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Ahumada-Vizcaino JC, Wuo-Silva R, Hernández MM, Chaddad-Neto F. The art of combining neuroanatomy and microsurgical skills in modern neurosurgery. Front Neurol 2023; 13:1076778. [PMID: 36712447 PMCID: PMC9877616 DOI: 10.3389/fneur.2022.1076778] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/12/2022] [Indexed: 01/13/2023] Open
Abstract
Neurosurgical training outside the operating room has become a priority for all neurosurgeons around the world. The exponential increase in the number of publications on training in neurosurgery reflects changes in the environment that future neurosurgeons are expected to work in. In modern practice, patients and medicolegal experts demand objective measures of competence and proficiency in the growing list of techniques available to treat complex neurosurgical conditions. It is important to ensure the myriad of training models available lead to tangible improvements in the operating room. While neuroanatomy textbooks and atlases are continually revised to teach the aspiring surgeon anatomy with a three-dimensional perspective, developing technical skills are integral to the pursuit of excellence in neurosurgery. Parapharsing William Osler, one of the fathers of neurosurgical training, without anatomical knowledge we are lost, but without the experience and skills from practice our journey is yet to begin. It is important to constantly aspire beyond competence to mastery, as we aim to deliver good outcomes for patients in an era of declining case volumes. In this article, we discuss, based on the literature, the most commonly used training models and how they are integrated into the treatment of some surgical brain conditions.
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Affiliation(s)
| | - Raphael Wuo-Silva
- Department of Neurology and Neurosurgery, Universidade Federal de São Paulo, São Paulo, SP, Brazil
| | - Manuel Moreno Hernández
- Department of Neurology and Neurosurgery, Universidade Federal de São Paulo, São Paulo, SP, Brazil
| | - Feres Chaddad-Neto
- Department of Neurology and Neurosurgery, Universidade Federal de São Paulo, São Paulo, SP, Brazil,Department of Neurosurgery, Beneficência Portuguesa Hospital, São Paulo, SP, Brazil,*Correspondence: Feres Chaddad-Neto ✉
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Prasad KM, Gertler J, Tollefson S, Wood JA, Roalf D, Gur RC, Gur RE, Almasy L, Pogue-Geile MF, Nimgaonkar VL. Heritable anisotropy associated with cognitive impairments among patients with schizophrenia and their non-psychotic relatives in multiplex families. Psychol Med 2022; 52:989-1000. [PMID: 32878667 PMCID: PMC8218223 DOI: 10.1017/s0033291720002883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND To test the functional implications of impaired white matter (WM) connectivity among patients with schizophrenia and their relatives, we examined the heritability of fractional anisotropy (FA) measured on diffusion tensor imaging data acquired in Pittsburgh and Philadelphia, and its association with cognitive performance in a unique sample of 175 multigenerational non-psychotic relatives of 23 multiplex schizophrenia families and 240 unrelated controls (total = 438). METHODS We examined polygenic inheritance (h2r) of FA in 24 WM tracts bilaterally, and also pleiotropy to test whether heritability of FA in multiple WM tracts is secondary to genetic correlation among tracts using the Sequential Oligogenic Linkage Analysis Routines. Partial correlation tests examined the correlation of FA with performance on eight cognitive domains on the Penn Computerized Neurocognitive Battery, controlling for age, sex, site and mother's education, followed by multiple comparison corrections. RESULTS Significant total additive genetic heritability of FA was observed in all three-categories of WM tracts (association, commissural and projection fibers), in total 33/48 tracts. There were significant genetic correlations in 40% of tracts. Diagnostic group main effects were observed only in tracts with significantly heritable FA. Correlation of FA with neurocognitive impairments was observed mainly in heritable tracts. CONCLUSIONS Our data show significant heritability of all three-types of tracts among relatives of schizophrenia. Significant heritability of FA of multiple tracts was not entirely due to genetic correlations among the tracts. Diagnostic group main effect and correlation with neurocognitive performance were mainly restricted to tracts with heritable FA suggesting shared genetic effects on these traits.
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Affiliation(s)
- KM Prasad
- Departments of Psychiatry and Bioengineering, University of Pittsburgh, VA Pittsburgh Healthcare System, Pittsburgh, PA
| | - J Gertler
- Departments of Psychiatry and Bioengineering, University of Pittsburgh, VA Pittsburgh Healthcare System, Pittsburgh, PA
| | - S Tollefson
- Departments of Psychiatry and Bioengineering, University of Pittsburgh, VA Pittsburgh Healthcare System, Pittsburgh, PA
| | - JA Wood
- Departments of Psychiatry and Bioengineering, University of Pittsburgh, VA Pittsburgh Healthcare System, Pittsburgh, PA
| | - D Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
| | - RC Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
| | - RE Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
| | - L Almasy
- Department of Genetics, University of Pennsylvania, Philadelphia, PA
| | - MF Pogue-Geile
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA
| | - VL Nimgaonkar
- Departments of Psychiatry and Bioengineering, University of Pittsburgh, VA Pittsburgh Healthcare System, Pittsburgh, PA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, PA
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Sotiropoulos SN, Zalesky A. Building connectomes using diffusion MRI: why, how and but. NMR Biomed 2019; 32:e3752. [PMID: 28654718 PMCID: PMC6491971 DOI: 10.1002/nbm.3752] [Citation(s) in RCA: 149] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 04/05/2017] [Accepted: 05/03/2017] [Indexed: 05/14/2023]
Abstract
Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.
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Affiliation(s)
- Stamatios N. Sotiropoulos
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Melbourne School of EngineeringUniversity of MelbourneVictoriaAustralia
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Wu JY, Zhang Y, Wu WB, Hu G, Xu Y. Impaired long contact white matter fibers integrity is related to depression in Parkinson's disease. CNS Neurosci Ther 2017; 24:108-114. [PMID: 29125694 DOI: 10.1111/cns.12778] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Revised: 10/18/2017] [Accepted: 10/19/2017] [Indexed: 12/22/2022] Open
Abstract
AIMS Depression is one of the most common nonmotor symptoms in Parkinson's disease (PD). But the pathogenesis is still unclear. Studies have shown that depression in PD is closely related to the white matter abnormalities, but the number of studies is still very small and lack of whole brain white matter lesions study. METHODS In this study, we investigated whole brain white matter integrity in 31 depressed PD patients and 37 nondepressed PD patients by diffusion tensor imaging. RESULTS There was no difference in age, gender, age of onset, disease duration, Hoehn-Yahr scale, Unified Parkinson's Disease Rating Scale scores-III, and Mini-Mental State Examination scores between the two groups. The only difference was the Hamilton Depression Rating Scale. Depressed PD patients showed reduced fractional anisotropy values in the left anterior corona radiata, left posterior thalamic radiation, left cingulum, left superior longitudinal fasciculus, left sagittal stratum (including inferior longitudinal fasciculus and inferior fronto-occipital fasciculus), and left uncinate fasciculus. In patients with depression, the Hamilton Depression Rating Scale (HDRS) was negatively correlated with the FA value in the left cingulum (r = -0.712, P = .032) and left superior longitudinal fasciculus (r = -0.699, P = .025). CONCLUSIONS This study suggested depression in PD was related to impaired white matter integrity especially the long contact fibers in the left hemisphere. These findings may be helpful for further understanding the potential mechanisms underlying depression in PD.
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Affiliation(s)
- Jia-Yong Wu
- Department of Neurology, Drum Tower Hospital of Nanjing Medical University, Nanjing, China.,Department of Neurology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Yang Zhang
- Department of Neurology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
| | - Wen-Bo Wu
- Department of Medical Imaging, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Gang Hu
- Jiangsu Key Laboratory of Neurodegeneration, Department of Pharmacology, Nanjing Medical University, Nanjing, Jiangsu, China.,Department of Pharmacology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Yun Xu
- Department of Neurology, Drum Tower Hospital of Nanjing Medical University, Nanjing, China.,Department of Neurology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, Jiangsu, China
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Song L, Mishra V, Ouyang M, Peng Q, Slinger M, Liu S, Huang H. Human Fetal Brain Connectome: Structural Network Development from Middle Fetal Stage to Birth. Front Neurosci 2017; 11:561. [PMID: 29081731 PMCID: PMC5645529 DOI: 10.3389/fnins.2017.00561] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 09/25/2017] [Indexed: 12/25/2022] Open
Abstract
Complicated molecular and cellular processes take place in a spatiotemporally heterogeneous and precisely regulated pattern in the human fetal brain, yielding not only dramatic morphological and microstructural changes, but also macroscale connectomic transitions. As the underlying substrate of the fetal brain structural network, both dynamic neuronal migration pathways and rapid developing fetal white matter (WM) fibers could fundamentally reshape early fetal brain connectome. Quantifying structural connectome development can not only shed light on the brain reconfiguration in this critical yet rarely studied developmental period, but also reveal alterations of the connectome under neuropathological conditions. However, transition of the structural connectome from the mid-fetal stage to birth is not yet known. The contribution of different types of neural fibers to the structural network in the mid-fetal brain is not known, either. In this study, diffusion tensor magnetic resonance imaging (DT-MRI or DTI) of 10 fetal brain specimens at the age of 20 postmenstrual weeks (PMW), 12 in vivo brains at 35 PMW, and 12 in vivo brains at term (40 PMW) were acquired. The structural connectome of each brain was established with evenly parcellated cortical regions as network nodes and traced fiber pathways based on DTI tractography as network edges. Two groups of fibers were categorized based on the fiber terminal locations in the cerebral wall in the 20 PMW fetal brains. We found that fetal brain networks become stronger and more efficient during 20–40 PMW. Furthermore, network strength and global efficiency increase more rapidly during 20–35 PMW than during 35–40 PMW. Visualization of the whole brain fiber distribution by the lengths suggested that the network reconfiguration in this developmental period could be associated with a significant increase of major long association WM fibers. In addition, non-WM neural fibers could be a major contributor to the structural network configuration at 20 PMW and small-world network organization could exist as early as 20 PMW. These findings offer a preliminary record of the fetal brain structural connectome maturation from the middle fetal stage to birth and reveal the critical role of non-WM neural fibers in structural network configuration in the middle fetal stage.
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Affiliation(s)
- Limei Song
- Shandong Provincial Key Laboratory of Mental Disorders, Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Jinan, China.,Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Virendra Mishra
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
| | - Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Qinmu Peng
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Michelle Slinger
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Shuwei Liu
- Shandong Provincial Key Laboratory of Mental Disorders, Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Jinan, China
| | - Hao Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Zimmerman-Moreno G, Ben Bashat D, Artzi M, Nefussy B, Drory V, Aizenstein O, Greenspan H. Whole brain fiber-based comparison (FBC)-A tool for diffusion tensor imaging-based cohort studies. Hum Brain Mapp 2015; 37:477-90. [PMID: 26518977 DOI: 10.1002/hbm.23043] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Revised: 10/15/2015] [Accepted: 10/19/2015] [Indexed: 12/13/2022] Open
Abstract
We present a novel method for fiber-based comparison of diffusion tensor imaging (DTI) scans of groups of subjects. The method entails initial preprocessing and fiber reconstruction by tractography of each brain in its native coordinate system. Several diffusion parameters are sampled along each fiber and used in subsequent comparisons. A spatial correspondence between subjects is established based on geometric similarity between fibers in a template set (several choices for template are explored), and fibers in all other subjects. Diffusion parameters between groups are compared statistically for each template fiber. Results are presented at single fiber resolution. As an initial exploratory step in neurological population studies this method points to the locations affected by the pathology of interest, without requiring a hypothesis. It does not make any grouping assumptions on the fibers and no manual intervention is needed. The framework was applied here to 18 healthy subjects and 23 amyotrophic lateral sclerosis (ALS) patients. The results are compatible with previous findings and with the tract based spatial statistics (TBSS) method. Hum Brain Mapp 37:477-490, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
| | - Dafna Ben Bashat
- The Functional Brain Center, the Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, 6 Weizmann St. Tel Aviv 64239, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Ramat-Aviv, 69978, Israel
| | - Moran Artzi
- The Functional Brain Center, the Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, 6 Weizmann St. Tel Aviv 64239, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Ramat-Aviv, 69978, Israel
| | - Beatrice Nefussy
- ALS Clinic, Department of Neurology, Tel Aviv Sourasky Medical Center, 6 Weizmann St. Tel Aviv 64239, Israel
| | - Vivian Drory
- Sackler Faculty of Medicine, Tel Aviv University, Ramat-Aviv, 69978, Israel.,ALS Clinic, Department of Neurology, Tel Aviv Sourasky Medical Center, 6 Weizmann St. Tel Aviv 64239, Israel
| | - Orna Aizenstein
- The Functional Brain Center, the Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, 6 Weizmann St. Tel Aviv 64239, Israel
| | - Hayit Greenspan
- Department of Biomedical Engineering, Tel Aviv University, 69978, Israel
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Abstract
Magnetic resonance diffusion tensor imaging is being widely used to reconstruct brain white matter fiber tracts. To characterize structural properties of the tracts, reconstructed fibers are often grouped into bundles that correspond to coherent anatomic structures. For further group analysis of fiber bundles, it is desirable that corresponding bundles from different studies are coregistered. To address these needs simultaneously, a unified fiber bundling and registration (UFIBRE) framework is proposed in this work. The framework is based on maximizing a posteriori Bayesian probabilities using an expectation maximization algorithm. Given a set of segmented template bundles and a whole-brain target fiber set, the UFIBRE algorithm optimally bundles the target fibers and registers them with the template. The bundling component in the UFIBRE algorithm simplifies fiber-based registration into bundle-to-bundle registration, and the registration component in turn guides the bundling process to find bundles consistent with the template. Experiments with in vivo data demonstrate that the estimated bundles have an approximately 80% consistency with ground truth and the root mean square error between their bundle medial axes is less than one voxel. The proposed algorithm is highly efficient, offering potential routine use for group analysis of white matter fibers.
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Affiliation(s)
- Qing Xu
- Vanderbilt University Institute of Imaging Science and the Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37232 USA
| | - Adam W. Anderson
- Vanderbilt University Institute of Imaging Science and the Department of Biomedical Engineering, Vanderbilt University, TN 37235 USA
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science and the Department of Biomedical Engineering, Vanderbilt University, TN 37235 USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, the Department of Electrical Engineering and Computer Science, the Department of Biomedical Engineering, and also with the Chemical and Physical Biology Program, Vanderbilt University, TN 37235 USA
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