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38 results on '"Osipowicz, Karol"'

3. Symptom-circuit mappings of the schizophrenia connectome

Author

Wang, Yingchan, Wang, Jijun, Su, Wenjun, Hu, Hao, Xia, Mengqing, Zhang, Tianhong, Xu, Lihua, Zhang, Xia, Taylor, Hugh, Osipowicz, Karol, Young, Isabella M., Lin, Yueh-Hsin, Nicholas, Peter, Tanglay, Onur, Sughrue, Michael E., Tang, Yingying, and Doyen, Stephane

Published
2023
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4. Application of individual brain connectome in chronic ischemia: mapping symptoms before and after reperfusion.

Author

Lei, Yu, Zhang, Xin, Ni, Wei, Gao, Chao, Li, Yanjiang, Yang, Heng, Gao, Xinjie, Xia, Ding, Zhang, Xia, Osipowicz, Karol, Doyen, Stephane, Sughrue, Michael E., Gu, Yuxiang, and Mao, Ying

Subjects
ELECTROENCEPHALOGRAPHY, REPERFUSION, CEREBRAL ischemia, FINGERPRINT databases, ISCHEMIA, MESENTERIC ischemia, REPERFUSION injury
Abstract

How brain functions in the distorted ischemic state before and after reperfusion is unclear. It is also uncertain whether there are any indicators within ischemic brain that could predict surgical outcomes. To alleviate these issues, we applied individual brain connectome in chronic steno‐occlusive vasculopathy (CSOV) to map both ischemic symptoms and their postbypass changes. A total of 499 bypasses in 455 CSOV patients were collected and followed up for 47.8 ± 20.5 months. Using multimodal parcellation with connectivity‐based and pathological distortion‐independent approach, areal MR features of brain connectome were generated with three measurements of functional connectivity (FC), structural connectivity, and PageRank centrality at the single‐subject level. Thirty‐three machine‐learning models were then trained with clinical and areal MR features to obtain acceptable classifiers for both ischemic symptoms and their postbypass changes, among which, 11 were deemed acceptable (AUC > 0.7). Notably, the FC feature‐based model for long‐term neurological outcomes performed very well (AUC > 0.8). Finally, a Shapley additive explanations plot was adopted to extract important individual features in acceptable models to generate "fingerprints" of brain connectome. This study not only establishes brain connectomic fingerprint databases for brain ischemia with distortion, but also provides informative insights for how brain functions before and after reperfusion. [ABSTRACT FROM AUTHOR]

Published
2024
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6. A parcellation‐based connectomic model of hemispatial neglect

Author

Ahsan, Syed A., primary, Dadario, Nicholas B., additional, Dhaliwal, Jasneet, additional, Briggs, Robert G., additional, Osipowicz, Karol, additional, Ahsan, Syed M., additional, Chendeb, Kassem, additional, Conner, Andrew K., additional, O'Neal, Christen M., additional, Glenn, Chad A., additional, and Sughrue, Michael E., additional

Published
2023
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7. A parcellation‐based connectomic model of hemispatial neglect.

Author

Ahsan, Syed A., Dadario, Nicholas B., Dhaliwal, Jasneet, Briggs, Robert G., Osipowicz, Karol, Ahsan, Syed M., Chendeb, Kassem, Conner, Andrew K., O'Neal, Christen M., Glenn, Chad A., and Sughrue, Michael E.

Subjects
UNILATERAL neglect, PARIETAL lobe, VOXEL-based morphometry, MAGNETIC resonance imaging
Abstract

Background and Purpose: Hemispatial neglect is characterized by a reduced awareness to stimuli on the contralateral side. Current literature suggesting that damage to the right parietal lobe and attention networks may cause hemispatial neglect is conflicting and can be improved by investigating a connectomic model of the "neglect system" and the anatomical specificity of regions involved in it. Methods: A meta‐analysis of voxel‐based morphometry magnetic resonance imaging (MRI) studies of hemispatial neglect was used to identify regions associated with neglect. We applied parcellation schemes to these regions and performed diffusion spectrum imaging (DSI) tractography to determine their connectivity. By overlaying neglect areas and maps of the attention networks, we studied the relationship between them. Results: The meta‐analysis generated a list of 13 right hemisphere parcellations. These 13 neglect‐related parcellations were predominantly linked by the superior longitudinal fasciculus (SLF) throughout a fronto‐parietal‐temporal network. We found that the dorsal and ventral attention networks showed partial overlap with the neglect system and included various other higher‐order networks. Conclusions: We provide an anatomically specific connectomic model of the neurobehavioral substrates underlying hemispatial neglect. Our model suggests a fronto‐parietal‐temporal network linked via the SLF supports the functions impaired in neglect and implicates various higher‐order networks which are not limited to the attention networks. [ABSTRACT FROM AUTHOR]

Published
2024
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9. An agile, data‐driven approach for target selection in rTMS therapy for anxiety symptoms: Proof of concept and preliminary data for two novel targets

Author

Young, Isabella M., primary, Taylor, Hugh M., additional, Nicholas, Peter J., additional, Mackenzie, Alana, additional, Tanglay, Onur, additional, Dadario, Nicholas B., additional, Osipowicz, Karol, additional, Davis, Ethan, additional, Doyen, Stephane, additional, Teo, Charles, additional, and Sughrue, Michael E., additional

Published
2023
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12. Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia

Author

Shen, Ying, primary, Lu, Qian, additional, Zhang, Tianjiao, additional, Yan, Hailang, additional, Mansouri, Negar, additional, Osipowicz, Karol, additional, Tanglay, Onur, additional, Young, Isabella, additional, Doyen, Stephane, additional, Lu, Xi, additional, Zhang, Xia, additional, Sughrue, Michael E., additional, and Wang, Tong, additional

Published
2022
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15. Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer’s Disease and Mild Cognitive Impairment

Author

Dong, Ningxin, primary, Fu, Changyong, additional, Li, Renren, additional, Zhang, Wei, additional, Liu, Meng, additional, Xiao, Weixin, additional, Taylor, Hugh M., additional, Nicholas, Peter J., additional, Tanglay, Onur, additional, Young, Isabella M., additional, Osipowicz, Karol Z., additional, Sughrue, Michael E., additional, Doyen, Stephane P., additional, and Li, Yunxia, additional

Published
2022
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18. Brain Connectometry Changes in Space Travelers After Long-Duration Spaceflight

Author

Doroshin, Andrei, primary, Jillings, Steven, additional, Jeurissen, Ben, additional, Tomilovskaya, Elena, additional, Pechenkova, Ekaterina, additional, Nosikova, Inna, additional, Rumshiskaya, Alena, additional, Litvinova, Liudmila, additional, Rukavishnikov, Ilya, additional, De Laet, Chloë, additional, Schoenmaekers, Catho, additional, Sijbers, Jan, additional, Laureys, Steven, additional, Petrovichev, Victor, additional, Van Ombergen, Angelique, additional, Annen, Jitka, additional, Sunaert, Stefan, additional, Parizel, Paul M., additional, Sinitsyn, Valentin, additional, zu Eulenburg, Peter, additional, Osipowicz, Karol, additional, and Wuyts, Floris L., additional

Published
2022
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21. Connectomic disturbances underlying insomnia disorder and predictors of treatment response.

Author

Qian Lu, Wentong Zhang, Hailang Yan, Mansouri, Negar, Tanglay, Onur, Osipowicz, Karol, Joyce, Angus W., Young, Isabella M., Xia Zhang, Doyen, Stephane, Sughrue, Michael E., and Chuan He

Subjects
RECEIVER operating characteristic curves, TRANSCRANIAL magnetic stimulation, DEFAULT mode network, MAGNETIC resonance imaging, SLEEP quality
Abstract

Objective: Despite 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 methods: 51 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. Results: Subjects 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. Conclusion: Machine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets. [ABSTRACT FROM AUTHOR]

Published
2022
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25. Elevated reward response to receipt of palatable food predicts future weight variability in healthy-weight adolescents12

Author

Winter, Samantha R, Yokum, Sonja, Stice, Eric, Osipowicz, Karol, and Lowe, Michael R

Subjects
Male, Brain Mapping, Adolescent, Obesity and Eating Disorders, Emotions, Brain, Weight Gain, Magnetic Resonance Imaging, Body Mass Index, Diet, Young Adult, Reward, Food, Reference Values, Humans, Female, Obesity, Cues, Energy Intake
Abstract

Background: Both an elevated brain-reward–region response to palatable food and elevated weight variability have been shown to predict future weight gain.

Published
2017

31. Functional Connectivity Evidence of Cortico-Cortico Inhibition in Temporal Lobe Epilepsy

Author

Tracy, Joseph I., Osipowicz, Karol, Spechler, Philip, Sharan, Ashwini, Skidmore, Christopher, Doucet, Gaelle, and Sperling, Michael R.

Subjects
Adult, Male, Electroencephalography, Middle Aged, Magnetic Resonance Imaging, Article, Functional Laterality, Temporal Lobe, nervous system, Epilepsy, Temporal Lobe, Neural Pathways, Image Processing, Computer-Assisted, Humans, Female
Abstract

Epileptic seizures can initiate a neural circuit and lead to aberrant neural communication with brain areas outside the epileptogenic region. We focus on interictal activity in focal temporal lobe epilepsy and evaluate functional connectivity differences that emerge as function of bilateral versus strictly unilateral epileptiform activity. We assess the strength of functional connectivity at rest between the ictal and non-ictal temporal lobes, in addition to whole brain connectivity with the ictal temporal lobe. Results revealed strong connectivity between the temporal lobes for both patient groups, but this did not vary as a function of unilateral versus bilateral interictal status. Both the left and right unilateral temporal lobe groups showed significant anti-correlated activity in regions outside the epileptogenic temporal lobe, primarily involving the contralateral (non-ictal/non-pathologic) hemisphere, with precuneus involvement prominent. The bilateral groups did not show this contralateral anti-correlated activity. This anti-correlated connectivity may represent a form of protective and adaptive inhibition, helping to constrain epileptiform activity to the pathologic temporal lobe. The absence of this activity in the bilateral groups may be indicative of flawed inhibitory mechanisms, helping to explain their more widespread epileptiform activity. Our data suggest that the location and build up of epilepsy networks in the brain are not truly random, and are not limited to the formation of strictly epileptogenic networks. Functional networks may develop to take advantage of the regulatory function of structures such as the precuneus to instantiate an anti-correlated network, generating protective cortico-cortico inhibition for the purpose of limiting seizure spread or epileptogenesis.

Published
2012

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