brain Struct Funct 2022; 227:1711-1734. [PMID: 35179638 PMCID: PMC9098625 DOI: 10.1007/s00429-022-02467-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 01/31/2022] [Indexed: 12/30/2022]
Abstract
Recent developments in network neuroscience suggest reconsidering what we thought we knew about the default mode network (DMN). Although this network has always been seen as unitary and associated with the resting state, a new deconstructive line of research is pointing out that the DMN could be divided into multiple subsystems supporting different functions. By now, it is well known that the DMN is not only deactivated by tasks, but also involved in affective, mnestic, and social paradigms, among others. Nonetheless, it is starting to become clear that the array of activities in which it is involved, might also be extended to more extrinsic functions. The present meta-analytic study is meant to push this boundary a bit further. The BrainMap database was searched for all experimental paradigms activating the DMN, and their activation likelihood estimation maps were then computed. An additional map of task-induced deactivations was also created. A multidimensional scaling indicated that such maps could be arranged along an anatomo-psychological gradient, which goes from midline core activations, associated with the most internal functions, to that of lateral cortices, involved in more external tasks. Further multivariate investigations suggested that such extrinsic mode is especially related to reward, semantic, and emotional functions. However, an important finding was that the various activation maps were often different from the canonical representation of the resting-state DMN, sometimes overlapping with it only in some peripheral nodes, and including external regions such as the insula. Altogether, our findings suggest that the intrinsic-extrinsic opposition may be better understood in the form of a continuous scale, rather than a dichotomy.
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Affiliation(s)
- Lorenzo Mancuso
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
| | | | - Donato Liloia
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Giulia Buzi
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
| | - Franco Cauda
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy.
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.
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Rivière D, Leprince Y, Labra N, Vindas N, Foubet O, Cagna B, Loh KK, Hopkins W, Balzeau A, Mancip M, Lebenberg J, Cointepas Y, Coulon O, Mangin JF. Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy. Front Neuroinform 2022; 16:803934. [PMID: 35311005 PMCID: PMC8928460 DOI: 10.3389/fninf.2022.803934] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/17/2022] [Indexed: 11/14/2022] Open
Abstract
Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. To this end, multiple atlases have been published to represent such structures based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However, this continuity is not always verified, and this "iconic" approach has limits. We present in this study an alternative, complementary, "structural" approach, which consists in extracting structures from the individual data, and comparing them without deformation. A "structural atlas" is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance, to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features make it a unique viewer for structural atlas browsing.
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Affiliation(s)
- Denis Rivière
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Yann Leprince
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Nicole Labra
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
- PaleoFED Team, UMR 7194, CNRS, Département Homme et Environnement, Muséum National d’Histoire Naturelle, Musée de l’Homme, Paris, France
| | - Nabil Vindas
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Ophélie Foubet
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Bastien Cagna
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Kep Kee Loh
- INT - Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France
| | - William Hopkins
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX, United States
| | - Antoine Balzeau
- PaleoFED Team, UMR 7194, CNRS, Département Homme et Environnement, Muséum National d’Histoire Naturelle, Musée de l’Homme, Paris, France
- Department of African Zoology, Royal Museum for Central Africa, Tervuren, Belgium
| | - Martial Mancip
- Maison de la Simulation, CNRS, CEA Saclay, Gif-sur-Yvette, France
| | - Jessica Lebenberg
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
- Université de Paris, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Yann Cointepas
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Olivier Coulon
- INT - Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France
| | - Jean-François Mangin
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
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Ge Y, Chen G, Waltz JA, Hong LE, Kochunov P, Chen S. An integrated cluster-wise significance measure for fMRI analysis. Hum Brain Mapp 2022; 43:2444-2459. [PMID: 35233859 PMCID: PMC9057103 DOI: 10.1002/hbm.25795] [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: 10/25/2021] [Revised: 12/31/2021] [Accepted: 01/17/2022] [Indexed: 11/07/2022] Open
Abstract
Cluster-wise inference is widely used in fMRI analysis. The cluster-level statistic is often obtained by counting the number of intra-cluster voxels which surpass a voxel-level statistical significance threshold. This measure can be sub-optimal regarding the power and false-positive error rate because the suprathreshold voxel count neglects the voxel-wise significance levels and ignores the dependence between voxels. This article aims to provide a new Integrated Cluster-wise significance Measure (ICM) for cluster-level significance determination in cluster-wise fMRI analysis by integrating cluster extent, voxel-level significance (e.g., p values), and activation dependence between within-cluster voxels. We develop a computationally efficient strategy for ICM based on probabilistic approximation theories. Consequently, the computational load for ICM-based cluster-wise inference (e.g., permutation tests) is affordable. We validate the proposed method via extensive simulations and then apply it to two fMRI data sets. The results demonstrate that ICM can improve the power with well-controlled family-wise error (FWE).
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Affiliation(s)
- Yunjiang Ge
- Department of Mathematics, University of Maryland-College Park, College Park, Maryland, USA
| | - Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institute of Health, Bethesda, Maryland, USA
| | - James A Waltz
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Catonsville, Maryland, USA
| | - Liyi Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Catonsville, Maryland, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Catonsville, Maryland, USA
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Catonsville, Maryland, USA.,Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, USA
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21
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Bulut T. Neural correlates of morphological processing: An activation likelihood estimation meta-analysis. Cortex 2022; 151:49-69. [DOI: 10.1016/j.cortex.2022.02.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 09/02/2021] [Accepted: 02/17/2022] [Indexed: 11/16/2022]
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22
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Miranda J, Tan GXV, Fernandes MC, Yildirim O, Sims JA, de Arimateia Batista Araujo-Filho J, Machado FADM, Assuncao AN, Nomura CH, Horvat N. Rectal MRI radiomics for predicting pathological complete response: Where we are. Clin Imaging 2022; 82:141-149. [PMID: 34826772 PMCID: PMC9119743 DOI: 10.1016/j.clinimag.2021.10.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/21/2021] [Accepted: 10/11/2021] [Indexed: 02/03/2023]
Abstract
Radiomics using rectal MRI radiomics has emerged as a promising approach in predicting pathological complete response. In this study, we present a typical pipeline of a radiomics analysis and review recent studies, exploring applications, development of radiomics methodologies and model construction in pCR prediction. Finally, we will offer our opinion about the future and discuss the next steps of rectal MRI radiomics for predicting pCR.
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Affiliation(s)
- Joao Miranda
- Department of Radiology, University of Sao Paulo, Sao Paulo, SP, Brazil,Department of Radiology, Diagnosticos da America SA (DASA), Sao Paulo, SP, Brazil
| | - Gary Xia Vern Tan
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Onur Yildirim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John A. Sims
- Department of Biomedical Engineering, Universidade Federal do ABC, Santo Andre, SP, Brazil
| | | | | | | | - Cesar Higa Nomura
- Department of Radiology, University of Sao Paulo, Sao Paulo, SP, Brazil,Department of Radiology, Hospital Sirio-Libanes, Sao Paulo, Brazil
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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23
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Paulk AC, Kfir Y, Khanna AR, Mustroph ML, Trautmann EM, Soper DJ, Stavisky SD, Welkenhuysen M, Dutta B, Shenoy KV, Hochberg LR, Richardson RM, Williams ZM, Cash SS. Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex. Nat Neurosci 2022; 25:252-263. [PMID: 35102333 DOI: 10.1038/s41593-021-00997-0] [Citation(s) in RCA: 92] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 12/07/2021] [Indexed: 12/20/2022]
Abstract
Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, current approaches restrict recordings to a few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here we describe a new probe variant and set of techniques that enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single-unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread and modulation by LFP events such as inter-ictal discharges and burst suppression. Although some challenges remain in creating a turnkey recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution.
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Affiliation(s)
- Angelique C Paulk
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
| | - Yoav Kfir
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Arjun R Khanna
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Martina L Mustroph
- Department of Neurosurgery, Harvard Medical School and Brigham & Women's Hospital, Boston, MA, USA
| | - Eric M Trautmann
- Department of Neuroscience, Columbia University Medical Center, New York City, NY, USA
- Zuckerman Institute, Columbia University, New York City, NY, USA
- Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York City, NY, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Columbia University, New York City, NY, USA
| | - Dan J Soper
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Sergey D Stavisky
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Neurological Surgery, University of California at Davis, Davis, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | | | | | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Neurobiology, Stanford University, Stanford, CA, USA
| | - Leigh R Hochberg
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, USA
- School of Engineering and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - R Mark Richardson
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, MA, USA.
| | - Sydney S Cash
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Neurology, Harvard Medical School, Boston, MA, USA.
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24
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Li H, Zhao D, Liu Y, Xv J, Huang H, Jin Y, Lu Y, Qi Y, Zhou Q. Are There Neural Overlaps of Reactivity to Illegal Drugs, Tobacco, and Alcohol Cues? With Evidence From ALE and CMA. Front Psychiatry 2022; 13:779239. [PMID: 35463497 PMCID: PMC9019580 DOI: 10.3389/fpsyt.2022.779239] [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: 09/18/2021] [Accepted: 03/07/2022] [Indexed: 11/13/2022] Open
Abstract
Abuses of most illegal drugs, including methamphetamine, marijuana, cocaine, heroin, and polydrug, are usually in conjunction with alcohol and tobacco. There are similarities and associations between the behavior, gene, and neurophysiology of such abusers, but the neural overlaps of their cue-reactivity and the correlation of neural overlap with drug craving still needs to be further explored. In this study, an Activation Likelihood Estimation (ALE) was performed on brain activation under legal (tobacco, alcohol) and illegal drug cues, for identifying the similarities in brain functions between different craving states. A Comprehensive meta-analysis (CMA) on the correlation coefficient between brain activation and craving scores in the selected literatures with subjective craving reports explained the degree of the craving via brain imaging results. In ALE, co-activation areas of the three cue-reactivity (posterior cingulate, caudate, and thalamus) suggest that the three cue-reactivity may all arouse drug-use identity which is a predictor of relapse and generation of conditioned reflexes under reward memory, thus leading to illegal drug relapses. In CMA, the brain activation was significantly correlated with subjective craving, with a correlation coefficient of 0.222. The neural overlap of tobacco, alcohol and most of the prevalent illegal drug cues not only further helps us understand the neural mechanism of substance co-abuse and relapse, but also provides implications to detoxification. Furthermore, the correlation between brain activation and craving is low, suggesting the accuracy of craving-based quantitative evaluation by neuroimaging remains unclear.
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Affiliation(s)
- HuiLing Li
- Department of Psychology, Wenzhou Medical University, Wenzhou, China
| | - Dong Zhao
- Department of Psychology, Wenzhou Medical University, Wenzhou, China
| | - YuQing Liu
- Department of Psychology, Wenzhou Medical University, Wenzhou, China
| | - JingWen Xv
- Department of Psychology, Wenzhou Medical University, Wenzhou, China
| | - HanZhi Huang
- Department of Psychology, Wenzhou Medical University, Wenzhou, China
| | - Yutong Jin
- Department of Psychology, Wenzhou Medical University, Wenzhou, China
| | - Yiying Lu
- Mental Health Education and Counseling Center, Lingnan Normal University, Zhanjiang, China
| | - YuanYuan Qi
- Zhejiang Moganshan Female Drug Detoxification Center, Huzhou, China
| | - Qiang Zhou
- Department of Psychology, Wenzhou Medical University, Wenzhou, China.,The Affiliated Kangning Hospital, Wenzhou Medical University, Wenzhou, China
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25
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Schulz S, Woerl AC, Jungmann F, Glasner C, Stenzel P, Strobl S, Fernandez A, Wagner DC, Haferkamp A, Mildenberger P, Roth W, Foersch S. Multimodal Deep Learning for Prognosis Prediction in Renal Cancer. Front Oncol 2021; 11:788740. [PMID: 34900744 PMCID: PMC8651560 DOI: 10.3389/fonc.2021.788740] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 11/02/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Clear-cell renal cell carcinoma (ccRCC) is common and associated with substantial mortality. TNM stage and histopathological grading have been the sole determinants of a patient's prognosis for decades and there are few prognostic biomarkers used in clinical routine. Management of ccRCC involves multiple disciplines such as urology, radiology, oncology, and pathology and each of these specialties generates highly complex medical data. Here, artificial intelligence (AI) could prove extremely powerful to extract meaningful information to benefit patients. OBJECTIVE In the study, we developed and evaluated a multimodal deep learning model (MMDLM) for prognosis prediction in ccRCC. DESIGN SETTING AND PARTICIPANTS Two mixed cohorts of non-metastatic and metastatic ccRCC patients were used: (1) The Cancer Genome Atlas cohort including 230 patients and (2) the Mainz cohort including 18 patients with ccRCC. For each of these patients, we trained the MMDLM on multiscale histopathological images, CT/MRI scans, and genomic data from whole exome sequencing. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Outcome measurements included Harrell's concordance index (C-index) and also various performance parameters for predicting the 5-year survival status (5YSS). Different visualization techniques were used to make our model more transparent. RESULTS The MMDLM showed great performance in predicting the prognosis of ccRCC patients with a mean C-index of 0.7791 and a mean accuracy of 83.43%. Training on a combination of data from different sources yielded significantly better results compared to when only one source was used. Furthermore, the MMDLM's prediction was an independent prognostic factor outperforming other clinical parameters. INTERPRETATION Multimodal deep learning can contribute to prognosis prediction in ccRCC and potentially help to improve the clinical management of this disease. PATIENT SUMMARY An AI-based computer program can analyze various medical data (microscopic images, CT/MRI scans, and genomic data) simultaneously and thereby predict the survival time of patients with renal cancer.
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Affiliation(s)
- Stefan Schulz
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Ann-Christin Woerl
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
- Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Florian Jungmann
- Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany
| | - Christina Glasner
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Philipp Stenzel
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Stephanie Strobl
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Aurélie Fernandez
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | | | - Axel Haferkamp
- Department of Urology and Pediatric Urology, University Medical Center Mainz, Mainz, Germany
| | - Peter Mildenberger
- Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany
| | - Wilfried Roth
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
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26
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Ge Y, Hare S, Chen G, Waltz JA, Kochunov P, Elliot Hong L, Chen S. Bayes estimate of primary threshold in clusterwise functional magnetic resonance imaging inferences. Stat Med 2021; 40:5673-5689. [PMID: 34309050 DOI: 10.1002/sim.9147] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 11/08/2022]
Abstract
Clusterwise statistical inference is the most widely used technique for functional magnetic resonance imaging (fMRI) data analyses. Clusterwise statistical inference consists of two steps: (i) primary thresholding that excludes less significant voxels by a prespecified cut-off (eg, p < . 001 ); and (ii) clusterwise thresholding that controls the familywise error rate caused by clusters consisting of false positive suprathreshold voxels. The selection of the primary threshold is critical because it determines both statistical power and false discovery rate (FDR). However, in most existing statistical packages, the primary threshold is selected based on prior knowledge (eg, p < . 001 ) without taking into account the information in the data. In this article, we propose a data-driven approach to algorithmically select the optimal primary threshold based on an empirical Bayes framework. We evaluate the proposed model using extensive simulation studies and real fMRI data. In the simulation, we show that our method can effectively increase statistical power by 20% to over 100% while effectively controlling the FDR. We then investigate the brain response to the dose-effect of chlorpromazine in patients with schizophrenia by analyzing fMRI scans and generate consistent results.
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Affiliation(s)
- Yunjiang Ge
- Department of Mathematics, University of Maryland-College Park, College Park, Maryland, USA
| | - Stephanie Hare
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, USA
| | - Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institute of Health, Bethesda, Maryland, USA
| | - James A Waltz
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, USA
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, USA.,Division of Biostatistics and Bioinformatics, School of Medicine, University of Maryland, Baltimore, Maryland, USA
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27
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Costa T, Manuello J, Ferraro M, Liloia D, Nani A, Fox PT, Lancaster J, Cauda F. BACON: A tool for reverse inference in brain activation and alteration. Hum Brain Mapp 2021; 42:3343-3351. [PMID: 33991154 PMCID: PMC8249901 DOI: 10.1002/hbm.25452] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 02/03/2021] [Accepted: 04/10/2021] [Indexed: 01/17/2023] Open
Abstract
Over the past decades, powerful MRI‐based methods have been developed, which yield both voxel‐based maps of the brain activity and anatomical variation related to different conditions. With regard to functional or structural MRI data, forward inferences try to determine which areas are involved given a mental function or a brain disorder. A major drawback of forward inference is its lack of specificity, as it suggests the involvement of brain areas that are not specific for the process/condition under investigation. Therefore, a different approach is needed to determine to what extent a given pattern of cerebral activation or alteration is specifically associated with a mental function or brain pathology. In this study, we present a new tool called BACON (Bayes fACtor mOdeliNg) for performing reverse inference both with functional and structural neuroimaging data. BACON implements the Bayes' factor and uses the activation likelihood estimation derived‐maps to obtain posterior probability distributions on the evidence of specificity with regard to a particular mental function or brain pathology.
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Affiliation(s)
- Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Department of Psychology, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Department of Psychology, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Mario Ferraro
- FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy.,Department of Physics, University of Turin, Turin, Italy
| | - Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Department of Psychology, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Department of Psychology, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA.,South Texas Veterans Health Care System, San Antonio, Texas, USA
| | - Jack Lancaster
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA.,South Texas Veterans Health Care System, San Antonio, Texas, USA
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.,Department of Psychology, University of Turin, Turin, Italy.,FOCUS Laboratory, Department of Psychology, University of Turin, Turin, Italy
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28
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Akeret K, van Niftrik CHB, Sebök M, Muscas G, Visser T, Staartjes VE, Marinoni F, Serra C, Regli L, Krayenbühl N, Piccirelli M, Fierstra J. Topographic volume-standardization atlas of the human brain. Brain Struct Funct 2021; 226:1699-1711. [PMID: 33961092 PMCID: PMC8203509 DOI: 10.1007/s00429-021-02280-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 04/10/2021] [Indexed: 11/29/2022]
Abstract
Specific anatomical patterns are seen in various diseases affecting the brain. Clinical studies on the topography of pathologies are often limited by the absence of a normalization of the prevalence of pathologies to the relative volume of the affected anatomical structures. A comprehensive reference on the relative volumes of clinically relevant anatomical structures serving for such a normalization, is currently lacking. The analyses are based on anatomical high-resolution three-dimensional T1-weighted magnetic resonance imaging data of 30 healthy Caucasian volunteers, including 14 females (mean age 37.79 years, SD 13.04) and 16 males (mean age 38.31 years, SD 16.91). Semi-automated anatomical segmentation was used, guided by a neuroanatomical parcellation algorithm differentiating 96 structures. Relative volumes were derived by normalizing parenchymal structures to the total individual encephalic volume and ventricular segments to the total individual ventricular volume. The present investigation provides the absolute and relative volumes of 96 anatomical parcellation units of the human encephalon. A larger absolute volume in males than in females is found for almost all parcellation units. While parenchymal structures display a trend towards decreasing volumes with increasing age, a significant inverse effect is seen with the ventricular system. The variances in volumes as well as the effects of gender and age are given for each structure before and after normalization. The provided atlas constitutes an anatomically detailed and comprehensive analysis of the absolute and relative volumes of the human encephalic structures using a clinically oriented parcellation algorithm. It is intended to serve as a reference for volume-standardization in clinical studies on the topographic prevalence of pathologies.
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Affiliation(s)
- Kevin Akeret
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
| | - Christiaan Hendrik Bas van Niftrik
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Martina Sebök
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Giovanni Muscas
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Thomas Visser
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Victor E Staartjes
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Federica Marinoni
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Carlo Serra
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Luca Regli
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Niklaus Krayenbühl
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.,Division of Pediatric Neurosurgery, University Children's Hospital, Zurich, Switzerland
| | - Marco Piccirelli
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Jorn Fierstra
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
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29
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Jenabi M, Young RJ, Moreno R, Gene M, Cho N, Otazo R, Holodny AI, Peck KK. Multiband diffusion tensor imaging for presurgical mapping of motor and language pathways in patients with brain tumors. J Neuroimaging 2021; 31:784-795. [PMID: 33817896 DOI: 10.1111/jon.12859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND PURPOSE Assessment of the essential white matter fibers of arcuate fasciculus and corticospinal tract (CST), required for preoperative planning in brain tumor patients, relies on the reliability of diffusion tensor imaging (DTI). The recent development of multiband DTI (mb-DTI) based on simultaneous multislice excitation could maintain the overall quality of tractography while not exceeding standard clinical care time. To address this potential, we performed quantitative analyses to evaluate tractography results of arcuate fasciculus and CST acquired by mb-DTI in brain tumor patients. METHODS We retrospectively analyzed 44 patients with brain lesions who underwent presurgical single-shot DTI (s-DTI) and mb-DTI. We measured DTI parameters: fractional anisotropy (FA) and mean diffusivity (MD [mm2 s-1 ]) in whole brain and tumor regions; and the tractography parameters: fiber FA, MD (mm2 s-1 ), volume (mm3 ), and length (mm) in the whole brain, arcuate fasciculus, and CST. Additionally, three neuroradiologists performed a blinded visual assessment comparing s-DTI with mb-DTI. RESULTS The mb-DTI showed higher mean FA and lower MD (r > .95, p < .002) in whole brain and tumor regions of interest; slightly higher fiber FA, volume, and length; and slightly lower fiber MD in whole brain, arcuate fasciculus, and CST than in s-DTI. These differences were significant for fiber FA in all tracts; length (mm) in arcuate fasciculus; and fiber MD (mm2 s-1 ) and volume (mm3 ) in all patients with tumor involved in the arcuate fasciculus, CST, and whole brain tracts (p = .001). Visual assessment demonstrated that both techniques produced visually similar tracts. CONCLUSIONS This study demonstrated the clinical potential and significant advantages of preoperative mb-DTI in brain tumor patients.
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Affiliation(s)
- Mehrnaz Jenabi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Raquel Moreno
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Madeleine Gene
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nicholas Cho
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.,Department of Neuroscience, Weill-Cornell Graduate School of the Medical Sciences, New York, New York, USA
| | - Kyung K Peck
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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30
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Pinto M, Pellegrino M, Lasaponara S, Scozia G, D'Onofrio M, Raffa G, Nigro S, Arnaud CR, Tomaiuolo F, Doricchi F. Number space is made by response space: Evidence from left spatial neglect. Neuropsychologia 2021; 154:107773. [PMID: 33567295 DOI: 10.1016/j.neuropsychologia.2021.107773] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 01/26/2021] [Accepted: 02/02/2021] [Indexed: 11/26/2022]
Abstract
Whether the semantic representation of numbers is endowed with an intrinsic spatial component, so that smaller numbers are inherently represented to the left of larger ones on a Mental Number Line (MNL), is a central matter of debate in numerical cognition. To gain an insight into this issue, we investigated the performance of right brain damaged patients with left spatial neglect (N+) in a bimanual Magnitude Comparison SNARC task and in a uni-manual Magnitude Comparison Go/No-Go task (i.e. "is the number smaller or larger than 5?"). While the first task requires the use of contrasting left/right spatial codes for response selection, the second task does not require the use of these codes. In line with previous evidence, in the SNARC task N+ patients displayed a significant asymmetry in Reaction Times (RTs), with slower RTs to number "4", that was immediately precedent to the numerical reference "5", with respect to the number "6", that immediately followed the same reference. This RTs asymmetry was correlated with lesion of white matter tracts, i.e. Fronto-Occipital-Fasciculus, that allows prefrontal Ba 8 and 46 to regulate the distribution of attention on sensory and memory traces in posterior occipital, temporal and parietal areas. In contrast, no similar RTs asymmetry was found in the Go/No-Go task. These findings suggest that while in the SNARC task numbers get mentally organised from left-to-right as a function of their increasing magnitude, so that N+ patients display a delay in the processing of number-magnitudes that are immediately smaller than a given numerical reference, in the Go/No-Go task no left-to-right organization is activated. These results support the idea that it is the use of contrasting left/right spatial codes, whether motor or conceptual, that triggers the generation of a spatially left-to-right organised MNL and that the representation of number magnitude is not endowed with an inherent spatial component.
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Affiliation(s)
| | - Michele Pellegrino
- Dipartimento di Psicologia, Università degli Studi di Roma 'La Sapienza', Roma, Italy; Fondazione Santa Lucia IRCCS, Roma, Italy
| | - Stefano Lasaponara
- Dipartimento di Psicologia, Università degli Studi di Roma 'La Sapienza', Roma, Italy; Libera Università Maria Santissima Assunta - LUMSA, Roma, Italy
| | - Gabriele Scozia
- Dipartimento di Psicologia, Università degli Studi di Roma 'La Sapienza', Roma, Italy
| | - Marianna D'Onofrio
- Dipartimento di Psicologia, Università degli Studi di Roma 'La Sapienza', Roma, Italy
| | - Giovanni Raffa
- Division of Neurosurgery, Dept. BIOMORF, University of Messina, Italy
| | - Salvatore Nigro
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
| | - Clelia Rossi Arnaud
- Dipartimento di Psicologia, Università degli Studi di Roma 'La Sapienza', Roma, Italy
| | - Francesco Tomaiuolo
- Dipartimento di Medicina Clinica e Sperimentale, Università degli studi di Messina, Messina, Italy
| | - Fabrizio Doricchi
- Dipartimento di Psicologia, Università degli Studi di Roma 'La Sapienza', Roma, Italy; Fondazione Santa Lucia IRCCS, Roma, Italy.
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Aytur SA, Ray KL, Meier SK, Campbell J, Gendron B, Waller N, Robin DA. Neural Mechanisms of Acceptance and Commitment Therapy for Chronic Pain: A Network-Based fMRI Approach. Front Hum Neurosci 2021; 15:587018. [PMID: 33613207 PMCID: PMC7892587 DOI: 10.3389/fnhum.2021.587018] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 01/12/2021] [Indexed: 01/29/2023] Open
Abstract
Over 100 million Americans suffer from chronic pain (CP), which causes more disability than any other medical condition in the United States at a cost of $560-$635 billion per year (Institute of Medicine, 2011). Opioid analgesics are frequently used to treat CP. However, long term use of opioids can cause brain changes such as opioid-induced hyperalgesia that, over time, increase pain sensation. Also, opioids fail to treat complex psychological factors that worsen pain-related disability, including beliefs about and emotional responses to pain. Cognitive behavioral therapy (CBT) can be efficacious for CP. However, CBT generally does not focus on important factors needed for long-term functional improvement, including attainment of personal goals and the psychological flexibility to choose responses to pain. Acceptance and Commitment Therapy (ACT) has been recognized as an effective, non-pharmacologic treatment for a variety of CP conditions (Gutierrez et al., 2004). However, little is known about the neurologic mechanisms underlying ACT. We conducted an ACT intervention in women (n = 9) with chronic musculoskeletal pain. Functional magnetic resonance imaging (fMRI) data were collected pre- and post-ACT, and changes in functional connectivity (FC) were measured using Network-Based Statistics (NBS). Behavioral outcomes were measured using validated assessments such as the Acceptance and Action Questionnaire (AAQ-II), the Chronic Pain Acceptance Questionnaire (CPAQ), the Center for Epidemiologic Studies Depression Scale (CES-D), and the NIH Toolbox Neuro-QoLTM (Quality of Life in Neurological Disorders) scales. Results suggest that, following the 4-week ACT intervention, participants exhibited reductions in brain activation within and between key networks including self-reflection (default mode, DMN), emotion (salience, SN), and cognitive control (frontal parietal, FPN). These changes in connectivity strength were correlated with changes in behavioral outcomes including decreased depression and pain interference, and increased participation in social roles. This study is one of the first to demonstrate that improved function across the DMN, SN, and FPN may drive the positive outcomes associated with ACT. This study contributes to the emerging evidence supporting the use of neurophysiological indices to characterize treatment effects of alternative and complementary mind-body therapies.
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Affiliation(s)
- Semra A. Aytur
- Department of Health Management and Policy, University of New Hampshire, Durham, NH, United States
| | - Kimberly L. Ray
- Department of Psychology, University of Texas at Austin, Austin, TX, United States
| | - Sarah K. Meier
- Department of Communication Sciences and Disorders, University of New Hampshire, Durham, NH, United States
| | - Jenna Campbell
- Department of Communication Sciences and Disorders, University of New Hampshire, Durham, NH, United States
| | - Barry Gendron
- Wentworth Health Partners Seacoast Physiatry, Somersworth, NH, United States
| | - Noah Waller
- Department of Communication Sciences and Disorders, University of New Hampshire, Durham, NH, United States
| | - Donald A. Robin
- Department of Communication Sciences and Disorders, University of New Hampshire, Durham, NH, United States
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Ramage AE, Aytur S, Ballard KJ. Resting-State Functional Magnetic Resonance Imaging Connectivity Between Semantic and Phonological Regions of Interest May Inform Language Targets in Aphasia. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2020; 63:3051-3067. [PMID: 32755498 PMCID: PMC7890222 DOI: 10.1044/2020_jslhr-19-00117] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 03/16/2020] [Accepted: 06/01/2020] [Indexed: 06/11/2023]
Abstract
Purpose Brain imaging has provided puzzle pieces in the understanding of language. In neurologically healthy populations, the structure of certain brain regions is associated with particular language functions (e.g., semantics, phonology). In studies on focal brain damage, certain brain regions or connections are considered sufficient or necessary for a given language function. However, few of these account for the effects of lesioned tissue on the "functional" dynamics of the brain for language processing. Here, functional connectivity (FC) among semantic-phonological regions of interest (ROIs) is assessed to fill a gap in our understanding about the neural substrates of impaired language and whether connectivity strength can predict language performance on a clinical tool in individuals with aphasia. Method Clinical assessment of language, using the Western Aphasia Battery-Revised, and resting-state functional magnetic resonance imaging data were obtained for 30 individuals with chronic aphasia secondary to left-hemisphere stroke and 18 age-matched healthy controls. FC between bilateral ROIs was contrasted by group and used to predict Western Aphasia Battery-Revised scores. Results Network coherence was observed in healthy controls and participants with stroke. The left-right premotor cortex connection was stronger in healthy controls, as reported by New et al. (2015) in the same data set. FC of (a) connections between temporal regions, in the left hemisphere and bilaterally, predicted lexical-semantic processing for auditory comprehension and (b) ipsilateral connections between temporal and frontal regions in both hemispheres predicted access to semantic-phonological representations and processing for verbal production. Conclusions Network connectivity of brain regions associated with semantic-phonological processing is predictive of language performance in poststroke aphasia. The most predictive connections involved right-hemisphere ROIs-particularly those for which structural adaptions are known to associate with recovered word retrieval performance. Predictions may be made, based on these findings, about which connections have potential as targets for neuroplastic functional changes with intervention in aphasia. Supplemental Material https://doi.org/10.23641/asha.12735785.
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Affiliation(s)
- Amy E. Ramage
- Department of Communication Sciences and Disorders, University of New Hampshire, Durham
| | - Semra Aytur
- Department of Health Policy and Management, University of New Hampshire, Durham
| | - Kirrie J. Ballard
- Faculty of Medicine and Health and the Brain and Mind Centre, The University of Sydney, New South Wales, Australia
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Shiri I, Hajianfar G, Sohrabi A, Abdollahi H, P Shayesteh S, Geramifar P, Zaidi H, Oveisi M, Rahmim A. Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test-retest and image registration analyses. Med Phys 2020; 47:4265-4280. [PMID: 32615647 DOI: 10.1002/mp.14368] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 06/17/2020] [Accepted: 06/18/2020] [Indexed: 02/06/2023] Open
Abstract
PURPOSE To assess the repeatability of radiomic features in magnetic resonance (MR) imaging of glioblastoma (GBM) tumors with respect to test-retest, different image registration approaches and inhomogeneity bias field correction. METHODS We analyzed MR images of 17 GBM patients including T1- and T2-weighted images (performed within the same imaging unit on two consecutive days). For image segmentation, we used a comprehensive segmentation approach including entire tumor, active area of tumor, necrotic regions in T1-weighted images, and edema regions in T2-weighted images (test studies only; registration to retest studies is discussed next). Analysis included N3, N4 as well as no bias correction performed on raw MR images. We evaluated 20 image registration approaches, generated by cross-combination of four transformation and five cost function methods. In total, 714 images (17 patients × 2 images × ((4 transformations × 5 cost functions) + 1 test image) and 2856 segmentations (714 images × 4 segmentations) were prepared for feature extraction. Various radiomic features were extracted, including the use of preprocessing filters, specifically wavelet (WAV) and Laplacian of Gaussian (LOG), as well as discretization into fixed bin width and fixed bin count (16, 32, 64, 128, and 256), Exponential, Gradient, Logarithm, Square and Square Root scales. Intraclass correlation coefficients (ICC) were calculated to assess the repeatability of MRI radiomic features (high repeatability defined as ICC ≥ 95%). RESULTS In our ICC results, we observed high repeatability (ICC ≥ 95%) with respect to image preprocessing, different image registration algorithms, and test-retest analysis, for example: RLNU and GLNU from GLRLM, GLNU and DNU from GLDM, Coarseness and Busyness from NGTDM, GLNU and ZP from GLSZM, and Energy and RMS from first order. Highest fraction (percent) of repeatable features was observed, among registration techniques, for the method Full Affine transformation with 12 degrees of freedom using Mutual Information cost function (mean 32.4%), and among image processing methods, for the method Laplacian of Gaussian (LOG) with Sigma (2.5-4.5 mm) (mean 78.9%). The trends were relatively consistent for N4, N3, or no bias correction. CONCLUSION Our results showed varying performances in repeatability of MR radiomic features for GBM tumors due to test-retest and image registration. The findings have implications for appropriate usage in diagnostic and predictive models.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland
| | - Ghasem Hajianfar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
| | - Ahmad Sohrabi
- Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Abdollahi
- Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical Science, Kerman, Iran
| | - Sajad P Shayesteh
- Department of Physiology, Pharmacology and Medical Physics, Faculty of Medicine, Alborz University of Medical Sciences, Karaj, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland.,Geneva University Neurocenter, Geneva University, Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mehrdad Oveisi
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.,Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada.,Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, BC, Canada
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Bouyeure A, Germanaud D, Bekha D, Delattre V, Lefèvre J, Pinabiaux C, Mangin JF, Rivière D, Fischer C, Chiron C, Hertz-Pannier L, Noulhiane M. Three-Dimensional Probabilistic Maps of Mesial Temporal Lobe Structures in Children and Adolescents' Brains. Front Neuroanat 2018; 12:98. [PMID: 30498435 PMCID: PMC6249374 DOI: 10.3389/fnana.2018.00098] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 10/29/2018] [Indexed: 11/13/2022] Open
Abstract
The hippocampus and the adjacent perirhinal, entorhinal, temporopolar, and parahippocampal cortices are interconnected in a hierarchical MTL system crucial for memory processes. A probabilistic description of the anatomical location and spatial variability of MTL cortices in the child and adolescent brain would help to assess structure-function relationships. The rhinal sulcus (RS) and the collateral sulcus (CS) that border MTL cortices and influence their morphology have never been described in these populations. In this study, we identified the aforementioned structures on magnetic resonance images of 38 healthy subjects aged 7-17 years old. Relative to sulcal morphometry in the MTL, we showed RS-CS conformation is an additional factor of variability in the MTL that is not explained by other variables such as age, sex and brain volume; with an innovative method using permutation testing of the extrema of structures of interest, we showed that RS-SC conformation was not associated with differences of location of MTL sulci. Relative to probabilistic maps, we offered for the first time a systematic mapping of MTL structures in children and adolescent, mapping all the structures of the MTL system while taking sulcal morphology into account. Our results, with the probabilistic maps described here being freely available for download, will help to understand the anatomy of this region and help functional and clinical studies to accurately test structure-function hypotheses in the MTL during development. Free access to MTL pediatric atlas: http://neurovault.org/collections/2381/.
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Affiliation(s)
- Antoine Bouyeure
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
| | - David Germanaud
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
- Université Paris Diderot, Sorbonne Paris Cité, AP-HP, Hôpital Robert-Debré, DHU Protect, Service de Neurologie Pédiatrique et des Maladies Métaboliques, Paris, France
| | - Dhaif Bekha
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
| | - Victor Delattre
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
| | - Julien Lefèvre
- CNRS, ENSAM, LSIS UMR 7296, Aix Marseille University, Toulon University, Toulon, France
| | - Charlotte Pinabiaux
- Université Paris Ouest Nanterre La Défense, Laboratoire CHArt (EA 4004), Nanterre, France
| | | | - Denis Rivière
- CEA, University Paris Saclay, NeuroSpin, UNATI, Gif-sur-Yvette, France
| | - Clara Fischer
- CEA, University Paris Saclay, NeuroSpin, UNATI, Gif-sur-Yvette, France
| | - Catherine Chiron
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
| | - Lucie Hertz-Pannier
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
| | - Marion Noulhiane
- INSERM, CEA, Université Paris Descartes, Sorbonne Paris Cité, Neurospin, UNIACT, UMR1129, Gif-sur-Yvette, France
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Bigliassi M, Karageorghis CI, Bishop DT, Nowicky AV, Wright MJ. Cerebral effects of music during isometric exercise: An fMRI study. Int J Psychophysiol 2018; 133:131-139. [PMID: 30059701 DOI: 10.1016/j.ijpsycho.2018.07.475] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 07/17/2018] [Accepted: 07/26/2018] [Indexed: 10/28/2022]
Abstract
A block-design experiment was conducted using fMRI to examine the brain regions that activate during the execution of an isometric handgrip exercise performed at light-to-moderate-intensity in the presence of music. Nineteen healthy adults (7 women and 12 men; Mage = 24.2, SD = 4.9 years) were exposed to an experimental condition (music [MU]) and a no-music control condition (CO) in a randomized order within a single session. Each condition lasted for 10 min and participants were required to execute 30 exercise trials (i.e., 1 trial = 10 s exercise + 10 s rest). Attention allocation, exertional responses, and affective changes were assessed immediately after each condition. The BOLD response was compared between conditions to identify the combined effects of music and exercise on neural activity. The findings indicate that music reallocated attention toward task-unrelated thoughts (d = 0.52) and upregulated affective arousal (d = 0.72) to a greater degree when compared to a no-music condition. The activity of the left inferior frontal gyrus (lIFG) also increased when participants executed the motor task in the presence of music (F = 24.65), and a significant negative correlation was identified between lIFG activity and perceived exertion for MU (limb discomfort: r = -0.54; overall exertion: r = -0.62). The authors hypothesize that the lIFG activates in response to motor tasks that are executed in the presence of environmental sensory stimuli. Activation of this region might also moderate processing of interoceptive signals - a neurophysiological mechanism responsible for reducing exercise consciousness and ameliorating fatigue-related symptoms.
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Affiliation(s)
- Marcelo Bigliassi
- Department of Life Sciences, Brunel University London, United Kingdom.
| | | | - Daniel T Bishop
- Department of Life Sciences, Brunel University London, United Kingdom
| | | | - Michael J Wright
- Department of Life Sciences, Brunel University London, United Kingdom
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Huang Y, Mohan A, De Ridder D, Sunaert S, Vanneste S. The neural correlates of the unified percept of alcohol-related craving: a fMRI and EEG study. Sci Rep 2018; 8:923. [PMID: 29343732 PMCID: PMC5772563 DOI: 10.1038/s41598-017-18471-y] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 12/12/2017] [Indexed: 12/24/2022] Open
Abstract
Alcohol addiction is accompanied by aberrant neural activity. Previously, task-based fMRI and resting-state EEG studies have revealed that craving, a critical component of addiction, is linked to abnormal activity in cortical regions including the dorsal anterior cingulate cortex (dACC), nucleus accumbens (NAcc), posterior cingulate cortex (PCC) and pregenual anterior cingulate cortex (pgACC), etc. In this study, we combine these two imaging techniques to investigate a group of alcohol-addicted patients and provide convergent evidence for the neural correlates of craving not only in alcohol but substance abuse in general. We observe abnormal BOLD signal levels in the dACC, NAcc, pgACC, PCC, amygdala, and parahippocampus (PHC) in a cue-reactivity fMRI experiment. These findings are consistent with increased beta-band activity in the dACC and pgACC in resting-state EEG. We further observe desynchronization characterized by decreased functional connectivity in cue-based fMRI and hypersynchronization characterized by increased functional connectivity between these regions in the theta frequency band. The results of our study show a consistent pattern of alcohol craving elicited by external cues and internal desires. Given the advantage of superior spatial and temporal resolution, we hypothesize a "central craving network" that integrates the different aspects of alcohol addiction into a unified percept.
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Affiliation(s)
- Yuefeng Huang
- Lab for Clinical & Integrative Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at, Dallas, USA
| | - Anusha Mohan
- Lab for Clinical & Integrative Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at, Dallas, USA
| | - Dirk De Ridder
- Department of Surgical Sciences, Section of Neurosurgery, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Stefan Sunaert
- Translational MRI, Department of Imaging and Pathology & Medical Imaging Research Center, Department of Radiology, Katholieke Universiteit Leuven - University of Leuven, Leuven, Belgium
| | - Sven Vanneste
- Lab for Clinical & Integrative Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at, Dallas, USA.
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Filgueiras A, Quintas Conde EF, Hall CR. The neural basis of kinesthetic and visual imagery in sports: an ALE meta − analysis. Brain Imaging Behav 2017; 12:1513-1523. [DOI: 10.1007/s11682-017-9813-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Abstract
Homeostasis is the basis of modern medicine and allostasis, a further elaboration of homeostasis, has been defined as stability through change, which was later modified to predictive reference resetting. It has been suggested that pleasure is related to salience (behavioral relevance), and withdrawal has been linked to allostasis in addictive types. The question arises how the clinical and neural signatures of pleasure, salience, allostasis and withdrawal relate, both in a non-addicted and addicted state. Resting state EEGs were performed in 66 people, involving a food-addicted obese group, a non-food addicted obese group and a lean control group. Correlation analyses were performed on behavioral data, and correlation, comparative and conjunction analyses were performed to extract electrophysiological relationships between pleasure, salience, allostasis and withdrawal. Pleasure/liking seems to be the phenomenological expression that enough salient stimuli are obtained, and withdrawal can be seen as a motivational incentive because due to allostatic reference resetting, more stimuli are required. In addition, in contrast to non-addiction, a pathological, non-adaptive salience attached to food results in withdrawal mediated through persistent allostatic reference resetting.
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De Ridder D, Manning P, Leong SL, Ross S, Sutherland W, Horwath C, Vanneste S. The brain, obesity and addiction: an EEG neuroimaging study. Sci Rep 2016; 6:34122. [PMID: 27658351 PMCID: PMC5034231 DOI: 10.1038/srep34122] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2016] [Accepted: 09/07/2016] [Indexed: 01/16/2023] Open
Abstract
Obesity is among the greatest challenges facing healthcare systems with 20% of the world’s population afflicted. Great controversy exists whether obesity can be regarded as an addictive disorder or not. Recently the Yale Food Addiction Scale questionnaire has been developed as a tool to identify individuals with traits of addiction towards food. Using clinical and source localized EEG data we dichotomize obesity. Brain activity in food-addicted and non-food-addicted obese people is compared to alcohol-addicted and non-addicted lean controls. We show that food addiction shares common neural brain activity with alcohol addiction. This ‘addiction neural brain activity’ consists of the dorsal and pregenual anterior cingulate cortex, parahippocampal area and precuneus. Furthermore, common neural obesity neural brain activity exists as well. The ‘obesity neural brain activity’ consists of dorsal and pregenual anterior cingulate cortex, posterior cingulate extending into the precuneus/cuneus as well as the parahippocampal and inferior parietal area. However food-addicted differ from non-food-addicted obese people by opposite activity in the anterior cingulate gyrus. This food addiction and non-food-addiction obesity dichotomy demonstrates there is at least 2 different kinds of obesity with overlapping network activity, but different in anterior cingulate cortex activity.
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Affiliation(s)
- Dirk De Ridder
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
| | - Patrick Manning
- Section of Endocrinology, Department of Medicine, Dunedin School of Medicine, University of Otago, New Zealand
| | - Sook Ling Leong
- Section of Neurosurgery, Department of Surgical Sciences, Dunedin School of Medicine, University of Otago, New Zealand
| | - Samantha Ross
- Section of Endocrinology, Department of Medicine, Dunedin School of Medicine, University of Otago, New Zealand
| | - Wayne Sutherland
- Section of Endocrinology, Department of Medicine, Dunedin School of Medicine, University of Otago, New Zealand
| | - Caroline Horwath
- Department of Human Nutrition, Dunedin School of Medicine, University of Otago, New Zealand
| | - Sven Vanneste
- School of Behavioral and Brain Sciences, University of Texas at Dallas, USA
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Working memory circuit as a function of increasing age in healthy adolescence: A systematic review and meta-analyses. NEUROIMAGE-CLINICAL 2015; 12:940-948. [PMID: 27995059 PMCID: PMC5153561 DOI: 10.1016/j.nicl.2015.12.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2015] [Revised: 11/19/2015] [Accepted: 12/07/2015] [Indexed: 12/15/2022]
Abstract
Working memory ability matures through puberty and early adulthood. Deficits in working memory are linked to the risk of onset of neurodevelopmental disorders such as schizophrenia, and there is a significant temporal overlap between the peak of first episode psychosis risk and working memory maturation. In order to characterize the normal working memory functional maturation process through this critical phase of cognitive development we conducted a systematic review and coordinate based meta-analyses of all the available primary functional magnetic resonance imaging studies (n = 382) that mapped WM function in healthy adolescents (10–17 years) and young adults (18–30 years). Activation Likelihood Estimation analyses across all WM tasks revealed increased activation with increasing subject age in the middle frontal gyrus (BA6) bilaterally, the left middle frontal gyrus (BA10), the left precuneus and left inferior parietal gyri (BA7; 40). Decreased activation with increasing age was found in the right superior frontal (BA8), left junction of postcentral and inferior parietal (BA3/40), and left limbic cingulate gyrus (BA31). These results suggest that brain activation during adolescence increased with age principally in higher order cortices, part of the core working memory network, while reductions were detected in more diffuse and potentially more immature neural networks. Understanding the process by which the brain and its cognitive functions mature through healthy adulthood may provide us with new clues to understanding the vulnerability to neurodevelopmental disorders. Healthy working memory functional maturation process in adolescence Brain activation increased with age in higher order cortices. Activation decreased in more diffuse and potentially more immature networks. Provide new clues to understanding vulnerability to neurodevelopmental disorders
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Neuroimaging in social anxiety disorder—A meta-analytic review resulting in a new neurofunctional model. Neurosci Biobehav Rev 2014; 47:260-80. [PMID: 25124509 DOI: 10.1016/j.neubiorev.2014.08.003] [Citation(s) in RCA: 237] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2014] [Revised: 06/26/2014] [Accepted: 08/01/2014] [Indexed: 01/30/2023]
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Zhang WN, Chang SH, Guo LY, Zhang KL, Wang J. The neural correlates of reward-related processing in major depressive disorder: a meta-analysis of functional magnetic resonance imaging studies. J Affect Disord 2013; 151:531-539. [PMID: 23856280 DOI: 10.1016/j.jad.2013.06.039] [Citation(s) in RCA: 251] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2013] [Revised: 05/27/2013] [Accepted: 06/18/2013] [Indexed: 10/26/2022]
Abstract
BACKGROUND A growing number of functional magnetic resonance imaging (fMRI) studies have been conducted in major depressive disorder (MDD) to elucidate reward-related brain functions. The aim of this meta-analysis was to examine the common reward network in the MDD brain and to further distinguish the brain activation patterns between positive stimuli and monetary rewards as well as reward anticipation and outcome. METHODS A series of activation likelihood estimation (ALE) meta-analyses were performed across 22 fMRI studies that examined reward-related processing, with a total of 341 MDD patients and 367 healthy controls. RESULTS We observed several frontostriatal regions that participated in reward processing in MDD. The common reward network in MDD was characterized by decreased subcortical and limbic areas activity and an increased cortical response. In addition, the cerebellum, lingual gyrus, parahippocampal gyrus and fusiform gyrus preferentially responded to positive stimuli in MDD, while the insula, precuneus, cuneus, PFC and inferior parietal lobule selectively responded to monetary rewards. Our results indicated a reduced caudate response during both monetary anticipation and outcome stages as well as increased activation in the middle frontal gyrus and dorsal anterior cingulate during reward anticipation in MDD. LIMITATIONS The reward-related tasks and mood states of patients included in our analysis were heterogeneous. CONCLUSIONS Our current findings suggest that there exist emotional or motivational pathway dysfunctions in MDD during reward-related processing. Future studies may be strengthened by paying careful attention to the types of reward used as well as the different components of reward processing examined.
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Affiliation(s)
- Wei-Na Zhang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Su-Hua Chang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Li-Yuan Guo
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Kun-Lin Zhang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Jing Wang
- The Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
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Kothari S, Phan JH, Wang MD. Scale normalization of histopathological images for batch invariant cancer diagnostic models. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:4406-9. [PMID: 23366904 DOI: 10.1109/embc.2012.6346943] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Histopathological images acquired from different experimental set-ups often suffer from batch-effects due to color variations and scale variations. In this paper, we develop a novel scale normalization model for histopathological images based on nuclear area distributions. Results indicate that the normalization model closely fits empirical values for two renal tumor datasets. We study the effect of scale normalization on classification of renal tumor images. Scale normalization improves classification performance in most cases. However, performance decreases in a few cases. In order to understand this, we propose two methods to filter extracted image features that are sensitive to image scaling and features that are uncorrelated with scaling factor. Feature filtering improves the classification performance of cases that were initially negatively affected by scale normalization.
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Affiliation(s)
- Sonal Kothari
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, GA, USA.
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Mapping the depressed brain: a meta-analysis of structural and functional alterations in major depressive disorder. J Affect Disord 2012; 140:142-8. [PMID: 21890211 DOI: 10.1016/j.jad.2011.08.001] [Citation(s) in RCA: 235] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2011] [Revised: 08/01/2011] [Accepted: 08/01/2011] [Indexed: 11/22/2022]
Abstract
BACKGROUND Depression has a lifetime prevalence of up to 20%. Neuroimaging methods have revealed various structural and functional changes that occur in a human brain during a depressive episode. However, we still lack information concerning the extent to which structural and functional changes co-occur in a depressed brain. Furthermore, it is difficult to evaluate from a merely qualitative literature review what regional brain changes in volume and activation are robust across depressed patient samples and consistent across imaging centers. METHODOLOGY AND PRINCIPLE FINDINGS This study is a meta-analysis from 10 selected studies published previously. We applied the statistical anatomical/activation likelihood estimate method (ALE) in a total of 176 depressed patients and 175 controls for the MRI modality and in a total of 102 depressed patients and 94 controls for the PET modality to quantitatively identify those brain regions that show concordant alteration in the midst of a depressive episode across imaging modalities and study sites. We find a convergent change in the limbic-cortical brain circuit in depression compared to controls of both Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) data. The specific changes include lower gray matter volumes in the amygdala, the dorsal frontomedian cortex, and the right paracingulate cortex, as well as increases in glucose metabolism in the right subgenual and pregenual anterior cingulate cortices. CONCLUSIONS/SIGNIFICANCE Our current findings represent an important first step towards a more focused approach to neuroimaging unipolar depression. The regions identified could serve as a specific region-of-interest-for-disease template for both individual in vivo imaging studies and postmortem histopathologic exploration.
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Lancaster JL, McKay DR, Cykowski MD, Martinez MJ, Tan X, Valaparla S, Zhang Y, Fox PT. Automated analysis of fundamental features of brain structures. Neuroinformatics 2012; 9:371-80. [PMID: 21360205 DOI: 10.1007/s12021-011-9108-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Automated image analysis of the brain should include measures of fundamental structural features such as size and shape. We used principal axes (P-A) measurements to measure overall size and shape of brain structures segmented from MR brain images. The rationale was that quantitative volumetric studies of brain structures would benefit from shape standardization as had been shown for whole brain studies. P-A analysis software was extended to include controls for variability in position and orientation to support individual structure spatial normalization (ISSN). The rationale was that ISSN would provide a bias-free means to remove elementary sources of a structure's spatial variability in preparation for more detailed analyses. We studied nine brain structures (whole brain, cerebral hemispheres, cerebellum, brainstem, caudate, putamen, hippocampus, inferior frontal gyrus, and precuneus) from the 40-brain LPBA40 atlas. This paper provides the first report of anatomical positions and principal axes orientations within a standard reference frame, in addition to "shape/size related" principal axes measures, for the nine brain structures from the LPBA40 atlas. Analysis showed that overall size (mean volume) for internal brain structures was preserved using shape standardization while variance was reduced by more than 50%. Shape standardization provides increased statistical power for between-group volumetric studies of brain structures compared to volumetric studies that control only for whole brain size. To test ISSN's ability to control for spatial variability of brain structures we evaluated the overlap of 40 regions of interest (ROIs) in a standard reference frame for the nine different brain structures before and after processing. Standardizations of orientation or shape were ineffective when not combined with position standardization. The greatest reduction in spatial variability was seen for combined standardizations of position, orientation and shape. These results show that ISSNs automated processing can be a valuable asset for measuring and controlling variability of fundamental features of brain structures.
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Affiliation(s)
- Jack L Lancaster
- Research Imaging Institute, Biomedical Image Analysis Division, University of Texas Health Science Center at San Antonio, 8403 Floyd Curl Drive, San Antonio, TX 78229-3900, USA.
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Kochunov P, Rogers W, Mangin JF, Lancaster J. A library of cortical morphology analysis tools to study development, aging and genetics of cerebral cortex. Neuroinformatics 2012; 10:81-96. [PMID: 21698393 DOI: 10.1007/s12021-011-9127-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Sharing of analysis techniques and tools is among the main driving forces of modern neuroscience. We describe a library of tools developed to quantify global and regional differences in cortical anatomy in high resolution structural MR images. This library is distributed as a plug-in application for popular structural analysis software, BrainVisa (BV). It contains tools to measure global and regional gyrification, gray matter thickness and sulcal and gyral white matter spans. We provide a description of each tool and examples for several case studies to demonstrate their use. These examples show how the BV library was used to study cortical folding process during antenatal development and recapitulation of this process during cerebral aging. Further, the BV library was used to perform translation research in humans and non-human primates on the genetics of cerebral gyrification. This library, including source code and self-contained binaries for popular computer platforms, is available from the NIH-Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) resource ( http://www.nitrc.org/projects/brainvisa_ext ).
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
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