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Fascher M, Nowaczynski S, Muehlhan M. Substance use disorders are characterised by increased voxel-wise intrinsic measures in sensorimotor cortices: An ALE meta-analysis. Neurosci Biobehav Rev 2024; 162:105712. [PMID: 38733896 DOI: 10.1016/j.neubiorev.2024.105712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 04/29/2024] [Accepted: 05/03/2024] [Indexed: 05/13/2024]
Abstract
Substance use disorders (SUDs) are severe psychiatric illnesses. Seed region and independent component analyses are currently the dominant connectivity measures but carry the risk of false negatives due to selection. They can be complemented by a data-driven and whole-brain usage of voxel-wise intrinsic measures (VIMs). We meta-analytically integrated VIMs, namely regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), voxel-mirrored homotopy connectivity (VMHC) and degree centrality (DC) across different SUDs using the Activation Likelihood Estimation (ALE) algorithm, functionally decoded emerging clusters, and analysed their connectivity profiles. Our systematic search identified 51 studies including 1439 SUD participants. Although no overall convergent pattern of alterations across VIMs in SUDs was found, sensitivity analyses demonstrated two ALE-derived clusters of increased ReHo and ALFF in SUDs, which peaked in the left pre- and postcentral cortices. Subsequent analyses showed their involvement in action execution, somesthesis, finger tapping and vibrotactile monitoring/discrimination. Their numerous clinical correlates across included studies highlight the under-discussed role of sensorimotor cortices in SUD, urging a more attentive exploration of their clinical significance.
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Affiliation(s)
- Maximilian Fascher
- Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Am Kaiserkai 1, Hamburg 20457, Germany; ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Am Kaiserkai 1, Hamburg 20457, Germany.
| | - Sandra Nowaczynski
- Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Am Kaiserkai 1, Hamburg 20457, Germany; ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Am Kaiserkai 1, Hamburg 20457, Germany; Department of Addiction Medicine, Carl-Friedrich-Flemming-Clinic, Helios Medical Center Schwerin, Wismarsche Str. 393, Schwerin 19055, Germany
| | - Markus Muehlhan
- Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Am Kaiserkai 1, Hamburg 20457, Germany; ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Am Kaiserkai 1, Hamburg 20457, Germany
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2
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Rangus I, Rios AS, Horn A, Fritsch M, Khalil A, Villringer K, Udke B, Ihrke M, Grittner U, Galinovic I, Al-Fatly B, Endres M, Kufner A, Nolte CH. Fronto-thalamic networks and the left ventral thalamic nuclei play a key role in aphasia after thalamic stroke. Commun Biol 2024; 7:700. [PMID: 38849518 PMCID: PMC11161613 DOI: 10.1038/s42003-024-06399-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 05/29/2024] [Indexed: 06/09/2024] Open
Abstract
Thalamic aphasia results from focal thalamic lesions that cause dysfunction of remote but functionally connected cortical areas due to language network perturbation. However, specific local and network-level neural substrates of thalamic aphasia remain incompletely understood. Using lesion symptom mapping, we demonstrate that lesions in the left ventrolateral and ventral anterior thalamic nucleus are most strongly associated with aphasia in general and with impaired semantic and phonemic fluency and complex comprehension in particular. Lesion network mapping (using a normative connectome based on fMRI data from 1000 healthy individuals) reveals a Thalamic aphasia network encompassing widespread left-hemispheric cerebral connections, with Broca's area showing the strongest associations, followed by the superior and middle frontal gyri, precentral and paracingulate gyri, and globus pallidus. Our results imply the critical involvement of the left ventrolateral and left ventral anterior thalamic nuclei in engaging left frontal cortical areas, especially Broca's area, during language processing.
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Affiliation(s)
- Ida Rangus
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Neurologie mit Experimenteller Neurologie, Berlin, Germany.
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin (CSB), Berlin, Germany.
| | - Ana Sofia Rios
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Neurologie mit Experimenteller Neurologie, Berlin, Germany
| | - Andreas Horn
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Neurologie mit Experimenteller Neurologie, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Neurologie mit experimenteller Neurologie, Movement Disorder and Neuromodulation Unit, Berlin, Germany
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Merve Fritsch
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Psychiatrie und Psychotherapie, Berlin, Germany
| | - Ahmed Khalil
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Neurologie mit Experimenteller Neurologie, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin (CSB), Berlin, Germany
| | - Kersten Villringer
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Neurologie mit Experimenteller Neurologie, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin (CSB), Berlin, Germany
| | - Birgit Udke
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Audiologie und Phoniatrie, Berlin, Germany
| | - Manuela Ihrke
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Audiologie und Phoniatrie, Berlin, Germany
| | - Ulrike Grittner
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institut für Biometrie und klinische Epidemiologie, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ivana Galinovic
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Neurologie mit Experimenteller Neurologie, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin (CSB), Berlin, Germany
| | - Bassam Al-Fatly
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Neurologie mit experimenteller Neurologie, Movement Disorder and Neuromodulation Unit, Berlin, Germany
| | - Matthias Endres
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Neurologie mit Experimenteller Neurologie, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin (CSB), Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- German Center for Cardiovascular Research (Deutsches Zentrum für Herz Kreislauferkrankungen, DZHK), Partner Site Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, NeuroCure Cluster of Excellence, NeuroCure Clinical Research Center (NCRC), Berlin, Germany
- German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen, DZNE), Partner Site Berlin, Berlin, Germany
| | - Anna Kufner
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Neurologie mit Experimenteller Neurologie, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin (CSB), Berlin, Germany
| | - Christian H Nolte
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik für Neurologie mit Experimenteller Neurologie, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin (CSB), Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- German Center for Cardiovascular Research (Deutsches Zentrum für Herz Kreislauferkrankungen, DZHK), Partner Site Berlin, Berlin, Germany
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Bae S, Liu K, Pouliopoulos AN, Ji R, Jiménez-Gambín S, Yousefian O, Kline-Schoder AR, Batts AJ, Tsitsos FN, Kokossis D, Mintz A, Honig LS, Konofagou EE. Transcranial Blood-Brain Barrier Opening in Alzheimer's Disease Patients Using a Portable Focused Ultrasound System with Real-Time 2-D Cavitation Mapping. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.12.21.23300222. [PMID: 38196636 PMCID: PMC10775403 DOI: 10.1101/2023.12.21.23300222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Background Focused ultrasound (FUS) in combination with microbubbles has recently shown great promise in facilitating blood-brain barrier (BBB) opening for drug delivery and immunotherapy in Alzheimer's disease (AD). However, it is currently limited to systems integrated within the MRI suites or requiring post-surgical implants, thus restricting its widespread clinical adoption. In this pilot study, we investigate the clinical safety and feasibility of a portable, non-invasive neuronavigation-guided FUS (NgFUS) system with integrated real-time 2-D microbubble cavitation mapping. Methods A phase 1 clinical study with mild to moderate AD patients (N=6) underwent a single session of microbubble-mediated NgFUS to induce transient BBB opening (BBBO). Microbubble activity under FUS was monitored with real-time 2-D cavitation maps and dosing to ensure the efficacy and safety of the NgFUS treatment. Post-operative MRI was used for BBB opening and closure confirmation as well as safety assessment. Changes in AD biomarker levels in both blood serum and extracellular vesicles (EVs) were evaluated, while changes in amyloid-beta (Aβ) load in the brain were assessed through 18F-Florbetapir PET. Results BBBO was achieved in 5 out of 6 subjects with an average volume of 983±626 mm3 following FUS at the right frontal lobe both in white and gray matter regions. The outpatient treatment was completed within 34.8±10.7 min. Cavitation dose significantly correlated with the BBBO volume (R2>0.9, N=4), demonstrating the portable NgFUS system's capability of predicting opening volumes. The cavitation maps co-localized closely with the BBBO location, representing the first report of real-time transcranial 2-D cavitation mapping in the human brain. Larger opening volumes correlated with increased levels of AD biomarkers, including Aβ42 (R2=0.74), Tau (R2=0.95), and P-Tau181 (R2=0.86), assayed in serum-derived EVs sampled 3 days after FUS (N=5). From PET scans, subjects showed a lower Aβ load increase in the treated frontal lobe region compared to the contralateral region. Reduction in asymmetry standardized uptake value ratios (SUVR) correlated with the cavitation dose (R2>0.9, N=3). Clinical changes in the mini-mental state examination over 6 months were within the expected range of cognitive decline with no additional changes observed as a result of FUS. Conclusion We showed the safety and feasibility of this cost-effective and time-efficient portable NgFUS treatment for BBBO in AD patients with the first demonstration of real-time 2-D cavitation mapping. The cavitation dose correlated with BBBO volume, a slowed increase in pathology, and serum detection of AD proteins. Our study highlights the potential for accessible FUS treatment in AD, with or without drug delivery.
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Affiliation(s)
- Sua Bae
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Keyu Liu
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | | | - Robin Ji
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | | | - Omid Yousefian
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | | | - Alec J. Batts
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Fotios N. Tsitsos
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
| | - Danae Kokossis
- Department of Radiation Oncology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Akiva Mintz
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Lawrence S. Honig
- Department of Neurology and Taub Institute, Columbia University Irving Medical Center 10032, New York, NY, USA
| | - Elisa E. Konofagou
- Department of Biomedical Engineering, Columbia University, New York, NY 10032, USA
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
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4
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Dörner M, Seebach K, Heneka MT, Menze I, von Känel R, Euler S, Schreiber F, Arndt P, Neumann K, Hildebrand A, John AC, Tyndall A, Kirchebner J, Tacik P, Jansen R, Grimm A, Henneicke S, Perosa V, Meuth SG, Peters O, Hellmann-Regen J, Preis L, Priller J, Spruth EJ, Schneider A, Fliessbach K, Wiltfang J, Jessen F, Rostamzadeh A, Glanz W, Schulze JB, Schiebler SLF, Buerger K, Janowitz D, Perneczky R, Rauchmann BS, Teipel S, Kilimann I, Laske C, Munk MH, Spottke A, Roy-Kluth N, Wagner M, Frommann I, Lüsebrink F, Dechent P, Hetzer S, Scheffler K, Kleineidam L, Stark M, Schmid M, Ersözlü E, Brosseron F, Ewers M, Schott BH, Düzel E, Ziegler G, Mattern H, Schreiber S, Bernal J. Inferior Frontal Sulcal Hyperintensities on Brain MRI Are Associated with Amyloid Positivity beyond Age-Results from the Multicentre Observational DELCODE Study. Diagnostics (Basel) 2024; 14:940. [PMID: 38732354 PMCID: PMC11083612 DOI: 10.3390/diagnostics14090940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024] Open
Abstract
Inferior frontal sulcal hyperintensities (IFSHs) on fluid-attenuated inversion recovery (FLAIR) sequences have been proposed to be indicative of glymphatic dysfunction. Replication studies in large and diverse samples are nonetheless needed to confirm them as an imaging biomarker. We investigated whether IFSHs were tied to Alzheimer's disease (AD) pathology and cognitive performance. We used data from 361 participants along the AD continuum, who were enrolled in the multicentre DELCODE study. The IFSHs were rated visually based on FLAIR magnetic resonance imaging. We performed ordinal regression to examine the relationship between the IFSHs and cerebrospinal fluid-derived amyloid positivity and tau positivity (Aβ42/40 ratio ≤ 0.08; pTau181 ≥ 73.65 pg/mL) and linear regression to examine the relationship between cognitive performance (i.e., Mini-Mental State Examination and global cognitive and domain-specific performance) and the IFSHs. We controlled the models for age, sex, years of education, and history of hypertension. The IFSH scores were higher in those participants with amyloid positivity (OR: 1.95, 95% CI: 1.05-3.59) but not tau positivity (OR: 1.12, 95% CI: 0.57-2.18). The IFSH scores were higher in older participants (OR: 1.05, 95% CI: 1.00-1.10) and lower in males compared to females (OR: 0.44, 95% CI: 0.26-0.76). We did not find sufficient evidence linking the IFSH scores with cognitive performance after correcting for demographics and AD biomarker positivity. IFSHs may reflect the aberrant accumulation of amyloid β beyond age.
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Affiliation(s)
- Marc Dörner
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
- Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (R.v.K.); (S.E.); (J.B.S.); (S.L.F.S.)
| | - Katharina Seebach
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
| | - Michael T. Heneka
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 4367 Belvaux, Luxembourg;
| | - Inga Menze
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Roland von Känel
- Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (R.v.K.); (S.E.); (J.B.S.); (S.L.F.S.)
| | - Sebastian Euler
- Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (R.v.K.); (S.E.); (J.B.S.); (S.L.F.S.)
| | - Frank Schreiber
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
- Department of Neurology, Otto-von-Guericke University, 39120 Magdeburg, Germany; (K.N.); (A.H.); (A.-C.J.)
| | - Philipp Arndt
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
- Department of Neurology, Otto-von-Guericke University, 39120 Magdeburg, Germany; (K.N.); (A.H.); (A.-C.J.)
| | - Katja Neumann
- Department of Neurology, Otto-von-Guericke University, 39120 Magdeburg, Germany; (K.N.); (A.H.); (A.-C.J.)
| | - Annkatrin Hildebrand
- Department of Neurology, Otto-von-Guericke University, 39120 Magdeburg, Germany; (K.N.); (A.H.); (A.-C.J.)
| | - Anna-Charlotte John
- Department of Neurology, Otto-von-Guericke University, 39120 Magdeburg, Germany; (K.N.); (A.H.); (A.-C.J.)
| | - Anthony Tyndall
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland;
| | - Johannes Kirchebner
- Department of Forensic Psychiatry, University Hospital of Psychiatry Zurich, University of Zurich, 8032 Zurich, Switzerland;
| | - Pawel Tacik
- Department of Parkinson’s Disease, Sleep and Movement Disorders, University Hospital Bonn, 53127 Bonn, Germany;
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 53127 Bonn, Germany; (A.S.); (K.F.); (F.J.); (A.S.); (N.R.-K.); (M.W.); (I.F.); (L.K.); (M.S.); (M.S.); (F.B.)
| | - Robin Jansen
- Department of Neurology, Heinrich Heine University, 40225 Düsseldorf, Germany; (R.J.); (S.G.M.)
| | - Alexander Grimm
- Center for Neurology, Tuebingen University Hospital and Hertie-Institute for Clinical Brain Research, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany;
| | - Solveig Henneicke
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
- Department of Neurology, Otto-von-Guericke University, 39120 Magdeburg, Germany; (K.N.); (A.H.); (A.-C.J.)
| | - Valentina Perosa
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Sven G. Meuth
- Department of Neurology, Heinrich Heine University, 40225 Düsseldorf, Germany; (R.J.); (S.G.M.)
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 10117 Berlin, Germany; (O.P.); (J.H.-R.); (J.P.); (E.J.S.); (E.E.)
- Institute of Psychiatry and Psychotherapy, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 14129 Berlin, Germany;
| | - Julian Hellmann-Regen
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 10117 Berlin, Germany; (O.P.); (J.H.-R.); (J.P.); (E.J.S.); (E.E.)
- Department of Psychiatry and Neurosciences, Campus Benjamin Franklin, Charité—Universitätsmedizin Berlin, 12203 Berlin, Germany
- German Center for Mental Health (DZPG), Partner Site Berlin, 10785 Berlin, Germany
| | - Lukas Preis
- Institute of Psychiatry and Psychotherapy, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 14129 Berlin, Germany;
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 10117 Berlin, Germany; (O.P.); (J.H.-R.); (J.P.); (E.J.S.); (E.E.)
- Department of Psychiatry and Psychotherapy, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University Munich, 81675 Munich, Germany
- UK Dementia Research Institute (UK DRI), University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Eike Jakob Spruth
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 10117 Berlin, Germany; (O.P.); (J.H.-R.); (J.P.); (E.J.S.); (E.E.)
- Department of Psychiatry and Psychotherapy, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 53127 Bonn, Germany; (A.S.); (K.F.); (F.J.); (A.S.); (N.R.-K.); (M.W.); (I.F.); (L.K.); (M.S.); (M.S.); (F.B.)
- Department of Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, 53127 Bonn, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 53127 Bonn, Germany; (A.S.); (K.F.); (F.J.); (A.S.); (N.R.-K.); (M.W.); (I.F.); (L.K.); (M.S.); (M.S.); (F.B.)
- Department of Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, 53127 Bonn, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 37075 Goettingen, Germany; (J.W.); (B.H.S.)
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, 37075 Goettingen, Germany
- Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 53127 Bonn, Germany; (A.S.); (K.F.); (F.J.); (A.S.); (N.R.-K.); (M.W.); (I.F.); (L.K.); (M.S.); (M.S.); (F.B.)
- Department of Psychiatry, Medical Faculty, University of Cologne, 50924 Cologne, Germany;
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany
| | - Ayda Rostamzadeh
- Department of Psychiatry, Medical Faculty, University of Cologne, 50924 Cologne, Germany;
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
| | - Jan Ben Schulze
- Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (R.v.K.); (S.E.); (J.B.S.); (S.L.F.S.)
| | - Sarah Lavinia Florence Schiebler
- Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (R.v.K.); (S.E.); (J.B.S.); (S.L.F.S.)
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 81377 Munich, Germany; (K.B.); (R.P.); (M.E.)
- Institute of Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany;
| | - Daniel Janowitz
- Institute of Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany;
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 81377 Munich, Germany; (K.B.); (R.P.); (M.E.)
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, 81377 Munich, Germany;
- Munich Cluster for Systems Neurology (SyNergy) Munich, 81377 Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, 81377 Munich, Germany;
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield S10 2HQ, UK
- Department of Neuroradiology, University Hospital, LMU Munich, 81377 Munich, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 18147 Rostock, Germany; (S.T.); (I.K.)
- Department of Psychosomatic Medicine, Rostock University Medical Center, 18147 Rostock, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 18147 Rostock, Germany; (S.T.); (I.K.)
- Department of Psychosomatic Medicine, Rostock University Medical Center, 18147 Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 72076 Tuebingen, Germany; (C.L.); (M.H.M.)
- Section for Dementia Research, Department of Psychiatry and Psychotherapy, Hertie Institute for Clinical Brain Research, University of Tuebingen, 72076 Tuebingen, Germany
| | - Matthias H. Munk
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 72076 Tuebingen, Germany; (C.L.); (M.H.M.)
- Department of Psychiatry and Psychotherapy, University of Tuebingen, 72076 Tuebingen, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 53127 Bonn, Germany; (A.S.); (K.F.); (F.J.); (A.S.); (N.R.-K.); (M.W.); (I.F.); (L.K.); (M.S.); (M.S.); (F.B.)
- Department of Neurology, University of Bonn, 53127 Bonn, Germany
| | - Nina Roy-Kluth
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 53127 Bonn, Germany; (A.S.); (K.F.); (F.J.); (A.S.); (N.R.-K.); (M.W.); (I.F.); (L.K.); (M.S.); (M.S.); (F.B.)
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 53127 Bonn, Germany; (A.S.); (K.F.); (F.J.); (A.S.); (N.R.-K.); (M.W.); (I.F.); (L.K.); (M.S.); (M.S.); (F.B.)
- Department of Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, 53127 Bonn, Germany
| | - Ingo Frommann
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 53127 Bonn, Germany; (A.S.); (K.F.); (F.J.); (A.S.); (N.R.-K.); (M.W.); (I.F.); (L.K.); (M.S.); (M.S.); (F.B.)
- Department of Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, 53127 Bonn, Germany
| | - Falk Lüsebrink
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Goettingen, 37073 Gottingen, Germany;
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité—Universitätsmedizin Berlin, 14129 Berlin, Germany;
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tuebingen, 72076 Tuebingen, Germany;
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 53127 Bonn, Germany; (A.S.); (K.F.); (F.J.); (A.S.); (N.R.-K.); (M.W.); (I.F.); (L.K.); (M.S.); (M.S.); (F.B.)
- Department of Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, 53127 Bonn, Germany
| | - Melina Stark
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 53127 Bonn, Germany; (A.S.); (K.F.); (F.J.); (A.S.); (N.R.-K.); (M.W.); (I.F.); (L.K.); (M.S.); (M.S.); (F.B.)
- Department of Cognitive Disorders and Old Age Psychiatry, University Hospital Bonn, 53127 Bonn, Germany
| | - Matthias Schmid
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 53127 Bonn, Germany; (A.S.); (K.F.); (F.J.); (A.S.); (N.R.-K.); (M.W.); (I.F.); (L.K.); (M.S.); (M.S.); (F.B.)
- Institute for Medical Biometry, University Hospital Bonn, 53127 Bonn, Germany
| | - Ersin Ersözlü
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 10117 Berlin, Germany; (O.P.); (J.H.-R.); (J.P.); (E.J.S.); (E.E.)
- Institute of Psychiatry and Psychotherapy, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 14129 Berlin, Germany;
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 53127 Bonn, Germany; (A.S.); (K.F.); (F.J.); (A.S.); (N.R.-K.); (M.W.); (I.F.); (L.K.); (M.S.); (M.S.); (F.B.)
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 81377 Munich, Germany; (K.B.); (R.P.); (M.E.)
- Institute of Stroke and Dementia Research (ISD), University Hospital, LMU Munich, 81377 Munich, Germany;
| | - Björn H. Schott
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 37075 Goettingen, Germany; (J.W.); (B.H.S.)
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, 37075 Goettingen, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Hendrik Mattern
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
- Center for Behavioural Brain Sciences (CBBS), 39120 Magdeburg, Germany
- Biomedical Magnetic Resonance, Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Stefanie Schreiber
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
- Department of Neurology, Otto-von-Guericke University, 39120 Magdeburg, Germany; (K.N.); (A.H.); (A.-C.J.)
- Center for Behavioural Brain Sciences (CBBS), 39120 Magdeburg, Germany
| | - Jose Bernal
- German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association, 39120 Magdeburg, Germany; (K.S.); (I.M.); (F.S.); (P.A.); (S.H.); (W.G.); (F.L.); (E.D.); (G.Z.); (H.M.); (S.S.); (J.B.)
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, 39120 Magdeburg, Germany
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5
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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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6
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Ait Bentaleb K, Boisvert M, Tourjman V, Potvin S. A Meta-Analysis of Functional Neuroimaging Studies of Ketamine Administration in Healthy Volunteers. J Psychoactive Drugs 2024; 56:211-224. [PMID: 36921026 DOI: 10.1080/02791072.2023.2190758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 02/22/2023] [Indexed: 03/17/2023]
Abstract
Ketamine administration leads to a psychotomimetic state when taken in large bolus doses, making it a valid model of psychosis. Therefore, understanding ketamine's effects on brain functioning is particularly relevant. This meta-analysis focused on neuroimaging studies that examined ketamine-induced brain activation at rest and during a task. Included are 10 resting-state studies and 23 task-based studies, 9 of which were measuring executive functions. Using a stringent statistical threshold (TFCE <0.05), the results showed increased activity at rest in the dorsal anterior cingulate cortex (ACC), and increased activation of the right Heschl's gyrus during executive tasks, following ketamine administration. Uncorrected results showed increased activation at rest in the right (anterior) insula and the right-fusiform gyrus, as well as increased activation during executive tasks in the rostral ACC. Rest-state studies highlighted alterations in core hubs of the salience network, while task-based studies suggested an impact on task-irrelevant brain regions. Increased activation in the rostral ACC may indicate a failure to deactivate the default mode network during executive tasks following ketamine administration. The results are coherent with alterations found in schizophrenia, which confer external validity to the ketamine model of psychosis. Studies investigating the neural mechanisms of ketamine's antidepressant action are warranted.
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Affiliation(s)
- Karim Ait Bentaleb
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, Canada
- Department of psychiatry and addiction, Université de Montréal, Montréal, Canada
| | - Mélanie Boisvert
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, Canada
- Department of psychiatry and addiction, Université de Montréal, Montréal, Canada
| | - Valérie Tourjman
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, Canada
- Department of psychiatry and addiction, Université de Montréal, Montréal, Canada
| | - Stéphane Potvin
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, Canada
- Department of psychiatry and addiction, Université de Montréal, Montréal, Canada
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7
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Muehlhan M, Spindler C, Nowaczynski S, Buchner C, Fascher M, Trautmann S. Where alcohol use disorder meets interoception: A meta-analytic view on structural and functional neuroimaging data. J Neurochem 2024. [PMID: 38528368 DOI: 10.1111/jnc.16104] [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: 01/31/2024] [Revised: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 03/27/2024]
Abstract
Alcohol use disorder (AUD) has been associated with changes in the processing of internal body signals, known as interoception. Changes in brain structure, particularly in the insula, are thought to underlie impaired interoception. As studies specifically investigating this association are largely lacking, this analysis takes an approach that compares meta-analytic results on interoception with recently published meta-analytic results on gray matter reduction in AUD. A systematic literature search identified 25 eligible interoception studies. Activation likelihood estimation (ALE) was used to test for spatial convergence of study results. Overlap between interoception and AUD clusters was tested using conjunction analysis. Meta-analytic connectivity modeling (MACM) and resting-state functional connectivity were used to identify the functional network of interoception and to test where this network overlapped with AUD meta-analytic clusters. The results were characterized using behavioral domain analysis. The interoception ALE identified a cluster in the left middle insula. There was no overlap with clusters of reduced gray matter in AUD. MACM analysis of the interoception cluster revealed a large network located in the insulae, thalami, basal nuclei, cingulate and medial frontal cortices, and pre- and postcentral gyri. Resting state analysis confirmed this result, showing the strongest connections to nodes of the salience- and somatomotor network. Five of the eight clusters that showed a structural reduction in AUD were located within these networks. The behavioral profiles of these clusters were suggestive of higher-level processes such as salience control, somatomotor functions, and skin sensations. The results suggest an altered salience mapping of interoceptive signals in AUD, consistent with current models. Connections to the somatomotor network may be related to action control and integration of skin sensations. Mindfulness-based interventions, pleasurable touch, and (deep) transcranial magnetic stimulation may be targeted interventions that reduce interoceptive deficits in AUD and thus contribute to drug use reduction and relapse prevention.
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Affiliation(s)
- Markus Muehlhan
- Department of Psychology, Faculty of Human Sciences, MSH Medical School Hamburg, Hamburg, Germany
- ICAN Institute of Cognitive and Affective Neuroscience, MSH Medical School Hamburg, Hamburg, Germany
| | - Carolin Spindler
- Department of Psychology, Faculty of Human Sciences, MSH Medical School Hamburg, Hamburg, Germany
| | - Sandra Nowaczynski
- Department of Psychology, Faculty of Human Sciences, MSH Medical School Hamburg, Hamburg, Germany
- ICAN Institute of Cognitive and Affective Neuroscience, MSH Medical School Hamburg, Hamburg, Germany
- Department of Addiction Medicine, Carl-Friedrich-Flemming-Clinic, Helios Medical Center Schwerin, Schwerin, Germany
| | - Claudius Buchner
- Department of Psychology, Faculty of Human Sciences, MSH Medical School Hamburg, Hamburg, Germany
| | - Maximilian Fascher
- Department of Psychology, Faculty of Human Sciences, MSH Medical School Hamburg, Hamburg, Germany
- ICAN Institute of Cognitive and Affective Neuroscience, MSH Medical School Hamburg, Hamburg, Germany
| | - Sebastian Trautmann
- Department of Psychology, Faculty of Human Sciences, MSH Medical School Hamburg, Hamburg, Germany
- ICPP Institute of Clinical Psychology and Psychotherapy, MSH Medical School Hamburg, Hamburg, Germany
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8
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Testa G, Sotgiu I, Rusconi ML, Cauda F, Costa T. The Functional Neuroimaging of Autobiographical Memory for Happy Events: A Coordinate-Based Meta-Analysis. Healthcare (Basel) 2024; 12:711. [PMID: 38610134 PMCID: PMC11011908 DOI: 10.3390/healthcare12070711] [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: 12/30/2023] [Revised: 02/14/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
Neuroimaging studies using autobiographical recall methods investigated the neural correlates of happy autobiographical memories (AMs). The scope of the present activation likelihood estimation (ALE) meta-analysis was to quantitatively analyze neuroimaging studies of happy AMs conducted with autobiographical recall paradigms. A total of 17 studies (12 fMRI; 5 PET) on healthy individuals were included in this meta-analysis. During recall of happy life events, consistent activation foci were found in the frontal gyrus, the cingulate cortex, the basal ganglia, the parahippocampus/hippocampus, the hypothalamus, and the thalamus. The result of this quantitative coordinate-based ALE meta-analysis provides an objective view of brain responses associated with AM recollection of happy events, thus identifying brain areas consistently activated across studies. This extended brain network included frontal and limbic regions involved in remembering emotionally relevant positive events. The frontal gyrus and the cingulate cortex may be responsible for cognitive appraisal processes during recollection of happy AMs, while the subthalamic nucleus and globus pallidus may be involved in pleasure reactions associated with recollection of happy life events. These findings shed light on the neural network involved in recalling positive AMs in healthy individuals, opening further avenues for future research in clinical populations with mood disorders.
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Affiliation(s)
- Giulia Testa
- Instituto de Transferencia e Investigación, Universidad Internacional de La Rioja, 26004 La Rioja, Spain
| | - Igor Sotgiu
- Department of Human and Social Sciences, University of Bergamo, 24129 Bergamo, Italy; (I.S.); (M.L.R.)
| | - Maria Luisa Rusconi
- Department of Human and Social Sciences, University of Bergamo, 24129 Bergamo, Italy; (I.S.); (M.L.R.)
| | - Franco Cauda
- Department of Psychology, University of Turin, 10124 Turin, Italy; (F.C.); (T.C.)
- GCS-fMRI Research Group, Koelliker Hospital, 10134 Turin, Italy
| | - Tommaso Costa
- Department of Psychology, University of Turin, 10124 Turin, Italy; (F.C.); (T.C.)
- GCS-fMRI Research Group, Koelliker Hospital, 10134 Turin, Italy
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9
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Long J, Song X, Wang C, Peng L, Niu L, Li Q, Huang R, Zhang R. Global-brain functional connectivity related with trait anxiety and its association with neurotransmitters and gene expression profiles. J Affect Disord 2024; 348:248-258. [PMID: 38159654 DOI: 10.1016/j.jad.2023.12.052] [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] [Received: 08/10/2023] [Revised: 11/30/2023] [Accepted: 12/23/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Numerous studies have explored the neural correlates of trait anxiety, a predisposing factor for several stress-related disorders. However, the findings from previous studies are inconsistent, which might be due to the limited regions of interest (ROI). A recent approach, named global-brain functional connectivity (GBC), has been demonstrated to address the shortcomings of ROI-based analysis. Furthermore, research on the transcriptome-connectome association has provided an approach to link the microlevel transcriptome profile with the macroscale brain network. In this paper, we aim to explore the neurobiology of trait anxiety with an imaging transcriptomic approach using GBC, biological neurotransmitters, and transcriptome profiles. METHODS Using a sample of resting-state fMRI data, we investigated trait anxiety-related alteration in GBC. We further used behavioral analysis, spatial correlation analysis, and postmortem gene expression to separately assess the cognitive functions, neurotransmitters, and transcriptional profiles related to alteration in GBC in individuals with trait anxiety. RESULTS GBC values in the ventromedial prefrontal cortex and the precuneus were negatively correlated with levels of trait anxiety. This alteration was correlated with behavioral terms including social cognition, emotion, and memory. A strong association was revealed between trait anxiety-related alteration in GBC and neurotransmitters, including dopaminergic, serotonergic, GABAergic, and glutamatergic systems in the ventromedial prefrontal cortex and the precuneus. The transcriptional profiles explained the functional connectivity, with correlated genes enriched in transmembrane signaling. LIMITATIONS Several limitations should be taken into account in this research. For example, future research should consider using some different approaches based on dynamic or task-based functional connectivity analysis, include more neurotransmitter receptors, additional gene expression data from different samples or more genes related to other stress-related disorders. Meanwhile, it is of great significance to include a larger sample size of individuals with a diagnosis of major depression disorder or other disorders for analysis and comparison and apply stricter multiple-comparison correction and threshold settings in future research. CONCLUSIONS Our research employed multimodal data to investigate GBC in the context of trait anxiety and to establish its associations with neurotransmitters and transcriptome profiles. This approach may improve understanding of the neural mechanism, together with the biological and molecular genetic foundations of GBC in trait anxiety.
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Affiliation(s)
- Jixin Long
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Xiaoqi Song
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Chanyu Wang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China; Faculty of Medicine and Health Sciences, Department of Head and Skin, Ghent Experimental Psychiatry (GHEP) lab, Ghent University, Ghent, Belgium
| | - Lanxin Peng
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Lijing Niu
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Qian Li
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Ruiwang Huang
- School of Psychology, South China Normal University, Guangzhou, China
| | - Ruibin Zhang
- Laboratory of Cognitive Control and Brain Healthy, Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China; Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
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Khanna AR, Muñoz W, Kim YJ, Kfir Y, Paulk AC, Jamali M, Cai J, Mustroph ML, Caprara I, Hardstone R, Mejdell M, Meszéna D, Zuckerman A, Schweitzer J, Cash S, Williams ZM. Single-neuronal elements of speech production in humans. Nature 2024; 626:603-610. [PMID: 38297120 PMCID: PMC10866697 DOI: 10.1038/s41586-023-06982-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 12/14/2023] [Indexed: 02/02/2024]
Abstract
Humans are capable of generating extraordinarily diverse articulatory movement combinations to produce meaningful speech. This ability to orchestrate specific phonetic sequences, and their syllabification and inflection over subsecond timescales allows us to produce thousands of word sounds and is a core component of language1,2. The fundamental cellular units and constructs by which we plan and produce words during speech, however, remain largely unknown. Here, using acute ultrahigh-density Neuropixels recordings capable of sampling across the cortical column in humans, we discover neurons in the language-dominant prefrontal cortex that encoded detailed information about the phonetic arrangement and composition of planned words during the production of natural speech. These neurons represented the specific order and structure of articulatory events before utterance and reflected the segmentation of phonetic sequences into distinct syllables. They also accurately predicted the phonetic, syllabic and morphological components of upcoming words and showed a temporally ordered dynamic. Collectively, we show how these mixtures of cells are broadly organized along the cortical column and how their activity patterns transition from articulation planning to production. We also demonstrate how these cells reliably track the detailed composition of consonant and vowel sounds during perception and how they distinguish processes specifically related to speaking from those related to listening. Together, these findings reveal a remarkably structured organization and encoding cascade of phonetic representations by prefrontal neurons in humans and demonstrate a cellular process that can support the production of speech.
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Affiliation(s)
- Arjun R Khanna
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - William Muñoz
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Yoav Kfir
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mohsen Jamali
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jing Cai
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Martina L Mustroph
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Irene Caprara
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Richard Hardstone
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mackenna Mejdell
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Domokos Meszéna
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jeffrey Schweitzer
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sydney Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA.
- Harvard Medical School, Program in Neuroscience, Boston, MA, USA.
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11
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Fascher M, Nowaczynski S, Spindler C, Strobach T, Muehlhan M. Neural underpinnings of response inhibition in substance use disorders: weak meta-analytic evidence for a widely used construct. Psychopharmacology (Berl) 2024; 241:1-17. [PMID: 37987836 PMCID: PMC10774166 DOI: 10.1007/s00213-023-06498-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 11/01/2023] [Indexed: 11/22/2023]
Abstract
RATIONALE Substance use disorders (SUDs) rank among the most severely debilitating psychiatric conditions. Among others, decreased response inhibition capacities could make it more difficult for patients to abstain from drug use and maintain abstinence. However, meta-analyses on the neural basis of response inhibition in SUDs yielded conflicting results. OBJECTIVE In this study, we revisited the neuroimaging research field and summarized the existing fMRI literature on overt response inhibition (Go/NoGo and stop-signal paradigms) across different SUDs. METHODS We performed a systematic literature review and an activation likelihood estimation (ALE) meta-analysis to investigate the actual convergence of functional deviations observed in SUD samples. Results were further supplied by consecutive robustness measures and a post-hoc random-effects meta-analysis of behavioural data. RESULTS We identified k = 21 eligible studies for our analysis. The ALE analysis indicated a significant cluster of convergence with its statistical peak in the right anterior insula. Consecutive analyses, however, indicated this result was not robust and susceptible towards publication bias. Additionally, a post-hoc random effects meta-analysis of the behavioural parameters of Go/NoGo and stop-signal paradigms reported by the included studies revealed no significant differences in task performance comparing SUD samples and controls. CONCLUSION We discuss that the role of task-based response inhibition may require some refinement as an overarching marker for SUD pathology. Finally, we give a few prospects for future research that should be further explored in this context.
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Affiliation(s)
- Maximilian Fascher
- Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Am Kaiserkai 1, 20457, Hamburg, Germany.
- Medical School Hamburg, ICAN Institute for Cognitive and Affective Neuroscience, Am Kaiserkai 1, 20457, Hamburg, Germany.
| | - Sandra Nowaczynski
- Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Am Kaiserkai 1, 20457, Hamburg, Germany
- Medical School Hamburg, ICAN Institute for Cognitive and Affective Neuroscience, Am Kaiserkai 1, 20457, Hamburg, Germany
- Department of Addiction Medicine, Carl‑Friedrich‑Flemming‑Clinic, Helios Medical Center Schwerin, Schwerin, Germany
| | - Carolin Spindler
- Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Am Kaiserkai 1, 20457, Hamburg, Germany
| | - Tilo Strobach
- Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Am Kaiserkai 1, 20457, Hamburg, Germany
- Medical School Hamburg, ICAN Institute for Cognitive and Affective Neuroscience, Am Kaiserkai 1, 20457, Hamburg, Germany
| | - Markus Muehlhan
- Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Am Kaiserkai 1, 20457, Hamburg, Germany
- Medical School Hamburg, ICAN Institute for Cognitive and Affective Neuroscience, Am Kaiserkai 1, 20457, Hamburg, Germany
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12
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Newsome MR, Martindale SL, Davenport N, Dennis EL, Diaz M, Esopenko C, Hodges C, Jackson GR, Liu Q, Kenney K, Mayer AR, Rowland JA, Scheibel RS, Steinberg JL, Taylor BA, Tate DF, Werner JK, Walker WC, Wilde EA. Subcortical functional connectivity and its association with walking performance following deployment related mild TBI. Front Neurol 2023; 14:1276437. [PMID: 38156092 PMCID: PMC10752967 DOI: 10.3389/fneur.2023.1276437] [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: 08/11/2023] [Accepted: 09/18/2023] [Indexed: 12/30/2023] Open
Abstract
Introduction The relation between traumatic brain injury (TBI), its acute and chronic symptoms, and the potential for remote neurodegenerative disease is a priority for military research. Structural and functional connectivity (FC) of the basal ganglia, involved in motor tasks such as walking, are altered in some samples of Service Members and Veterans with TBI, but any behavioral implications are unclear and could further depend on the context in which the TBI occurred. Methods In this study, FC from caudate and pallidum seeds was measured in Service Members and Veterans with a history of mild TBI that occurred during combat deployment, Service Members and Veterans whose mild TBI occurred outside of deployment, and Service Members and Veterans who had no lifetime history of TBI. Results FC patterns differed for the two contextual types of mild TBI. Service Members and Veterans with deployment-related mild TBI demonstrated increased FC between the right caudate and lateral occipital regions relative to both the non-deployment mild TBI and TBI-negative groups. When evaluating the association between FC from the caudate and gait, the non-deployment mild TBI group showed a significant positive relationship between walking time and FC with the frontal pole, implicated in navigational planning, whereas the deployment-related mild TBI group trended towards a greater negative association between walking time and FC within the occipital lobes, associated with visuo-spatial processing during navigation. Discussion These findings have implications for elucidating subtle motor disruption in Service Members and Veterans with deployment-related mild TBI. Possible implications for future walking performance are discussed.
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Affiliation(s)
- Mary R. Newsome
- Research Service Line, George E. Wahlen VA Medical Center, Salt Lake City, UT, United States
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, United States
| | - Sarah L. Martindale
- Research and Academic Affairs Service Line, W. G. (Bill) Hefner VA Healthcare System, Salisbury, NC, United States
- Veterans Integrated Service Networks (VISN)-6 Mid-Atlantic Mental Illness, Research Education and Clinical Center (MIRECC), Durham, NC, United States
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Nicholas Davenport
- Research Service Line, Minneapolis VA Health Care System, Minneapolis, MN, United States
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
| | - Emily L. Dennis
- Research Service Line, George E. Wahlen VA Medical Center, Salt Lake City, UT, United States
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
| | - Marlene Diaz
- Research Service Line, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, United States
| | - Carrie Esopenko
- Department of Rehabilitation and Human Performance, Icahn School of Medicine, New York, NY, United States
| | - Cooper Hodges
- Department of Psychology, Brigham Young University, Provo, UT, United States
| | - George R. Jackson
- Parkinson's Disease Research, Education and Clinical Center (PADRECC), Michael E. DeBakey VA Medical Center, Houston, TX, United States
- Department of Neurology, Baylor College of Medicine, Houston, TX, United States
| | - Qisheng Liu
- Research Service Line, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, United States
- Center for Translational Research on Inflammatory Diseases (CTRID), Baylor College of Medicine, Houston, TX, United States
| | - Kimbra Kenney
- Department of Neurology, Uniform Services University, Bethesda, MD, United States
| | - Andrew R. Mayer
- The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, United States
- Departments of Psychiatry and Behavioral Sciences, Psychology and Neurology, University of New Mexico, Albuquerque, NM, United States
| | - Jared A. Rowland
- Research and Academic Affairs Service Line, W. G. (Bill) Hefner VA Healthcare System, Salisbury, NC, United States
- Veterans Integrated Service Networks (VISN)-6 Mid-Atlantic Mental Illness, Research Education and Clinical Center (MIRECC), Durham, NC, United States
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Randall S. Scheibel
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, United States
- Research Service Line, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, United States
| | - Joel L. Steinberg
- Department of Psychiatry, Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States
| | - Brian A. Taylor
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - David F. Tate
- Research Service Line, George E. Wahlen VA Medical Center, Salt Lake City, UT, United States
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
| | - J. Kent Werner
- Department of Neurology, Uniform Services University, Bethesda, MD, United States
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, United States
| | - William C. Walker
- Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University, Richmond, VA, United States
| | - Elisabeth A. Wilde
- Research Service Line, George E. Wahlen VA Medical Center, Salt Lake City, UT, United States
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah, Salt Lake City, UT, United States
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, United States
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13
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Taipale M, Tiihonen J, Korhonen J, Popovic D, Vaurio O, Lähteenvuo M, Lieslehto J. Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia. Schizophr Bull 2023; 49:1568-1578. [PMID: 37449305 PMCID: PMC10686357 DOI: 10.1093/schbul/sbad103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
BACKGROUND AND HYPOTHESIS Neuroimaging-based machine learning (ML) algorithms have the potential to aid the clinical diagnosis of schizophrenia. However, literature on the effect of prevalent comorbidities such as substance use disorder (SUD) and antisocial personality (ASPD) on these models' performance has remained unexplored. We investigated whether the presence of SUD or ASPD affects the performance of neuroimaging-based ML models trained to discern patients with schizophrenia (SCH) from controls. STUDY DESIGN We trained an ML model on structural MRI data from public datasets to distinguish between SCH and controls (SCH = 347, controls = 341). We then investigated the model's performance in two independent samples of individuals undergoing forensic psychiatric examination: sample 1 was used for sensitivity analysis to discern ASPD (N = 52) from SCH (N = 66), and sample 2 was used for specificity analysis to discern ASPD (N = 26) from controls (N = 25). Both samples included individuals with SUD. STUDY RESULTS In sample 1, 94.4% of SCH with comorbid ASPD and SUD were classified as SCH, followed by patients with SCH + SUD (78.8% classified as SCH) and patients with SCH (60.0% classified as SCH). The model failed to discern SCH without comorbidities from ASPD + SUD (AUC = 0.562, 95%CI = 0.400-0.723). In sample 2, the model's specificity to predict controls was 84.0%. In both samples, about half of the ASPD + SUD were misclassified as SCH. Data-driven functional characterization revealed associations between the classification as SCH and cognition-related brain regions. CONCLUSION Altogether, ASPD and SUD appear to have effects on ML prediction performance, which potentially results from converging cognition-related brain abnormalities between SCH, ASPD, and SUD.
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Affiliation(s)
- Matias Taipale
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland
| | - Jari Tiihonen
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Psychiatry Research, Stockholm City Council, Stockholm, Sweden
| | - Juuso Korhonen
- Department of Computer Science, Aalto University, Espoo, Finland
| | - David Popovic
- Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Olli Vaurio
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland
| | - Markku Lähteenvuo
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland
| | - Johannes Lieslehto
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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14
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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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15
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Liang B, Li Y, Zhao W, Du Y. Bilateral human laryngeal motor cortex in perceptual decision of lexical tone and voicing of consonant. Nat Commun 2023; 14:4710. [PMID: 37543659 PMCID: PMC10404239 DOI: 10.1038/s41467-023-40445-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 07/27/2023] [Indexed: 08/07/2023] Open
Abstract
Speech perception is believed to recruit the left motor cortex. However, the exact role of the laryngeal subregion and its right counterpart in speech perception, as well as their temporal patterns of involvement remain unclear. To address these questions, we conducted a hypothesis-driven study, utilizing transcranial magnetic stimulation on the left or right dorsal laryngeal motor cortex (dLMC) when participants performed perceptual decision on Mandarin lexical tone or consonant (voicing contrast) presented with or without noise. We used psychometric function and hierarchical drift-diffusion model to disentangle perceptual sensitivity and dynamic decision-making parameters. Results showed that bilateral dLMCs were engaged with effector specificity, and this engagement was left-lateralized with right upregulation in noise. Furthermore, the dLMC contributed to various decision stages depending on the hemisphere and task difficulty. These findings substantially advance our understanding of the hemispherical lateralization and temporal dynamics of bilateral dLMC in sensorimotor integration during speech perceptual decision-making.
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Affiliation(s)
- Baishen Liang
- Institute of Psychology, CAS Key Laboratory of Behavioral Science, Chinese Academy of Sciences, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yanchang Li
- Institute of Psychology, CAS Key Laboratory of Behavioral Science, Chinese Academy of Sciences, Beijing, 100101, China
| | - Wanying Zhao
- Institute of Psychology, CAS Key Laboratory of Behavioral Science, Chinese Academy of Sciences, Beijing, 100101, China
| | - Yi Du
- Institute of Psychology, CAS Key Laboratory of Behavioral Science, Chinese Academy of Sciences, Beijing, 100101, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, 200031, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
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16
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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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17
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Bedini M, Olivetti E, Avesani P, Baldauf D. Accurate localization and coactivation profiles of the frontal eye field and inferior frontal junction: an ALE and MACM fMRI meta-analysis. Brain Struct Funct 2023; 228:997-1017. [PMID: 37093304 PMCID: PMC10147761 DOI: 10.1007/s00429-023-02641-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 04/08/2023] [Indexed: 04/25/2023]
Abstract
The frontal eye field (FEF) and the inferior frontal junction (IFJ) are prefrontal structures involved in mediating multiple aspects of goal-driven behavior. Despite being recognized as prominent nodes of the networks underlying spatial attention and oculomotor control, and working memory and cognitive control, respectively, the limited quantitative evidence on their precise localization has considerably impeded the detailed understanding of their structure and connectivity. In this study, we performed an activation likelihood estimation (ALE) fMRI meta-analysis by selecting studies that employed standard paradigms to accurately infer the localization of these regions in stereotaxic space. For the FEF, we found the highest spatial convergence of activations for prosaccade and antisaccade paradigms at the junction of the precentral sulcus and superior frontal sulcus. For the IFJ, we found consistent activations across oddball/attention, working memory, task-switching and Stroop paradigms at the junction of the inferior precentral sulcus and inferior frontal sulcus. We related these clusters to previous meta-analyses, sulcal/gyral neuroanatomy, and a comprehensive brain parcellation, highlighting important differences compared to their results and taxonomy. Finally, we leveraged the ALE peak coordinates as seeds to perform a meta-analytic connectivity modeling (MACM) analysis, which revealed systematic coactivation patterns spanning the frontal, parietal, and temporal cortices. We decoded the behavioral domains associated with these coactivations, suggesting that these may allow FEF and IFJ to support their specialized roles in flexible behavior. Our study provides the meta-analytic groundwork for investigating the relationship between functional specialization and connectivity of two crucial control structures of the prefrontal cortex.
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Affiliation(s)
- Marco Bedini
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy.
- Department of Psychology, University of California, San Diego, McGill Hall 9500 Gilman Dr, La Jolla, CA, 92093-0109, USA.
| | - Emanuele Olivetti
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
- NILab, Bruno Kessler Foundation (FBK), Via delle Regole 101, 38123, Trento, Italy
| | - Paolo Avesani
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
- NILab, Bruno Kessler Foundation (FBK), Via delle Regole 101, 38123, Trento, Italy
| | - Daniel Baldauf
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
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18
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Yeung AWK, Robertson M, Uecker A, Fox PT, Eickhoff SB. Trends in the sample size, statistics, and contributions to the BrainMap database of activation likelihood estimation meta-analyses: An empirical study of 10-year data. Hum Brain Mapp 2023; 44:1876-1887. [PMID: 36479854 PMCID: PMC9980884 DOI: 10.1002/hbm.26177] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/18/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
The literature of neuroimaging meta-analysis has been thriving for over a decade. A majority of them were coordinate-based meta-analyses, particularly the activation likelihood estimation (ALE) approach. A meta-evaluation of these meta-analyses was performed to qualitatively evaluate their design and reporting standards. The publications listed from the BrainMap website were screened. Six hundred and three ALE papers published during 2010-2019 were included and analysed. For reporting standards, most of the ALE papers reported their total number of Papers involved and mentioned the inclusion/exclusion criteria on Paper selection. However, most papers did not describe how data redundancy was avoided when multiple related Experiments were reported within one paper. The most prevalent repeated-measures correction methods were voxel-level FDR (54.4%) and cluster-level FWE (33.8%), with the latter quickly replacing the former since 2016. For study characteristics, sample size in terms of number of Papers included per ALE paper and number of Experiments per analysis seemed to be stable over the decade. One-fifth of the surveyed ALE papers failed to meet the recommendation of having >17 Experiments per analysis. For data sharing, most of them did not provide input and output data. In conclusion, the field has matured well in terms of rising dominance of cluster-level FWE correction, and slightly improved reporting on elimination of data redundancy and providing input data. The provision of Data and Code availability statements and flow chart of literature screening process, as well as data submission to BrainMap, should be more encouraged.
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Affiliation(s)
- Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China
| | - Michaela Robertson
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Angela Uecker
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA.,Department of Radiology, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
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19
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Scheliga S, Kellermann T, Lampert A, Rolke R, Spehr M, Habel U. Neural correlates of multisensory integration in the human brain: an ALE meta-analysis. Rev Neurosci 2023; 34:223-245. [PMID: 36084305 DOI: 10.1515/revneuro-2022-0065] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 07/22/2022] [Indexed: 02/07/2023]
Abstract
Previous fMRI research identified superior temporal sulcus as central integration area for audiovisual stimuli. However, less is known about a general multisensory integration network across senses. Therefore, we conducted activation likelihood estimation meta-analysis with multiple sensory modalities to identify a common brain network. We included 49 studies covering all Aristotelian senses i.e., auditory, visual, tactile, gustatory, and olfactory stimuli. Analysis revealed significant activation in bilateral superior temporal gyrus, middle temporal gyrus, thalamus, right insula, and left inferior frontal gyrus. We assume these regions to be part of a general multisensory integration network comprising different functional roles. Here, thalamus operate as first subcortical relay projecting sensory information to higher cortical integration centers in superior temporal gyrus/sulcus while conflict-processing brain regions as insula and inferior frontal gyrus facilitate integration of incongruent information. We additionally performed meta-analytic connectivity modelling and found each brain region showed co-activations within the identified multisensory integration network. Therefore, by including multiple sensory modalities in our meta-analysis the results may provide evidence for a common brain network that supports different functional roles for multisensory integration.
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Affiliation(s)
- Sebastian Scheliga
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Thilo Kellermann
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany.,JARA-Institute Brain Structure Function Relationship, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Angelika Lampert
- Institute of Physiology, Medical Faculty RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Roman Rolke
- Department of Palliative Medicine, Medical Faculty RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Marc Spehr
- Department of Chemosensation, RWTH Aachen University, Institute for Biology, Worringerweg 3, 52074 Aachen, Germany
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany.,JARA-Institute Brain Structure Function Relationship, Pauwelsstraße 30, 52074 Aachen, Germany
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20
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Xu X, Li Q, Qian Y, Cai H, Zhang C, Zhao W, Zhu J, Yu Y. Genetic mechanisms underlying gray matter volume changes in patients with drug-naive first-episode schizophrenia. Cereb Cortex 2023; 33:2328-2341. [PMID: 35640648 DOI: 10.1093/cercor/bhac211] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Brain structural damage is a typical feature of schizophrenia. Investigating such disease phenotype in patients with drug-naive first-episode schizophrenia (DFSZ) may exclude the confounds of antipsychotics and illness chronicity. However, small sample sizes and marked clinical heterogeneity have precluded definitive identification of gray matter volume (GMV) changes in DFSZ as well as their underlying genetic mechanisms. Here, GMV changes in DFSZ were assessed using a neuroimaging meta-analysis of 19 original studies, including 605 patients and 637 controls. Gene expression data were derived from the Allen Human Brain Atlas and processed with a newly proposed standardized pipeline. Then, we used transcriptome-neuroimaging spatial correlations to identify genes associated with GMV changes in DFSZ, followed by a set of gene functional feature analyses. Meta-analysis revealed consistent GMV reduction in the right superior temporal gyrus, right insula and left inferior temporal gyrus in DFSZ. Moreover, we found that these GMV changes were spatially correlated with expression levels of 1,201 genes, which exhibited a wide range of functional features. Our findings may provide important insights into the genetic mechanisms underlying brain morphological abnormality in schizophrenia.
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Affiliation(s)
- Xiaotao Xu
- Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei 230012, China.,Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Qian Li
- Department of Radiology, Chaohu Hospital of Anhui Medical University, Hefei 238000, China.,Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Cun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China.,Department of Radiology, Chaohu Hospital of Anhui Medical University, Hefei 238000, China.,Department of Radiology, The Fourth Affiliated Hospital of Anhui Medical University, Hefei 230012, China
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21
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Schroeter ML, Godulla J, Thiel F, Taskin B, Beutner F, Dubovoy VK, Teren A, Camilleri J, Eickhoff S, Villringer A, Mueller K. Heart failure decouples the precuneus in interaction with social cognition and executive functions. Sci Rep 2023; 13:1236. [PMID: 36690723 PMCID: PMC9870947 DOI: 10.1038/s41598-023-28338-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 01/17/2023] [Indexed: 01/24/2023] Open
Abstract
Aging increases the risk to develop Alzheimer's disease. Cardiovascular diseases might accelerate this process. Our study aimed at investigating the impact of heart failure on brain connectivity using functional magnetic resonance imaging at resting state. Here we show brain connectivity alterations related to heart failure and cognitive performance. Heart failure decreases brain connectivity in the precuneus. Precuneus dysconnectivity was associated with biomarkers of heart failure-left ventricular ejection fraction and N-terminal prohormone of brain natriuretic peptide-and cognitive performance, predominantly executive function. Meta-analytical data-mining approaches-conducted in the BrainMap and Neurosynth databases-revealed that social and executive cognitive functions are mainly associated with those neural networks. Remarkably, the precuneus, as identified in our study in a mid-life cohort, represents one central functional hub affected by Alzheimer's disease. A long-term follow-up investigation in our cohort after approximately nine years revealed more severe cognitive impairment in the group with heart failure than controls, where social cognition was the cognitive domain mainly affected, and not memory such as in Alzheimer's disease. In sum, our results indicate consistently an association between heart failure and decoupling of the precuneus from other brain regions being associated with social and executive functions. Further longitudinal studies are warranted elucidating etiopathological mechanisms.
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Affiliation(s)
- Matthias L Schroeter
- Clinic for Cognitive Neurology, University Hospital Leipzig, Liebigstr. 16, 04103, Leipzig, Germany.
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A, 04103, Leipzig, Germany.
- Leipzig Research Center for Civilization Diseases, Leipzig, Germany.
| | - Jannis Godulla
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A, 04103, Leipzig, Germany
- Ludwig Maximilians University Munich, Munich, Germany
| | - Friederike Thiel
- Clinic for Cognitive Neurology, University Hospital Leipzig, Liebigstr. 16, 04103, Leipzig, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A, 04103, Leipzig, Germany
| | - Birol Taskin
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A, 04103, Leipzig, Germany
| | - Frank Beutner
- Leipzig Research Center for Civilization Diseases, Leipzig, Germany
- Leipzig Heart Center, Leipzig, Germany
| | - Vladimir K Dubovoy
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A, 04103, Leipzig, Germany
- Karazin Kharkiv National University, Kharkiv, Ukraine
| | - Andrej Teren
- Leipzig Research Center for Civilization Diseases, Leipzig, Germany
- Leipzig Heart Center, Leipzig, Germany
- Department of Cardiology and Intensive Care Medicine, Klinikum Bielefeld, Bielefeld, Germany
| | - Julia Camilleri
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Jülich, Germany
| | - Simon Eickhoff
- Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7 Brain and Behaviour), Forschungszentrum Jülich, Jülich, Germany
| | - Arno Villringer
- Clinic for Cognitive Neurology, University Hospital Leipzig, Liebigstr. 16, 04103, Leipzig, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A, 04103, Leipzig, Germany
- Leipzig Research Center for Civilization Diseases, Leipzig, Germany
| | - Karsten Mueller
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1A, 04103, Leipzig, Germany.
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22
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Dugré JR, Potvin S. Clarifying the role of Cortico-Cortical and Amygdalo-Cortical brain dysconnectivity associated with Conduct Problems. Neuroimage Clin 2023; 37:103346. [PMID: 36791489 PMCID: PMC9958059 DOI: 10.1016/j.nicl.2023.103346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 01/14/2023] [Accepted: 02/05/2023] [Indexed: 02/10/2023]
Abstract
A recent meta-analysis of resting-state functional connectivity studies revealed that individuals exhibiting antisocial behaviors or conduct problems may show disrupted brain connectivity in networks underpinning socio-affective and attentional processes. However, studies included in the meta-analysis generally rely on small sample sizes and substantially differ in terms of psychometric scales and neuroimaging methodologies. Therefore, we aimed to identify reliable functional brain connectivity alterations associated with severity of conduct problems using a large sample of adolescents and two measures of conduct problems. In a sample of 1416 children and adolescents, mass-univariate analyses of connectivity measures between 333 cortical parcels were conducted to examine the relationship between resting-state functional cortical-cortical connectome and the severity of conduct problems using the Child Behavior Checklist (CBCL) and the Strengths and Difficulties Questionnaire (SDQ). At a liberal threshold, results showed that the functional brain connectivity significantly associated with conduct problems largely differ between the two scales. Indeed, only 21 pairs of brain regions overlapped between the CBCL and SDQ. Permutation feature importance of these 21 brain connectivity measures revealed that connectivity between precentral/postcentral gyri and lateral prefrontal cortex (both ventral and dorsal) were the most important features in explaining variance in conduct problems. The current study highlights that psychometric measures may yield distinct functional connectivity results. Moreover, severity of conduct problems in children and adolescents was mainly associated with deficient functional connectivity of somatomotor and ventral attention networks indicating potential alterations in motor, cognitive and reward processes.
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Affiliation(s)
- Jules R Dugré
- Research Center of the Institut Universitaire en Santé Mentale de Montréal, Montreal, Canada; Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, Canada.
| | - Stéphane Potvin
- Research Center of the Institut Universitaire en Santé Mentale de Montréal, Montreal, Canada; Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, Canada.
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23
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D’Elia A, Schiavi S, Manduca A, Rava A, Buzzelli V, Ascone F, Orsini T, Putti S, Soluri A, Galli F, Soluri A, Mattei M, Cicconi R, Massari R, Trezza V. FMR1 deletion in rats induces hyperactivity with no changes in striatal dopamine transporter availability. Sci Rep 2022; 12:22535. [PMID: 36581671 PMCID: PMC9800572 DOI: 10.1038/s41598-022-26986-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 12/22/2022] [Indexed: 12/30/2022] Open
Abstract
Autism Spectrum Disorder (ASD) is a pervasive neurodevelopmental disorder emerging in early life characterized by impairments in social interaction, poor verbal and non-verbal communication, and repetitive patterns of behaviors. Among the best-known genetic risk factors for ASD, there are mutations causing the loss of the Fragile X Messenger Ribonucleoprotein 1 (FMRP) leading to Fragile X syndrome (FXS), a common form of inherited intellectual disability and the leading monogenic cause of ASD. Being a pivotal regulator of motor activity, motivation, attention, and reward processing, dopaminergic neurotransmission has a key role in several neuropsychiatric disorders, including ASD. Fmr1 Δexon 8 rats have been validated as a genetic model of ASD based on FMR1 deletion, and they are also a rat model of FXS. Here, we performed behavioral, biochemical and in vivo SPECT neuroimaging experiments to investigate whether Fmr1 Δexon 8 rats display ASD-like repetitive behaviors associated with changes in striatal dopamine transporter (DAT) availability assessed through in vivo SPECT neuroimaging. At the behavioral level, Fmr1 Δexon 8 rats displayed hyperactivity in the open field test in the absence of repetitive behaviors in the hole board test. However, these behavioral alterations were not associated with changes in striatal DAT availability as assessed by non-invasive in vivo SPECT and Western blot analyses.
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Affiliation(s)
- Annunziata D’Elia
- grid.5326.20000 0001 1940 4177Institute of Biochemistry and Cell Biology (IBBC), National Research Council of Italy (CNR), c/o International Campus “A. Buzzati-Traverso”, Via E. Ramarini, 32, 00015 Monterotondo Scalo (Rome), Italy ,grid.8509.40000000121622106Department of Science, Section of Biomedical Sciences and Technologies, Roma Tre University, Viale G. Marconi 446, 00146 Rome, Italy
| | - Sara Schiavi
- grid.8509.40000000121622106Department of Science, Section of Biomedical Sciences and Technologies, Roma Tre University, Viale G. Marconi 446, 00146 Rome, Italy
| | - Antonia Manduca
- grid.8509.40000000121622106Department of Science, Section of Biomedical Sciences and Technologies, Roma Tre University, Viale G. Marconi 446, 00146 Rome, Italy ,grid.417778.a0000 0001 0692 3437Neuroendocrinology, Metabolism and Neuropharmacology Unit, IRCSS Fondazione Santa Lucia, Rome, Italy
| | - Alessandro Rava
- grid.8509.40000000121622106Department of Science, Section of Biomedical Sciences and Technologies, Roma Tre University, Viale G. Marconi 446, 00146 Rome, Italy
| | - Valeria Buzzelli
- grid.8509.40000000121622106Department of Science, Section of Biomedical Sciences and Technologies, Roma Tre University, Viale G. Marconi 446, 00146 Rome, Italy
| | - Fabrizio Ascone
- grid.8509.40000000121622106Department of Science, Section of Biomedical Sciences and Technologies, Roma Tre University, Viale G. Marconi 446, 00146 Rome, Italy
| | - Tiziana Orsini
- grid.5326.20000 0001 1940 4177Institute of Biochemistry and Cell Biology (IBBC), National Research Council of Italy (CNR), c/o International Campus “A. Buzzati-Traverso”, Via E. Ramarini, 32, 00015 Monterotondo Scalo (Rome), Italy
| | - Sabrina Putti
- grid.5326.20000 0001 1940 4177Institute of Biochemistry and Cell Biology (IBBC), National Research Council of Italy (CNR), c/o International Campus “A. Buzzati-Traverso”, Via E. Ramarini, 32, 00015 Monterotondo Scalo (Rome), Italy
| | - Andrea Soluri
- grid.5326.20000 0001 1940 4177Institute of Biochemistry and Cell Biology (IBBC), National Research Council of Italy (CNR), c/o International Campus “A. Buzzati-Traverso”, Via E. Ramarini, 32, 00015 Monterotondo Scalo (Rome), Italy ,grid.9657.d0000 0004 1757 5329Unit of Molecular Neurosciences, University Campus Bio-Medico, Rome, Rome, Italy
| | - Filippo Galli
- grid.7841.aNuclear Medicine Unit, Department of Medical-Surgical Sciences and of Translational Medicine, Faculty of Medicine and Psychology, “Sapienza” University of Rome, Rome, Italy
| | - Alessandro Soluri
- grid.5326.20000 0001 1940 4177Institute of Biochemistry and Cell Biology (IBBC), National Research Council of Italy (CNR), c/o International Campus “A. Buzzati-Traverso”, Via E. Ramarini, 32, 00015 Monterotondo Scalo (Rome), Italy
| | - Maurizio Mattei
- grid.6530.00000 0001 2300 0941Department of Biology and Centro di Servizi Interdipartimentale-Stazione per la Tecnologia Animale, “Tor Vergata” University, Rome, Italy
| | - Rosella Cicconi
- grid.6530.00000 0001 2300 0941Department of Biology and Centro di Servizi Interdipartimentale-Stazione per la Tecnologia Animale, “Tor Vergata” University, Rome, Italy
| | - Roberto Massari
- grid.5326.20000 0001 1940 4177Institute of Biochemistry and Cell Biology (IBBC), National Research Council of Italy (CNR), c/o International Campus “A. Buzzati-Traverso”, Via E. Ramarini, 32, 00015 Monterotondo Scalo (Rome), Italy
| | - Viviana Trezza
- grid.8509.40000000121622106Department of Science, Section of Biomedical Sciences and Technologies, Roma Tre University, Viale G. Marconi 446, 00146 Rome, Italy
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24
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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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25
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Gray JP, Manuello J, Alexander-Bloch AF, Leonardo C, Franklin C, Choi KS, Cauda F, Costa T, Blangero J, Glahn DC, Mayberg HS, Fox PT. Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects. Neuroinformatics 2022; 21:443-455. [PMID: 36469193 DOI: 10.1007/s12021-022-09614-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/06/2022] [Indexed: 12/12/2022]
Abstract
Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.
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26
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Singh S, Rogers H, Kanber B, Clemente J, Pye H, Johnston EW, Parry T, Grey A, Dinneen E, Shaw G, Heavey S, Stopka-Farooqui U, Haider A, Freeman A, Giganti F, Atkinson D, Moore CM, Whitaker HC, Alexander DC, Panagiotaki E, Punwani S. Avoiding Unnecessary Biopsy after Multiparametric Prostate MRI with VERDICT Analysis: The INNOVATE Study. Radiology 2022; 305:623-630. [PMID: 35916679 DOI: 10.1148/radiol.212536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background In men suspected of having prostate cancer (PCa), up to 50% of men with positive multiparametric MRI (mpMRI) findings (Prostate Imaging Reporting and Data System [PI-RADS] or Likert score of 3 or higher) have no clinically significant (Gleason score ≤3+3, benign) biopsy findings. Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumor (VERDICT) MRI analysis could improve the stratification of positive mpMRI findings. Purpose To evaluate VERDICT MRI, mpMRI-derived apparent diffusion coefficient (ADC), and prostate-specific antigen density (PSAD) as determinants of clinically significant PCa (csPCa). Materials and Methods Between April 2016 and December 2019, men suspected of having PCa were prospectively recruited from two centers and underwent VERDICT MRI and mpMRI at one center before undergoing targeted biopsy. Biopsied lesion ADC, lesion-derived fractional intracellular volume (FIC), and PSAD were compared between men with csPCa and those without csPCa, using nonparametric tests subdivided by Likert scores. Area under the receiver operating characteristic curve (AUC) was calculated to test diagnostic performance. Results Among 303 biopsy-naive men, 165 study participants (mean age, 65 years ± 7 [SD]) underwent targeted biopsy; of these, 73 had csPCa. Median lesion FIC was higher in men with csPCa (FIC, 0.53) than in those without csPCa (FIC, 0.18) for Likert 3 (P = .002) and Likert 4 (0.60 vs 0.28, P < .001) lesions. Median lesion ADC was lower for Likert 4 lesions with csPCa (0.86 × 10-3 mm2/sec) compared with lesions without csPCa (1.12 × 10-3 mm2/sec, P = .03), but there was no evidence of a difference for Likert 3 lesions (0.97 × 10-3 mm2/sec vs 1.20 × 10-3 mm2/sec, P = .09). PSAD also showed no difference for Likert 3 (0.17 ng/mL2 vs 0.12 ng/mL2, P = .07) or Likert 4 (0.14 ng/mL2 vs 0.12 ng/mL2, P = .47) lesions. The diagnostic performance of FIC (AUC, 0.96; 95% CI: 0.93, 1.00) was higher (P = .02) than that of ADC (AUC, 0.85; 95% CI: 0.79, 0.91) and PSAD (AUC, 0.74; 95% CI: 0.66, 0.82) for the presence of csPCa in biopsied lesions. Conclusion Lesion fractional intracellular volume enabled better classification of clinically significant prostate cancer than did apparent diffusion coefficient and prostate-specific antigen density. Clinical trial registration no. NCT02689271 © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
- Saurabh Singh
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Harriet Rogers
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Baris Kanber
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Joey Clemente
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Hayley Pye
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Edward W Johnston
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Tom Parry
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Alistair Grey
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Eoin Dinneen
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Greg Shaw
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Susan Heavey
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Urszula Stopka-Farooqui
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Aiman Haider
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Alex Freeman
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Francesco Giganti
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - David Atkinson
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Caroline M Moore
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Hayley C Whitaker
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Daniel C Alexander
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Eleftheria Panagiotaki
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
| | - Shonit Punwani
- From the Centre for Medical Imaging, Division of Medicine (S.S., H.R., J.C., E.W.J., T.P., D.A., S.P.), Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering (B.K.), Molecular Diagnostics and Therapeutics Group (H.P., S.H., U.S.F., H.C.W.), Division of Surgery and Interventional Sciences (F.G., C.M.M.), and Centre for Medical Image Computing, Department of Computer Science (D.C.A., E.P.), University College London, Charles Bell House, 43-45 Foley St, London W1W 7TS, England; Department of Diagnostic Radiology, Royal Marsden Hospital, London, England (E.W.J.); Departments of Urology (A.G., E.D., G.S., C.M.M.), Pathology (A.H., A.F.), and Radiology (F.G.), University College London Hospitals NHS Foundation Trust, London, England; and Department of Urology, Barts Health, NHS Foundation Trust, London, England (A.G., G.S.)
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27
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Liu S, Zhang C, Meng C, Wang R, Jiang P, Cai H, Zhao W, Yu Y, Zhu J. Frequency-dependent genetic modulation of neuronal oscillations: a combined transcriptome and resting-state functional MRI study. Cereb Cortex 2022; 32:5132-5144. [PMID: 35106539 DOI: 10.1093/cercor/bhac003] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 01/01/2022] [Accepted: 01/02/2022] [Indexed: 12/27/2022] Open
Abstract
Neuronal oscillations within certain frequency bands are assumed to associate with specific neural processes and cognitive functions. To examine this hypothesis, transcriptome-neuroimaging spatial correlation analysis was applied to resting-state functional magnetic resonance imaging data from 793 healthy individuals and gene expression data from the Allen Human Brain Atlas. We found that expression measures of 336 genes were correlated with fractional amplitude of low-frequency fluctuations (fALFF) in the slow-4 band (0.027-0.073 Hz), whereas there were no expression-fALFF correlations for the other frequency bands. Furthermore, functional enrichment analyses showed that these slow-4 fALFF-related genes were mainly enriched for ion channel, synaptic function, and neuronal system as well as many neuropsychiatric disorders. Specific expression analyses demonstrated that these genes were specifically expressed in brain tissue, in neurons, and during the late stage of cortical development. Concurrently, the fALFF-related genes were linked to multiple behavioral domains, including dementia, attention, and emotion. In addition, these genes could construct a protein-protein interaction network supported by 30 hub genes. Our findings of a frequency-dependent genetic modulation of spontaneous neuronal activity may support the concept that neuronal oscillations within different frequency bands capture distinct neurobiological processes from the perspective of underlying molecular mechanisms.
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Affiliation(s)
- Siyu Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Cun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Chun Meng
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Department of Radiology, Anhui No.2 Provincial People's Hospital, Hefei 230041, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Ping Jiang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China.,Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
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28
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Reveley C, Ye FQ, Mars RB, Matrov D, Chudasama Y, Leopold DA. Diffusion MRI anisotropy in the cerebral cortex is determined by unmyelinated tissue features. Nat Commun 2022; 13:6702. [PMID: 36335105 PMCID: PMC9637141 DOI: 10.1038/s41467-022-34328-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/19/2022] [Indexed: 11/07/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) is commonly used to assess the tissue and cellular substructure of the human brain. In the white matter, myelinated axons are the principal neural elements that shape dMRI through the restriction of water diffusion; however, in the gray matter the relative contributions of myelinated axons and other tissue features to dMRI are poorly understood. Here we investigate the determinants of diffusion in the cerebral cortex. Specifically, we ask whether myelinated axons significantly shape dMRI fractional anisotropy (dMRI-FA), a measure commonly used to characterize tissue properties in humans. We compared ultra-high resolution ex vivo dMRI data from the brain of a marmoset monkey with both myelin- and Nissl-stained histological sections obtained from the same brain after scanning. We found that the dMRI-FA did not match the spatial distribution of myelin in the gray matter. Instead dMRI-FA was more closely related to the anisotropy of stained tissue features, most prominently those revealed by Nissl staining and to a lesser extent those revealed by myelin staining. Our results suggest that unmyelinated neurites such as large caliber apical dendrites are the primary features shaping dMRI measures in the cerebral cortex.
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Affiliation(s)
- Colin Reveley
- grid.4991.50000 0004 1936 8948Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU UK ,grid.12082.390000 0004 1936 7590Department of Informatics, University of Sussex, Falmer, Brighton, BN1 9QJ UK
| | - Frank Q. Ye
- grid.94365.3d0000 0001 2297 5165Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD USA
| | - Rogier B. Mars
- grid.4991.50000 0004 1936 8948Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU UK ,grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Denis Matrov
- grid.94365.3d0000 0001 2297 5165Section on Behavioral Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - Yogita Chudasama
- grid.94365.3d0000 0001 2297 5165Section on Behavioral Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - David A. Leopold
- grid.94365.3d0000 0001 2297 5165Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD USA ,grid.94365.3d0000 0001 2297 5165Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
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29
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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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30
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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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31
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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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32
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Zhao H, Cai H, Mo F, Lu Y, Yao S, Yu Y, Zhu J. Genetic mechanisms underlying brain functional homotopy: a combined transcriptome and resting-state functional MRI study. Cereb Cortex 2022; 33:3387-3400. [PMID: 35851912 DOI: 10.1093/cercor/bhac279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/22/2022] [Accepted: 06/23/2022] [Indexed: 11/15/2022] Open
Abstract
Abstract
Functional homotopy, the high degree of spontaneous activity synchrony and functional coactivation between geometrically corresponding interhemispheric regions, is a fundamental characteristic of the intrinsic functional architecture of the brain. However, little is known about the genetic mechanisms underlying functional homotopy. Resting-state functional magnetic resonance imaging data from a discovery dataset (656 healthy subjects) and 2 independent cross-race, cross-scanner validation datasets (103 and 329 healthy subjects) were used to calculate voxel-mirrored homotopic connectivity (VMHC) indexing brain functional homotopy. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging spatial correlation analysis was conducted to identify genes linked to VMHC. We found 1,001 genes whose expression measures were spatially associated with VMHC. Functional enrichment analyses demonstrated that these VMHC-related genes were enriched for biological functions including protein kinase activity, ion channel regulation, and synaptic function as well as many neuropsychiatric disorders. Concurrently, specific expression analyses showed that these genes were specifically expressed in the brain tissue, in neurons and immune cells, and during nearly all developmental periods. In addition, the VMHC-associated genes were linked to multiple behavioral domains, including vision, execution, and attention. Our findings suggest that interhemispheric communication and coordination involve a complex interaction of polygenes with a rich range of functional features.
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Affiliation(s)
- Han Zhao
- Department of Radiology , The First Affiliated Hospital of Anhui Medical University, Hefei 230022 , China
- Research Center of Clinical Medical Imaging , Anhui Province, Hefei 230032 , China
- Anhui Provincial Institute of Translational Medicine , Hefei 230032 , China
| | - Huanhuan Cai
- Department of Radiology , The First Affiliated Hospital of Anhui Medical University, Hefei 230022 , China
- Research Center of Clinical Medical Imaging , Anhui Province, Hefei 230032 , China
- Anhui Provincial Institute of Translational Medicine , Hefei 230032 , China
| | - Fan Mo
- Department of Radiology , The First Affiliated Hospital of Anhui Medical University, Hefei 230022 , China
- Research Center of Clinical Medical Imaging , Anhui Province, Hefei 230032 , China
- Anhui Provincial Institute of Translational Medicine , Hefei 230032 , China
| | - Yun Lu
- Department of Radiology , The First Affiliated Hospital of Anhui Medical University, Hefei 230022 , China
- Research Center of Clinical Medical Imaging , Anhui Province, Hefei 230032 , China
- Anhui Provincial Institute of Translational Medicine , Hefei 230032 , China
| | - Shanwen Yao
- Department of Radiology , The First Affiliated Hospital of Anhui Medical University, Hefei 230022 , China
- Research Center of Clinical Medical Imaging , Anhui Province, Hefei 230032 , China
- Anhui Provincial Institute of Translational Medicine , Hefei 230032 , China
| | - Yongqiang Yu
- Department of Radiology , The First Affiliated Hospital of Anhui Medical University, Hefei 230022 , China
- Research Center of Clinical Medical Imaging , Anhui Province, Hefei 230032 , China
- Anhui Provincial Institute of Translational Medicine , Hefei 230032 , China
| | - Jiajia Zhu
- Department of Radiology , The First Affiliated Hospital of Anhui Medical University, Hefei 230022 , China
- Research Center of Clinical Medical Imaging , Anhui Province, Hefei 230032 , China
- Anhui Provincial Institute of Translational Medicine , Hefei 230032 , China
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Abstract
Through long-term training, music experts acquire complex and specialized sensorimotor skills, which are paralleled by continuous neuro-anatomical and -functional adaptations. The underlying neuroplasticity mechanisms have been extensively explored in decades of research in music, cognitive, and translational neuroscience. However, the absence of a comprehensive review and quantitative meta-analysis prevents the plethora of variegated findings to ultimately converge into a unified picture of the neuroanatomy of musical expertise. Here, we performed a comprehensive neuroimaging meta-analysis of publications investigating neuro-anatomical and -functional differences between musicians (M) and non-musicians (NM). Eighty-four studies were included in the qualitative synthesis. From these, 58 publications were included in coordinate-based meta-analyses using the anatomic/activation likelihood estimation (ALE) method. This comprehensive approach delivers a coherent cortico-subcortical network encompassing sensorimotor and limbic regions bilaterally. Particularly, M exhibited higher volume/activity in auditory, sensorimotor, interoceptive, and limbic brain areas and lower volume/activity in parietal areas as opposed to NM. Notably, we reveal topographical (dis-)similarities between the identified functional and anatomical networks and characterize their link to various cognitive functions by means of meta-analytic connectivity modelling. Overall, we effectively synthesized decades of research in the field and provide a consistent and controversies-free picture of the neuroanatomy of musical expertise.
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Neural substrates of brand equity: applying a quantitative meta-analytical method for neuroimage studies. Heliyon 2022; 8:e09702. [PMID: 35734557 PMCID: PMC9207674 DOI: 10.1016/j.heliyon.2022.e09702] [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: 09/12/2021] [Revised: 01/29/2022] [Accepted: 06/06/2022] [Indexed: 11/24/2022] Open
Abstract
Although the concept of brand equity has been investigated using various approaches, a comprehensive neural basis for brand equity remains unclear. The default mode network (DMN) as a mental process might influence brand equity related consumers' decision-making, as reported in the marketing literature. While studies on the overlapping regions between the DMN and value-based decision-making related brain regions have been reported in neuroscience literature, relationships between the DMN and a neural mechanism of brand equity have not been clarified. The aim of our study is to identify neural substrates of brand equity and examine brand equity-related mental processes by comparing them to the DMN. To determine the neural substrates of brand equity, we first carried out the activation likelihood estimation (ALE) meta-analysis. We examined 26 studies using branded objects as experimental stimuli for the ALE. Next, we set the output regions from ALE as the region of interest for meta-analytic connectivity modeling (MACM). Further, we compared the brand equity-related brain network (BE-RBN) revealed by the MACM with the DMN. We confirmed that the BE-RBN brain regions overlap with the medial temporal lobule (MTL) sub-system, a module composed of the DMN but excluding the retrosplenial cortex. Further, we discovered that several brain regions apart from the DMN are also distinctive BE-RBN brain regions (i.e., the insula, the inferior frontal gyrus, amygdala, ventral striatum, parietal region). We decoded the BE-RBN brain regions using the BrandMap module. The decoded results revealed that the brand equity-related mental processes are complex constructs integrated via multiple mental processes such as self-referential, reward, emotional, memory, and sensorimotor processing. Our study demonstrated that the DMN alone is insufficient to engage in brand equity-related mental processes. Therefore, marketers are required to make strategic plans to integrate the five consumer's multiple mental processes while building brand equity.
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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: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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36
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Lu QY, Towne JM, Lock M, Jiang C, Cheng ZX, Habes M, Zuo XN, Zang YF. Toward coordinate-based cognition dictionaries: A brainmap and neurosynth demo. Neuroscience 2022; 493:109-118. [DOI: 10.1016/j.neuroscience.2022.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/09/2022] [Accepted: 02/14/2022] [Indexed: 10/18/2022]
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Dugré JR, Eickhoff SB, Potvin S. Meta-analytical transdiagnostic neural correlates in common pediatric psychiatric disorders. Sci Rep 2022; 12:4909. [PMID: 35318371 PMCID: PMC8941086 DOI: 10.1038/s41598-022-08909-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/09/2022] [Indexed: 01/04/2023] Open
Abstract
In the last decades, neuroimaging studies have attempted to unveil the neurobiological markers underlying pediatric psychiatric disorders. Yet, the vast majority of neuroimaging studies still focus on a single nosological category, which limit our understanding of the shared/specific neural correlates between these disorders. Therefore, we aimed to investigate the transdiagnostic neural correlates through a novel and data-driven meta-analytical method. A data-driven meta-analysis was carried out which grouped similar experiments’ topographic map together, irrespectively of nosological categories and task-characteristics. Then, activation likelihood estimation meta-analysis was performed on each group of experiments to extract spatially convergent brain regions. One hundred forty-seven experiments were retrieved (3124 cases compared to 3100 controls): 79 attention-deficit/hyperactivity disorder, 32 conduct/oppositional defiant disorder, 14 anxiety disorders, 22 major depressive disorders. Four significant groups of experiments were observed. Functional characterization suggested that these groups of aberrant brain regions may be implicated internally/externally directed processes, attentional control of affect, somato-motor and visual processes. Furthermore, despite that some differences in rates of studies involving major depressive disorders were noticed, nosological categories were evenly distributed between these four sets of regions. Our results may reflect transdiagnostic neural correlates of pediatric psychiatric disorders, but also underscore the importance of studying pediatric psychiatric disorders simultaneously rather than independently to examine differences between disorders.
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Affiliation(s)
- Jules R Dugré
- Research Center of the Institut Universitaire en Santé Mentale de Montréal, 7331 Hochelaga, Montreal, QC, H1N 3V2, Canada. .,Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, Canada.
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Jülich, Germany.,Institute for Systems Neuroscience, Heinrich Heine University, Düsseldorf, Germany
| | - Stéphane Potvin
- Research Center of the Institut Universitaire en Santé Mentale de Montréal, 7331 Hochelaga, Montreal, QC, H1N 3V2, Canada. .,Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, Canada.
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38
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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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39
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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: 76] [Impact Index Per Article: 38.0] [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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40
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Spindler C, Mallien L, Trautmann S, Alexander N, Muehlhan M. A coordinate-based meta-analysis of white matter alterations in patients with alcohol use disorder. Transl Psychiatry 2022; 12:40. [PMID: 35087021 PMCID: PMC8795454 DOI: 10.1038/s41398-022-01809-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 01/04/2022] [Accepted: 01/13/2022] [Indexed: 12/18/2022] Open
Abstract
INTRODUCTION Besides the commonly described gray matter (GM) deficits, there is growing evidence of significant white matter (WM) alterations in patients with alcohol use disorder (AUD). WM changes can be assessed using volumetric and diffusive magnetic resonance imaging methods, such as voxel-based morphometry (VBM) and diffusion tensor imaging (DTI). The aim of the present meta-analysis is to investigate the spatial convergence of the reported findings on WM alterations in AUD. METHODS Systematic literature search on PubMed and further databases revealed 18 studies eligible for inclusion, entailing a total of 462 AUD patients and 416 healthy controls (up to January 18, 2021). All studies that had used either VBM or DTI whole-brain analyzing methods and reported results as peak-coordinates in standard reference space were considered for inclusion. We excluded studies using approaches non-concordant with recent guidelines for neuroimaging meta-analyses and studies investigating patient groups with Korsakoff syndrome or other comorbid substance use disorders (except tobacco). RESULTS Anatomical likelihood estimation (ALE) revealed four significant clusters of convergent macro- and microstructural WM alterations in AUD patients that were assigned to the genu and body of the corpus callosum, anterior and posterior cingulum, fornix, and the right posterior limb of the internal capsule. DISCUSSION The changes in WM could to some extent explain the deteriorations in motor, cognitive, affective, and perceptual functions seen in AUD. Future studies are needed to clarify how WM alterations vary over the course of the disorder and to what extent they are reversible with prolonged abstinence.
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Affiliation(s)
- Carolin Spindler
- grid.461732.5Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Am Kaiserkai 1, 20457 Hamburg, Germany ,grid.461732.5ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Am Kaiserkai 1, 20457 Hamburg, Germany
| | - Louisa Mallien
- grid.461732.5Department of Human Medicine, Faculty of Medicine, Medical School Hamburg, Am Kaiserkai 1, 20457 Hamburg, Germany
| | - Sebastian Trautmann
- grid.461732.5Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Am Kaiserkai 1, 20457 Hamburg, Germany ,grid.461732.5ICPP Institute for Clinical Psychology and Psychotherapy, Medical School Hamburg, Am Kaiserkai 1, 20457 Hamburg, Germany
| | - Nina Alexander
- grid.461732.5Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Am Kaiserkai 1, 20457 Hamburg, Germany ,grid.10253.350000 0004 1936 9756Department of Psychiatry and Psychotherapy, Philipps University Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany ,grid.10253.350000 0004 1936 9756Center for Mind, Brain and Behavior, Philipps University Marburg, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Markus Muehlhan
- Department of Psychology, Faculty of Human Sciences, Medical School Hamburg, Am Kaiserkai 1, 20457, Hamburg, Germany. .,ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Am Kaiserkai 1, 20457, Hamburg, Germany.
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41
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Pando-Naude V, Patyczek A, Bonetti L, Vuust P. An ALE meta-analytic review of top-down and bottom-up processing of music in the brain. Sci Rep 2021; 11:20813. [PMID: 34675231 PMCID: PMC8531391 DOI: 10.1038/s41598-021-00139-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/06/2021] [Indexed: 12/01/2022] Open
Abstract
A remarkable feature of the human brain is its ability to integrate information from the environment with internally generated content. The integration of top-down and bottom-up processes during complex multi-modal human activities, however, is yet to be fully understood. Music provides an excellent model for understanding this since music listening leads to the urge to move, and music making entails both playing and listening at the same time (i.e., audio-motor coupling). Here, we conducted activation likelihood estimation (ALE) meta-analyses of 130 neuroimaging studies of music perception, production and imagery, with 2660 foci, 139 experiments, and 2516 participants. We found that music perception and production rely on auditory cortices and sensorimotor cortices, while music imagery recruits distinct parietal regions. This indicates that the brain requires different structures to process similar information which is made available either by an interaction with the environment (i.e., bottom-up) or by internally generated content (i.e., top-down).
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Affiliation(s)
- Victor Pando-Naude
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music Aarhus/Aalborg, Universitetsbyen, 3-0-17, 8000, Aarhus C, Denmark.
| | - Agata Patyczek
- MR Center of Excellence, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Leonardo Bonetti
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music Aarhus/Aalborg, Universitetsbyen, 3-0-17, 8000, Aarhus C, Denmark
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music Aarhus/Aalborg, Universitetsbyen, 3-0-17, 8000, Aarhus C, Denmark
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Huang K, Kang Y, Wu Z, Wang Y, Cai S, Huang L. Asymmetrical alterations of grey matter among psychiatric disorders: A systematic analysis by voxel-based activation likelihood estimation. Prog Neuropsychopharmacol Biol Psychiatry 2021; 110:110322. [PMID: 33838150 DOI: 10.1016/j.pnpbp.2021.110322] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 04/02/2021] [Accepted: 04/04/2021] [Indexed: 10/21/2022]
Abstract
Schizophrenia (SZ), bipolar disorder (BD) and major depression disorder (MDD) have been regarded as highly diverged independent entities in current psychiatric diagnosis. However, ample new evidence suggests that they may have common biological traits. Neuroimaging studies showed that psychiatric disorders might associated with altered grey matter (GM) asymmetry compared to controls; however, the degree to which SZ, BD and MDD have common and/or distinct asymmetrical alterations in GM is still ambiguous. In this study, we analysed 169 voxel-based studies (including 3517 SZ patients, 1575 BD patients, 3280 MDD patients and 9733 controls) using activation likelihood estimation (ALE) meta-analysis to systematically review the existence of similar GM atrophy and asymmetrical alteration patterns among these psychiatric disorders, and the functional association between behaviour domains and topological alterations. We found that the right parahippocampal gyrus and left superior frontal gyrus showed commonly altered GM volume across all three illnesses, but did not identify common asymmetrical alteration. The asymmetrical alteration with leftward bias appeared in SZ and bipolar disorder at different locations, but more asymmetrical alteration with rightward bias appeared in MDD. Moreover, these changes have been confirmed to be associate with several symptoms and may have roles in functional networks. Our findings support the existence of common neurobiological damnification in these psychiatric disorders and provides valuable insights for the neural commonalties among different psychiatric disorders based on a large sample size.
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Affiliation(s)
- Kexin Huang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Yafei Kang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Zhongcheng Wu
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Yubo Wang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Suping Cai
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
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43
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Meier SK, Ray KL, Mastan JC, Salvage SR, Robin DA. Meta-analytic connectivity modelling of deception-related brain regions. PLoS One 2021; 16:e0248909. [PMID: 34432808 PMCID: PMC8386837 DOI: 10.1371/journal.pone.0248909] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 08/10/2021] [Indexed: 11/30/2022] Open
Abstract
Brain-based deception research began only two decades ago and has since included a wide variety of contexts and response modalities for deception paradigms. Investigations of this sort serve to better our neuroscientific and legal knowledge of the ways in which individuals deceive others. To this end, we conducted activation likelihood estimation (ALE) and meta-analytic connectivity modelling (MACM) using BrainMap software to examine 45 task-based fMRI brain activation studies on deception. An activation likelihood estimation comparing activations during deceptive versus honest behavior revealed 7 significant peak activation clusters (bilateral insula, left superior frontal gyrus, bilateral supramarginal gyrus, and bilateral medial frontal gyrus). Meta-analytic connectivity modelling revealed an interconnected network amongst the 7 regions comprising both unidirectional and bidirectional connections. Together with subsequent behavioral and paradigm decoding, these findings implicate the supramarginal gyrus as a key component for the sociocognitive process of deception.
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Affiliation(s)
- Sarah K. Meier
- Department of Communication Sciences and Disorders Research Laboratories, University of New Hampshire, Durham, New Hampshire, United States of America
- * E-mail: (SKM); (DAR)
| | - Kimberly L. Ray
- Department of Psychology, University of Texas, Austin, Texas, United States of America
| | - Juliana C. Mastan
- Department of Communication Sciences and Disorders Research Laboratories, University of New Hampshire, Durham, New Hampshire, United States of America
| | - Savannah R. Salvage
- Department of Communication Sciences and Disorders Research Laboratories, University of New Hampshire, Durham, New Hampshire, United States of America
| | - Donald A. Robin
- Department of Communication Sciences and Disorders Research Laboratories, University of New Hampshire, Durham, New Hampshire, United States of America
- Interdisciplinary Program in Neuroscience and Behavior, University of New Hampshire, Durham, New Hampshire, United States of America
- Department of Biological Sciences, University of New Hampshire, Durham, New Hampshire, United States of America
- * E-mail: (SKM); (DAR)
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44
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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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45
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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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46
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Zhang D. Intelligent recognition of dance training movements based on machine learning and embedded system. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189954] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Recognizing human movement is an important research topic in the field of human-computer interaction, and people expect it to be used in smart homes, virtual reality, and electronic games. Based on the interaction between humans and computers, more and more attention has been paid, especially in the field of smart home action recognition. Through observation, people can understand the intention of intelligent interaction is included in the main part. However, the current recognition algorithms still cannot meet the actual requirements of the accuracy, real-time and robustness of human motion recognition. Especially in order to recognize complex human movements in real time, it is imperative to solve several problems in motion capture and recognition. Establishing the feature parameter angle of the feature vector space of motion data, using the pre-recognition algorithm is based on multi-class support vector machines. The motion recognition algorithm takes advantage of the accurate and fast classification function of svm. Based on the structural differences of the motion data, most of the data can be correctly identified. The optimal motion recognition algorithm uses hmm to correct the svm error recognition result through the random constraint relationship between the error recognition data and the actual label. Based on data simulation and analysis, each variable determined by the grid search algorithm has the highest accuracy in the optimization of each variable of the support vector machine. Finally, a smart home simulation experiment interactive system was built, and a local database was created, including 1,300 processes. The real-time algorithm uses the data in the local database for training and testing. Experimental results show that the motion recognition algorithm in this paper improves the accuracy and robustness of complex motion recognition. While meeting the real-time recognition conditions, the correct answer rate of the final operation can reach 9.6%. The human motion trajectory recognition system uses the three-dimensional trajectory of gestures to recognize motion. The information in the three-dimensional space is more comprehensive, and the orbit recognition is more robust.
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Affiliation(s)
- Dixin Zhang
- College of Music and Dance, Zhengzhou University of Technology, Zhengzhou, China
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Efraim M, Kirwan CB, Muncy NM, Tucker LA, Kwon S, Bailey BW. Acute after-school screen time in children decreases impulse control and activation toward high-calorie food stimuli in brain regions related to reward and attention. Brain Imaging Behav 2021; 15:177-189. [PMID: 32128716 DOI: 10.1007/s11682-019-00244-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The purpose of this study was to examine the effects of after-school sedentary screen time on children's brain activation in reward and cognitive control regions in response to pictures of high- and low-calorie foods. Thirty-two children participated in a randomized crossover study with counterbalanced treatment conditions. Conditions took place on separate days after school and included three hours of active or sedentary play. After each condition, neural activation was assessed using functional magnetic resonance imaging (fMRI) while participants completed a go/no-go task involving pictures of high- and low-calorie foods. General response inhibition was also measured using the Stroop task. Hunger was measured upon arrival to the testing facility and just prior to fMRI scans. Mixed effects models were used to evaluate main effects and interactions. Significant stimulus by condition interactions were found in the right superior parietal cortex, and left anterior cingulate cortex (Ps ≤ 0.05). High-calorie pictures elicited significantly more activation bilaterally in the orbitofrontal cortex compared to low-calorie pictures (Ps ≤ 0.05). Stroop task performance diminished significantly following the sedentary condition compared to the active (P ≤ 0.05). Subjective feelings of hunger were not different between conditions at any point. Sedentary screen time was associated with significantly decreased response inhibition and a reversed brain activation pattern to pictures of high- and low-calorie foods compared to active play, in areas of the brain important to the modulation of food intake. Decreased attention, and impulse control following sedentary screen time may contribute to disinhibited eating that can lead to overweight and obesity.
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Affiliation(s)
- Mary Efraim
- Department of Exercise Sciences, Brigham Young University, 267 Smith Fieldhouse, Provo, UT, 84602, USA
| | - C Brock Kirwan
- Department of Psychology, Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Nathan M Muncy
- Department of Psychology, Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Larry A Tucker
- Department of Exercise Sciences, Brigham Young University, 267 Smith Fieldhouse, Provo, UT, 84602, USA
| | - Sunku Kwon
- Department of Exercise Sciences, Brigham Young University, 267 Smith Fieldhouse, Provo, UT, 84602, USA
| | - Bruce W Bailey
- Department of Exercise Sciences, Brigham Young University, 267 Smith Fieldhouse, Provo, UT, 84602, USA.
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48
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Auerbach RP, Chase HW, Brent DA. The Elusive Phenotype of Preadolescent Suicidal Thoughts and Behaviors: Can Neuroimaging Deliver on Its Promise? Am J Psychiatry 2021; 178:285-287. [PMID: 33789457 PMCID: PMC8023751 DOI: 10.1176/appi.ajp.2020.21010022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Randy P. Auerbach
- Department of Psychiatry, Columbia University Irving Medical Center and Vagelos College of Physicians and Surgeons, New York, NY
| | - Henry W. Chase
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - David A. Brent
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA,Corresponding Author: David A. Brent, MD, University of Pittsburgh School of Medicine, Western Psychiatric Hospital of the University of Pittsburgh Medical Center, 3811 O’Hara St. BFT 311, Pittsburgh PA., 15213;
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49
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Meta-analysis of grey matter changes and their behavioral characterization in patients with alcohol use disorder. Sci Rep 2021; 11:5238. [PMID: 33664372 PMCID: PMC7933165 DOI: 10.1038/s41598-021-84804-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 02/04/2021] [Indexed: 01/31/2023] Open
Abstract
Alcohol Use Disorder (AUD) is associated with reductions in grey matter (GM) volume which can lead to changes in numerous brain functions. The results of previous studies on altered GM in AUD differ considerably in the regions identified. Three meta-analyses carried out between 2014 and 2017 yielded different results. The present study includes the considerable amount of newer research and delivers a state-of-the art meta-analysis in line with recently published guidelines. Additionally, we behaviorally characterized affected regions using fMRI metadata and identified related brain networks by determining their meta-analytic connectivity patterns. Twenty-seven studies with 1,045 AUD patients and 1,054 healthy controls were included in the analysis and analyzed by means of Anatomical Likelihood Estimation (ALE). GM alterations were identified in eight clusters covering different parts of the cingulate and medial frontal gyri, paracentral lobes, left post- and precentral gyri, left anterior and right posterior insulae and left superior frontal gyrus. The behavioral characterization associated these regions with specific cognitive, emotional, somatosensory and motor functions. Moreover, the clusters represent nodes within behaviorally relevant brain networks. Our results suggest that GM reduction in AUD could disrupt network communication responsible for the neurocognitive impairments associated with high chronic alcohol consumption.
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50
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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: 4] [Impact Index Per Article: 1.3] [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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