A5043743232- Functional Brain Connectivity Studies
- Epilepsy research and treatment
- EEG and Brain-Computer Interfaces
- Neural dynamics and brain function
- AI in cancer detection
- Mental Health Research Topics
- Radiomics and Machine Learning in Medical Imaging
- Advanced Neuroimaging Techniques and Applications
- Renal cell carcinoma treatment
- Renal and related cancers
- Artificial Intelligence in Healthcare and Education
- Bladder and Urothelial Cancer Treatments
- Fractal and DNA sequence analysis
- Pancreatic and Hepatic Oncology Research
- Advanced MRI Techniques and Applications
- Fetal and Pediatric Neurological Disorders
- Advanced X-ray and CT Imaging
- Brain Tumor Detection and Classification
- Neuroscience and Neuropharmacology Research
- Genetic and Kidney Cyst Diseases
- Neural and Behavioral Psychology Studies
- Advanced Neural Network Applications
- COVID-19 diagnosis using AI
- Pediatric Urology and Nephrology Studies
- Renal and Vascular Pathologies
Mayo Clinic in Arizona
2023-2024
University of Wisconsin–Madison
2018-2023
This study explored the taxonomy of cognitive impairment within temporal lobe epilepsy and characterized sociodemographic, clinical neurobiological correlates identified phenotypes. 111 patients 83 controls (mean ages 33 39, 57% 61% female, respectively) from Epilepsy Connectome Project underwent neuropsychological assessment, interview, high resolution 3T structural resting-state functional MRI. A comprehensive test battery was reduced to core domains (language, memory, executive,...
To evaluate the performance of an internally developed and previously validated artificial intelligence (AI) algorithm for magnetic resonance (MR)-derived total kidney volume (TKV) in autosomal dominant polycystic disease (ADPKD) when implemented clinical practice.
The association of epilepsy with structural brain changes and cognitive abnormalities in midlife has raised concern regarding the possibility future accelerated aging increased risk later life neurocognitive disorders. To address this issue we examined age-related processes both functional neuroimaging among individuals temporal lobe (TLE, N = 104) who were participants Epilepsy Connectome Project (ECP). Support vector regression (SVR) models trained from 151 healthy controls used to predict...
The Epilepsy Connectome Project examines the differences in connectomes between temporal lobe epilepsy (TLE) patients and healthy controls. Using these data, effective connectivity of default mode network (DMN) with left TLE compared controls was investigated using spectral dynamic causal modeling (spDCM) resting-state functional magnetic resonance imaging data. Group comparisons were made two parametric empirical Bayes (PEB) models. first level each PEB model consisted participant's spDCM....
The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. magnetic resonance imaging protocol follows that used the Human Project, and includes 20 min resting-state functional acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with FreeSurfer subcortical regions generate (RSFC), amplitude low-frequency fluctuations (ALFFs), fractional ALFF measures. Seven...
Multiple magnetic resonance imaging (MRI) modalities are currently used for the diagnosis and characterization of temporal lobe epilepsy (TLE). The objective this study is to assess performance individual combination multimodal MRI datasets provide an accurate classification TLE by employing a multi-channel deep neural network. Several network models were trained, validated, tested using brain structure metrics from structural MRI, MRI-based region interest correlation features, personal...
Automated segmentation tools often encounter accuracy and adaptability issues when applied to images of different pathology. The purpose this study is explore the feasibility building a workflow efficiently route specifically trained models. By implementing deep learning classifier automatically classify them appropriate models, we hope that our can segment with pathology accurately. data used in are 350 CT from patients affected by polycystic liver disease presenting metastases colorectal...
You have accessJournal of UrologyImaging/Uroradiology II (MP30)1 May 2024MP30-07 AUTOMATED RENAL VOLUME MEASUREMENT USING ARTIFICIAL INTELLIGENCE: CORRELATION TO POST-OPERATIVE FUNCTION AFTER RADICAL AND PARTIAL NEPHRECTOMY Abhinav Khanna, Vidit Sharma, Ekamjit S. Deol, Adriana Gregory, Harrison C. Gottlich, Cole Cook, Jason Klug, Christine Lohse, Theodora Potretzke, Aaron Stephen A. Boorjian, R. Houston Thompson, Andrew Rule, Naoki Takahashi, Alexander Denic, Bradley Erickson, Timothy...
There is large interest in the early diagnosis of Alzheimer's disease (AD) using machine learning. The NIH-sponsored Disease Connectome Project (ADCP), a multi-center MRI, PET, and behavioral study brain connectivity AD, has specific aim accurately staging AD throughout its progression on an individual basis. It uses state-of-the-art MRI imaging techniques which allow for building reliable learning models. In this ongoing project, we are training models with structural features to separate...
The default mode network, implicated in various types of memory, is often investigated Alzheimer's disease (AD). AD patients exhibit both memory impairments and executive dysfunction. control network (ECN), thought to be linked with the function, differs functional connectivity, measured using Pearson correlation, between healthy controls individuals (Agosta et al., 2012). Differences effective a measure causal neuronal interaction, as Granger causality were noted controls, mild cognitive...
Brain gender differences have been known for a long time and are the possible reason many psychological, psychiatric behavioral between males females. Predicting genders from brain functional connectivity (FC) can build relationship activities gender, extracting important related FC features prediction model offers way to investigate difference. Current predictive models applied demonstrate good accuracies, but usually extract individual connections instead of patterns in whole matrix as...
Abstract We propose a unique, minimal assumption, approach based on variance analyses (compared with standard approaches) to investigate genetic influence individual differences the functional connectivity of brain using 65 monozygotic and dizygotic healthy young adult twin pairs' low‐frequency oscillation resting state Magnetic Resonance Imaging (fMRI) data from Human Connectome Project. Overall, we found high number genetically‐influenced (GIF) connections involving posterior regions...
Abstract Determining genetic versus environmental influences on the human brain is of crucial importance to understand healthy as well in a variety disease and disorder states. Here we propose unique, minimal assumption, approach investigate influence functional connectivity using 260 subjects” (65 monozygotic (MZ) 65 dizygotic (DZ) young adult twin pairs) resting state fMRI (rsfMRI) data from Human Connectome Project (HCP). For any given connection between pairs, strengths across pairs were...
You have accessJournal of UrologyCME1 Apr 2023PD08-04 VIRTUAL RENAL MASS BIOPSY: PREDICTING TUMOR HISTOLOGY ON ABDOMINAL CT IMAGES USING MACHINE LEARNING Abhinav Khanna, Vidit Sharma, Adriana Gregory, H. Chase Gottlich, Cole J. Cook, Jason Klug, Christine Lohse, Theodora Potretzke, Aaron Stephen A. Boorjian, R. Houston Thompson, Naoki Takahashi, Bradley Erickson, John Cheville, Timothy Kline, and Leibovich KhannaAbhinav Khanna More articles by this author , SharmaVidit Sharma GregoryAdriana...
Kline, Timothy L.; Cook, Cole J.; Gregory, Adriana; Klug, Jason R.; Potretzke, Theodora A.; Ron, Eyal; Takahashi, Naoki; Erickson, Bradley Khanna, Abhinav; Sharma, Vidit; Leibovich, Author Information
The default mode network (DMN) is well-recognized as being involved in memory and affected by dementia due to Alzheimer's disease (AD), with differences this previously noted between healthy controls AD patients. Previous studies of DMN effective connectivity used models hemodynamic along t-tests compare connections groups. Dynamic causal modeling (DCM) was explore neuronal a group mild cognitive impairment (MCI) Resting state fMRI data were acquired on 3T GE 750 scanners at the University...
The default mode network (DMN) is well-recognized as being involved in memory and affected by dementia due to Alzheimer's disease (AD), with differences this previously noted between healthy controls AD patients. Previous studies of DMN effective connectivity AD, such used models hemodynamic along t-tests compare connections groups. Dynamic causal modeling (DCM) was explore neuronal a group mild cognitive impairment (MCI) Resting state fMRI data were acquired on 3T GE 750 scanners at the...
April 23, 2018April 10, 2018Free AccessImpact of Seizures in Temporal Lobe Epilepsy on Cognitive Functions and Imaging Features (P2.265)Gyujoon Hwang, Jedidiah Mathis, Veena Nair, Megan Rozman, Taylor McMillan, Courtney Forseth, Dace Almane, … Show All , Bruce Hermann, Neelima Tellapragada, Onyekachi Nwoke, Cole Cook, Andrew Nencka, Rasmus Birn, Elizabeth Felton, Aaron Struck, Rama Maganti, Lisa Conant, Colin Humphries, Edgar DeYoe, Manoj Raghavan, Vivek Prabhakaran, Jeffrey Binder, Mary...
Resting state networks (RSNs) have been associated with mental illness and disease may serve as endophenotypes such in Alzheimer's Disease. Criteria to define an endophenotype necessitates heritability. While several studies investigated heritability at the macroscopic resting network level using functional Magnetic Resonance Imaging (rs-fMRI), few derived from connections network-network interaction whole brain level. The purpose of this study was: 1) determine most heritable individual...
The default mode network, implicated in various types of memory, is often investigated Alzheimer's disease (AD). AD patients exhibit both memory impairments and executive dysfunction. control network (ECN), thought to be linked with the function, differs functional connectivity, measured using Pearson correlation, between healthy controls individuals (Agosta et al., 2012). Differences effective a measure causal neuronal interaction, as Granger causality were noted controls, mild cognitive...