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Hormovas J, Dadario NB, Tang SJ, Nicholas P, Dhanaraj V, Young I, Doyen S, Sughrue ME. Parcellation-Based Connectivity Model of the Judgement Core. J Pers Med 2023; 13:1384. [PMID: 37763153 PMCID: PMC10532823 DOI: 10.3390/jpm13091384] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/12/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Judgement is a higher-order brain function utilized in the evaluation process of problem solving. However, heterogeneity in the task methodology based on the many definitions of judgement and its expansive and nuanced applications have prevented the identification of a unified cortical model at a level of granularity necessary for clinical translation. Forty-six task-based fMRI studies were used to generate activation-likelihood estimations (ALE) across moral, social, risky, and interpersonal judgement paradigms. Cortical parcellations overlapping these ALEs were used to delineate patterns in neurocognitive network engagement for the four judgement tasks. Moral judgement involved the bilateral superior frontal gyri, right temporal gyri, and left parietal lobe. Social judgement demonstrated a left-dominant frontoparietal network with engagement of right-sided temporal limbic regions. Moral and social judgement tasks evoked mutual engagement of the bilateral DMN. Both interpersonal and risk judgement were shown to involve a right-sided frontoparietal network with accompanying engagement of the left insular cortex, converging at the right-sided CEN. Cortical activation in normophysiological judgement function followed two separable patterns involving the large-scale neurocognitive networks. Specifically, the DMN was found to subserve judgement centered around social inferences and moral cognition, while the CEN subserved tasks involving probabilistic reasoning, risk estimation, and strategic contemplation.
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Affiliation(s)
- Jorge Hormovas
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Level 7 Prince of Wales Private Hospital, Randwick, NSW 2031, Australia; (J.H.); (V.D.)
| | - Nicholas B. Dadario
- Robert Wood Johnson Medical School, Rutgers University, 125 Paterson St., New Brunswick, NJ 08901, USA;
| | - Si Jie Tang
- School of Medicine, 21772 University of California Davis Medical Center, 2315 Stockton Blvd., Sacramento, CA 95817, USA
| | - Peter Nicholas
- Omniscient Neurotechnology, Level 10/580 George Street, Haymarket, NSW 2000, Australia; (P.N.); (I.Y.); (S.D.)
| | - Vukshitha Dhanaraj
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Level 7 Prince of Wales Private Hospital, Randwick, NSW 2031, Australia; (J.H.); (V.D.)
| | - Isabella Young
- Omniscient Neurotechnology, Level 10/580 George Street, Haymarket, NSW 2000, Australia; (P.N.); (I.Y.); (S.D.)
| | - Stephane Doyen
- Omniscient Neurotechnology, Level 10/580 George Street, Haymarket, NSW 2000, Australia; (P.N.); (I.Y.); (S.D.)
| | - Michael E. Sughrue
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Level 7 Prince of Wales Private Hospital, Randwick, NSW 2031, Australia; (J.H.); (V.D.)
- Omniscient Neurotechnology, Level 10/580 George Street, Haymarket, NSW 2000, Australia; (P.N.); (I.Y.); (S.D.)
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Taylor H, Nicholas P, Hoy K, Bailey N, Tanglay O, Young IM, Dobbin L, Doyen S, Sughrue ME, Fitzgerald PB. Functional connectivity analysis of the depression connectome provides potential markers and targets for transcranial magnetic stimulation. J Affect Disord 2023; 329:539-547. [PMID: 36841298 DOI: 10.1016/j.jad.2023.02.082] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 02/02/2023] [Accepted: 02/19/2023] [Indexed: 02/27/2023]
Abstract
BACKGROUND Despite efforts to improve targeting accuracy of the dorsolateral prefrontal cortex (DLPFC) as a repetitive transcranial magnetic stimulation (rTMS) target for Major Depressive Disorder (MDD), the heterogeneity in clinical response remains unexplained. OBJECTIVE We sought to compare the patterns of functional connectivity from the DLPFC treatment site in patients with MDD who were TMS responders to those who were TMS non-responders. METHODS Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 37 participants before they underwent a course of rTMS to left Brodmann area 46. A novel machine learning method was utilized to identify brain regions associated with each item of the Beck's Depression Inventory II (BDI-II), and for 26 participants who underwent rTMS treatment over the left Brodmann area 46, identify regions differentiating rTMS responders and non-responders. RESULTS Nine parcels of the Human Connectome Project Multimodal Parcellation Atlas matched to at least three items of the Beck's Depression Inventory II (BDI-II) as predictors of response to rTMS, with many in the temporal, parietal and cingulate cortices. Additionally, pre-treatment mapping for 17 items of the BDI-II demonstrated significant variability in symptom to parcel mapping. When parcels associated with symptom presence and symptom resolution were compared, 15 parcels were uniquely associated with resolution (potential targets), and 12 parcels were associated with both symptom presence and resolution (blockers or biomarkers). CONCLUSIONS Machine learning approaches show promise for the development of pathoanatomical diagnosis and treatment algorithms for MDD. Prospective studies are required to facilitate clinical translation.
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Affiliation(s)
- Hugh Taylor
- Omniscient Neurotechnology, Sydney, Australia
| | | | - Kate Hoy
- Central Clinical School Department of Psychiatry, Monash University, Camberwell, Victoria, Australia; Bionics Institute, 384-388 Albert St, East Melbourne, Vic 3002, Australia
| | - Neil Bailey
- Central Clinical School Department of Psychiatry, Monash University, Camberwell, Victoria, Australia; Monarch Research Institute Monarch Mental Health Group, Sydney, New South Wales, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia
| | | | | | | | | | | | - Paul B Fitzgerald
- School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia
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Gou C, Yang S, Hou Q, Rudder P, Tanglay O, Young I, Peng T, He W, Yang L, Osipowicz K, Doyen S, Mansouri N, Sughrue ME, Wang X. Functional connectivity of the language area in migraine: a preliminary classification model. BMC Neurol 2023; 23:142. [PMID: 37016325 PMCID: PMC10071619 DOI: 10.1186/s12883-023-03183-w] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/25/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND Migraine is a complex disorder characterized by debilitating headaches. Despite its prevalence, its pathophysiology remains unknown, with subsequent gaps in diagnosis and treatment. We combined machine learning with connectivity analysis and applied a whole-brain network approach to identify potential targets for migraine diagnosis and treatment. METHODS Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI(rfMRI), and diffusion weighted scans were obtained from 31 patients with migraine, and 17 controls. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into diagnostic groups based on functional connectivity (FC) and derive networks and parcels contributing to the model. PageRank centrality analysis was also performed on the structural connectome to identify changes in hubness. RESULTS Our model attained an area under the receiver operating characteristic curve (AUC-ROC) of 0.68, which rose to 0.86 following hyperparameter tuning. FC of the language network was most predictive of the model's classification, though patients with migraine also demonstrated differences in the accessory language, visual and medial temporal regions. Several analogous regions in the right hemisphere demonstrated changes in PageRank centrality, suggesting possible compensation. CONCLUSIONS Although our small sample size demands caution, our preliminary findings demonstrate the utility of our method in providing a network-based perspective to diagnosis and treatment of migraine.
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Affiliation(s)
- Chen Gou
- Institute of Neurological Diseases, North Sichuan Medical College, Nanchong, 637000, China
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Shuangfeng Yang
- Institute of Neurological Diseases, North Sichuan Medical College, Nanchong, 637000, China
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Qianmei Hou
- Institute of Neurological Diseases, North Sichuan Medical College, Nanchong, 637000, China
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Peter Rudder
- Omniscient Neurotechnology, Sydney, NSW, 2000, Australia
| | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, NSW, 2000, Australia
| | - Isabella Young
- Omniscient Neurotechnology, Sydney, NSW, 2000, Australia
| | - Tingting Peng
- Institute of Neurological Diseases, North Sichuan Medical College, Nanchong, 637000, China
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Weiwei He
- Institute of Neurological Diseases, North Sichuan Medical College, Nanchong, 637000, China
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China
| | - Liuyi Yang
- Shenzhen Xijia Medical Technology Company, Shenzhen, Guangdong Province, 518052, China
| | | | - Stephane Doyen
- Omniscient Neurotechnology, Sydney, NSW, 2000, Australia
| | - Negar Mansouri
- Omniscient Neurotechnology, Sydney, NSW, 2000, Australia
| | | | - Xiaoming Wang
- Institute of Neurological Diseases, North Sichuan Medical College, Nanchong, 637000, China.
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, China.
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Young IM, Taylor HM, Nicholas PJ, Mackenzie A, Tanglay O, Dadario NB, Osipowicz K, Davis E, Doyen S, Teo C, Sughrue ME. An agile, data-driven approach for target selection in rTMS therapy for anxiety symptoms: Proof of concept and preliminary data for two novel targets. Brain Behav 2023; 13:e2914. [PMID: 36949668 PMCID: PMC10175990 DOI: 10.1002/brb3.2914] [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] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 12/04/2022] [Accepted: 01/22/2023] [Indexed: 03/24/2023] Open
Abstract
INTRODUCTION Data-driven approaches to transcranial magnetic stimulation (TMS) might yield more consistent and symptom-specific results based on individualized functional connectivity analyses compared to previous traditional approaches due to more precise targeting. We provide a proof of concept for an agile target selection paradigm based on using connectomic methods that can be used to detect patient-specific abnormal functional connectivity, guide treatment aimed at the most abnormal regions, and optimize the rapid development of new hypotheses for future study. METHODS We used the resting-state functional MRI data of 28 patients with medically refractory generalized anxiety disorder to perform agile target selection based on abnormal functional connectivity patterns between the Default Mode Network (DMN) and Central Executive Network (CEN). The most abnormal areas of connectivity within these regions were selected for subsequent targeted TMS treatment by a machine learning based on an anomalous functional connectivity detection matrix. Areas with mostly hyperconnectivity were stimulated with continuous theta burst stimulation and the converse with intermittent theta burst stimulation. An image-guided accelerated theta burst stimulation paradigm was used for treatment. RESULTS Areas 8Av and PGs demonstrated consistent abnormalities, particularly in the left hemisphere. Significant improvements were demonstrated in anxiety symptoms, and few, minor complications were reported (fatigue (n = 2) and headache (n = 1)). CONCLUSIONS Our study suggests that a left-lateralized DMN is likely the primary functional network disturbed in anxiety-related disorders, which can be improved by identifying and targeting abnormal regions with a rapid, data-driven, agile aTBS treatment on an individualized basis.
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Grants
- Omniscient Neurotechnology provided support in the form of salaries for authors IY, HT, PN, AM, OT, KO, SD, MS, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This research did not receive any other specific grant from funding agencies in the public, commercial, or not-for-profit sectors
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Affiliation(s)
- Isabella M Young
- Omniscient Neurotechnology, Sydney, New South Wales, Australia
- Cingulum Health, Sydney, New South Wales, Australia
| | - Hugh M Taylor
- Omniscient Neurotechnology, Sydney, New South Wales, Australia
| | | | - Alana Mackenzie
- Omniscient Neurotechnology, Sydney, New South Wales, Australia
| | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, New South Wales, Australia
| | - Nicholas B Dadario
- Rutgers Robert Wood Johnson School of Medicine, New Brunswick, New Jersey
| | - Karol Osipowicz
- Omniscient Neurotechnology, Sydney, New South Wales, Australia
| | - Ethan Davis
- Cingulum Health, Sydney, New South Wales, Australia
| | - Stephane Doyen
- Omniscient Neurotechnology, Sydney, New South Wales, Australia
| | - Charles Teo
- Cingulum Health, Sydney, New South Wales, Australia
| | - Michael E Sughrue
- Omniscient Neurotechnology, Sydney, New South Wales, Australia
- Cingulum Health, Sydney, New South Wales, Australia
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Osipowicz K, Profyris C, Mackenzie A, Nicholas P, Rudder P, Taylor HM, Young IM, Joyce AW, Dobbin L, Tanglay O, Thompson L, Mashilwane T, Sughrue ME, Doyen S. Real world demonstration of hand motor mapping using the structural connectivity atlas. Clin Neurol Neurosurg 2023; 228:107679. [PMID: 36965417 DOI: 10.1016/j.clineuro.2023.107679] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 03/12/2023] [Accepted: 03/14/2023] [Indexed: 03/18/2023]
Abstract
BACKGROUND Locating the hand-motor-cortex (HMC) is an essential component within many neurosurgeries. Despite advancements in these localization methods there are still downfalls for each. Additionally, the importance of presurgical planning calls for increasingly accurate and efficient methods of locating specific cortical regions. OBJECTIVE In this study we aimed to test the ability of the Structural Connectivity Atlas (SCA), a machine-learning based method to parcellate the human cortex, to locate the HMC in a small cohort study. METHODS Using MRI and DTI images obtained from adult subjects (n = 11), personalized brain maps were created for each individual based on a SCA paired with the Brainnetome region for the HMC. Subjects received single pulse TMS, over the HMC region through the use of a neuronavigation system. If they responded with motor movement, this was recorded. The SCA identified HMC region was compared to the visual-determined HMC through identifying the Omega fold on the Precentral Gyrus, which was completed by a trained neuroanatomist. A Kendall's Tau B correlation was conducted between anatomical match and visual movement. RESULTS This study concluded that the SCA was capable of locating the HMC in healthy and distorted brains. Overall, the SCA defined the anatomical area of the HMC in 90 % of subjects and triggered a motor response in 61 %. CONCLUSION The SCA could be suitable for incorporation into presurgical planning practices due to its ability to map anatomically abnormal brains. Further studies on larger cohorts and targeting different areas of cortex could be beneficial.
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Wang Y, Wang J, Su W, Hu H, Xia M, Zhang T, Xu L, Zhang X, Taylor H, Osipowicz K, Young IM, Lin YH, Nicholas P, Tanglay O, Sughrue ME, Tang Y, Doyen S. Symptom-circuit mappings of the schizophrenia connectome. Psychiatry Res 2023; 323:115122. [PMID: 36889161 DOI: 10.1016/j.psychres.2023.115122] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 02/15/2023] [Accepted: 02/18/2023] [Indexed: 02/27/2023]
Abstract
OBJECTIVE This paper aims to model the anatomical circuits underlying schizophrenia symptoms, and to explore patterns of abnormal connectivity among brain networks affected by psychopathology. METHODS T1 magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), and resting-state functional MRI (rsfMRI) were obtained from a total of 126 patients with schizophrenia who were recruited for the study. The images were processed using the Omniscient software (https://www.o8t. com). We further apply the use of the Hollow-tree Super (HoTS) method to gain insights into what brain regions had abnormal connectivity that might be linked to the symptoms of schizophrenia. RESULTS The Positive and Negative Symptom Scale is characterised into 6 factors. Each symptom is mapped with specific anatomical abnormalities and circuits. Comparison between factors reveals co-occurrence in parcels in Factor 1 and Factor 2. Multiple large-scale networks are involved in SCZ symptomatology, with functional connectivity within Default Mode Network (DMN) and Central Executive Network (CEN) regions most frequently associated with measures of psychopathology. CONCLUSION We present a summary of the relevant anatomy for regions of the cortical areas as part of a larger effort to understand its contribution in schizophrenia. This unique machine learning-type approach maps symptoms to specific brain regions and circuits by bridging the diagnostic subtypes and analysing the features of the connectome.
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Affiliation(s)
- Yingchan Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
| | - Wenjun Su
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Hao Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Mengqing Xia
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Xia Zhang
- Xijia Medical Technology Company Limited, Shenzhen 518000, China; International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi'an 710082, China
| | - Hugh Taylor
- Omniscient Neurotechnology, Sydney, Australia
| | | | | | - Yueh-Hsin Lin
- Department of Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
| | | | | | - Michael E Sughrue
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi'an 710082, China; Omniscient Neurotechnology, Sydney, Australia
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
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Bian R, Huo M, Liu W, Mansouri N, Tanglay O, Young I, Osipowicz K, Hu X, Zhang X, Doyen S, Sughrue ME, Liu L. Connectomics underlying motor functional outcomes in the acute period following stroke. Front Aging Neurosci 2023; 15:1131415. [PMID: 36875697 PMCID: PMC9975347 DOI: 10.3389/fnagi.2023.1131415] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Accepted: 01/30/2023] [Indexed: 02/17/2023] Open
Abstract
Objective Stroke remains the number one cause of morbidity in many developing countries, and while effective neurorehabilitation strategies exist, it remains difficult to predict the individual trajectories of patients in the acute period, making personalized therapies difficult. Sophisticated and data-driven methods are necessary to identify markers of functional outcomes. Methods Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 79 patients following stroke. Sixteen models were constructed to predict performance across six tests of motor impairment, spasticity, and activities of daily living, using either whole-brain structural or functional connectivity. Feature importance analysis was also performed to identify brain regions and networks associated with performance in each test. Results The area under the receiver operating characteristic curve ranged from 0.650 to 0.868. Models utilizing functional connectivity tended to have better performance than those utilizing structural connectivity. The Dorsal and Ventral Attention Networks were among the top three features in several structural and functional models, while the Language and Accessory Language Networks were most commonly implicated in structural models. Conclusions Our study highlights the potential of machine learning methods combined with connectivity analysis in predicting outcomes in neurorehabilitation and disentangling the neural correlates of functional impairments, though further longitudinal studies are necessary.
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Affiliation(s)
- Rong Bian
- Department of Rehabilitation, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ming Huo
- University of Health and Rehabilitation Sciences, Qingdao, China
| | - Wan Liu
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | | | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, NSW, Australia
| | | | | | - Xiaorong Hu
- Xijia Medical Technology Company Limited, Shenzhen, China
| | - Xia Zhang
- Xijia Medical Technology Company Limited, Shenzhen, China.,International Joint Research Center on Precision Brain Medicine, Xidian Group Hospital, Xi'an, China
| | | | - Michael E Sughrue
- Omniscient Neurotechnology, Sydney, NSW, Australia.,International Joint Research Center on Precision Brain Medicine, Xidian Group Hospital, Xi'an, China
| | - Li Liu
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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Kim SJ, Tanglay O, Chong EHN, Young IM, Fonseka RD, Taylor H, Nicholas P, Doyen S, Sughrue ME. Functional connectivity in ADHD children doing Go/No-Go tasks: An fMRI systematic review and meta-analysis. Transl Neurosci 2023; 14:20220299. [PMID: 38410259 PMCID: PMC10896184 DOI: 10.1515/tnsci-2022-0299] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 07/01/2023] [Accepted: 07/10/2023] [Indexed: 02/28/2024] Open
Abstract
Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders diagnosed in childhood. Two common features of ADHD are impaired behavioural inhibition and sustained attention. The Go/No-Go experimental paradigm with concurrent functional magnetic resonance imaging (fMRI) scanning has previously revealed important neurobiological correlates of ADHD such as the supplementary motor area and the prefrontal cortex. The coordinate-based meta-analysis combined with quantitative techniques, such as activation likelihood estimate (ALE) generation, provides an unbiased and objective method of summarising these data to understand the brain network architecture and connectivity in ADHD children. Go/No-Go task-based fMRI studies involving children and adolescent subjects were selected. Coordinates indicating foci of activation were collected to generate ALEs using threshold values (voxel-level: p < 0.001; cluster-level: p < 0.05). ALEs were matched to one of seven canonical brain networks based on the cortical parcellation scheme derived from the Human Connectome Project. Fourteen studies involving 457 children met the eligibility criteria. No significant convergence of Go/No-Go related brain activation was found for ADHD groups. Three significant ALE clusters were detected for brain activation relating to controls or ADHD < controls. Significant clusters were related to specific areas of the default mode network (DMN). Network-based analysis revealed less extensive DMN, dorsal attention network, and limbic network activation in ADHD children compared to controls. The presence of significant ALE clusters may be due to reduced homogeneity in the selected sample demographic and experimental paradigm. Further investigations regarding hemispheric asymmetry in ADHD subjects would be beneficial.
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Affiliation(s)
- Sihyong J Kim
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
| | - Onur Tanglay
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
- Omniscient Neurotechnology, Sydney, Australia
| | - Elizabeth H N Chong
- National University of Singapore Yong Loo Lin School of Medicine, Singapore, Singapore
| | | | - Rannulu D Fonseka
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
| | - Hugh Taylor
- Omniscient Neurotechnology, Sydney, Australia
| | | | | | - Michael E Sughrue
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Sydney, Australia
- Omniscient Neurotechnology, Sydney, Australia
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Yueh-Hsin L, Dadario N, Crawford L, Tanglay O, Dow HK, Young I, Ahsan SA, Doyen S, Sughrue M. MODL-37. DISCERNIBLE INTERINDIVIDUAL PATTERNS OF GLOBAL EFFICIENCY DECLINE DURING THEORETICAL BRAIN SURGERY. Neuro Oncol 2022. [PMCID: PMC9661259 DOI: 10.1093/neuonc/noac209.1164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Abstract
Resection of infiltrating brain tumors, such as diffuse gliomas, generally involves resecting a portion of a lobe of the brain. While the concept of localized functions, and the risk to removing these areas, is well established in neurosurgical thinking, the potential that the overall global efficiency of the connectome could be disproportionately disturbed by an intervention in ways which are not immediately obvious have not been formally studied. The current article provides evidence that structural patterns exist in the impact resection of various lobes of the brain has, which also differs between subjects. We utilized diffusion tractography to create structural connectivity graphs from the brains of 80 healthy adults, and then performed every plausible brain surgery in every gross anatomic region of the cerebrum by deleting every possible combination of nodes in the graph which were adjacent to each other, and measured the drop in global efficiency (GE) at each nodal deletion. Not surprisingly, the deletion of some nodes was worse than others, such that in every lobe we studied in every subject, there were combinations of deletions which were worse for GE than removing a greater number of nodes in a different part of the brain. Interestingly, while the worst nodes differed between subjects, there were specific nodes which typically showed up as particularly detrimental regardless of which node was the worst in that person, but that there were patterns of so-called ―connectotype, which could determine which nodes were the worst. Progressive removal of a lobe of the brain leads to patterns of global efficiency decline which are reasonably predictable, but which are not the same between subjects. Given evidence that global efficiency relates to specific neuro-cognitive abilities, this provides a path towards reducing the cognitive footprint of brain surgery.
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Affiliation(s)
| | | | | | | | - Hsu-Kang Dow
- University of New South Wales , Sydney , Australia
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10
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Young IM, Osipowicz K, Mackenzie A, Clarke O, Taylor H, Nicholas P, Ryan M, Holle J, Tanglay O, Doyen S, Sughrue ME. Comparison of consistency between image guided and craniometric transcranial magnetic stimulation coil placement. Brain Stimul 2022; 15:1465-1466. [PMID: 36309343 DOI: 10.1016/j.brs.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/16/2022] [Accepted: 10/20/2022] [Indexed: 12/30/2022] Open
Affiliation(s)
| | | | | | | | - Hugh Taylor
- Omniscient Neurotechnology, Sydney, 2000, Australia
| | | | - Mark Ryan
- Cingulum Health, Rosebery, 2018, Australia
| | | | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, 2000, Australia
| | | | - Michael E Sughrue
- Omniscient Neurotechnology, Sydney, 2000, Australia; Cingulum Health, Rosebery, 2018, Australia.
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11
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Shen Y, Lu Q, Zhang T, Yan H, Mansouri N, Osipowicz K, Tanglay O, Young I, Doyen S, Lu X, Zhang X, Sughrue ME, Wang T. Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia. Front Aging Neurosci 2022; 14:962319. [PMID: 36118683 PMCID: PMC9475065 DOI: 10.3389/fnagi.2022.962319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveProgressive conditions characterized by cognitive decline, including mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are clinical conditions representing a major risk factor to develop dementia, however, the diagnosis of these pre-dementia conditions remains a challenge given the heterogeneity in clinical trajectories. Earlier diagnosis requires data-driven approaches for improved and targeted treatment modalities.MethodsNeuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI (rsfMRI), and diffusion weighted scans were obtained from 35 patients with SCD, 19 with MCI, and 36 age-matched healthy controls (HC). A recently developed machine learning technique, Hollow Tree Super (HoTS) was utilized to classify subjects into diagnostic categories based on their FC, and derive network and parcel-based FC features contributing to each model. The same approach was used to identify features associated with performance in a range of neuropsychological tests. We concluded our analysis by looking at changes in PageRank centrality (a measure of node hubness) between the diagnostic groups.ResultsSubjects were classified into diagnostic categories with a high area under the receiver operating characteristic curve (AUC-ROC), ranging from 0.73 to 0.84. The language networks were most notably associated with classification. Several central networks and sensory brain regions were predictors of poor performance in neuropsychological tests, suggesting maladaptive compensation. PageRank analysis highlighted that basal and limbic deep brain region, along with the frontal operculum demonstrated a reduction in centrality in both SCD and MCI patients compared to controls.ConclusionOur methods highlight the potential to explore the underlying neural networks contributing to the cognitive changes and neuroplastic responses in prodromal dementia.
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Affiliation(s)
- Ying Shen
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | - Qian Lu
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | - Tianjiao Zhang
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hailang Yan
- Department of Radiology, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | | | | | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, NSW, Australia
| | | | | | - Xi Lu
- Department of Rehabilitation Medicine, China-Japan Friendship Hospital, Beijing, China
| | - Xia Zhang
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
- Shenzhen Xijia Medical Technology Company, Shenzhen, China
| | - Michael E. Sughrue
- Omniscient Neurotechnology, Sydney, NSW, Australia
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
- Michael E. Sughrue,
| | - Tong Wang
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Tong Wang,
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Lu Q, Zhang W, Yan H, Mansouri N, Tanglay O, Osipowicz K, Joyce AW, Young IM, Zhang X, Doyen S, Sughrue ME, He C. Connectomic disturbances underlying insomnia disorder and predictors of treatment response. Front Hum Neurosci 2022; 16:960350. [PMID: 36034119 PMCID: PMC9399490 DOI: 10.3389/fnhum.2022.960350] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 07/19/2022] [Indexed: 01/23/2023] Open
Abstract
ObjectiveDespite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation (rTMS) and pharmacotherapy.Materials and methods51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging (MRI), resting-stage functional MRI (rsfMRI), and diffusion weighted imaging (DWI). Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with or without rTMS. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up.ResultsSubjects were classified into ID and control with an area under the receiver operating characteristic curve (AUC-ROC) of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index (ISI) was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change.ConclusionMachine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets.
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Affiliation(s)
- Qian Lu
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | - Wentong Zhang
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | - Hailang Yan
- Department of Radiology, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | | | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, NSW, Australia
| | | | | | | | - Xia Zhang
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
- Shenzhen Xijia Medical Technology Company, Shenzhen, China
| | | | - Michael E. Sughrue
- Omniscient Neurotechnology, Sydney, NSW, Australia
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
- Michael E. Sughrue,
| | - Chuan He
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
- *Correspondence: Chuan He,
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13
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Shahab QS, Young IM, Dadario NB, Tanglay O, Nicholas PJ, Lin YH, Fonseka RD, Yeung JT, Bai MY, Teo C, Doyen S, Sughrue ME. A connectivity model of the anatomic substrates underlying Gerstmann syndrome. Brain Commun 2022; 4:fcac140. [PMID: 35706977 PMCID: PMC9189613 DOI: 10.1093/braincomms/fcac140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 04/05/2022] [Accepted: 05/26/2022] [Indexed: 11/29/2022] Open
Abstract
The Gerstmann syndrome is a constellation of neurological deficits that include agraphia, acalculia, left–right discrimination and finger agnosia. Despite a growing interest in this clinical phenomenon, there remains controversy regarding the specific neuroanatomic substrates involved. Advancements in data-driven, computational modelling provides an opportunity to create a unified cortical model with greater anatomic precision based on underlying structural and functional connectivity across complex cognitive domains. A literature search was conducted for healthy task-based functional MRI and PET studies for the four cognitive domains underlying Gerstmann’s tetrad using the electronic databases PubMed, Medline, and BrainMap Sleuth (2.4). Coordinate-based, meta-analytic software was utilized to gather relevant regions of interest from included studies to create an activation likelihood estimation (ALE) map for each cognitive domain. Machine-learning was used to match activated regions of the ALE to the corresponding parcel from the cortical parcellation scheme previously published under the Human Connectome Project (HCP). Diffusion spectrum imaging-based tractography was performed to determine the structural connectivity between relevant parcels in each domain on 51 healthy subjects from the HCP database. Ultimately 102 functional MRI studies met our inclusion criteria. A frontoparietal network was found to be involved in the four cognitive domains: calculation, writing, finger gnosis, and left–right orientation. There were three parcels in the left hemisphere, where the ALE of at least three cognitive domains were found to be overlapping, specifically the anterior intraparietal area, area 7 postcentral (7PC) and the medial intraparietal sulcus. These parcels surround the anteromedial portion of the intraparietal sulcus. Area 7PC was found to be involved in all four domains. These regions were extensively connected in the intraparietal sulcus, as well as with a number of surrounding large-scale brain networks involved in higher-order functions. We present a tractographic model of the four neural networks involved in the functions which are impaired in Gerstmann syndrome. We identified a ‘Gerstmann Core’ of extensively connected functional regions where at least three of the four networks overlap. These results provide clinically actionable and precise anatomic information which may help guide clinical translation in this region, such as during resective brain surgery in or near the intraparietal sulcus, and provides an empiric basis for future study.
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Affiliation(s)
- Qazi S. Shahab
- University of New South Wales School of Medicine, , 2052, Sydney, Australia
| | | | - Nicholas B. Dadario
- Rutgers Robert Wood Johnson Medical School , New Brunswick, New Jersey 08901, United States of America
| | - Onur Tanglay
- Omniscient Neurotechnology , Sydney, 2000, Australia
| | | | - Yueh-Hsin Lin
- Prince of Wales Private Hospital Centre for Minimally Invasive Neurosurgery, , Randwick, 2031, Australia
| | - R. Dineth Fonseka
- Prince of Wales Private Hospital Centre for Minimally Invasive Neurosurgery, , Randwick, 2031, Australia
| | - Jacky T. Yeung
- Prince of Wales Private Hospital Centre for Minimally Invasive Neurosurgery, , Randwick, 2031, Australia
| | - Michael Y. Bai
- Prince of Wales Private Hospital Centre for Minimally Invasive Neurosurgery, , Randwick, 2031, Australia
| | - Charles Teo
- Prince of Wales Private Hospital Centre for Minimally Invasive Neurosurgery, , Randwick, 2031, Australia
| | | | - Michael E. Sughrue
- Omniscient Neurotechnology , Sydney, 2000, Australia
- Prince of Wales Private Hospital Centre for Minimally Invasive Neurosurgery, , Randwick, 2031, Australia
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14
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Doyen S, Dadario NB. 12 Plagues of AI in Healthcare: A Practical Guide to Current Issues With Using Machine Learning in a Medical Context. Front Digit Health 2022; 4:765406. [PMID: 35592460 PMCID: PMC9110785 DOI: 10.3389/fdgth.2022.765406] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 04/11/2022] [Indexed: 12/23/2022] Open
Abstract
The healthcare field has long been promised a number of exciting and powerful applications of Artificial Intelligence (AI) to improve the quality and delivery of health care services. AI techniques, such as machine learning (ML), have proven the ability to model enormous amounts of complex data and biological phenomena in ways only imaginable with human abilities alone. As such, medical professionals, data scientists, and Big Tech companies alike have all invested substantial time, effort, and funding into these technologies with hopes that AI systems will provide rigorous and systematic interpretations of large amounts of data that can be leveraged to augment clinical judgments in real time. However, despite not being newly introduced, AI-based medical devices have more than often been limited in their true clinical impact that was originally promised or that which is likely capable, such as during the current COVID-19 pandemic. There are several common pitfalls for these technologies that if not prospectively managed or adjusted in real-time, will continue to hinder their performance in high stakes environments outside of the lab in which they were created. To address these concerns, we outline and discuss many of the problems that future developers will likely face that contribute to these failures. Specifically, we examine the field under four lenses: approach, data, method and operation. If we continue to prospectively address and manage these concerns with reliable solutions and appropriate system processes in place, then we as a field may further optimize the clinical applicability and adoption of medical based AI technology moving forward.
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Affiliation(s)
- Stephane Doyen
- Omniscient Neurotechnology, Sydney, NSW, Australia
- *Correspondence: Stephane Doyen
| | - Nicholas B. Dadario
- Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, NJ, United States
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15
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Dadario NB, Nicholas P, Henkin A, Sin B, Dyer K, Sughrue ME, Doyen S. The Z-Shift: A Need for Quality Management System Level Testing and Standardization in Neuroimaging Pipelines. AJNR Am J Neuroradiol 2022; 43:320-323. [PMID: 35241419 PMCID: PMC8910812 DOI: 10.3174/ajnr.a7435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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16
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Doyen S, Nicholas P, Poologaindran A, Crawford L, Young IM, Romero‐Garcia R, Sughrue ME. Cover Image. Hum Brain Mapp 2022. [DOI: 10.1002/hbm.25485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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17
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Yeung JT, Young IM, Doyen S, Teo C, Sughrue ME. Changes in the Brain Connectome Following Repetitive Transcranial Magnetic Stimulation for Stroke Rehabilitation. Cureus 2021; 13:e19105. [PMID: 34858752 PMCID: PMC8614179 DOI: 10.7759/cureus.19105] [Citation(s) in RCA: 1] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2021] [Indexed: 11/09/2022] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is a promising approach for post-stroke rehabilitation but there lacks a rationale strategy to plan, execute, and monitor treatment. We present a case of targeted rTMS using the Omniscient Infinitome software to devise targets for treatment in a post-stroke patient and describe the functional connectomic changes after treatment. A 19-year-old female with no medical history presented 19 months after suffering a left middle cerebral artery (MCA) superior division ischemic stroke, resulting in language impairment and diminished right upper extremity motor function. She underwent a resting-state MRI (rsMRI) with tractography and images were processed using the Omniscient Infinitome software. Analysis using the anomaly detection within the software enabled us to identify three targets for rTMS (left area 1, left area 45, and right area SFL). These areas were treated with 25 sessions of intermittent Theta Burst Stimulation (iTBS) over five days at 80% of motor threshold concomitantly with targeted physical therapy and speech therapy. At five months follow-up, her language and right upper extremity functions significantly improved. Her connectomic analysis revealed substantial neural changes, including normalization of the sensorimotor network, substantially thicker callosal fiber bundle connecting the two hemispheres, and increased cortical recruitment in her language network. We present the first description of robust connectomic alterations in a post-stroke patient following targeted rTMS treatment. Further studies on the use of rTMS with an emphasis on functional connectomics are warranted.
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Affiliation(s)
- Jacky T Yeung
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Sydney, AUS
| | | | | | - Charles Teo
- Neurological Surgery, Prince of Wales Private Hospital, University of New South Wales, Sydney, AUS.,Research, Omniscient Neurotechnology, Sydney, AUS
| | - Michael E Sughrue
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Sydney, AUS.,Research, Omniscient Neurotechnology, Sydney, AUS
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18
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Doyen S, Nicholas P, Poologaindran A, Crawford L, Young IM, Romero-Garcia R, Sughrue ME. Connectivity-based parcellation of normal and anatomically distorted human cerebral cortex. Hum Brain Mapp 2021; 43:1358-1369. [PMID: 34826179 PMCID: PMC8837585 DOI: 10.1002/hbm.25728] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [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: 07/18/2021] [Revised: 11/03/2021] [Accepted: 11/13/2021] [Indexed: 12/29/2022] Open
Abstract
For over a century, neuroscientists have been working toward parcellating the human cortex into distinct neurobiological regions. Modern technologies offer many parcellation methods for healthy cortices acquired through magnetic resonance imaging. However, these methods are suboptimal for personalized neurosurgical application given that pathology and resection distort the cerebrum. We sought to overcome this problem by developing a novel connectivity‐based parcellation approach that can be applied at the single‐subject level. Utilizing normative diffusion data, we first developed a machine‐learning (ML) classifier to learn the typical structural connectivity patterns of healthy subjects. Specifically, the Glasser HCP atlas was utilized as a prior to calculate the streamline connectivity between each voxel and each parcel of the atlas. Using the resultant feature vector, we determined the parcel identity of each voxel in neurosurgical patients (n = 40) and thereby iteratively adjusted the prior. This approach enabled us to create patient‐specific maps independent of brain shape and pathological distortion. The supervised ML classifier re‐parcellated an average of 2.65% of cortical voxels across a healthy dataset (n = 178) and an average of 5.5% in neurosurgical patients. Our patient dataset consisted of subjects with supratentorial infiltrating gliomas operated on by the senior author who then assessed the validity and practical utility of the re‐parcellated diffusion data. We demonstrate a rapid and effective ML parcellation approach to parcellation of the human cortex during anatomical distortion. Our approach overcomes limitations of indiscriminately applying atlas‐based registration from healthy subjects by employing a voxel‐wise connectivity approach based on individual data.
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Affiliation(s)
- Stephane Doyen
- Omniscient Neurotechnology, Sydney, New South Wales, Australia
| | - Peter Nicholas
- Omniscient Neurotechnology, Sydney, New South Wales, Australia
| | - Anujan Poologaindran
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.,The Alan Turing Institute, British Library, London, UK
| | - Lewis Crawford
- Omniscient Neurotechnology, Sydney, New South Wales, Australia
| | | | - Rafeael Romero-Garcia
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
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Doyen S, Taylor H, Nicholas P, Crawford L, Young I, Sughrue ME. Hollow-tree super: A directional and scalable approach for feature importance in boosted tree models. PLoS One 2021; 16:e0258658. [PMID: 34695143 PMCID: PMC8544862 DOI: 10.1371/journal.pone.0258658] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 10/03/2021] [Indexed: 12/02/2022] Open
Abstract
Purpose Current limitations in methodologies used throughout machine-learning to investigate feature importance in boosted tree modelling prevent the effective scaling to datasets with a large number of features, particularly when one is investigating both the magnitude and directionality of various features on the classification into a positive or negative class. This manuscript presents a novel methodology, “Hollow-tree Super” (HOTS), designed to resolve and visualize feature importance in boosted tree models involving a large number of features. Further, this methodology allows for accurate investigation of the directionality and magnitude various features have on classification and incorporates cross-validation to improve the accuracy and validity of the determined features of importance. Methods Using the Iris dataset, we first highlight the characteristics of HOTS by comparing it to other commonly used techniques for feature importance, including Gini Importance, Partial Dependence Plots, and Permutation Importance, and explain how HOTS resolves the weaknesses present in these three strategies for investigating feature importance. We then demonstrate how HOTS can be utilized in high dimensional spaces such as neuroscientific setting, by taking 60 Schizophrenic subjects from the publicly available SchizConnect database and applying the method to determine which regions of the brain were most important for the positive and negative classification of schizophrenia as determined by the positive and negative syndrome scale (PANSS). Results HOTS effectively replicated and supported the findings of feature importance for classification of the Iris dataset when compared to Gini importance, Partial Dependence Plots and Permutation importance, determining ‘petal length’ as the most important feature for positive and negative classification. When applied to the Schizconnect dataset, HOTS was able to resolve from 379 independent features, the top 10 most important features for classification, as well as their directionality for classification and magnitude compared to other features. Cross-validation supported that these same 10 features were consistently used in the decision-making process across multiple trees, and these features were localised primarily to the occipital and parietal cortices, commonly disturbed brain regions in those afflicted with Schizophrenia. Conclusion HOTS effectively overcomes previous challenges of identifying feature importance at scale, and can be utilized across a swathe of disciplines. As computational power and data quantity continues to expand, it is imperative that a methodology is developed that is able to handle the demands of working with large datasets that contain a large number of features. This approach represents a unique way to investigate both the directionality and magnitude of feature importance when working at scale within a boosted tree model that can be easily visualized within commonly used software.
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Affiliation(s)
| | - Hugh Taylor
- Omniscient Neurotechnology, Sydney, Australia
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Yeung JT, Taylor HM, Nicholas PJ, Young IM, Jiang I, Doyen S, Sughrue ME, Teo C. Using Quicktome for Intracerebral Surgery: Early Retrospective Study and Proof of Concept. World Neurosurg 2021; 154:e734-e742. [PMID: 34358688 DOI: 10.1016/j.wneu.2021.07.127] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [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: 06/17/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Neurosurgeons have limited tools in their armamentarium to visualize critical brain networks during surgical planning. Quicktome was designed using machine-learning to generate robust visualization of important brain networks that can be used with standard neuronavigation to minimize those deficits. We sought to see whether Quicktome could help localize important cerebral networks and tracts during intracerebral surgery. METHODS We report on all patients who underwent keyhole intracranial surgery with available Quicktome-enabled neuronavigation. We retrospectively analyzed the locations of the lesions and determined functional networks at risks, including chief executive network, default mode network, salience, corticospinal/sensorimotor, language, neglect, and visual networks. We report on the postoperative neurologic outcomes of the patients and retrospectively determined whether the outcomes could be explained by Quicktome's functional localizations. RESULTS Fifteen high-risk patients underwent craniotomies for intra-axial tumors, with the exception of one meningioma and one case of leukoencephalopathy. Eight patients were male. The median age was 49.6 years. Quicktome was readily integrated in our existing navigation system in every case. New postoperative neurologic deficits occurred in 8 patients. All new deficits, except for one resulting from a postoperative stroke, were expected and could be explained by preoperative findings by Quicktome. In addition, in those who did not have new neurologic deficits, Quicktome offered explanations for their outcomes. CONCLUSIONS Quicktome helps to visualize complex functional connectomic networks and tracts by seamlessly integrating into existing neuronavigation platforms. The added information may assist in reducing neurological deficits and offer explanations for postsurgical outcomes.
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Affiliation(s)
- Jacky T Yeung
- Centre for Minimally Invasive Neurosurgery, Randwick, NSW, Australia
| | | | | | | | - Ivy Jiang
- Omniscient Neurotechnology, Sydney, Australia
| | | | | | - Charles Teo
- Centre for Minimally Invasive Neurosurgery, Randwick, NSW, Australia; Omniscient Neurotechnology, Sydney, Australia
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