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Edlow BL, Olchanyi M, Freeman HJ, Li J, Maffei C, Snider SB, Zöllei L, Iglesias JE, Augustinack J, Bodien YG, Haynes RL, Greve DN, Diamond BR, Stevens A, Giacino JT, Destrieux C, van der Kouwe A, Brown EN, Folkerth RD, Fischl B, Kinney HC. Multimodal MRI reveals brainstem connections that sustain wakefulness in human consciousness. Sci Transl Med 2024; 16:eadj4303. [PMID: 38691619 DOI: 10.1126/scitranslmed.adj4303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 04/10/2024] [Indexed: 05/03/2024]
Abstract
Consciousness is composed of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that underlie awareness in the human brain, but knowledge about the subcortical networks that sustain arousal in humans is incomplete. Here, we aimed to map the connectivity of a proposed subcortical arousal network that sustains wakefulness in the human brain, analogous to the cortical default mode network (DMN) that has been shown to contribute to awareness. We integrated data from ex vivo diffusion magnetic resonance imaging (MRI) of three human brains, obtained at autopsy from neurologically normal individuals, with immunohistochemical staining of subcortical brain sections. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain. Deterministic and probabilistic tractography analyses of the ex vivo diffusion MRI data revealed projection, association, and commissural pathways linking dAAN nodes with one another and with DMN nodes. Complementary analyses of in vivo 7-tesla resting-state functional MRI data from the Human Connectome Project identified the dopaminergic ventral tegmental area in the midbrain as a widely connected hub node at the nexus of the subcortical arousal and cortical awareness networks. Our network-based autopsy methods and connectivity data provide a putative neuroanatomic architecture for the integration of arousal and awareness in human consciousness.
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Affiliation(s)
- Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Mark Olchanyi
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Holly J Freeman
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Jian Li
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Chiara Maffei
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Samuel B Snider
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - J Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Yelena G Bodien
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Robin L Haynes
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Bram R Diamond
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Allison Stevens
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Joseph T Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Christophe Destrieux
- UMR 1253, iBrain, Université de Tours, Inserm, 10 Boulevard Tonnellé, 37032, Tours, France
- CHRU de Tours, 2 Boulevard Tonnellé, Tours, France
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Emery N Brown
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | | | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Hannah C Kinney
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
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Uceda-Heras A, Aparicio-Rodríguez G, García-Cabezas MÁ. Hyperphosphorylated tau in Alzheimer's disease disseminates along pathways predicted by the Structural Model for Cortico-cortical Connections. J Comp Neurol 2024; 532:e25623. [PMID: 38803103 DOI: 10.1002/cne.25623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/05/2024] [Accepted: 04/27/2024] [Indexed: 05/29/2024]
Abstract
In Alzheimer´s disease (AD), hyperphosphorylated tau spreads along the cerebral cortex in a stereotypical pattern that parallels cognitive deterioration. Tau seems to spread transsynaptically along cortico-cotical pathways that, according to synaptic tract-tracing studies in nonhuman primates, have specific laminar patterns related to the cortical type of the connected areas. This relation is described in the Structural Model. In the present article, we study the laminar distribution of hyperphosphorylated tau, labeled with the antibody AT8, along temporal cortical types in postmortem human brains with different AD stages to test the predictions of the Structural Model. Brains from donors without dementia had scant AT8-immunorreactive (AT8-ir) neurons in allo-, meso-, and isocortical types. In early AD stages, the mesocortical dysgranular type, including part of the transentorhinal cortex, had the highest AT8 immunostaining and AT8-ir neurons density. In advanced AD stages, AT8 immunostaining increased along the isocortical types until reaching the auditory koniocortex. Regarding laminar patterns, in early AD stages there were more AT8-ir neurons in supragranular layers in each de novo involved neocortical type; in advanced AD stages, AT8-ir neurons were equally distributed in supra- and infragranular layers. These AT8-ir laminar patterns are compatible with the predictions of the Structural Model. In summary, we show that hyperphosphorylated tau initially accumulates in allo-, meso-, and isocortical types, offer a proof of concept for the validity of the Structural Model to predict synaptic pathway organization in the human cerebral cortex, and highlight the relevance of nonhuman primate tract-tracing studies to understand human neuropathology.
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Affiliation(s)
- Alicia Uceda-Heras
- Department of Anatomy, Histology and Neuroscience, School of Medicine, Autónoma University of Madrid, Madrid, Spain
- PhD Program in Neuroscience UAM-Cajal, Autónoma University of Madrid, Madrid, Spain
| | - Gonzalo Aparicio-Rodríguez
- Department of Anatomy, Histology and Neuroscience, School of Medicine, Autónoma University of Madrid, Madrid, Spain
- Master´s Program in Neuroscience, Autónoma University of Madrid, Madrid, Spain
| | - Miguel Ángel García-Cabezas
- Department of Anatomy, Histology and Neuroscience, School of Medicine, Autónoma University of Madrid, Madrid, Spain
- PhD Program in Neuroscience UAM-Cajal, Autónoma University of Madrid, Madrid, Spain
- Master´s Program in Neuroscience, Autónoma University of Madrid, Madrid, Spain
- Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, Massachusetts, USA
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Blume J, Dhanasekara CS, Kahathuduwa CN, Mastergeorge AM. Central Executive and Default Mode Networks: An Appraisal of Executive Function and Social Skill Brain-Behavior Correlates in Youth with Autism Spectrum Disorder. J Autism Dev Disord 2024; 54:1882-1896. [PMID: 36988766 DOI: 10.1007/s10803-023-05961-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2023] [Indexed: 03/30/2023]
Abstract
Atypical connectivity patterns have been observed for individuals with autism spectrum disorders (ASD), particularly across the triple-network model. The current study investigated brain-behavior relationships in the context of social skills and executive function profiles for ASD youth. We calculated connectivity measures from diffusion tensor imaging using Bayesian estimation and probabilistic tractography. We replicated prior structural equation modeling of behavioral measures with total default mode network (DMN) connectivity to include comparisons with central executive network (CEN) connectivity and CEN-DMN connectivity. Increased within-CEN connectivity was related to metacognitive strengths. Our findings indicate behavior regulation difficulties in youth with ASD may be attributable to impaired connectivity between the CEN and DMN and social skill difficulties may be exacerbated by impaired within-DMN connectivity.
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Affiliation(s)
- Jessica Blume
- Department of Human Development and Family Sciences, Texas Tech University, P.O. Box 41230, Lubbock, TX, 79409-1230, USA.
| | | | - Chanaka N Kahathuduwa
- Department of Psychiatry and Neurology, Texas Tech University Health Sciences Center, Lubbock, USA
| | - Ann M Mastergeorge
- Department of Human Development and Family Sciences, Texas Tech University, P.O. Box 41230, Lubbock, TX, 79409-1230, USA
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Tagliaferri M, Amorosino G, Voltolini L, Giampiccolo D, Avesani P, Cattaneo L. A revision of the dorsal origin of the frontal aslant tract (FAT) in the superior frontal gyrus: a DWI-tractographic study. Brain Struct Funct 2024; 229:987-999. [PMID: 38502328 DOI: 10.1007/s00429-024-02778-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 02/19/2024] [Indexed: 03/21/2024]
Abstract
The frontal aslant tract (FAT) is a white matter tract connecting the superior frontal gyrus (SFG) to the inferior frontal gyrus (IFG). Its dorsal origin is identified in humans in the medial wall of the SFG, in the supplementary motor complex (SM-complex). However, empirical observation shows that many FAT fibres appear to originate from the dorsal, rather than medial, portion of the SFG. We quantitatively investigated the actual origin of FAT fibres in the SFG, specifically discriminating between terminations in the medial wall and in the convexity of the SFG. We analysed data from 105 subjects obtained from the Human Connectome Project (HCP) database. We parcelled the cortex of the IFG, dorsal SFG and medial SFG in several regions of interest (ROIs) ordered in a caudal-rostral direction, which served as seed locations for the generation of streamlines. Diffusion imaging data (DWI) was processed using a multi-shell multi-tissue CSD-based algorithm. Results showed that the number of streamlines originating from the dorsal wall of the SFG significantly exceeds those from the medial wall of the SFG. Connectivity patterns between ROIs indicated that FAT sub-bundles are segregated in parallel circuits ordered in a caudal-rostral direction. Such high degree of coherence in the streamline trajectory allows to establish pairs of homologous cortical parcels in the SFG and IFG. We conclude that the frontal origin of the FAT is found in both dorsal and medial surfaces of the superior frontal gyrus.
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Affiliation(s)
- Marco Tagliaferri
- Centro Interdipartimentale Mente e Cervello (CIMeC), University of Trento, Trento, Italy
| | - Gabriele Amorosino
- Centro Interdipartimentale Mente e Cervello (CIMeC), University of Trento, Trento, Italy
- Neuroinformatics Laboratory, Center for Digital Health & Well Being, Fondazione Bruno Kessler, Trento, Italy
| | - Linda Voltolini
- Centro Interdipartimentale Mente e Cervello (CIMeC), University of Trento, Trento, Italy
| | - Davide Giampiccolo
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Institute of Neuroscience, Cleveland Clinic London, Grosvenor Place, London, UK
| | - Paolo Avesani
- Centro Interdipartimentale Mente e Cervello (CIMeC), University of Trento, Trento, Italy
- Neuroinformatics Laboratory, Center for Digital Health & Well Being, Fondazione Bruno Kessler, Trento, Italy
| | - Luigi Cattaneo
- Centro Interdipartimentale Mente e Cervello (CIMeC), University of Trento, Trento, Italy.
- Centro Interdipartimentale di Scienze Mediche (CISMed) - University of Trento, Trento, Italy.
- Center for Mind/Brain Sciences (CIMeC) - Center for Medical Sciences (CISMed), University of Trento Center for Medical Sciences (CISMed), Via delle Regole 101, Trento, 38123, Italy.
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Zhu K, Chang J, Zhang S, Li Y, Zuo J, Ni H, Xie B, Yao J, Xu Z, Bian S, Yan T, Wu X, Chen S, Jin W, Wang Y, Xu P, Song P, Wu Y, Shen C, Zhu J, Yu Y, Dong F. The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain. Neuroimage 2024; 290:120558. [PMID: 38437909 DOI: 10.1016/j.neuroimage.2024.120558] [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: 10/28/2023] [Revised: 02/22/2024] [Accepted: 02/27/2024] [Indexed: 03/06/2024] Open
Abstract
The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.
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Affiliation(s)
- Kun Zhu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Jianchao Chang
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Siya Zhang
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; School of Basic Medical Sciences, Anhui Medical University, Hefei, PR China
| | - Yan Li
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Junxun Zuo
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Haoyu Ni
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Bingyong Xie
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Jiyuan Yao
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Zhibin Xu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Sicheng Bian
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Tingfei Yan
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Xianyong Wu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Orthopedics, Anqing First People's Hospital of Anhui Medical University, Anqing, PR China
| | - Senlin Chen
- Department of Orthopedics, Dongcheng branch of The First Affiliated Hospital of Anhui Medical University (Feidong People's Hospital), Hefei, PR China
| | - Weiming Jin
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Ying Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Peng Xu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Peiwen Song
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Yuanyuan Wu
- Department of Medical Imaging, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Cailiang Shen
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Fulong Dong
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; School of Basic Medical Sciences, Anhui Medical University, Hefei, PR China.
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Kram L, Schroeder A, Meyer B, Krieg SM, Ille S. Function-guided differences of arcuate fascicle and inferior fronto-occipital fascicle tractography as diagnostic indicators for surgical risk stratification. Brain Struct Funct 2024:10.1007/s00429-024-02787-3. [PMID: 38597941 DOI: 10.1007/s00429-024-02787-3] [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: 09/17/2023] [Accepted: 03/05/2024] [Indexed: 04/11/2024]
Abstract
BACKGROUND Several patients with language-eloquent gliomas face language deterioration postoperatively. Persistent aphasia is frequently associated with damage to subcortical language pathways. Underlying mechanisms still need to be better understood, complicating preoperative risk assessment. This study compared qualitative and quantitative functionally relevant subcortical differences pre- and directly postoperatively in glioma patients with and without aphasia. METHODS Language-relevant cortical sites were defined using navigated transcranial magnetic stimulation (nTMS) language mapping in 74 patients between 07/2016 and 07/2019. Post-hoc nTMS-based diffusion tensor imaging tractography was used to compare a tract's pre- and postoperative visualization, volume and fractional anisotropy (FA), and the preoperative distance between tract and lesion and postoperative overlap with the resection cavity between the following groups: no aphasia (NoA), tumor- or previous resection induced aphasia persistent pre- and postoperatively (TIA_P), and surgery-induced transient or permanent aphasia (SIA_T or SIA_P). RESULTS Patients with NoA, TIA_P, SIA_T, and SIA_P showed distinct fasciculus arcuatus (AF) and inferior-fronto-occipital fasciculus (IFOF) properties. The AF was more frequently reconstructable, and the FA of IFOF was higher in NoA than TIA_P cases (all p ≤ 0.03). Simultaneously, SIA_T cases showed higher IFOF fractional anisotropy than TIA_P cases (p < 0.001) and the most considerable AF volume loss overall. While not statistically significant, the four SIA_P cases showed complete loss of ventral language streams postoperatively, the highest resection-cavity-AF-overlap, and the shortest AF to tumor distance. CONCLUSION Functionally relevant qualitative and quantitative differences in AF and IFOF provide a pre- and postoperative pathophysiological and clinically relevant diagnostic indicator that supports surgical risk stratification.
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Affiliation(s)
- Leonie Kram
- Department of Neurosurgery, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Munich, Germany
- Department of Neurosurgery, Heidelberg University Hospital, Ruprecht-Karls-University, Heidelberg, Germany
| | - Axel Schroeder
- Department of Neurosurgery, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Munich, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Munich, Germany
| | - Sandro M Krieg
- Department of Neurosurgery, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Munich, Germany
- Department of Neurosurgery, Heidelberg University Hospital, Ruprecht-Karls-University, Heidelberg, Germany
| | - Sebastian Ille
- Department of Neurosurgery, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Munich, Germany.
- Department of Neurosurgery, Heidelberg University Hospital, Ruprecht-Karls-University, Heidelberg, Germany.
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7
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Borghei A, Kelly R, Pearce JJ, Stoub TR, Sani S. Structural Connectivity of the Human Piriform Cortex: an Exploratory Study. Neurosurgery 2024; 94:856-863. [PMID: 37955443 DOI: 10.1227/neu.0000000000002756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/21/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND AND OBJECTIVES The piriform cortex (PC) is part of the primary olfactory network in humans. Recent findings suggest that it plays a role in pathophysiology of epilepsy. Therefore, studying its connectivity can further our understanding of seizure propagation in epilepsy. We aimed to explore the structural connectivity of PC using high-quality human connectome project data coupled with segmentation of PC on anatomic MRI. METHODS Twenty subjects were randomly selected from the human connectome project database, and PC was traced on each hemisphere. Probabilistic whole-brain tractography was then used to visualize PC connectivity. RESULTS The strongest connectivity was noted between PC and ipsilateral insula in both hemispheres. Specifically, the posterior long gyrus of each insula was predominantly connected to PC. This was followed by connections between PC and basal ganglia as well as orbital frontal cortices. CONCLUSION The PC has the strongest connectivity with the insula bilaterally. Specifically, the posterior long gyri of insula have the strongest connectivity. This finding may provide additional insight for localizing and treating temporo-insular epilepsy.
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Affiliation(s)
- Alireza Borghei
- Department of Neurosurgery, Rush University Medical Center, Chicago , Illinois , USA
| | - Ryan Kelly
- Department of Neurosurgery, Rush University Medical Center, Chicago , Illinois , USA
| | - John J Pearce
- Department of Neurosurgery, Rush University Medical Center, Chicago , Illinois , USA
| | - Travis R Stoub
- Department of Neurological Sciences, Rush University Medical Center, Chicago , Illinois , USA
| | - Sepehr Sani
- Department of Neurosurgery, Rush University Medical Center, Chicago , Illinois , USA
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Kram L, Ohlerth AK, Ille S, Meyer B, Krieg SM. CompreTAP: Feasibility and reliability of a new language comprehension mapping task via preoperative navigated transcranial magnetic stimulation. Cortex 2024; 171:347-369. [PMID: 38086145 DOI: 10.1016/j.cortex.2023.09.023] [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: 11/01/2022] [Revised: 02/01/2023] [Accepted: 09/25/2023] [Indexed: 02/12/2024]
Abstract
OBJECTIVE Stimulation-based language mapping approaches that are used pre- and intraoperatively employ predominantly overt language tasks requiring sufficient language production abilities. Yet, these production-based setups are often not feasible in brain tumor patients with severe expressive aphasia. This pilot study evaluated the feasibility and reliability of a newly developed language comprehension task with preoperative navigated transcranial magnetic stimulation (nTMS). METHODS Fifteen healthy subjects and six brain tumor patients with severe expressive aphasia unable to perform classic overt naming tasks underwent preoperative nTMS language mapping based on an auditory single-word Comprehension TAsk for Perioperative mapping (CompreTAP). Comprehension was probed by button-press responses to auditory stimuli, hence not requiring overt language responses. Positive comprehension areas were identified when stimulation elicited an incorrect or delayed button press. Error categories, case-wise cortical error rate distribution and inter-rater reliability between two experienced specialists were examined. RESULTS Overall, the new setup showed to be feasible. Comprehension-disruptions induced by nTMS manifested in no responses, delayed or hesitant responses, searching behavior or selection of wrong target items across all patients and controls and could be performed even in patients with severe expressive aphasia. The analysis agreement between both specialists was substantial for classifying comprehension-positive and -negative sites. Extensive left-hemispheric individual cortical comprehension sites were identified for all patients. Apart from one case presenting with transient worsening of aphasic symptoms, pre-existing language deficits did not aggravate if results were used for subsequent surgical planning. CONCLUSION Employing this new comprehension-based nTMS setup allowed to identify language relevant cortical sites in all healthy subjects and severely aphasic patients who were thus far precluded from classic production-based mapping. This pilot study, moreover, provides first indications that the CompreTAP mapping results may support the preservation of residual language function if used for subsequent surgical planning.
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Affiliation(s)
- Leonie Kram
- Department of Neurosurgery, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Germany; Department of Neurosurgery, Heidelberg University Hospital, Ruprecht-Karls-University Heidelberg, Germany
| | - Ann-Katrin Ohlerth
- Department of Neurosurgery, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Germany; Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, the Netherlands
| | - Sebastian Ille
- Department of Neurosurgery, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Germany; TUM Neuroimaging Center, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Germany; Department of Neurosurgery, Heidelberg University Hospital, Ruprecht-Karls-University Heidelberg, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Germany
| | - Sandro M Krieg
- Department of Neurosurgery, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Germany; TUM Neuroimaging Center, Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Germany; Department of Neurosurgery, Heidelberg University Hospital, Ruprecht-Karls-University Heidelberg, Germany.
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9
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Mihailovic JM, Sanganahalli BG, Hyder F, Chitturi J, Elkabes S, Heary RF, Kannurpatti SS. Cross-hemicord spinal fiber reorganization associates with cortical sensory and motor network expansion in the rat model of hemicontusion cervical spinal cord injury. Neurosci Lett 2024; 820:137607. [PMID: 38141752 PMCID: PMC10797561 DOI: 10.1016/j.neulet.2023.137607] [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: 09/13/2023] [Revised: 12/01/2023] [Accepted: 12/17/2023] [Indexed: 12/25/2023]
Abstract
Magnetic resonance imaging plays an important role in characterizing microstructural changes and reorganization after traumatic injuries to the nervous system. In this study, we tested the feasibility of ex-vivo spinal cord diffusion tensor imaging (DTI) in combination with in vivo brain functional MRI to characterize spinal reorganization and its supraspinal association after a hemicontusion cervical spinal cord injury (SCI). DTI parameters (fractional anisotropy [FA], mean diffusion [MD]) and fiber orientation changes related to reorganization in the contused cervical spinal cord were compared to sham specimens. Altered fiber density and fiber directions occurred across the ipsilateral and contralateral hemicords but with only ipsilateral FA and MD changes. The hemicontusion SCI resulted in ipsilateral fiber breaks, voids and vivid fiber reorientations along the injury epicenter. Fiber directional changes below the injury level were primarily inter-hemispheric, indicating prominent below-level cross-hemispheric reorganization. In vivo resting state functional connectivity of the brain from the respective rats before obtaining the spinal cord samples indicated spatial expansion and increased connectivity strength across both the sensory and motor networks after SCI. The consistency of the neuroplastic changes along the neuraxis (both brain and spinal cord) at the single-subject level, indicates that distinctive reorganizational relationships exist between the spinal cord and the brain post-SCI.
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Affiliation(s)
- Jelena M Mihailovic
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520, United States.
| | - Basavaraju G Sanganahalli
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520, United States.
| | - Fahmeed Hyder
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 300 Cedar St, New Haven, CT 06520, United States.
| | - Jyothsna Chitturi
- Department of Radiology, Rutgers Biomedical and Health Sciences-New Jersey Medical School, 30 Bergen Street, Newark, NJ 07103, United States
| | - Stella Elkabes
- Department of Neurosurgery, Rutgers Biomedical and Health Sciences-New Jersey Medical School. 205 South Orange Avenue, Newark, NJ 07103, United States.
| | - Robert F Heary
- Division of Neurosurgery, Hackensack Meridian School of Medicine, Mountainside Medical Center, Montclair, NJ, United States.
| | - Sridhar S Kannurpatti
- Department of Radiology, Rutgers Biomedical and Health Sciences-New Jersey Medical School, 30 Bergen Street, Newark, NJ 07103, United States.
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10
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Kamagata K, Andica C, Uchida W, Takabayashi K, Saito Y, Lukies M, Hagiwara A, Fujita S, Akashi T, Wada A, Hori M, Kamiya K, Zalesky A, Aoki S. Advancements in Diffusion MRI Tractography for Neurosurgery. Invest Radiol 2024; 59:13-25. [PMID: 37707839 DOI: 10.1097/rli.0000000000001015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
ABSTRACT Diffusion magnetic resonance imaging tractography is a noninvasive technique that enables the visualization and quantification of white matter tracts within the brain. It is extensively used in preoperative planning for brain tumors, epilepsy, and functional neurosurgical procedures such as deep brain stimulation. Over the past 25 years, significant advancements have been made in imaging acquisition, fiber direction estimation, and tracking methods, resulting in considerable improvements in tractography accuracy. The technique enables the mapping of functionally critical pathways around surgical sites to avoid permanent functional disability. When the limitations are adequately acknowledged and considered, tractography can serve as a valuable tool to safeguard critical white matter tracts and provides insight regarding changes in normal white matter and structural connectivity of the whole brain beyond local lesions. In functional neurosurgical procedures such as deep brain stimulation, it plays a significant role in optimizing stimulation sites and parameters to maximize therapeutic efficacy and can be used as a direct target for therapy. These insights can aid in patient risk stratification and prognosis. This article aims to discuss state-of-the-art tractography methodologies and their applications in preoperative planning and highlight the challenges and new prospects for the use of tractography in daily clinical practice.
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Affiliation(s)
- Koji Kamagata
- From the Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan (K.K., C.A., W.U., K.T., Y.S., A.H., S.F., T.A., A.W., S.A.); Faculty of Health Data Science, Juntendo University, Chiba, Japan (C.A., S.A.); Department of Radiology, Alfred Health, Melbourne, Victoria, Australia (M.L.); Department of Radiology, University of Tokyo, Tokyo, Japan (S.F.); Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan (M.H., K.K.); Melbourne Neuropsychiatry Center, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia (A.Z.); and Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia (A.Z.)
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11
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Sundaresan V, Lehman JF, Maffei C, Haber SN, Yendiki A. Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.30.560310. [PMID: 37873366 PMCID: PMC10592842 DOI: 10.1101/2023.09.30.560310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Anatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histological sections. This is a very time-consuming process, which limits the amount of annotated tracer data that are available for validation studies. Thus, there is a need to accelerate this process by developing a method for computer-assisted segmentation. Such a method must be robust to the common artifacts in tracer data, including variations in the intensity of stained axons and background, as well as spatial distortions introduced by sectioning and mounting the tissue. The method should also achieve satisfactory performance using limited manually charted data for training. Here we propose the first deeplearning method, with a self-supervised loss function, for segmentation of fiber bundles on histological sections from macaque brains that have received tracer injections. We address the limited availability of manual labels with a semi-supervised training technique that takes advantage of unlabeled data to improve performance. We also introduce anatomic and across-section continuity constraints to improve accuracy. We show that our method can be trained on manually charted sections from a single case and segment unseen sections from different cases, with a true positive rate of ~0.80. We further demonstrate the utility of our method by quantifying the density of fiber bundles as they travel through different white-matter pathways. We show that fiber bundles originating in the same injection site have different levels of density when they travel through different pathways, a finding that can have implications for microstructure-informed tractography methods. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing.
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Affiliation(s)
- Vaanathi Sundaresan
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, Karnataka 560012, India
| | - Julia F. Lehman
- Department of Pharmacology and Physiology, University of Rochester School of Medicine, Rochester, NY, United States
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Suzanne N. Haber
- Department of Pharmacology and Physiology, University of Rochester School of Medicine, Rochester, NY, United States
- McLean Hospital, Belmont, MA, United States
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
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12
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Uddin LQ, Betzel RF, Cohen JR, Damoiseaux JS, De Brigard F, Eickhoff SB, Fornito A, Gratton C, Gordon EM, Laird AR, Larson-Prior L, McIntosh AR, Nickerson LD, Pessoa L, Pinho AL, Poldrack RA, Razi A, Sadaghiani S, Shine JM, Yendiki A, Yeo BTT, Spreng RN. Controversies and progress on standardization of large-scale brain network nomenclature. Netw Neurosci 2023; 7:864-905. [PMID: 37781138 PMCID: PMC10473266 DOI: 10.1162/netn_a_00323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/10/2023] [Indexed: 10/03/2023] Open
Abstract
Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
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Affiliation(s)
- Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences and Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Jessica S. Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Linda Larson-Prior
- Deptartment of Psychiatry and Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - A. Randal McIntosh
- Institute for Neuroscience and Neurotechnology, Simon Fraser University, Vancouver, BC, Canada
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Ana Luísa Pinho
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | | | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana Champaign, IL, USA
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Anastasia Yendiki
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - R. Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
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13
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Liu ZQ, Shafiei G, Baillet S, Misic B. Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks. Neuroimage 2023; 278:120276. [PMID: 37451374 DOI: 10.1016/j.neuroimage.2023.120276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.
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Affiliation(s)
- Zhen-Qi Liu
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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14
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Valdes PA, Ng S, Bernstock JD, Duffau H. Development of an educational method to rethink and learn oncological brain surgery in an "a la carte" connectome-based perspective. Acta Neurochir (Wien) 2023; 165:2489-2500. [PMID: 37199758 DOI: 10.1007/s00701-023-05626-2] [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: 03/31/2023] [Accepted: 05/03/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND Understanding the structural connectivity of white matter tracts (WMT) and their related functions is a prerequisite to implementing an "a la carte" "connectomic approach" to glioma surgery. However, accessible resources facilitating such an approach are lacking. Here we present an educational method that is readily accessible, simple, and reproducible that enables the visualization of WMTs on individual patient images via an atlas-based approach. METHODS Our method uses the patient's own magnetic resonance imaging (MRI) images and consists of three main steps: data conversion, normalization, and visualization; these are accomplished using accessible software packages and WMT atlases. We implement our method on three common cases encountered in glioma surgery: a right supplementary motor area tumor, a left insular tumor, and a left temporal tumor. RESULTS Using patient-specific perioperative MRIs with open-sourced and co-registered atlas-derived WMTs, we highlight the critical subnetworks requiring specific surgical monitoring identified intraoperatively using direct electrostimulation mapping with cognitive monitoring. The aim of this didactic method is to provide the neurosurgical oncology community with an accessible and ready-to-use educational tool, enabling neurosurgeons to improve their knowledge of WMTs and to better learn their oncologic cases, especially in glioma surgery using awake mapping. CONCLUSIONS Taking no more than 3-5 min per patient and irrespective of their resource settings, we believe that this method will enable junior surgeons to develop an intuition, and a robust 3-dimensional imagery of WMT by regularly applying it to their cases both before and after surgery to develop an "a la carte" connectome-based perspective to glioma surgery.
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Affiliation(s)
- Pablo A Valdes
- Department of Neurosurgery, University of Texas Medical Branch, Galveston, TX, 77555, USA.
- Department of Neurosurgery, Hôpital Gui de Chauliac, CHU Montpellier, 80 Av Augustin Fliche, 34295, Montpellier, France.
| | - Sam Ng
- Department of Neurosurgery, Hôpital Gui de Chauliac, CHU Montpellier, 80 Av Augustin Fliche, 34295, Montpellier, France
- Team "Plasticity of Central Nervous System, Human Stem Cells and Glial Tumors", Institute of Functional Genomics, INSERM U1191, University of Montpellier, 141 Rue de la cardonille, 34091, Montpellier, France
| | - Joshua D Bernstock
- Department of Neurosurgery, Harvard Medical School/Brigham and Women's Hospital, Boston, MA, 02115, USA
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hugues Duffau
- Department of Neurosurgery, Hôpital Gui de Chauliac, CHU Montpellier, 80 Av Augustin Fliche, 34295, Montpellier, France
- Team "Plasticity of Central Nervous System, Human Stem Cells and Glial Tumors", Institute of Functional Genomics, INSERM U1191, University of Montpellier, 141 Rue de la cardonille, 34091, Montpellier, France
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15
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Barbosa DAN, Gattas S, Salgado JS, Kuijper FM, Wang AR, Huang Y, Kakusa B, Leuze C, Luczak A, Rapp P, Malenka RC, Hermes D, Miller KJ, Heifets BD, Bohon C, McNab JA, Halpern CH. An orexigenic subnetwork within the human hippocampus. Nature 2023; 621:381-388. [PMID: 37648849 PMCID: PMC10499606 DOI: 10.1038/s41586-023-06459-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 07/20/2023] [Indexed: 09/01/2023]
Abstract
Only recently have more specific circuit-probing techniques become available to inform previous reports implicating the rodent hippocampus in orexigenic appetitive processing1-4. This function has been reported to be mediated at least in part by lateral hypothalamic inputs, including those involving orexigenic lateral hypothalamic neuropeptides, such as melanin-concentrating hormone5,6. This circuit, however, remains elusive in humans. Here we combine tractography, intracranial electrophysiology, cortico-subcortical evoked potentials, and brain-clearing 3D histology to identify an orexigenic circuit involving the lateral hypothalamus and converging in a hippocampal subregion. We found that low-frequency power is modulated by sweet-fat food cues, and this modulation was specific to the dorsolateral hippocampus. Structural and functional analyses of this circuit in a human cohort exhibiting dysregulated eating behaviour revealed connectivity that was inversely related to body mass index. Collectively, this multimodal approach describes an orexigenic subnetwork within the human hippocampus implicated in obesity and related eating disorders.
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Affiliation(s)
- Daniel A N Barbosa
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandra Gattas
- Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, USA
| | - Juliana S Salgado
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Fiene Marie Kuijper
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
- Université Paris Cité, Paris, France
- Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Allan R Wang
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Yuhao Huang
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Bina Kakusa
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Christoph Leuze
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Artur Luczak
- Canadian Centre for Behavioral Neuroscience, University of Lethbridge, Lethbridge, Alberta, Canada
| | - Paul Rapp
- Department of Military & Emergency Medicine, Uniformed Services University, Bethesda, MD, USA
| | - Robert C Malenka
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Nancy Pritzker Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Dora Hermes
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA
| | - Boris D Heifets
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Cara Bohon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Casey H Halpern
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA.
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16
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Edlow BL, Olchanyi M, Freeman HJ, Li J, Maffei C, Snider SB, Zöllei L, Iglesias JE, Augustinack J, Bodien YG, Haynes RL, Greve DN, Diamond BR, Stevens A, Giacino JT, Destrieux C, van der Kouwe A, Brown EN, Folkerth RD, Fischl B, Kinney HC. Sustaining wakefulness: Brainstem connectivity in human consciousness. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548265. [PMID: 37502983 PMCID: PMC10369992 DOI: 10.1101/2023.07.13.548265] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Consciousness is comprised of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that modulate awareness in the human brain, but knowledge about the subcortical networks that sustain arousal is lacking. We integrated data from ex vivo diffusion MRI, immunohistochemistry, and in vivo 7 Tesla functional MRI to map the connectivity of a subcortical arousal network that we postulate sustains wakefulness in the resting, conscious human brain, analogous to the cortical default mode network (DMN) that is believed to sustain self-awareness. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain by correlating ex vivo diffusion MRI with immunohistochemistry in three human brain specimens from neurologically normal individuals scanned at 600-750 μm resolution. We performed deterministic and probabilistic tractography analyses of the diffusion MRI data to map dAAN intra-network connections and dAAN-DMN internetwork connections. Using a newly developed network-based autopsy of the human brain that integrates ex vivo MRI and histopathology, we identified projection, association, and commissural pathways linking dAAN nodes with one another and with cortical DMN nodes, providing a structural architecture for the integration of arousal and awareness in human consciousness. We release the ex vivo diffusion MRI data, corresponding immunohistochemistry data, network-based autopsy methods, and a new brainstem dAAN atlas to support efforts to map the connectivity of human consciousness.
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Affiliation(s)
- Brian L. Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Mark Olchanyi
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Holly J. Freeman
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Jian Li
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Chiara Maffei
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Samuel B. Snider
- Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - J. Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Yelena G. Bodien
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129 USA
| | - Robin L. Haynes
- Department of Pathology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Douglas N. Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Bram R. Diamond
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Allison Stevens
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Joseph T. Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129 USA
| | - Christophe Destrieux
- UMR 1253, iBrain, Université de Tours, Inserm, 10 Boulevard Tonnellé, 37032, Tours, France
- CHRU de Tours, 2 Boulevard Tonnellé, Tours, France
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Emery N. Brown
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hannah C. Kinney
- Department of Pathology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
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17
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Liang Z, Arefin TM, Lee CH, Zhang J. Using mesoscopic tract-tracing data to guide the estimation of fiber orientation distributions in the mouse brain from diffusion MRI. Neuroimage 2023; 270:119999. [PMID: 36871795 PMCID: PMC10052941 DOI: 10.1016/j.neuroimage.2023.119999] [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: 12/30/2022] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 03/07/2023] Open
Abstract
Diffusion MRI (dMRI) tractography is the only tool for non-invasive mapping of macroscopic structural connectivity over the entire brain. Although it has been successfully used to reconstruct large white matter tracts in the human and animal brains, the sensitivity and specificity of dMRI tractography remained limited. In particular, the fiber orientation distributions (FODs) estimated from dMRI signals, key to tractography, may deviate from histologically measured fiber orientation in crossing fibers and gray matter regions. In this study, we demonstrated that a deep learning network, trained using mesoscopic tract-tracing data from the Allen Mouse Brain Connectivity Atlas, was able to improve the estimation of FODs from mouse brain dMRI data. Tractography results based on the network generated FODs showed improved specificity while maintaining sensitivity comparable to results based on FOD estimated using a conventional spherical deconvolution method. Our result is a proof-of-concept of how mesoscale tract-tracing data can guide dMRI tractography and enhance our ability to characterize brain connectivity.
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Affiliation(s)
- Zifei Liang
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA
| | - Tanzil Mahmud Arefin
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA
| | - Choong H Lee
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA
| | - Jiangyang Zhang
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA.
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18
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Lerma-Usabiaga G, Liu M, Paz-Alonso PM, Wandell BA. Reproducible Tract Profiles 2 (RTP2) suite, from diffusion MRI acquisition to clinical practice and research. Sci Rep 2023; 13:6010. [PMID: 37045891 PMCID: PMC10097625 DOI: 10.1038/s41598-023-32924-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Diffusion MRI is a complex technique, where new discoveries and implementations occur at a fast pace. The expertise needed for data analyses and accurate and reproducible results is increasingly demanding and requires multidisciplinary collaborations. In the present work we introduce Reproducible Tract Profiles 2 (RTP2), a set of flexible and automated methods to analyze anatomical MRI and diffusion weighted imaging (DWI) data for reproducible tractography. RTP2 reads structural MRI data and processes them through a succession of serialized containerized analyses. We describe the DWI algorithms used to identify white-matter tracts and their summary metrics, the flexible architecture of the platform, and the tools to programmatically access and control the computations. The combination of these three components provides an easy-to-use automatized tool developed and tested over 20 years, to obtain usable and reliable state-of-the-art diffusion metrics at the individual and group levels for basic research and clinical practice.
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Affiliation(s)
- Garikoitz Lerma-Usabiaga
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, Stanford, CA, 94305, USA.
- BCBL, Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua 69, 20009, Donostia-San Sebastián, Gipuzkoa, Spain.
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, 94305, USA.
- IKERBASQUE, Basque Foundation for Science, 48013, Bilbao, Spain.
| | - Mengxing Liu
- BCBL, Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua 69, 20009, Donostia-San Sebastián, Gipuzkoa, Spain
| | - Pedro M Paz-Alonso
- BCBL, Basque Center on Cognition, Brain and Language, Mikeletegi Pasealekua 69, 20009, Donostia-San Sebastián, Gipuzkoa, Spain
- IKERBASQUE, Basque Foundation for Science, 48013, Bilbao, Spain
| | - Brian A Wandell
- Department of Psychology, Stanford University, 450 Serra Mall, Jordan Hall Building, Stanford, CA, 94305, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, 94305, USA
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19
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Charvet CJ. Mapping Human Brain Pathways: Challenges and Opportunities in the Integration of Scales. BRAIN, BEHAVIOR AND EVOLUTION 2023; 98:194-209. [PMID: 36972574 DOI: 10.1159/000530317] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/16/2023] [Indexed: 03/29/2023]
Abstract
The human brain is composed of a complex web of pathways. Diffusion magnetic resonance (MR) tractography is a neuroimaging technique that relies on the principle of diffusion to reconstruct brain pathways. Its tractography is broadly applicable to a range of problems as it is amenable for study in individuals of any age and from any species. However, it is well known that this technique can generate biologically implausible pathways, especially in regions of the brain where multiple fibers cross. This review highlights potential misconnections in two cortico-cortical association pathways with a focus on the aslant tract and inferior frontal occipital fasciculus. The lack of alternative methods to validate observations from diffusion MR tractography means there is a need to develop new integrative approaches to trace human brain pathways. This review discusses integrative approaches in neuroimaging, anatomical, and transcriptional variation as having much potential to trace the evolution of human brain pathways.
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Affiliation(s)
- Christine J Charvet
- Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, Alabama, USA
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20
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Sip V, Hashemi M, Dickscheid T, Amunts K, Petkoski S, Jirsa V. Characterization of regional differences in resting-state fMRI with a data-driven network model of brain dynamics. SCIENCE ADVANCES 2023; 9:eabq7547. [PMID: 36930710 PMCID: PMC10022900 DOI: 10.1126/sciadv.abq7547] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 02/16/2023] [Indexed: 06/18/2023]
Abstract
Model-based data analysis of whole-brain dynamics links the observed data to model parameters in a network of neural masses. Recently, studies focused on the role of regional variance of model parameters. Such analyses however necessarily depend on the properties of preselected neural mass model. We introduce a method to infer from the functional data both the neural mass model representing the regional dynamics and the region- and subject-specific parameters while respecting the known network structure. We apply the method to human resting-state fMRI. We find that the underlying dynamics can be described as noisy fluctuations around a single fixed point. The method reliably discovers three regional parameters with clear and distinct role in the dynamics, one of which is strongly correlated with the first principal component of the gene expression spatial map. The present approach opens a novel way to the analysis of resting-state fMRI with possible applications for understanding the brain dynamics during aging or neurodegeneration.
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Affiliation(s)
- Viktor Sip
- Aix-Marseille Université, INSERM, Institut de Neurosciences de Systèmes (INS), Marseille, France
| | - Meysam Hashemi
- Aix-Marseille Université, INSERM, Institut de Neurosciences de Systèmes (INS), Marseille, France
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Spase Petkoski
- Aix-Marseille Université, INSERM, Institut de Neurosciences de Systèmes (INS), Marseille, France
| | - Viktor Jirsa
- Aix-Marseille Université, INSERM, Institut de Neurosciences de Systèmes (INS), Marseille, France
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21
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Shastin D, Genc S, Parker GD, Koller K, Tax CMW, Evans J, Hamandi K, Gray WP, Jones DK, Chamberland M. Surface-based tracking for short association fibre tractography. Neuroimage 2022; 260:119423. [PMID: 35809886 PMCID: PMC10009610 DOI: 10.1016/j.neuroimage.2022.119423] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/30/2022] [Accepted: 06/29/2022] [Indexed: 11/16/2022] Open
Abstract
It is estimated that in the human brain, short association fibres (SAF) represent more than half of the total white matter volume and their involvement has been implicated in a range of neurological and psychiatric conditions. This population of fibres, however, remains relatively understudied in the neuroimaging literature. Some of the challenges pertinent to the mapping of SAF include their variable anatomical course and proximity to the cortical mantle, leading to partial volume effects and potentially affecting streamline trajectory estimation. This work considers the impact of seeding and filtering strategies and choice of scanner, acquisition, data resampling to propose a whole-brain, surface-based short (≤30-40 mm) SAF tractography approach. The framework is shown to produce longer streamlines with a predilection for connecting gyri as well as high cortical coverage. We further demonstrate that certain areas of subcortical white matter become disproportionally underrepresented in diffusion-weighted MRI data with lower angular and spatial resolution and weaker diffusion weighting; however, collecting data with stronger gradients than are usually available clinically has minimal impact, making our framework translatable to data collected on commonly available hardware. Finally, the tractograms are examined using voxel- and surface-based measures of consistency, demonstrating moderate reliability, low repeatability and high between-subject variability, urging caution when streamline count-based analyses of SAF are performed.
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Affiliation(s)
- Dmitri Shastin
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Department of Neurosurgery, University Hospital of Wales, Cardiff, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom.
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Greg D Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Kristin Koller
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom; Department of Neurology, University Hospital of Wales, Cardiff, United Kingdom
| | - William P Gray
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Department of Neurosurgery, University Hospital of Wales, Cardiff, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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22
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In vivo probabilistic atlas of white matter tracts of the human subthalamic area combining track density imaging and optimized diffusion tractography. Brain Struct Funct 2022; 227:2647-2665. [PMID: 36114861 PMCID: PMC9618529 DOI: 10.1007/s00429-022-02561-3] [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: 05/06/2022] [Accepted: 08/29/2022] [Indexed: 11/18/2022]
Abstract
The human subthalamic area is a region of high anatomical complexity, tightly packed with tiny fiber bundles. Some of them, including the pallidothalamic, cerebello-thalamic, and mammillothalamic tracts, are relevant targets in functional neurosurgery for various brain diseases. Diffusion-weighted imaging-based tractography has been suggested as a useful tool to map white matter pathways in the human brain in vivo and non-invasively, though the reconstruction of these specific fiber bundles is challenging due to their small dimensions and complex anatomy. To the best of our knowledge, a population-based, in vivo probabilistic atlas of subthalamic white matter tracts is still missing. In the present work, we devised an optimized tractography protocol for reproducible reconstruction of the tracts of subthalamic area in a large data sample from the Human Connectome Project repository. First, we leveraged the super-resolution properties and high anatomical detail provided by short tracks track-density imaging (stTDI) to identify the white matter bundles of the subthalamic area on a group-level template. Tracts identification on the stTDI template was also aided by visualization of histological sections of human specimens. Then, we employed this anatomical information to drive tractography at the subject-level, optimizing tracking parameters to maximize between-subject and within-subject similarities as well as anatomical accuracy. Finally, we gathered subject level tracts reconstructed with optimized tractography into a large-scale, normative population atlas. We suggest that this atlas could be useful in both clinical anatomy and functional neurosurgery settings, to improve our understanding of the complex morphology of this important brain region.
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23
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Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI. Neuroimage 2022; 257:119327. [PMID: 35636227 PMCID: PMC9453851 DOI: 10.1016/j.neuroimage.2022.119327] [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] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/06/2022] [Accepted: 05/19/2022] [Indexed: 01/25/2023] Open
Abstract
Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.
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24
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Kai J, Khan AR, Haast RA, Lau JC. Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability. Neuroimage 2022; 262:119553. [PMID: 35961469 DOI: 10.1016/j.neuroimage.2022.119553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 07/15/2022] [Accepted: 08/08/2022] [Indexed: 10/31/2022] Open
Abstract
Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectome in vivo.
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Affiliation(s)
- Jason Kai
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada; Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
| | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada; Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
| | - Roy Am Haast
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada; Aix-Marseille University, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Jonathan C Lau
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada; Department of Clinical Neurological Sciences, Division of Neurosurgery, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada.
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25
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Dopamine and Beyond: Implications of Psychophysical Studies of Intracranial Self-Stimulation for the Treatment of Depression. Brain Sci 2022; 12:brainsci12081052. [PMID: 36009115 PMCID: PMC9406029 DOI: 10.3390/brainsci12081052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 07/28/2022] [Accepted: 08/02/2022] [Indexed: 11/16/2022] Open
Abstract
Major depressive disorder is a leading cause of disability and suicide worldwide. Consecutive rounds of conventional interventions are ineffective in a significant sub-group of patients whose disorder is classified as treatment-resistant depression. Significant progress in managing this severe form of depression has been achieved through the use of deep brain stimulation of the medial forebrain bundle (MFB). The beneficial effect of such stimulation appears strong, safe, and enduring. The proposed neural substrate for this promising clinical finding includes midbrain dopamine neurons and a subset of their cortical afferents. Here, we aim to broaden the discussion of the candidate circuitry by exploring potential implications of a new “convergence” model of brain reward circuitry in rodents. We chart the evolution of the new model from its predecessors, which held that midbrain dopamine neurons constituted an obligatory stage of the final common path for reward seeking. In contrast, the new model includes a directly activated, non-dopaminergic pathway whose output ultimately converges with that of the dopaminergic neurons. On the basis of the new model and the relative ineffectiveness of dopamine agonists in the treatment of depression, we ask whether non-dopaminergic circuitry may contribute to the clinical efficacy of deep brain stimulation of the MFB.
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26
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Yendiki A, Aggarwal M, Axer M, Howard AF, van Cappellen van Walsum AM, Haber SN. Post mortem mapping of connectional anatomy for the validation of diffusion MRI. Neuroimage 2022; 256:119146. [PMID: 35346838 PMCID: PMC9832921 DOI: 10.1016/j.neuroimage.2022.119146] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 03/02/2022] [Accepted: 03/23/2022] [Indexed: 01/13/2023] Open
Abstract
Diffusion MRI (dMRI) is a unique tool for the study of brain circuitry, as it allows us to image both the macroscopic trajectories and the microstructural properties of axon bundles in vivo. The Human Connectome Project ushered in an era of impressive advances in dMRI acquisition and analysis. As a result of these efforts, the quality of dMRI data that could be acquired in vivo improved substantially, and large collections of such data became widely available. Despite this progress, the main limitation of dMRI remains: it does not image axons directly, but only provides indirect measurements based on the diffusion of water molecules. Thus, it must be validated by methods that allow direct visualization of axons but that can only be performed in post mortem brain tissue. In this review, we discuss methods for validating the various features of connectional anatomy that are extracted from dMRI, both at the macro-scale (trajectories of axon bundles), and at micro-scale (axonal orientations and other microstructural properties). We present a range of validation tools, including anatomic tracer studies, Klingler's dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.
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Affiliation(s)
- Anastasia Yendiki
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States,Corresponding author (A. Yendiki)
| | - Manisha Aggarwal
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Markus Axer
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine, Jülich, Germany,Department of Physics, University of Wuppertal Germany
| | - Amy F.D. Howard
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Anne-Marie van Cappellen van Walsum
- Department of Medical Imaging, Anatomy, Radboud University Medical Center, Nijmegen, the Netherland,Cognition and Behaviour, Donders Institute for Brain, Nijmegen, the Netherland
| | - Suzanne N. Haber
- Department of Pharmacology and Physiology, University of Rochester, Rochester, NY, United States,McLean Hospital, Belmont, MA, United States
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27
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Pallikaras V, Shizgal P. The Convergence Model of Brain Reward Circuitry: Implications for Relief of Treatment-Resistant Depression by Deep-Brain Stimulation of the Medial Forebrain Bundle. Front Behav Neurosci 2022; 16:851067. [PMID: 35431828 PMCID: PMC9011331 DOI: 10.3389/fnbeh.2022.851067] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 02/10/2022] [Indexed: 12/28/2022] Open
Abstract
Deep-brain stimulation of the medial forebrain bundle (MFB) can provide effective, enduring relief of treatment-resistant depression. Panksepp provided an explanatory framework: the MFB constitutes the core of the neural circuitry subserving the anticipation and pursuit of rewards: the “SEEKING” system. On that view, the SEEKING system is hypoactive in depressed individuals; background electrical stimulation of the MFB alleviates symptoms by normalizing activity. Panksepp attributed intracranial self-stimulation to excitation of the SEEKING system in which the ascending projections of midbrain dopamine neurons are an essential component. In parallel with Panksepp’s qualitative work, intracranial self-stimulation has long been studied quantitatively by psychophysical means. That work argues that the predominant directly stimulated substrate for MFB self-stimulation are myelinated, non-dopaminergic fibers, more readily excited by brief electrical current pulses than the thin, unmyelinated axons of the midbrain dopamine neurons. The series-circuit hypothesis reconciles this view with the evidence implicating dopamine in MFB self-stimulation as follows: direct activation of myelinated MFB fibers is rewarding due to their trans-synaptic activation of midbrain dopamine neurons. A recent study in which rats worked for optogenetic stimulation of midbrain dopamine neurons challenges the series-circuit hypothesis and provides a new model of intracranial self-stimulation in which the myelinated non-dopaminergic neurons and the midbrain dopamine projections access the behavioral final common path for reward seeking via separate, converging routes. We explore the potential implications of this convergence model for the interpretation of the antidepressant effect of MFB stimulation. We also discuss the consistent finding that psychomotor stimulants, which boost dopaminergic neurotransmission, fail to provide a monotherapy for depression. We propose that non-dopaminergic MFB components may contribute to the therapeutic effect in parallel to, in synergy with, or even instead of, a dopaminergic component.
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28
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Sitek KR, Calabrese E, Johnson GA, Ghosh SS, Chandrasekaran B. Structural Connectivity of Human Inferior Colliculus Subdivisions Using in vivo and post mortem Diffusion MRI Tractography. Front Neurosci 2022; 16:751595. [PMID: 35392412 PMCID: PMC8981148 DOI: 10.3389/fnins.2022.751595] [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: 08/01/2021] [Accepted: 01/27/2022] [Indexed: 12/05/2022] Open
Abstract
Inferior colliculus (IC) is an obligatory station along the ascending auditory pathway that also has a high degree of top-down convergence via efferent pathways, making it a major computational hub. Animal models have attributed critical roles for the IC in in mediating auditory plasticity, egocentric selection, and noise exclusion. IC contains multiple functionally distinct subdivisions. These include a central nucleus that predominantly receives ascending inputs and external and dorsal nuclei that receive more heterogeneous inputs, including descending and multisensory connections. Subdivisions of human IC have been challenging to identify and quantify using standard brain imaging techniques such as MRI, and the connectivity of each of these subnuclei has not been identified in the human brain. In this study, we estimated the connectivity of human IC subdivisions with diffusion MRI (dMRI) tractography, using both anatomical-based seed analysis as well as unsupervised k-means clustering. We demonstrate sensitivity of tractography to overall IC connections in both high resolution post mortem and in vivo datasets. k-Means clustering of the IC streamlines in both the post mortem and in vivo datasets generally segregated streamlines based on their terminus beyond IC, such as brainstem, thalamus, or contralateral IC. Using fine-grained anatomical segmentations of the major IC subdivisions, the post mortem dataset exhibited unique connectivity patterns from each subdivision, including commissural connections through dorsal IC and lateral lemniscal connections to central and external IC. The subdivisions were less distinct in the context of in vivo connectivity, although lateral lemniscal connections were again highest to central and external IC. Overall, the unsupervised and anatomically driven methods provide converging evidence for distinct connectivity profiles for each of the IC subdivisions in both post mortem and in vivo datasets, suggesting that dMRI tractography with high quality data is sensitive to neural pathways involved in auditory processing as well as top-down control of incoming auditory information.
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Affiliation(s)
- Kevin R. Sitek
- SoundBrain Lab, Brain and Auditory Sciences Research Initiative, Department of Communication and Science Disorders, University of Pittsburgh, Pittsburgh, PA, United States
- *Correspondence: Kevin R. Sitek,
| | - Evan Calabrese
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - G. Allan Johnson
- Center for In Vivo Microscopy, Duke University, Durham, NC, United States
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Otolaryngology – Head and Neck Surgery, Harvard Medical School, Boston, MA, United States
| | - Bharath Chandrasekaran
- SoundBrain Lab, Brain and Auditory Sciences Research Initiative, Department of Communication and Science Disorders, University of Pittsburgh, Pittsburgh, PA, United States
- Bharath Chandrasekaran,
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29
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Fan Q, Eichner C, Afzali M, Mueller L, Tax CMW, Davids M, Mahmutovic M, Keil B, Bilgic B, Setsompop K, Lee HH, Tian Q, Maffei C, Ramos-Llordén G, Nummenmaa A, Witzel T, Yendiki A, Song YQ, Huang CC, Lin CP, Weiskopf N, Anwander A, Jones DK, Rosen BR, Wald LL, Huang SY. Mapping the Human Connectome using Diffusion MRI at 300 mT/m Gradient Strength: Methodological Advances and Scientific Impact. Neuroimage 2022; 254:118958. [PMID: 35217204 DOI: 10.1016/j.neuroimage.2022.118958] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 12/20/2022] Open
Abstract
Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in Continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength dMRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for dMRI and where the field is headed in the coming years.
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Affiliation(s)
- Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Cornelius Eichner
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, LS2 9JT, UK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, LS2 9JT, UK
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Image Sciences Institute, University Medical Center (UMC) Utrecht, Utrecht, Netherlands
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mirsad Mahmutovic
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Yi-Qiao Song
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA USA
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Shanghai Changning Mental Health Center, Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Alfred Anwander
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States.
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30
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Giampiccolo D, Duffau H. Controversy over the temporal cortical terminations of the left arcuate fasciculus: a reappraisal. Brain 2022; 145:1242-1256. [PMID: 35142842 DOI: 10.1093/brain/awac057] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 12/19/2021] [Accepted: 01/20/2022] [Indexed: 11/12/2022] Open
Abstract
The arcuate fasciculus has been considered a major dorsal fronto-temporal white matter pathway linking frontal language production regions with auditory perception in the superior temporal gyrus, the so-called Wernicke's area. In line with this tradition, both historical and contemporary models of language function have assigned primacy to superior temporal projections of the arcuate fasciculus. However, classical anatomical descriptions and emerging behavioural data are at odds with this assumption. On one hand, fronto-temporal projections to Wernicke's area may not be unique to the arcuate fasciculus. On the other hand, dorsal stream language deficits have been reported also for damage to middle, inferior and basal temporal gyri which may be linked to arcuate disconnection. These findings point to a reappraisal of arcuate projections in the temporal lobe. Here, we review anatomical and functional evidence regarding the temporal cortical terminations of the left arcuate fasciculus by incorporating dissection and tractography findings with stimulation data using cortico-cortical evoked potentials and direct electrical stimulation mapping in awake patients. Firstly, we discuss the fibers of the arcuate fasciculus projecting to the superior temporal gyrus and the functional rostro-caudal gradient in this region where both phonological encoding and auditory-motor transformation may be performed. Caudal regions within the temporoparietal junction may be involved in articulation and associated with temporoparietal projections of the third branch of the superior longitudinal fasciculus, while more rostral regions may support encoding of acoustic phonetic features, supported by arcuate fibres. We then move to examine clinical data showing that multimodal phonological encoding is facilitated by projections of the arcuate fasciculus to superior, but also middle, inferior and basal temporal regions. Hence, we discuss how projections of the arcuate fasciculus may contribute to acoustic (middle-posterior superior and middle temporal gyri), visual (posterior inferior temporal/fusiform gyri comprising the visual word form area) and lexical (anterior-middle inferior temporal/fusiform gyri in the basal temporal language area) information in the temporal lobe to be processed, encoded and translated into a dorsal phonological route to the frontal lobe. Finally, we point out surgical implications for this model in terms of the prediction and avoidance of neurological deficit.
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Affiliation(s)
- Davide Giampiccolo
- Section of Neurosurgery, Department of Neurosciences, Biomedicine and Movement Sciences, University Hospital, Verona, Italy.,Institute of Neuroscience, Cleveland Clinic London, Grosvenor Place, London, UK.,Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK.,Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, Montpellier, France.,Team "Neuroplasticity, Stem Cells and Low-grade Gliomas," INSERM U1191, Institute of Genomics of Montpellier, University of Montpellier, Montpellier, France
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31
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Yeh FC, Irimia A, Bastos DCDA, Golby AJ. Tractography methods and findings in brain tumors and traumatic brain injury. Neuroimage 2021; 245:118651. [PMID: 34673247 PMCID: PMC8859988 DOI: 10.1016/j.neuroimage.2021.118651] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 10/05/2021] [Accepted: 10/11/2021] [Indexed: 12/31/2022] Open
Abstract
White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.
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Affiliation(s)
- Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA; Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | | | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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32
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Jones R, Maffei C, Augustinack J, Fischl B, Wang H, Bilgic B, Yendiki A. High-fidelity approximation of grid- and shell-based sampling schemes from undersampled DSI using compressed sensing: Post mortem validation. Neuroimage 2021; 244:118621. [PMID: 34587516 PMCID: PMC8631240 DOI: 10.1016/j.neuroimage.2021.118621] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/02/2021] [Accepted: 09/24/2021] [Indexed: 12/31/2022] Open
Abstract
While many useful microstructural indices, as well as orientation distribution functions, can be obtained from multi-shell dMRI data, there is growing interest in exploring the richer set of microstructural features that can be extracted from the full ensemble average propagator (EAP). The EAP can be readily computed from diffusion spectrum imaging (DSI) data, at the cost of a very lengthy acquisition. Compressed sensing (CS) has been used to make DSI more practical by reducing its acquisition time. CS applied to DSI (CS-DSI) attempts to reconstruct the EAP from significantly undersampled q-space data. We present a post mortem validation study where we evaluate the ability of CS-DSI to approximate not only fully sampled DSI but also multi-shell acquisitions with high fidelity. Human brain samples are imaged with high-resolution DSI at 9.4T and with polarization-sensitive optical coherence tomography (PSOCT). The latter provides direct measurements of axonal orientations at microscopic resolutions, allowing us to evaluate the mesoscopic orientation estimates obtained from diffusion MRI, in terms of their angular error and the presence of spurious peaks. We test two fast, dictionary-based, L2-regularized algorithms for CS-DSI reconstruction. We find that, for a CS acceleration factor of R=3, i.e., an acquisition with 171 gradient directions, one of these methods is able to achieve both low angular error and low number of spurious peaks. With a scan length similar to that of high angular resolution multi-shell acquisition schemes, this CS-DSI approach is able to approximate both fully sampled DSI and multi-shell data with high accuracy. Thus it is suitable for orientation reconstruction and microstructural modeling techniques that require either grid- or shell-based acquisitions. We find that the signal-to-noise ratio (SNR) of the training data used to construct the dictionary can have an impact on the accuracy of CS-DSI, but that there is substantial robustness to loss of SNR in the test data. Finally, we show that, as the CS acceleration factor increases beyond R=3, the accuracy of these reconstruction methods degrade, either in terms of the angular error, or in terms of the number of spurious peaks. Our results provide useful benchmarks for the future development of even more efficient q-space acceleration techniques.
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Affiliation(s)
- Robert Jones
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA.
| | - Chiara Maffei
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - Jean Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Hui Wang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
| | - Berkin Bilgic
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Anastasia Yendiki
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA 02129, USA
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33
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White matter variability, cognition, and disorders: a systematic review. Brain Struct Funct 2021; 227:529-544. [PMID: 34731328 PMCID: PMC8844174 DOI: 10.1007/s00429-021-02382-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 09/03/2021] [Indexed: 11/23/2022]
Abstract
Inter-individual differences can inform treatment procedures and—if accounted for—have the potential to significantly improve patient outcomes. However, when studying brain anatomy, these inter-individual variations are commonly unaccounted for, despite reports of differences in gross anatomical features, cross-sectional, and connectional anatomy. Brain connections are essential to facilitate functional organization and, when severed, cause impairments or complete loss of function. Hence, the study of cerebral white matter may be an ideal compromise to capture inter-individual variability in structure and function. We reviewed the wealth of studies that associate cognitive functions and clinical symptoms with individual tracts using diffusion tractography. Our systematic review indicates that tractography has proven to be a sensitive method in neurology, psychiatry, and healthy populations to identify variability and its functional correlates. However, the literature may be biased, as the most commonly studied tracts are not necessarily those with the highest sensitivity to cognitive functions and pathologies. Additionally, the hemisphere of the studied tract is often unreported, thus neglecting functional laterality and asymmetries. Finally, we demonstrate that tracts, as we define them, are not correlated with one, but multiple cognitive domains or pathologies. While our systematic review identified some methodological caveats, it also suggests that tract–function correlations might still be a promising tool in identifying biomarkers for precision medicine. They can characterize variations in brain anatomy, differences in functional organization, and predicts resilience and recovery in patients.
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34
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Schilling KG, Rheault F, Petit L, Hansen CB, Nath V, Yeh FC, Girard G, Barakovic M, Rafael-Patino J, Yu T, Fischi-Gomez E, Pizzolato M, Ocampo-Pineda M, Schiavi S, Canales-Rodríguez EJ, Daducci A, Granziera C, Innocenti G, Thiran JP, Mancini L, Wastling S, Cocozza S, Petracca M, Pontillo G, Mancini M, Vos SB, Vakharia VN, Duncan JS, Melero H, Manzanedo L, Sanz-Morales E, Peña-Melián Á, Calamante F, Attyé A, Cabeen RP, Korobova L, Toga AW, Vijayakumari AA, Parker D, Verma R, Radwan A, Sunaert S, Emsell L, De Luca A, Leemans A, Bajada CJ, Haroon H, Azadbakht H, Chamberland M, Genc S, Tax CMW, Yeh PH, Srikanchana R, Mcknight CD, Yang JYM, Chen J, Kelly CE, Yeh CH, Cochereau J, Maller JJ, Welton T, Almairac F, Seunarine KK, Clark CA, Zhang F, Makris N, Golby A, Rathi Y, O'Donnell LJ, Xia Y, Aydogan DB, Shi Y, Fernandes FG, Raemaekers M, Warrington S, Michielse S, Ramírez-Manzanares A, Concha L, Aranda R, Meraz MR, Lerma-Usabiaga G, Roitman L, Fekonja LS, Calarco N, Joseph M, Nakua H, Voineskos AN, Karan P, Grenier G, Legarreta JH, Adluru N, Nair VA, Prabhakaran V, Alexander AL, Kamagata K, Saito Y, Uchida W, Andica C, Abe M, Bayrak RG, Wheeler-Kingshott CAMG, D'Angelo E, Palesi F, Savini G, Rolandi N, Guevara P, Houenou J, López-López N, Mangin JF, Poupon C, Román C, Vázquez A, Maffei C, Arantes M, Andrade JP, Silva SM, Calhoun VD, Caverzasi E, Sacco S, Lauricella M, Pestilli F, Bullock D, Zhan Y, Brignoni-Perez E, Lebel C, Reynolds JE, Nestrasil I, Labounek R, Lenglet C, Paulson A, Aulicka S, Heilbronner SR, Heuer K, Chandio BQ, Guaje J, Tang W, Garyfallidis E, Raja R, Anderson AW, Landman BA, Descoteaux M. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset? Neuroimage 2021; 243:118502. [PMID: 34433094 PMCID: PMC8855321 DOI: 10.1016/j.neuroimage.2021.118502] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 08/10/2021] [Accepted: 08/21/2021] [Indexed: 10/20/2022] Open
Abstract
White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.
| | | | - Laurent Petit
- Groupe dImagerie Neurofonctionnelle, Institut Des Maladies Neurodegeneratives, CNRS, CEA University of Bordeaux, Bordeaux, France
| | - Colin B Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Vishwesh Nath
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States
| | - Gabriel Girard
- CIBM Center for BioMedical Imaging, Lausanne, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK), Department of Medicine and Biomedical Engineering, University Hospital and University of Basel, Basel, Switzerland
| | - Jonathan Rafael-Patino
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Thomas Yu
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Elda Fischi-Gomez
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marco Pizzolato
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Simona Schiavi
- Department of Computer Science, University of Verona, Italy
| | | | | | - Cristina Granziera
- Translational Imaging in Neurology (ThINK), Department of Medicine and Biomedical Engineering, University Hospital and University of Basel, Basel, Switzerland
| | - Giorgio Innocenti
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jean-Philippe Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology & Neurosurgery, UCL Hospitals NHS Foundation Trust, London, United Kingdom
| | - Stephen Wastling
- Lysholm Department of Neuroradiology, National Hospital for Neurology & Neurosurgery, UCL Hospitals NHS Foundation Trust, London, United Kingdom
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Maria Petracca
- Department of Neurosciences and Reproductive and Odontostomatological Sciences, University "Federico II", Naples, Italy
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom
| | - Sjoerd B Vos
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Vejay N Vakharia
- Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom
| | - John S Duncan
- Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
| | - Helena Melero
- Departamento de Psicobiología y Metodología en Ciencias del Comportamiento - Universidad Complutense de Madrid, Spain Laboratorio de Análisis de Imagen Médica y Biometría (LAIMBIO), Universidad Rey Juan Carlos, Madrid, Spain
| | - Lidia Manzanedo
- Facultad de Ciencias de la Salud, Universidad Rey Juan Carlos, Madrid, Spain
| | - Emilio Sanz-Morales
- Laboratorio de Análisis de Imagen Médica y Biometría (LAIMBIO), Universidad Rey Juan Carlos, Madrid, Spain
| | - Ángel Peña-Melián
- Departamento de Anatomía, Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Fernando Calamante
- Sydney Imaging and School of Biomedical Engineering, The University of Sydney, Sydney, Australia
| | - Arnaud Attyé
- School of Biomedical Engineering, The University of Sydney, Sydney, Australia
| | - Ryan P Cabeen
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Laura Korobova
- Center for Integrative Connectomics, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Arthur W Toga
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | | | - Drew Parker
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Ragini Verma
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Ahmed Radwan
- KU Leuven, Department of Imaging and Pathology, Translational MRI, B-3000, Leuven, Belgium
| | - Stefan Sunaert
- KU Leuven, Department of Imaging and Pathology, Translational MRI, B-3000, Leuven, Belgium
| | - Louise Emsell
- KU Leuven, Department of Imaging and Pathology, Translational MRI, B-3000, Leuven, Belgium
| | | | | | - Claude J Bajada
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Malta
| | - Hamied Haroon
- Division of Neuroscience & Experimental Psychology, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | | | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Ping-Hong Yeh
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Rujirutana Srikanchana
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Colin D Mcknight
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Joseph Yuan-Mou Yang
- Department of Neurosurgery, Neuroscience Advanced Clinical Imaging Suite (NACIS), Royal Children's Hospital, Parkville, Melbourne, Australia
| | - Jian Chen
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - Claire E Kelly
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University & Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | | | - Jerome J Maller
- MRI Clinical Science Specialist, General Electric Healthcare, Australia
| | | | - Fabien Almairac
- Neurosurgery department, Hôpital Pasteur, University Hospital of Nice, Côte d'Azur University, France
| | - Kiran K Seunarine
- Developmental Imaging and Biophysics Section, UCL GOS Institute of Child Health, London
| | - Chris A Clark
- Developmental Imaging and Biophysics Section, UCL GOS Institute of Child Health, London
| | - Fan Zhang
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Nikos Makris
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Alexandra Golby
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Yogesh Rathi
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lauren J O'Donnell
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Yihao Xia
- University of Southern California, Keck School of Medicine, Neuroimaging and Informatics Institute, Los Angeles, California, United States
| | - Dogu Baran Aydogan
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Yonggang Shi
- University of Southern California, Keck School of Medicine, Neuroimaging and Informatics Institute, Los Angeles, California, United States
| | | | - Mathijs Raemaekers
- UMC Utrecht Brain Center, Department of Neurology&Neurosurgery, Utrecht, the Netherlands
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
| | - Stijn Michielse
- Department of Neurosurgery, School for Mental Health and Neuroscience, Maastricht University
| | | | - Luis Concha
- Universidad Nacional Autonoma de Mexico, Institute of Neurobiology, Mexico City, Mexico
| | - Ramón Aranda
- Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE-UT3), Cátedras-CONACyT, Ensenada, Mexico
| | | | | | - Lucas Roitman
- Department of Psychology, Stanford University, Stanford, California, USA
| | - Lucius S Fekonja
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Navona Calarco
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Michael Joseph
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Hajer Nakua
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | | | | | | | | | - Veena A Nair
- University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Masahiro Abe
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Roza G Bayrak
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Italy
| | - Giovanni Savini
- Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Nicolò Rolandi
- Department of Brain and Behavioral Sciences, University of Pavia, Italy
| | - Pamela Guevara
- Universidad de Concepción, Faculty of Engineering, Concepción, Chile
| | - Josselin Houenou
- Université Paris-Saclay, CEA, CNRS, Neurospin, Gif-sur-Yvette, France
| | | | | | - Cyril Poupon
- Université Paris-Saclay, CEA, CNRS, Neurospin, Gif-sur-Yvette, France
| | - Claudio Román
- Universidad de Concepción, Faculty of Engineering, Concepción, Chile
| | - Andrea Vázquez
- Universidad de Concepción, Faculty of Engineering, Concepción, Chile
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Mavilde Arantes
- Department of Biomedicine, Unit of Anatomy, Faculty of Medicine of the University of Porto, Al. Professor Hernâni Monteiro, Porto, Portugal
| | - José Paulo Andrade
- Department of Biomedicine, Unit of Anatomy, Faculty of Medicine of the University of Porto, Al. Professor Hernâni Monteiro, Porto, Portugal
| | - Susana Maria Silva
- Department of Biomedicine, Unit of Anatomy, Faculty of Medicine of the University of Porto, Al. Professor Hernâni Monteiro, Porto, Portugal
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, United States
| | - Eduardo Caverzasi
- Neurology Department UCSF Weill Institute for Neurosciences, University of California, San Francisco
| | - Simone Sacco
- Neurology Department UCSF Weill Institute for Neurosciences, University of California, San Francisco
| | - Michael Lauricella
- Memory and Aging Center. UCSF Weill Institute for Neurosciences, University of California, San Francisco, USA
| | - Franco Pestilli
- Department of Psychology, The University of Texas at Austin, TX 78731, USA
| | - Daniel Bullock
- Department of Psychology, The University of Texas at Austin, TX 78731, USA
| | - Yang Zhan
- Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Edith Brignoni-Perez
- Developmental-Behavioral Pediatrics Division, Department of Pediatrics, Stanford School of Medicine, Stanford, CA, United States
| | - Catherine Lebel
- Department of Radiology, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4
| | - Jess E Reynolds
- Department of Radiology, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4
| | - Igor Nestrasil
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Amy Paulson
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Stefania Aulicka
- Department of Paediatric Neurology, University Hospital and Medicine Faculty, Masaryk University, Brno, Czech Republic
| | | | - Katja Heuer
- Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, Paris, France
| | - Bramsh Qamar Chandio
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Javier Guaje
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Wei Tang
- Department of Computer Science, Indiana University, Bloomington, IN, USA
| | | | - Rajikha Raja
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Adam W Anderson
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
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35
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Coenen VA, Döbrössy MD, Teo SJ, Wessolleck J, Sajonz BEA, Reinacher PC, Thierauf-Emberger A, Spittau B, Leupold J, von Elverfeldt D, Schlaepfer TE, Reisert M. Diverging prefrontal cortex fiber connection routes to the subthalamic nucleus and the mesencephalic ventral tegmentum investigated with long range (normative) and short range (ex-vivo high resolution) 7T DTI. Brain Struct Funct 2021; 227:23-47. [PMID: 34482443 PMCID: PMC8741702 DOI: 10.1007/s00429-021-02373-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/24/2021] [Indexed: 11/30/2022]
Abstract
Uncertainties
concerning anatomy and function of cortico-subcortical projections have arisen during the recent years. A clear distinction between cortico-subthalamic (hyperdirect) and cortico-tegmental projections (superolateral medial forebrain bundle, slMFB) so far is elusive. Deep Brain Stimulation (DBS) of the slMFB (for major depression, MD and obsessive compulsive disorders, OCD) has on the one hand been interpreted as actually involving limbic (prefrontal) hyperdirect pathways. On the other hand slMFB’s stimulation region in the mesencephalic ventral tegmentum is said to impact on other structures too, going beyond the antidepressant (or anti OCD) efficacy of sole modulation of the cortico-tegmental reward-associated pathways. We have here used a normative diffusion MRT template (HCP, n = 80) for long-range tractography and augmented this dataset with ex-vivo high resolution data (n = 1) in a stochastic brain space. We compared this data with histological information and used the high resolution ex-vivo data set to scrutinize the mesencephalic tegmentum for small fiber pathways present. Our work resolves an existing ambiguity between slMFB and prefrontal hyperdirect pathways which—for the first time—are described as co-existent. DBS of the slMFB does not appear to modulate prefrontal hyperdirect cortico-subthalamic but rather cortico-tegmental projections. Smaller fiber structures in the target region—as far as they can be discerned—appear not to be involved in slMFB DBS. Our work enfeebles previous anatomical criticism and strengthens the position of the slMFB DBS target for its use in MD and OCD.
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Affiliation(s)
- Volker A Coenen
- Department of Stereotactic and Functional Neurosurgery, Medical Center of Freiburg University, Breisacher STraße 64, 79106, Freiburg, Germany. .,Medical Faculty of Freiburg University, Freiburg, Germany. .,Center for Deep Brain Stimulation, Medical Center of Freiburg University, Freiburg, Germany. .,Laboratory of Stereotaxy and Interventional Neurosciences, Department of Stereotactic and Functional Neurosurgery, Medical Center of Freiburg University, Freiburg, Germany.
| | - Máté D Döbrössy
- Department of Stereotactic and Functional Neurosurgery, Medical Center of Freiburg University, Breisacher STraße 64, 79106, Freiburg, Germany.,Medical Faculty of Freiburg University, Freiburg, Germany.,Laboratory of Stereotaxy and Interventional Neurosciences, Department of Stereotactic and Functional Neurosurgery, Medical Center of Freiburg University, Freiburg, Germany
| | - Shi Jia Teo
- Medical Faculty of Freiburg University, Freiburg, Germany.,Department of Diagnostic and Interventional Radiology, Medical Physics, Medical Center, University of Freiburg, Freiburg, Germany
| | - Johanna Wessolleck
- Department of Stereotactic and Functional Neurosurgery, Medical Center of Freiburg University, Breisacher STraße 64, 79106, Freiburg, Germany.,Laboratory of Stereotaxy and Interventional Neurosciences, Department of Stereotactic and Functional Neurosurgery, Medical Center of Freiburg University, Freiburg, Germany
| | - Bastian E A Sajonz
- Department of Stereotactic and Functional Neurosurgery, Medical Center of Freiburg University, Breisacher STraße 64, 79106, Freiburg, Germany.,Medical Faculty of Freiburg University, Freiburg, Germany
| | - Peter C Reinacher
- Department of Stereotactic and Functional Neurosurgery, Medical Center of Freiburg University, Breisacher STraße 64, 79106, Freiburg, Germany.,Medical Faculty of Freiburg University, Freiburg, Germany.,Fraunhofer Institute for Laser Technology (ILT), Aachen, Germany
| | - Annette Thierauf-Emberger
- Medical Faculty of Freiburg University, Freiburg, Germany.,Institute of Forensic Medicine, Medical Center of Freiburg University, Freiburg, Germany
| | - Björn Spittau
- Anatomy and Cell Biology, Medical School OWL, Bielefeld University, Bielefeld, Germany.,Institute for Anatomy and Cell Biology, Department of Molecular Embryologie, Faculty of Medicine, Freiburg University, Freiburg, Germany
| | - Jochen Leupold
- Medical Faculty of Freiburg University, Freiburg, Germany.,Department of Diagnostic and Interventional Radiology, Medical Physics, Medical Center, University of Freiburg, Freiburg, Germany
| | - Dominik von Elverfeldt
- Medical Faculty of Freiburg University, Freiburg, Germany.,Department of Diagnostic and Interventional Radiology, Medical Physics, Medical Center, University of Freiburg, Freiburg, Germany
| | - Thomas E Schlaepfer
- Medical Faculty of Freiburg University, Freiburg, Germany.,Center for Deep Brain Stimulation, Medical Center of Freiburg University, Freiburg, Germany.,Division of Interventional Biological Psychiatry, Department of Psychiatry and Psychotherapy, Medical Center of Freiburg University, Freiburg, Germany
| | - Marco Reisert
- Department of Stereotactic and Functional Neurosurgery, Medical Center of Freiburg University, Breisacher STraße 64, 79106, Freiburg, Germany.,Medical Faculty of Freiburg University, Freiburg, Germany.,Department of Diagnostic and Interventional Radiology, Medical Physics, Medical Center, University of Freiburg, Freiburg, Germany
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