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Ariaei A, Ghorbani A, Habibzadeh E, Moghaddam N, Chegeni Nezhad N, Abdoli A, Mazinanian S, Sadeghi M, Mayeli M. Investigating the association between the GAP-43 concentration with diffusion tensor imaging indices in Alzheimer's dementia continuum. BMC Neurol 2024; 24:397. [PMID: 39420261 PMCID: PMC11484424 DOI: 10.1186/s12883-024-03904-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2024] [Accepted: 10/04/2024] [Indexed: 10/19/2024] Open
Abstract
BACKGROUND Synaptic degeneration, axonal injury, and white matter disintegration are among the pathological events in Alzheimer's disease (AD), for which growth-associated protein 43 (GAP-43) and diffusion tensor imaging (DTI) could be an indicator. In this study, the cerebrospinal fluid (CSF) GAP-43 clinical trajectories and their association with progression and AD hallmarks with white matter microstructural changes were evaluated. METHODS A total number of 133 participants were enrolled in GAP-43 and DTI values were compared between groups, both cross-sectionally and longitudinally with two and four-year follow-ups. Subsequently, the correlation between GAP-43 levels in the CSF and DTI values was investigated using Spearman's correlation. RESULTS The CSF level of GAP-43 is negatively correlated with the mean diffusivity measures in Fornix (Cres)/Stria terminals in early and late MCI (rs=-0.478 p = 0.021 and rs=-0.425 p = 0.038). Additionally, the CSF level of GAP-43 is negatively correlated with fractional anisotropy in the cingulum in late MCI (rs=-0.437 p = 0.033). Moreover, the axial diffusivity in superior corona radiate (rs=-0.562 p = 0.005 and rs=-0.484 p = 0.036) and radial diffusivity in superior fronto-occipital fasciculus was negatively correlated with GAP-43 level in the early and mid-MCI participants (rs=-0.520 p = 0.011 and rs=-0.498 p = 0.030). CONCLUSIONS Presynaptic marker GAP-43 in combination with DTI can be used as a novel biomarker to identify microstructural synaptic degeneration in the early MCI. In addition, it can be used as a biomarker for tracking the progression of AD and monitoring treatment efficacy.
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Affiliation(s)
- Armin Ariaei
- School of Medicine, Iran University of Medical Science, Hemmat Highway, Next to Milad Tower, Tehran, 1449614535, Iran.
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Sciences, Tehran, Iran.
| | - Atousa Ghorbani
- Department of Biology, Islamic Azad University East Tehran Branch, Tehran, Iran
| | - Elham Habibzadeh
- School of Medicine, Tabriz University of Medical Science, Tabriz, Iran
| | - Nazanin Moghaddam
- Department of Clinical Biochemistry, Islamic Azad University Shahrood Branch, Shahrood, Iran
| | - Negar Chegeni Nezhad
- Department of Advanced Sciences and Technology, Islamic Azad University Tehran Medical Sciences, Tehran, Iran
| | - Amirabbas Abdoli
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Sciences, Centre Hospitalier Universitaire de Sherbrooke (CHUS), Université de Sherbrooke, Sherbrooke, Canada
| | - Samira Mazinanian
- Department of Psychology, Islamic Azad University Semnan Branch, Semnan, Iran
| | - Mohammad Sadeghi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahsa Mayeli
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Fama R, Sassoon SA, Müller-Oehring EM, Saranathan M, Pohl KM, Zahr NM, Pfefferbaum A, Sullivan EV. Anterior and posterior thalamic volumes differentially correlate with memory, attention, and motor processes in HIV infection and alcohol use disorder comorbidity. Brain Res Bull 2024; 217:111085. [PMID: 39343322 DOI: 10.1016/j.brainresbull.2024.111085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 09/16/2024] [Accepted: 09/19/2024] [Indexed: 10/01/2024]
Abstract
The thalamus, with its reciprocal connections to and from cortical, subcortical, and cerebellar regions, is a central active participant in multiple functional brain networks. Structural MRI studies measuring the entire thalamus without respect to its regional or nuclear divisions report volume shrinkage in diseases including HIV infection, alcohol use disorder (AUD), and their comorbidity (HIV+AUD). Here, we examined relations between thalamic subregions (anterior, ventral, medial, and posterior) and neuropsychological functions (attention/working memory, executive functioning, episodic memory, and motor skills). Volumes of thalamic subregions were derived from automatic segmentations of standard T1 weighted MRIs of 65 individuals with HIV, 189 with AUD, 80 with HIV+AUD comorbidity, and 141 healthy controls (CTRL). Total thalamic volume was smaller and cognitive and motor composite scores were lower in the three diagnostic groups relative to the CTRL group. The AUD and HIV+AUD groups had significantly smaller thalamic subregional volumes than the CTRL group. The HIV+AUD group had smaller anterior thalamic volume than the HIV-only group and smaller ventral thalamic volume than the AUD-only group. In the HIV+AUD group, memory scores correlated with anterior thalamic volumes, attention/working memory scores correlated with posterior and medial thalamic volumes, and motor skill scores correlated with posterior thalamic volumes. Exploratory analyses focused on the HIV+AUD group indicated that within the posterior thalamic region, the pulvinar and medial geniculate nuclei were related to attention/working memory scores, and the pulvinar was related to motor skills scores. This study is novel in locating volume deficits in specific thalamic subregions, in addition to the thalamus as a whole, in HIV, AUD, and their comorbidity and in identifying functional ramifications of these deficits. Taken together, this study highlights the relevance of thalamic subregional volume deficits to dissociable cognitive and motor processes.
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Affiliation(s)
- Rosemary Fama
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA; Center for Health Sciences, SRI International, Menlo Park, CA, USA.
| | - Stephanie A Sassoon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA; Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | - Eva M Müller-Oehring
- Center for Health Sciences, SRI International, Menlo Park, CA, USA; Department of Neurology, Stanford University School of Medicine, Stanford, CA, USA
| | - Manojkumar Saranathan
- Department of Radiology, University of Massachusetts Chan School of Medicine, Worcester, MA, USA
| | - Kilian M Pohl
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Natalie M Zahr
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA; Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | - Adolf Pfefferbaum
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA; Center for Health Sciences, SRI International, Menlo Park, CA, USA
| | - Edith V Sullivan
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
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Pfefferbaum A, Zahr NM, Sassoon SA, Fama R, Saranathan M, Pohl KM, Sullivan EV. Aging, HIV infection, and alcohol exert synergist effects on regional thalamic volumes resulting in functional impairment. Neuroimage Clin 2024; 44:103684. [PMID: 39423567 DOI: 10.1016/j.nicl.2024.103684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2024] [Revised: 09/23/2024] [Accepted: 10/06/2024] [Indexed: 10/21/2024]
Abstract
OBJECTIVE Pharmacologically-treated people living with HIV infection have near-normal life spans with more than 50 % living into at-risk age for dementia and a disproportionate number relative to uninfected people engaging in unhealthy drinking. Accelerated aging in HIV occurs in some brain structures including the multinucleated thalamus. Unknown is whether aging with HIV affects thalamic nuclei and associated functions differentially and whether the common comorbidity of alcohol use disorder (AUD) + HIV accelerates aging. METHODS This mixed cross-sectional/longitudinal design examined 216 control, 69 HIV, and 74 HIV + AUD participants, age 25-75 years old at initial visit, examined 1-8 times. MRI thalamic volumetry, parcellated using THalamus Optimized Multi-Atlas Segmentation (THOMAS), identified 10 nuclei grouped into 4 functional regions for correlation with age and measures of neuropsychological, clinical, and hematological status. RESULTS Aging in the control group was best modeled with quadratic functions in the Anterior and Ventral regions and with linear functions in the Medial and Posterior regions. Relative to controls, age-related decline was even steeper in the Anterior and Ventral regions of the HIV group and in the Anterior region of the comorbid group. Anterior volumes of each HIV group declined significantly faster after age 50 (HIV = -2.4 %/year; HIV + AUD = -2.8 %/year) than that of controls (-1.8 %/year). Anterior and Ventral volumes were significantly smaller in the HIV + AUD than HIV-only group when controlling for infection factors. Although compared with controls HIV + AUD declined faster than HIV alone, the two HIV groups did not differ significantly from each other in aging rates. Declining Attention/Working Memory and Motor Skills performance correlated with Anterior and Posterior volume declines in the HIV + AUD group. CONCLUSIONS Regional thalamic volumetry detected normal aging declines, differential and accelerated volume losses in HIV, relations between age-related nuclear and performance declines, and exacerbation of volume declines in comorbid AUD contributing to functional deficits.
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Affiliation(s)
- Adolf Pfefferbaum
- Center for Health Sciences, SRI International, Menlo Park, CA, United States; Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Natalie M Zahr
- Center for Health Sciences, SRI International, Menlo Park, CA, United States; Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Stephanie A Sassoon
- Center for Health Sciences, SRI International, Menlo Park, CA, United States; Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Rosemary Fama
- Center for Health Sciences, SRI International, Menlo Park, CA, United States; Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Manojkumar Saranathan
- Department of Radiology, University of Massachusetts Chan School of Medicine, Worcester, MA, United States
| | - Kilian M Pohl
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Edith V Sullivan
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States.
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Agarwal N, Frigerio G, Rizzato G, Ciceri T, Mani E, Lanteri F, Molteni M, Carare RO, Losa L, Peruzzo D. Parasagittal dural volume correlates with cerebrospinal fluid volume and developmental delay in children with autism spectrum disorder. COMMUNICATIONS MEDICINE 2024; 4:191. [PMID: 39367270 PMCID: PMC11452566 DOI: 10.1038/s43856-024-00622-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 09/24/2024] [Indexed: 10/06/2024] Open
Abstract
BACKGROUND The parasagittal dura, a tissue that lines the walls of the superior sagittal sinus, acts as an active site for immune-surveillance, promotes the reabsorption of cerebrospinal fluid, and facilitates the removal of metabolic waste products from the brain. Cerebrospinal fluid is important for the distribution of growth factors that signal immature neurons to proliferate and migrate. Autism spectrum disorder is characterized by altered cerebrospinal fluid dynamics. METHODS In this retrospective study, we investigated potential correlations between parasagittal dura volume, brain structure volumes, and clinical severity scales in young children with autism spectrum disorder. We employed a semi-supervised two step pipeline to extract parasagittal dura volume from 3D-T2 Fluid Attenuated Inversion Recovery sequences, based on U-Net followed by manual refinement of the extracted parasagittal dura masks. RESULTS Here we show that the parasagittal dura volume does not change with age but is significantly correlated with cerebrospinal fluid (p-value = 0.002), extra-axial cerebrospinal fluid volume (p-value = 0.0003) and severity of developmental delay (p-value = 0.024). CONCLUSIONS These findings suggest that autism spectrum disorder children with severe developmental delay may have a maldeveloped parasagittal dura that potentially perturbs cerebrospinal fluid dynamics.
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Affiliation(s)
- Nivedita Agarwal
- Diagnostic Imaging and Neuroradiology Unit, IRCCS Scientific Institute E. Medea, Bosisio Parini, Lecco LC, Italy.
| | - Giulia Frigerio
- Diagnostic Imaging and Neuroradiology Unit, IRCCS Scientific Institute E. Medea, Bosisio Parini, Lecco LC, Italy
| | - Gloria Rizzato
- Diagnostic Imaging and Neuroradiology Unit, IRCCS Scientific Institute E. Medea, Bosisio Parini, Lecco LC, Italy
| | - Tommaso Ciceri
- Neuroimaging Unit, IRCCS Scientific Institute E. Medea, Bosisio Parini, Lecco LC, Italy
- Department of Information Engineering, University of Padua, Padua, Italy
| | - Elisa Mani
- Child Psychopathology Unit, IRCCS Scientific Institute E. Medea, Bosisio Parini, Lecco LC, Italy
| | - Fabiola Lanteri
- Child Psychopathology Unit, IRCCS Scientific Institute E. Medea, Bosisio Parini, Lecco LC, Italy
| | - Massimo Molteni
- Child Psychopathology Unit, IRCCS Scientific Institute E. Medea, Bosisio Parini, Lecco LC, Italy
| | - Roxana O Carare
- Faculty of Medicine, University of Southampton, Southampton, UK
- University of Medicine, Pharmacy, Science, and Technology, Targu-Mures, Romania
| | - Letizia Losa
- Diagnostic Imaging and Neuroradiology Unit, IRCCS Scientific Institute E. Medea, Bosisio Parini, Lecco LC, Italy
| | - Denis Peruzzo
- Neuroimaging Unit, IRCCS Scientific Institute E. Medea, Bosisio Parini, Lecco LC, Italy
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Sullivan EV, Zahr NM, Zhao Q, Pohl KM, Sassoon SA, Pfefferbaum A. Contributions of Cerebral White Matter Hyperintensities to Postural Instability in Aging With and Without Alcohol Use Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:998-1009. [PMID: 38569932 PMCID: PMC11442683 DOI: 10.1016/j.bpsc.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/29/2024] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
BACKGROUND Both postural instability and brain white matter hyperintensities (WMHs) are noted markers of normal aging and alcohol use disorder (AUD). Here, we questioned what variables contribute to the sway path-WMH relationship in individuals with AUD and healthy control participants. METHODS The data comprised 404 balance platform sessions, yielding sway path length and magnetic resonance imaging data acquired cross-sectionally or longitudinally in 102 control participants and 158 participants with AUD ages 25 to 80 years. Balance sessions were typically conducted on the same day as magnetic resonance imaging fluid-attenuated inversion recovery acquisitions, permitting WMH volume quantification. Factors considered in multiple regression analyses as potential contributors to the relationship between WMH volumes and postural instability were age, sex, socioeconomic status, education, pedal 2-point discrimination, systolic and diastolic blood pressure, body mass index, depressive symptoms, total alcohol consumed in the past year, and race. RESULTS Initial analysis identified diagnosis, age, sex, and race as significant contributors to observed sway path-WMH relationships. Inclusion of these factors as predictors in multiple regression analyses substantially attenuated the sway path-WMH relationships in both AUD and healthy control groups. Women, irrespective of diagnosis or race, had shorter sway paths than men. Black participants, irrespective of diagnosis or sex, had shorter sway paths than non-Black participants despite having modestly larger WMH volumes than non-Black participants, which is possibly a reflection of the younger age of the Black sample. CONCLUSIONS Longer sway paths were related to larger WMH volumes in healthy men and women with and without AUD. Critically, however, age almost fully accounted for these associations.
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Affiliation(s)
- Edith V Sullivan
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California.
| | - Natalie M Zahr
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Center for Health Sciences, SRI International, Menlo Park, California
| | - Qingyu Zhao
- Department of Radiology, Weill Cornell Medicine, New York, New York
| | - Kilian M Pohl
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Stephanie A Sassoon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Center for Health Sciences, SRI International, Menlo Park, California
| | - Adolf Pfefferbaum
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Center for Health Sciences, SRI International, Menlo Park, California
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6
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Bonetti L, Vænggård AK, Iorio C, Vuust P, Lumaca M. Decreased inter-hemispheric connectivity predicts a coherent retrieval of auditory symbolic material. Biol Psychol 2024; 193:108881. [PMID: 39332661 DOI: 10.1016/j.biopsycho.2024.108881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 09/19/2024] [Accepted: 09/24/2024] [Indexed: 09/29/2024]
Abstract
Investigating the transmission of information between individuals is essential to better understand how humans communicate. Coherent information transmission (i.e., transmission without significant modifications or loss of fidelity) helps preserving cultural traits and traditions over time, while innovation may lead to new cultural variants. Although much research has focused on the cognitive mechanisms underlying cultural transmission, little is known on the brain features which correlates with coherent transmission of information. To address this gap, we combined structural (from high-resolution diffusion imaging) and functional connectivity (from resting-state functional magnetic resonance imaging [fMRI]) with a laboratory model of cultural transmission, the signalling games, implemented outside the MRI scanner. We found that individuals who exhibited more coherence in the transmission of auditory symbolic information were characterized by lower levels of both structural and functional inter-hemispheric connectivity. Specifically, higher coherence negatively correlated with the strength of bilateral structural connections between frontal and subcortical, insular and temporal brain regions. Similarly, we observed increased inter-hemispheric functional connectivity between inferior frontal brain regions derived from structural connectivity analysis in individuals who exhibited lower transmission coherence. Our results suggest that lateralization of cognitive processes involved in semantic mappings in the brain may be related to the stability over time of auditory symbolic systems.
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Affiliation(s)
- Leonardo Bonetti
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Denmark; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom.
| | - Anna Kildall Vænggård
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Denmark
| | - Claudia Iorio
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Denmark; LEAD-CNRS UMR 5022, Université de Bourgogne, Dijon 21000, France
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Denmark
| | - Massimo Lumaca
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Denmark.
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Fisch L, Zumdick S, Barkhau C, Emden D, Ernsting J, Leenings R, Sarink K, Winter NR, Risse B, Dannlowski U, Hahn T. deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks. Comput Biol Med 2024; 179:108845. [PMID: 39002314 DOI: 10.1016/j.compbiomed.2024.108845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 07/01/2024] [Accepted: 07/02/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods. METHOD Here, we used a unique dataset compilation comprising 7837 T1-weighted (T1w) MR images from 191 different OpenNeuro datasets in combination with advanced deep learning methods to build a fast, high-precision brain extraction tool called deepbet. RESULTS deepbet sets a novel state-of-the-art performance during cross-dataset validation with a median Dice score (DSC) of 99.0 on unseen datasets, outperforming the current best performing deep learning (DSC=97.9) and classic (DSC=96.5) methods. While current methods are more sensitive to outliers, deepbet achieves a Dice score of >97.4 across all 7837 images from 191 different datasets. This robustness was additionally tested in 5 external datasets, which included challenging clinical MR images. During visual exploration of each method's output which resulted in the lowest Dice score, major errors could be found for all of the tested tools except deepbet. Finally, deepbet uses a compute efficient variant of the UNet architecture, which accelerates brain extraction by a factor of ≈10 compared to current methods, enabling the processing of one image in ≈2 s on low level hardware. CONCLUSIONS In conclusion, deepbet demonstrates superior performance and reliability in brain extraction across a wide range of T1w MR images of adults, outperforming existing top tools. Its high minimal Dice score and minimal objective errors, even in challenging conditions, validate deepbet as a highly dependable tool for accurate brain extraction. deepbet can be conveniently installed via "pip install deepbet" and is publicly accessible at https://github.com/wwu-mmll/deepbet.
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Affiliation(s)
- Lukas Fisch
- University of Münster, Institute for Translational Psychiatry, Münster, Germany.
| | - Stefan Zumdick
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Carlotta Barkhau
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Daniel Emden
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Jan Ernsting
- University of Münster, Institute for Translational Psychiatry, Münster, Germany; Department of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Ramona Leenings
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Kelvin Sarink
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Nils R Winter
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Benjamin Risse
- Department of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Udo Dannlowski
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Tim Hahn
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
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Keeling EG, Bergamino M, Ragunathan S, Quarles CC, Newton AT, Stokes AM. Optimization and validation of multi-echo, multi-contrast SAGE acquisition in fMRI. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:1-20. [PMID: 39449748 PMCID: PMC11497078 DOI: 10.1162/imag_a_00217] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 05/10/2024] [Accepted: 06/05/2024] [Indexed: 10/26/2024]
Abstract
The purpose of this study was to optimize and validate a multi-contrast, multi-echo fMRI method using a combined spin- and gradient-echo (SAGE) acquisition. It was hypothesized that SAGE-based blood oxygen level-dependent (BOLD) functional MRI (fMRI) will improve sensitivity and spatial specificity while reducing signal dropout. SAGE-fMRI data were acquired with five echoes (2 gradient-echoes, 2 asymmetric spin-echoes, and 1 spin-echo) across 12 protocols with varying acceleration factors, and temporal SNR (tSNR) was assessed. The optimized protocol was then implemented in working memory and vision tasks in 15 healthy subjects. Task-based analysis was performed using individual echoes, quantitative dynamic relaxation times T2 * and T2, and echo time-dependent weighted combinations of dynamic signals. These methods were compared to determine the optimal analysis method for SAGE-fMRI. Implementation of a multiband factor of 2 and sensitivity encoding (SENSE) factor of 2.5 yielded adequate spatiotemporal resolution while minimizing artifacts and loss in tSNR. Higher BOLD contrast-to-noise ratio (CNR) and tSNR were observed for SAGE-fMRI relative to single-echo fMRI, especially in regions with large susceptibility effects and for T2-dominant analyses. Using a working memory task, the extent of activation was highest with T2 *-weighting, while smaller clusters were observed with quantitative T2 * and T2. SAGE-fMRI couples the high BOLD sensitivity from multi-gradient-echo acquisitions with improved spatial localization from spin-echo acquisitions, providing two contrasts for analysis. SAGE-fMRI provides substantial advantages, including improving CNR and tSNR for more accurate analysis.
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Affiliation(s)
- Elizabeth G. Keeling
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States
- School of Life Sciences, Arizona State University, Tempe, AZ, United States
| | - Maurizio Bergamino
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Sudarshan Ragunathan
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States
- Hyperfine, Inc., Guilford, CT, United States
| | - C. Chad Quarles
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States
- The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Allen T. Newton
- Vanderbilt University Institute of Imaging Science, Nashville, TN, United States
| | - Ashley M. Stokes
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States
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Hoffmann M, Hoopes A, Greve DN, Fischl B, Dalca AV. Anatomy-aware and acquisition-agnostic joint registration with SynthMorph. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:1-33. [PMID: 39015335 PMCID: PMC11247402 DOI: 10.1162/imag_a_00197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 04/27/2024] [Accepted: 05/21/2024] [Indexed: 07/18/2024]
Abstract
Affine image registration is a cornerstone of medical-image analysis. While classical algorithms can achieve excellent accuracy, they solve a time-consuming optimization for every image pair. Deep-learning (DL) methods learn a function that maps an image pair to an output transform. Evaluating the function is fast, but capturing large transforms can be challenging, and networks tend to struggle if a test-image characteristic shifts from the training domain, such as the resolution. Most affine methods are agnostic to the anatomy the user wishes to align, meaning the registration will be inaccurate if algorithms consider all structures in the image. We address these shortcomings with SynthMorph, a fast, symmetric, diffeomorphic, and easy-to-use DL tool for joint affine-deformable registration of any brain image without preprocessing. First, we leverage a strategy that trains networks with widely varying images synthesized from label maps, yielding robust performance across acquisition specifics unseen at training. Second, we optimize the spatial overlap of select anatomical labels. This enables networks to distinguish anatomy of interest from irrelevant structures, removing the need for preprocessing that excludes content which would impinge on anatomy-specific registration. Third, we combine the affine model with a deformable hypernetwork that lets users choose the optimal deformation-field regularity for their specific data, at registration time, in a fraction of the time required by classical methods. This framework is applicable to learning anatomy-aware, acquisition-agnostic registration of any anatomy with any architecture, as long as label maps are available for training. We analyze how competing architectures learn affine transforms and compare state-of-the-art registration tools across an extremely diverse set of neuroimaging data, aiming to truly capture the behavior of methods in the real world. SynthMorph demonstrates high accuracy and is available at https://w3id.org/synthmorph, as a single complete end-to-end solution for registration of brain magnetic resonance imaging (MRI) data.
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Affiliation(s)
- Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Andrew Hoopes
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Douglas N. Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Adrian V. Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
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10
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Valverde S, Coll L, Valencia L, Clèrigues A, Oliver A, Vilanova JC, Ramió-Torrentà L, Rovira À, Lladó X. Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm. J Magn Reson Imaging 2024; 59:1991-2000. [PMID: 34137113 DOI: 10.1002/jmri.27776] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 05/31/2021] [Accepted: 06/01/2021] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Manual brain extraction from magnetic resonance (MR) images is time-consuming and prone to intra- and inter-rater variability. Several automated approaches have been developed to alleviate these constraints, including deep learning pipelines. However, these methods tend to reduce their performance in unseen magnetic resonance imaging (MRI) scanner vendors and different imaging protocols. PURPOSE To present and evaluate for clinical use PARIETAL, a pre-trained deep learning brain extraction method. We compare its reproducibility in a scan/rescan analysis and its robustness among scanners of different manufacturers. STUDY TYPE Retrospective. POPULATION Twenty-one subjects (12 women) with age range 22-48 years acquired using three different MRI scanner machines including scan/rescan in each of them. FIELD STRENGTH/SEQUENCE T1-weighted images acquired in a 3-T Siemens with magnetization prepared rapid gradient-echo sequence and two 1.5 T scanners, Philips and GE, with spin-echo and spoiled gradient-recalled (SPGR) sequences, respectively. ASSESSMENT Analysis of the intracranial cavity volumes obtained for each subject on the three different scanners and the scan/rescan acquisitions. STATISTICAL TESTS Parametric permutation tests of the differences in volumes to rank and statistically evaluate the performance of PARIETAL compared to state-of-the-art methods. RESULTS The mean absolute intracranial volume differences obtained by PARIETAL in the scan/rescan analysis were 1.88 mL, 3.91 mL, and 4.71 mL for Siemens, GE, and Philips scanners, respectively. PARIETAL was the best-ranked method on Siemens and GE scanners, while decreasing to Rank 2 on the Philips images. Intracranial differences for the same subject between scanners were 5.46 mL, 27.16 mL, and 30.44 mL for GE/Philips, Siemens/Philips, and Siemens/GE comparison, respectively. The permutation tests revealed that PARIETAL was always in Rank 1, obtaining the most similar volumetric results between scanners. DATA CONCLUSION PARIETAL accurately segments the brain and it generalizes to images acquired at different sites without the need of training or fine-tuning it again. PARIETAL is publicly available. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Sergi Valverde
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Llucia Coll
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Liliana Valencia
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Albert Clèrigues
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Arnau Oliver
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
- REEM, Red Española de Esclerosis Múltiple
| | | | - Lluís Ramió-Torrentà
- REEM, Red Española de Esclerosis Múltiple
- Multiple Sclerosis and Neuroimmunology Unit, Neurology Department, Dr. Josep Trueta University Hospital, Institut d'Investigació Biomèdica, Girona, Spain
- Medical Sciences Department, University of Girona, Girona, Spain
| | - Àlex Rovira
- Magnetic Resonance Unit, Department of Radiology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Xavier Lladó
- Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
- REEM, Red Española de Esclerosis Múltiple
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11
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Zahr NM, Pfefferbaum A. Serum albumin and white matter hyperintensities. Transl Psychiatry 2024; 14:233. [PMID: 38824150 PMCID: PMC11144249 DOI: 10.1038/s41398-024-02953-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 05/15/2024] [Accepted: 05/20/2024] [Indexed: 06/03/2024] Open
Abstract
People living with HIV and those diagnosed with alcohol use disorders (AUD) relative to healthy individuals commonly have low levels of serum albumin, substantiated as an independent predictor of cardiovascular events. White matter hyperintensities (WMH)-a neuroimaging feature of cerebral small vessel disease-are also related to cardiovascular disease. Despite consensus regarding associations between high levels of urine albumin and WMH prevalence, and low serum albumin levels and impaired cognitive functioning, relations between serum albumin and WMH burdens have rarely been evaluated. Here, a sample including 160 individuals with AUD, 142 living with HIV, and 102 healthy controls was used to test the hypothesis that serum albumin would be inversely related to WMH volumes and directly related to cognitive performance in the two diagnostic groups. Although serum albumin and periventricular WMH volumes showed an inverse relationship in both AUD and HIV groups, this relationship persisted only in the HIV group after consideration of traditional cardiovascular (i.e., age, sex, body mass index (BMI), nicotine use, hypertension, diabetes), study-relevant (i.e., race, socioeconomic status, hepatitis C virus status), and disease-specific (i.e., CD4 nadir, HIV viral load, HIV duration) factors. Further, serum albumin contributed more significantly than periventricular WMH volume to variance in performance on a verbal learning and memory composite score in the HIV group only. Relations in both HIV and AUD groups between albumin and hematological red blood cell markers (e.g., hemoglobin, hematocrit) suggest that in this sample, serum albumin reflects hematological abnormalities. Albumin, a simple serum biomarker available in most clinical settings, may therefore help identify periventricular WMH burden and performance levels in specific cognitive domains in people living with HIV. Whether serum albumin contributes mechanistically to periventricular WMH in HIV will require additional investigation.
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Affiliation(s)
- Natalie M Zahr
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Neuroscience Program, SRI International, Menlo Park, CA, USA.
| | - Adolf Pfefferbaum
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Neuroscience Program, SRI International, Menlo Park, CA, USA
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12
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Kelley W, Ngo N, Dalca AV, Fischl B, Zöllei L, Hoffmann M. BOOSTING SKULL-STRIPPING PERFORMANCE FOR PEDIATRIC BRAIN IMAGES. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2024; 2024:10.1109/isbi56570.2024.10635307. [PMID: 39371473 PMCID: PMC11451993 DOI: 10.1109/isbi56570.2024.10635307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/08/2024]
Abstract
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.
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Affiliation(s)
- William Kelley
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nathan Ngo
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science & Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA
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13
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Duan C, Bian X, Cheng K, Lyu J, Xiong Y, Xiao S, Wang X, Duan Q, Li C, Huang J, Hu J, Wang ZJ, Zhou X, Lou X. Synthesized 7T MPRAGE From 3T MPRAGE Using Generative Adversarial Network and Validation in Clinical Brain Imaging: A Feasibility Study. J Magn Reson Imaging 2024; 59:1620-1629. [PMID: 37559435 DOI: 10.1002/jmri.28944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Ultra-high field 7T MRI can provide excellent tissue contrast and anatomical details, but is often cost prohibitive, and is not widely accessible in clinical practice. PURPOSE To generate synthetic 7T images from widely acquired 3T images with deep learning and to evaluate the feasibility of this approach for brain imaging. STUDY TYPE Prospective. POPULATION 33 healthy volunteers and 89 patients with brain diseases, divided into training, and evaluation datasets in the ratio 4:1. SEQUENCE AND FIELD STRENGTH T1-weighted nonenhanced or contrast-enhanced magnetization-prepared rapid acquisition gradient-echo sequence at both 3T and 7T. ASSESSMENT A generative adversarial network (SynGAN) was developed to produce synthetic 7T images from 3T images as input. SynGAN training and evaluation were performed separately for nonenhanced and contrast-enhanced paired acquisitions. Qualitative image quality of acquired 3T and 7T images and of synthesized 7T images was evaluated by three radiologists in terms of overall image quality, artifacts, sharpness, contrast, and visualization of vessel using 5-point Likert scales. STATISTICAL TESTS Wilcoxon signed rank tests to compare synthetic 7T images with acquired 7T and 3T images and intraclass correlation coefficients to evaluate interobserver variability. P < 0.05 was considered significant. RESULTS Of the 122 paired 3T and 7T MRI scans, 66 were acquired without contrast agent and 56 with contrast agent. The average time to generate synthetic images was ~11.4 msec per slice (2.95 sec per participant). The synthetic 7T images achieved significantly improved tissue contrast and sharpness in comparison to 3T images in both nonenhanced and contrast-enhanced subgroups. Meanwhile, there was no significant difference between acquired 7T and synthetic 7T images in terms of all the evaluation criteria for both nonenhanced and contrast-enhanced subgroups (P ≥ 0.180). DATA CONCLUSION The deep learning model has potential to generate synthetic 7T images with similar image quality to acquired 7T images. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Caohui Duan
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Xiangbing Bian
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Kun Cheng
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Jinhao Lyu
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Yongqin Xiong
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Sa Xiao
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China
| | - Xueyang Wang
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Qi Duan
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Chenxi Li
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Jiayu Huang
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Jianxing Hu
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Z Jane Wang
- Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Xin Zhou
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China
| | - Xin Lou
- Department of Radiology, Chinese PLA General Hospital, Beijing, China
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14
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Doering S, McCullough A, Gordon BA, Chen CD, McKay N, Hobbs D, Keefe S, Flores S, Scott J, Smith H, Jarman S, Jackson K, Hornbeck RC, Ances BM, Xiong C, Aschenbrenner AJ, Hassenstab J, Cruchaga C, Daniels A, Bateman RJ, Morris JC, Benzinger TLS. Deconstructing pathological tau by biological process in early stages of Alzheimer disease: a method for quantifying tau spatial spread in neuroimaging. EBioMedicine 2024; 103:105080. [PMID: 38552342 PMCID: PMC10995809 DOI: 10.1016/j.ebiom.2024.105080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Neuroimaging studies often quantify tau burden in standardized brain regions to assess Alzheimer disease (AD) progression. However, this method ignores another key biological process in which tau spreads to additional brain regions. We have developed a metric for calculating the extent tau pathology has spread throughout the brain and evaluate the relationship between this metric and tau burden across early stages of AD. METHODS 445 cross-sectional participants (aged ≥ 50) who had MRI, amyloid PET, tau PET, and clinical testing were separated into disease-stage groups based on amyloid positivity and cognitive status (older cognitively normal control, preclinical AD, and symptomatic AD). Tau burden and tau spatial spread were calculated for all participants. FINDINGS We found both tau metrics significantly elevated across increasing disease stages (p < 0.0001) and as a function of increasing amyloid burden for participants with preclinical (p < 0.0001, p = 0.0056) and symptomatic (p = 0.010, p = 0.0021) AD. An interaction was found between tau burden and tau spatial spread when predicting amyloid burden (p = 0.00013). Analyses of slope between tau metrics demonstrated more spread than burden in preclinical AD (β = 0.59), but then tau burden elevated relative to spread (β = 0.42) once participants had symptomatic AD, when the tau metrics became highly correlated (R = 0.83). INTERPRETATION Tau burden and tau spatial spread are both strong biomarkers for early AD but provide unique information, particularly at the preclinical stage. Tau spatial spread may demonstrate earlier changes than tau burden which could have broad impact in clinical trial design. FUNDING This research was supported by the Knight Alzheimer Disease Research Center (Knight ADRC, NIH grants P30AG066444, P01AG026276, P01AG003991), Dominantly Inherited Alzheimer Network (DIAN, NIH grants U01AG042791, U19AG03243808, R01AG052550-01A1, R01AG05255003), and the Barnes-Jewish Hospital Foundation Willman Scholar Fund.
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Affiliation(s)
- Stephanie Doering
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Austin McCullough
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Brian A Gordon
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Charles D Chen
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Nicole McKay
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Diana Hobbs
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Sarah Keefe
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Shaney Flores
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Jalen Scott
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Hunter Smith
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Stephen Jarman
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Kelley Jackson
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Russ C Hornbeck
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Beau M Ances
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Chengjie Xiong
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | | | - Jason Hassenstab
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Carlos Cruchaga
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Alisha Daniels
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - Randall J Bateman
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
| | - John C Morris
- Washington University in Saint Louis School of Medicine, Saint Louis, MO, USA
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15
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Jaiswal S, Chakravarthula LNC, Padmala S. Additive Effects of Monetary Loss and Positive Emotion in the Human Brain. eNeuro 2024; 11:ENEURO.0374-23.2024. [PMID: 38565297 PMCID: PMC11026344 DOI: 10.1523/eneuro.0374-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/26/2024] [Accepted: 03/22/2024] [Indexed: 04/04/2024] Open
Abstract
In many real-life scenarios, our decisions could lead to multiple outcomes that conflict with value. Hence, an appropriate neural representation of the net experienced value of conflicting outcomes, which play a crucial role in guiding future decisions, is critical for adaptive behavior. As some recent functional neuroimaging work has primarily focused on the concurrent processing of monetary gains and aversive information, very little is known regarding the integration of conflicting value signals involving monetary losses and appetitive information in the human brain. To address this critical gap, we conducted a functional MRI study involving healthy human male participants to examine the nature of integrating positive emotion and monetary losses. We employed a novel experimental design where the valence (positive or neutral) of an emotional stimulus indicated the type of outcome (loss or no loss) in a choice task. Specifically, we probed two plausible integration patterns while processing conflicting value signals involving positive emotion and monetary losses: interactive versus additive. We found overlapping main effects of positive (vs neutral) emotion and loss (vs no loss) in multiple brain regions, including the ventromedial prefrontal cortex, striatum, and amygdala, notably with a lack of evidence for interaction. Thus, our findings revealed the additive integration pattern of monetary loss and positive emotion outcomes, suggesting that the experienced value of the monetary loss was not modulated by the valence of the image signaling those outcomes. These findings contribute to our limited understanding of the nature of integrating conflicting outcomes in the healthy human brain with potential clinical relevance.
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Affiliation(s)
- Sagarika Jaiswal
- Centre for Neuroscience, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | | | - Srikanth Padmala
- Centre for Neuroscience, Indian Institute of Science, Bangalore, Karnataka 560012, India
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16
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Carass A, Greenman D, Dewey BE, Calabresi PA, Prince JL, Pham DL. Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI. NEUROIMAGE. REPORTS 2024; 4:100195. [PMID: 38370461 PMCID: PMC10871705 DOI: 10.1016/j.ynirp.2024.100195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) in multiple sclerosis, this lack of intensity standardization becomes a critical problem affecting both the staging and tracking of the disease and its treatment. This paper presents a study of harmonization on WML segmentation consistency, which is evaluated using an object detection classification scheme that incorporates manual delineations from both the original and harmonized MRIs. A cohort of ten people scanned on two different imaging platforms was studied. An expert rater, blinded to the image source, manually delineated WMLs on images from both scanners before and after harmonization. It was found that there is closer agreement in both global and per-lesion WML volume and spatial distribution after harmonization, demonstrating the importance of image harmonization prior to the creation of manual delineations. These results could lead to better truth models in both the development and evaluation of automated lesion segmentation algorithms.
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Affiliation(s)
- Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Danielle Greenman
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA
| | - Blake E. Dewey
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Peter A. Calabresi
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Dzung L. Pham
- Department of Radiology, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA
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17
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Chen JV, Li Y, Tang F, Chaudhari G, Lew C, Lee A, Rauschecker AM, Haskell-Mendoza AP, Wu YW, Calabrese E. Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset. Sci Rep 2024; 14:4583. [PMID: 38403673 PMCID: PMC10894871 DOI: 10.1038/s41598-024-54436-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/13/2024] [Indexed: 02/27/2024] Open
Abstract
Brain extraction, or skull-stripping, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there are limited extraction algorithms for the neonatal brain. We aim to adapt an established deep learning algorithm for the automatic segmentation of neonatal brains from MRI, trained on a large multi-institutional dataset for improved generalizability across image acquisition parameters. Our model, ANUBEX (automated neonatal nnU-Net brain MRI extractor), was designed using nnU-Net and was trained on a subset of participants (N = 433) enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) study. We compared the performance of our model to five publicly available models (BET, BSE, CABINET, iBEATv2, ROBEX) across conventional and machine learning methods, tested on two public datasets (NIH and dHCP). We found that our model had a significantly higher Dice score on the aggregate of both data sets and comparable or significantly higher Dice scores on the NIH (low-resolution) and dHCP (high-resolution) datasets independently. ANUBEX performs similarly when trained on sequence-agnostic or motion-degraded MRI, but slightly worse on preterm brains. In conclusion, we created an automatic deep learning-based neonatal brain extraction algorithm that demonstrates accurate performance with both high- and low-resolution MRIs with fast computation time.
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Affiliation(s)
- Joshua V Chen
- Department of Radiology, University of California San Francisco, San Francisco, CA, USA
| | - Yi Li
- Department of Radiology, University of California San Francisco, San Francisco, CA, USA
| | - Felicia Tang
- Department of Radiology, University of California San Francisco, San Francisco, CA, USA
| | - Gunvant Chaudhari
- Department of Radiology, University of California San Francisco, San Francisco, CA, USA
| | - Christopher Lew
- Division of Neuroradiology, Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Amanda Lee
- Division of Neuroradiology, Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA
| | - Andreas M Rauschecker
- Department of Radiology, University of California San Francisco, San Francisco, CA, USA
| | | | - Yvonne W Wu
- University of California San Francisco Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Evan Calabrese
- Division of Neuroradiology, Department of Radiology, Duke University Medical Center, Durham, NC, 27710, USA.
- Duke Center for Artificial Intelligence in Radiology (DAIR), Durham, NC, USA.
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Hallquist MN, Hwang K, Luna B, Dombrovski AY. Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space. SCIENCE ADVANCES 2024; 10:eadj2219. [PMID: 38394198 PMCID: PMC10889364 DOI: 10.1126/sciadv.adj2219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 01/23/2024] [Indexed: 02/25/2024]
Abstract
Primates exploring and exploiting a continuous sensorimotor space rely on dynamic maps in the dorsal stream. Two complementary perspectives exist on how these maps encode rewards. Reinforcement learning models integrate rewards incrementally over time, efficiently resolving the exploration/exploitation dilemma. Working memory buffer models explain rapid plasticity of parietal maps but lack a plausible exploration/exploitation policy. The reinforcement learning model presented here unifies both accounts, enabling rapid, information-compressing map updates and efficient transition from exploration to exploitation. As predicted by our model, activity in human frontoparietal dorsal stream regions, but not in MT+, tracks the number of competing options, as preferred options are selectively maintained on the map, while spatiotemporally distant alternatives are compressed out. When valuable new options are uncovered, posterior β1/α oscillations desynchronize within 0.4 to 0.7 s, consistent with option encoding by competing β1-stabilized subpopulations. Together, outcomes matching locally cached reward representations rapidly update parietal maps, biasing choices toward often-sampled, rewarded options.
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Affiliation(s)
| | - Kai Hwang
- Department of Psychological and Brain Sciences, Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
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Park JS, Fadnavis S, Garyfallidis E. Multi-scale V-net architecture with deep feature CRF layers for brain extraction. COMMUNICATIONS MEDICINE 2024; 4:29. [PMID: 38396078 PMCID: PMC10891085 DOI: 10.1038/s43856-024-00452-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Brain extraction is a computational necessity for researchers using brain imaging data. However, the complex structure of the interfaces between the brain, meninges and human skull have not allowed a highly robust solution to emerge. While previous methods have used machine learning with structural and geometric priors in mind, with the development of Deep Learning (DL), there has been an increase in Neural Network based methods. Most proposed DL models focus on improving the training data despite the clear gap between groups in the amount and quality of accessible training data between. METHODS We propose an architecture we call Efficient V-net with Additional Conditional Random Field Layers (EVAC+). EVAC+ has 3 major characteristics: (1) a smart augmentation strategy that improves training efficiency, (2) a unique way of using a Conditional Random Fields Recurrent Layer that improves accuracy and (3) an additional loss function that fine-tunes the segmentation output. We compare our model to state-of-the-art non-DL and DL methods. RESULTS Results show that even with limited training resources, EVAC+ outperforms in most cases, achieving a high and stable Dice Coefficient and Jaccard Index along with a desirable lower Surface (Hausdorff) Distance. More importantly, our approach accurately segmented clinical and pediatric data, despite the fact that the training dataset only contains healthy adults. CONCLUSIONS Ultimately, our model provides a reliable way of accurately reducing segmentation errors in complex multi-tissue interfacing areas of the brain. We expect our method, which is publicly available and open-source, to be beneficial to a wide range of researchers.
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Affiliation(s)
- Jong Sung Park
- Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, USA.
| | - Shreyas Fadnavis
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Finkelstein O, Levakov G, Kaplan A, Zelicha H, Meir AY, Rinott E, Tsaban G, Witte AV, Blüher M, Stumvoll M, Shelef I, Shai I, Riklin Raviv T, Avidan G. Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention. Hum Brain Mapp 2024; 45:e26595. [PMID: 38375968 PMCID: PMC10878010 DOI: 10.1002/hbm.26595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 11/16/2023] [Accepted: 01/03/2024] [Indexed: 02/21/2024] Open
Abstract
Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain morphology. In the DIRECT-PLUS clinical trial, participants with criterion for metabolic syndrome underwent an 18-month lifestyle intervention. Structural brain MRIs were acquired before and after the intervention. We utilized an ensemble learning framework to predict Body-Mass Index (BMI) scores, which correspond to adiposity-related clinical measurements from brain MRIs. We revealed that patient-specific reduction in BMI predictions was associated with actual weight loss and was significantly higher in active diet groups compared to a control group. Moreover, explainable AI (XAI) maps highlighted brain regions contributing to BMI predictions that were distinct from regions associated with age prediction. Our DIRECT-PLUS analysis results imply that predicted BMI and its reduction are unique neural biomarkers for obesity-related brain modifications and weight loss.
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Affiliation(s)
- Ofek Finkelstein
- Department of Cognitive and Brain SciencesBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Gidon Levakov
- Department of Cognitive and Brain SciencesBen‐Gurion University of the NegevBeer ShevaIsrael
| | - Alon Kaplan
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
- The Chaim Sheba Medical Center, Tel HashomerRamat‐GanIsrael
| | - Hila Zelicha
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
| | - Anat Yaskolka Meir
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
| | - Ehud Rinott
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
| | - Gal Tsaban
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
- Soroka University Medical CenterBeer ShevaIsrael
| | - Anja Veronica Witte
- Department of Neurology, Max Planck‐Institute for Human Cognitive and Brain Sciences, and Cognitive NeurologyUniversity of Leipzig Medical CenterLeipzigGermany
| | | | | | - Ilan Shelef
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
- Soroka University Medical CenterBeer ShevaIsrael
| | - Iris Shai
- The Health & Nutrition Innovative International Research Center, Faculty of Health SciencesBen Gurion University of the NegevBeer ShevaIsrael
- Department of Nutrition, Harvard T.H. Chan School of Public HealthBostonMassachusettsUSA
| | - Tammy Riklin Raviv
- The School of Electrical and Computer EngineeringBen Gurion University of the NegevBeer ShevaIsrael
| | - Galia Avidan
- Department of PsychologyBen‐Gurion University of the NegevBeer ShevaIsrael
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Levitas D, Hayashi S, Vinci-Booher S, Heinsfeld A, Bhatia D, Lee N, Galassi A, Niso G, Pestilli F. ezBIDS: Guided standardization of neuroimaging data interoperable with major data archives and platforms. Sci Data 2024; 11:179. [PMID: 38332144 PMCID: PMC10853279 DOI: 10.1038/s41597-024-02959-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 01/12/2024] [Indexed: 02/10/2024] Open
Abstract
Data standardization promotes a common framework through which researchers can utilize others' data and is one of the leading methods neuroimaging researchers use to share and replicate findings. As of today, standardizing datasets requires technical expertise such as coding and knowledge of file formats. We present ezBIDS, a tool for converting neuroimaging data and associated metadata to the Brain Imaging Data Structure (BIDS) standard. ezBIDS contains four major features: (1) No installation or programming requirements. (2) Handling of both imaging and task events data and metadata. (3) Semi-automated inference and guidance for adherence to BIDS. (4) Multiple data management options: download BIDS data to local system, or transfer to OpenNeuro.org or to brainlife.io. In sum, ezBIDS requires neither coding proficiency nor knowledge of BIDS, and is the first BIDS tool to offer guided standardization, support for task events conversion, and interoperability with OpenNeuro.org and brainlife.io.
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Affiliation(s)
- Daniel Levitas
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Soichi Hayashi
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Sophia Vinci-Booher
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, TN, 37203, USA
| | - Anibal Heinsfeld
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Dheeraj Bhatia
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Nicholas Lee
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA
| | - Anthony Galassi
- Center for Multimodal Neuroimaging, National Institute of Mental Health, Bethesda, MD, USA
| | | | - Franco Pestilli
- Department of Psychology, Department of Neuroscience, Center for Perceptual Systems, Center for Learning and Memory, Center for Aging Population Sciences, University of Texas, Austin, TX, 78712, USA.
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22
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Teng Y, Chen C, Shu X, Zhao F, Zhang L, Xu J. Automated, fast, robust brain extraction on contrast-enhanced T1-weighted MRI in presence of brain tumors: an optimized model based on multi-center datasets. Eur Radiol 2024; 34:1190-1199. [PMID: 37615767 PMCID: PMC10853304 DOI: 10.1007/s00330-023-10078-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/12/2023] [Accepted: 07/14/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVES Existing brain extraction models should be further optimized to provide more information for oncological analysis. We aimed to develop an nnU-Net-based deep learning model for automated brain extraction on contrast-enhanced T1-weighted (T1CE) images in presence of brain tumors. METHODS This is a multi-center, retrospective study involving 920 patients. A total of 720 cases with four types of intracranial tumors from private institutions were collected and set as the training group and the internal test group. Mann-Whitney U test (U test) was used to investigate if the model performance was associated with pathological types and tumor characteristics. Then, the generalization of model was independently tested on public datasets consisting of 100 glioma and 100 vestibular schwannoma cases. RESULTS In the internal test, the model achieved promising performance with median Dice similarity coefficient (DSC) of 0.989 (interquartile range (IQR), 0.988-0.991), and Hausdorff distance (HD) of 6.403 mm (IQR, 5.099-8.426 mm). U test suggested a slightly descending performance in meningioma and vestibular schwannoma group. The results of U test also suggested that there was a significant difference in peritumoral edema group, with median DSC of 0.990 (IQR, 0.989-0.991, p = 0.002), and median HD of 5.916 mm (IQR, 5.000-8.000 mm, p = 0.049). In the external test, our model also showed to be robust performance, with median DSC of 0.991 (IQR, 0.983-0.998) and HD of 8.972 mm (IQR, 6.164-13.710 mm). CONCLUSIONS For automated processing of MRI neuroimaging data presence of brain tumors, the proposed model can perform brain extraction including important superficial structures for oncological analysis. CLINICAL RELEVANCE STATEMENT The proposed model serves as a radiological tool for image preprocessing in tumor cases, focusing on superficial brain structures, which could streamline the workflow and enhance the efficiency of subsequent radiological assessments. KEY POINTS • The nnU-Net-based model is capable of segmenting significant superficial structures in brain extraction. • The proposed model showed feasible performance, regardless of pathological types or tumor characteristics. • The model showed generalization in the public datasets.
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Affiliation(s)
- Yuen Teng
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyue Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
- West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China.
| | - Xin Shu
- College of Computer Science, Sichuan University, Chengdu, People's Republic of China
| | - Fumin Zhao
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Lei Zhang
- College of Computer Science, Sichuan University, Chengdu, People's Republic of China.
| | - Jianguo Xu
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
- West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China.
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23
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Zahr N, Sullivan E, Pfefferbaum A. [WITHDRAWN] Serum biomarkers of liver fibrosis identify changes in striatal metabolite levels. RESEARCH SQUARE 2024:rs.3.rs-2729490. [PMID: 37034697 PMCID: PMC10081358 DOI: 10.21203/rs.3.rs-2729490/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
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24
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Zahr N, Pfefferbaum A. Serum albumin and white matter hyperintensities. RESEARCH SQUARE 2024:rs.3.rs-3822513. [PMID: 38260299 PMCID: PMC10802700 DOI: 10.21203/rs.3.rs-3822513/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Urine albumin, high in kidney disease, predicts cardiovascular incidents and CNS white matter hyperintensity (WMH) burdens. Serum albumin - a more general biomarker which can be low in several disorders - including kidney and liver disease, malnutrition, and inflammation - also predicts cardiovascular events and is associated with cognitive impairment in several clinical populations; relations between serum albumin and WMH prevalence, however, have rarely been evaluated. In a sample of 160 individuals with alcohol use disorder (AUD), 142 infected with HIV, and 102 healthy controls, the hypothesis was tested that lower serum albumin levels would predict larger WMH volumes and worse cognitive performance irrespective of diagnosis. After considering traditional cardiovascular risk factors (e.g., age, sex, body mass index (BMI), nicotine use, hypertension, diabetes) and study-relevant variables (i.e., primary diagnoses, race, socioeconomic status, hepatitis C virus status), serum albumin survived false discovery rate (FDR)-correction in contributing variance to larger periventricular but not deep WMH volumes. This relationship was salient in the AUD and HIV groups, but not the control group. In secondary analyses, serum albumin and periventricular WMH along with age, sex, diagnoses, BMI, and hypertension were considered for hierarchical contribution to variance in performance in 4 cognitive domains. Albumin survived FDR-correction for significantly contributing to visual and verbal learning and memory performance after accounting for diagnosis. Relations between albumin and markers of liver integrity [e.g., aspartate transaminase (AST)] and blood status (e.g., hemoglobin, red blood cell count, red cell distribution width) suggest that in this sample, albumin reflects both liver dysfunction and hematological abnormalities. The current results suggest that albumin, a simple serum biomarker available in most clinical settings, can predict variance in periventricular WMH volumes and performance in visual and verbal learning and memory cognitive domains. Whether serum albumin contributes mechanistically to periventricular WMH prevalence will require additional investigation.
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25
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Xiong L, Yi C, Xiong Q, Jiang S. SEA-NET: medical image segmentation network based on spiral squeeze-and-excitation and attention modules. BMC Med Imaging 2024; 24:17. [PMID: 38212684 PMCID: PMC10785532 DOI: 10.1186/s12880-024-01194-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 01/06/2024] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Medical image segmentation is an important processing step in most of medical image analysis. Thus, high accuracy and robustness are required for them. The current deep neural network based medical segmentation methods have good effect on image with balanced foreground and background, but it will loss the characteristics of small targets on image with imbalanced foreground and background after multiple convolutions. METHODS In order to retain the features of small targets in the deep network, we proposed a new medical image segmentation model based on the U-Net with squeeze-and-excitation and attention modules which form a spiral closed path,callled as Spiral Squeeze-and-Excitation and Attention NET (SEA-NET) in this paper. The segmentation model used squeeze-and-extraction modules to adjust the channel information to enhance the useful information and used attention modules to adjust the spatial information of the feature map to highlight the target area for small target segmentation when up-sampling. The deep semantic information is integrated into the shallow feature map by the attention model. Therefore, the deep semantic information cannot be scattered by continuous up-sampling. We used cross entropy loss + Tversky loss function for fast convergence and well processing the imbalanced data sets. Our proposed SEA-NET was tested on the brain MRI dataset LPBA40 and peripheral blood smear images. CONCLUSIONS On brain MRI data, the average value of the Dice coefficient we obtained reached 98.1[Formula: see text]. On the peripheral blood smear dataset, our proposed model has a good segmentation effect on adhesion cells. RESULTS The experimental results proved that the proposed SEA-Net performed better than U-Net, U-Net++, etc. in medical image segmentation.
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Affiliation(s)
- Liangli Xiong
- Department of BMC Medical Imaging, Nanchang Hangkong University, 330063, Nanchang, China
| | - Chen Yi
- Department of BMC Medical Imaging, Nanchang Hangkong University, 330063, Nanchang, China
| | - Qiliang Xiong
- Department of BMC Medical Imaging, Nanchang Hangkong University, 330063, Nanchang, China
| | - Shaofeng Jiang
- Department of BMC Medical Imaging, Nanchang Hangkong University, 330063, Nanchang, China.
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26
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Dhakal K, Rosenthal ES, Kulpanowski AM, Dodelson JA, Wang Z, Cudemus-Deseda G, Villien M, Edlow BL, Presciutti AM, Januzzi JL, Ning M, Taylor Kimberly W, Amorim E, Brandon Westover M, Copen WA, Schaefer PW, Giacino JT, Greer DM, Wu O. Increased task-relevant fMRI responsiveness in comatose cardiac arrest patients is associated with improved neurologic outcomes. J Cereb Blood Flow Metab 2024; 44:50-65. [PMID: 37728641 PMCID: PMC10905635 DOI: 10.1177/0271678x231197392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 09/21/2023]
Abstract
Early prediction of the recovery of consciousness in comatose cardiac arrest patients remains challenging. We prospectively studied task-relevant fMRI responses in 19 comatose cardiac arrest patients and five healthy controls to assess the fMRI's utility for neuroprognostication. Tasks involved instrumental music listening, forward and backward language listening, and motor imagery. Task-specific reference images were created from group-level fMRI responses from the healthy controls. Dice scores measured the overlap of individual subject-level fMRI responses with the reference images. Task-relevant responsiveness index (Rindex) was calculated as the maximum Dice score across the four tasks. Correlation analyses showed that increased Dice scores were significantly associated with arousal recovery (P < 0.05) and emergence from the minimally conscious state (EMCS) by one year (P < 0.001) for all tasks except motor imagery. Greater Rindex was significantly correlated with improved arousal recovery (P = 0.002) and consciousness (P = 0.001). For patients who survived to discharge (n = 6), the Rindex's sensitivity was 75% for predicting EMCS (n = 4). Task-based fMRI holds promise for detecting covert consciousness in comatose cardiac arrest patients, but further studies are needed to confirm these findings. Caution is necessary when interpreting the absence of task-relevant fMRI responses as a surrogate for inevitable poor neurological prognosis.
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Affiliation(s)
- Kiran Dhakal
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Eric S Rosenthal
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Annelise M Kulpanowski
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Jacob A Dodelson
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Zihao Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Gaston Cudemus-Deseda
- Department of Cardiac Anesthesiology and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Marjorie Villien
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Brian L Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Alexander M Presciutti
- Department of Psychiatry, Center for Health Outcomes and Interdisciplinary Research, Massachusetts General Hospital, Boston, MA, USA
| | - James L Januzzi
- Department of Medicine, Cardiology Division, Massachusetts General Hospital and Baim Institute for Clinical Research, Boston, MA, USA
| | - MingMing Ning
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - W Taylor Kimberly
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Edilberto Amorim
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - William A Copen
- Department of Radiology, Neuroradiology Division, Massachusetts General Hospital, Boston, MA, USA
| | - Pamela W Schaefer
- Department of Radiology, Neuroradiology Division, Massachusetts General Hospital, Boston, MA, USA
| | - Joseph T Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Charlestown, MA, USA
| | - David M Greer
- Department of Neurology, Boston University School of Medicine, Boston Medical Center, Boston, MA, USA
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
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27
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Hoebel KV, Bridge CP, Ahmed S, Akintola O, Chung C, Huang RY, Johnson JM, Kim A, Ly KI, Chang K, Patel J, Pinho M, Batchelor TT, Rosen BR, Gerstner ER, Kalpathy-Cramer J. Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation. Radiol Artif Intell 2024; 6:e220231. [PMID: 38197800 PMCID: PMC10831514 DOI: 10.1148/ryai.220231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 09/13/2023] [Accepted: 11/01/2023] [Indexed: 01/11/2024]
Abstract
Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting on brain tumor segmentation algorithms were surveyed for the reported quality evaluation. Additionally, ratings of segmentation quality on a four-point scale were collected from medical professionals for 60 brain tumor segmentation cases. Results Of the surveyed articles, Dice score, sensitivity, and Hausdorff distance were the most popular metrics to report segmentation performance. Notably, only 2.8% of the articles included clinical experts' evaluation of segmentation quality. The experimental results revealed a low interrater agreement (Krippendorff α, 0.34) in experts' segmentation quality perception. Furthermore, the correlations between the ratings and commonly used quantitative quality metrics were low (Kendall tau between Dice score and mean rating, 0.23; Kendall tau between Hausdorff distance and mean rating, 0.51), with large variability among the experts. Conclusion The results demonstrate that quality ratings are prone to variability due to the ambiguity of tumor boundaries and individual perceptual differences, and existing metrics do not capture the clinical perception of segmentation quality. Keywords: Brain Tumor Segmentation, Deep Learning Algorithms, Glioblastoma, Cancer, Machine Learning Clinical trial registration nos. NCT00756106 and NCT00662506 Supplemental material is available for this article. © RSNA, 2023.
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Affiliation(s)
- Katharina V. Hoebel
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Christopher P. Bridge
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Sara Ahmed
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Oluwatosin Akintola
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Caroline Chung
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Raymond Y. Huang
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Jason M. Johnson
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Albert Kim
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - K. Ina Ly
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Ken Chang
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Jay Patel
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Marco Pinho
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Tracy T. Batchelor
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Bruce R. Rosen
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Elizabeth R. Gerstner
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Jayashree Kalpathy-Cramer
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
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Honkamaa J, Khan U, Koivukoski S, Valkonen M, Latonen L, Ruusuvuori P, Marttinen P. Deformation equivariant cross-modality image synthesis with paired non-aligned training data. Med Image Anal 2023; 90:102940. [PMID: 37666115 DOI: 10.1016/j.media.2023.102940] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 08/14/2023] [Accepted: 08/18/2023] [Indexed: 09/06/2023]
Abstract
Cross-modality image synthesis is an active research topic with multiple medical clinically relevant applications. Recently, methods allowing training with paired but misaligned data have started to emerge. However, no robust and well-performing methods applicable to a wide range of real world data sets exist. In this work, we propose a generic solution to the problem of cross-modality image synthesis with paired but non-aligned data by introducing new deformation equivariance encouraging loss functions. The method consists of joint training of an image synthesis network together with separate registration networks and allows adversarial training conditioned on the input even with misaligned data. The work lowers the bar for new clinical applications by allowing effortless training of cross-modality image synthesis networks for more difficult data sets.
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Affiliation(s)
- Joel Honkamaa
- Department of Computer Science, Aalto University, Finland.
| | - Umair Khan
- Institute of Biomedicine, University of Turku, Finland
| | - Sonja Koivukoski
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Mira Valkonen
- Faculty of Medicine and Health Technology, Tampere University, Finland
| | - Leena Latonen
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Pekka Ruusuvuori
- Institute of Biomedicine, University of Turku, Finland; Faculty of Medicine and Health Technology, Tampere University, Finland
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Pérez Hinestroza J, Mazo C, Trujillo M, Herrera A. MRI and CT Fusion in Stereotactic Electroencephalography (SEEG). Diagnostics (Basel) 2023; 13:3420. [PMID: 37998556 PMCID: PMC10670384 DOI: 10.3390/diagnostics13223420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/03/2023] [Accepted: 09/05/2023] [Indexed: 11/25/2023] Open
Abstract
Epilepsy is a neurological disorder characterized by spontaneous recurrent seizures. While 20% to 30% of epilepsy cases are untreatable with Anti-Epileptic Drugs, some of these cases can be addressed through surgical intervention. The success of such interventions greatly depends on accurately locating the epileptogenic tissue, a task achieved using diagnostic techniques like Stereotactic Electroencephalography (SEEG). SEEG utilizes multi-modal fusion to aid in electrode localization, using pre-surgical resonance and post-surgical computer tomography images as inputs. To ensure the absence of artifacts or misregistrations in the resultant images, a fusion method that accounts for electrode presence is required. We proposed an image fusion method in SEEG that incorporates electrode segmentation from computed tomography as a sampling mask during registration to address the fusion problem in SEEG. The method was validated using eight image pairs from the Retrospective Image Registration Evaluation Project (RIRE). After establishing a reference registration for the MRI and identifying eight points, we assessed the method's efficacy by comparing the Euclidean distances between these reference points and those derived using registration with a sampling mask. The results showed that the proposed method yielded a similar average error to the registration without a sampling mask, but reduced the dispersion of the error, with a standard deviation of 0.86 when a mask was used and 5.25 when no mask was used.
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Affiliation(s)
- Jaime Pérez Hinestroza
- Multimedia and Computer Vision Group, Universidad del Valle, Cali 760042, Colombia; (C.M.); (M.T.); (A.H.)
| | - Claudia Mazo
- Multimedia and Computer Vision Group, Universidad del Valle, Cali 760042, Colombia; (C.M.); (M.T.); (A.H.)
- School of Computing, Faculty of Engineering and Computing, Glasnevin Campus, Dublin City University, 9 Dublin, Ireland
| | - Maria Trujillo
- Multimedia and Computer Vision Group, Universidad del Valle, Cali 760042, Colombia; (C.M.); (M.T.); (A.H.)
| | - Alejandro Herrera
- Multimedia and Computer Vision Group, Universidad del Valle, Cali 760042, Colombia; (C.M.); (M.T.); (A.H.)
- Clinica Imbanaco Grupo Quironsalud, Cali 760042, Colombia
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Du W, Yin K, Shi J. Dimensionality Reduction Hybrid U-Net for Brain Extraction in Magnetic Resonance Imaging. Brain Sci 2023; 13:1549. [PMID: 38002509 PMCID: PMC10669566 DOI: 10.3390/brainsci13111549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023] Open
Abstract
In various applications, such as disease diagnosis, surgical navigation, human brain atlas analysis, and other neuroimage processing scenarios, brain extraction is typically regarded as the initial stage in MRI image processing. Whole-brain semantic segmentation algorithms, such as U-Net, have demonstrated the ability to achieve relatively satisfactory results even with a limited number of training samples. In order to enhance the precision of brain semantic segmentation, various frameworks have been developed, including 3D U-Net, slice U-Net, and auto-context U-Net. However, the processing methods employed in these models are relatively complex when applied to 3D data models. In this article, we aim to reduce the complexity of the model while maintaining appropriate performance. As an initial step to enhance segmentation accuracy, the preprocessing extraction of full-scale information from magnetic resonance images is performed with a cluster tool. Subsequently, three multi-input hybrid U-Net model frameworks are tested and compared. Finally, we propose utilizing a fusion of two-dimensional segmentation outcomes from different planes to attain improved results. The performance of the proposed framework was tested using publicly accessible benchmark datasets, namely LPBA40, in which we obtained Dice overlap coefficients of 98.05%. Improvement was achieved via our algorithm against several previous studies.
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Affiliation(s)
- Wentao Du
- Nanjing Research Institute of Electronic Technology, Nanjing 210019, China;
| | - Kuiying Yin
- Nanjing Research Institute of Electronic Technology, Nanjing 210019, China;
| | - Jingping Shi
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China;
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31
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Okamoto RJ, Escarcega JD, Alshareef A, Carass A, Prince JL, Johnson CL, Bayly PV. Effect of Direction and Frequency of Skull Motion on Mechanical Vulnerability of the Human Brain. J Biomech Eng 2023; 145:111005. [PMID: 37432674 PMCID: PMC10578077 DOI: 10.1115/1.4062937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 06/26/2023] [Accepted: 07/06/2023] [Indexed: 07/12/2023]
Abstract
Strain energy and kinetic energy in the human brain were estimated by magnetic resonance elastography (MRE) during harmonic excitation of the head, and compared to characterize the effect of loading direction and frequency on brain deformation. In brain MRE, shear waves are induced by external vibration of the skull and imaged by a modified MR imaging sequence; the resulting harmonic displacement fields are typically "inverted" to estimate mechanical properties, like stiffness or damping. However, measurements of tissue motion from MRE also illuminate key features of the response of the brain to skull loading. In this study, harmonic excitation was applied in two different directions and at five different frequencies from 20 to 90 Hz. Lateral loading induced primarily left-right head motion and rotation in the axial plane; occipital loading induced anterior-posterior head motion and rotation in the sagittal plane. The ratio of strain energy to kinetic energy (SE/KE) depended strongly on both direction and frequency. The ratio of SE/KE was approximately four times larger for lateral excitation than for occipital excitation and was largest at the lowest excitation frequencies studied. These results are consistent with clinical observations that suggest lateral impacts are more likely to cause injury than occipital or frontal impacts, and also with observations that the brain has low-frequency (∼10 Hz) natural modes of oscillation. The SE/KE ratio from brain MRE is potentially a simple and powerful dimensionless metric of brain vulnerability to deformation and injury.
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Affiliation(s)
- Ruth J. Okamoto
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, One Brookings Drive, MSC 1185-208-125, St. Louis, MO 63130
| | - Jordan D. Escarcega
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130
| | - Ahmed Alshareef
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218
| | - Curtis L. Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE 19713
| | - Philip V. Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130
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Koshmanova E, Berger A, Beckers E, Campbell I, Mortazavi N, Sharifpour R, Paparella I, Balda F, Berthomier C, Degueldre C, Salmon E, Lamalle L, Bastin C, Van Egroo M, Phillips C, Maquet P, Collette F, Muto V, Chylinski D, Jacobs HI, Talwar P, Sherif S, Vandewalle G. Locus coeruleus activity while awake is associated with REM sleep quality in older individuals. JCI Insight 2023; 8:e172008. [PMID: 37698926 PMCID: PMC10619502 DOI: 10.1172/jci.insight.172008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/06/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUNDThe locus coeruleus (LC) is the primary source of norepinephrine in the brain and regulates arousal and sleep. Animal research shows that it plays important roles in the transition between sleep and wakefulness, and between slow wave sleep and rapid eye movement sleep (REMS). It is unclear, however, whether the activity of the LC predicts sleep variability in humans.METHODSWe used 7-Tesla functional MRI, sleep electroencephalography (EEG), and a sleep questionnaire to test whether the LC activity during wakefulness was associated with sleep quality in 33 healthy younger (~22 years old; 28 women, 5 men) and 19 older (~61 years old; 14 women, 5 men) individuals.RESULTSWe found that, in older but not in younger participants, higher LC activity, as probed during an auditory attentional task, was associated with worse subjective sleep quality and with lower power over the EEG theta band during REMS. The results remained robust even when accounting for the age-related changes in the integrity of the LC.CONCLUSIONThese findings suggest that LC activity correlates with the perception of the sleep quality and an essential oscillatory mode of REMS, and we found that the LC may be an important target in the treatment of sleep- and age-related diseases.FUNDINGThis work was supported by Fonds National de la Recherche Scientifique (FRS-FNRS, T.0242.19 & J. 0222.20), Action de Recherche Concertée - Fédération Wallonie-Bruxelles (ARC SLEEPDEM 17/27-09), Fondation Recherche Alzheimer (SAO-FRA 2019/0025), ULiège, and European Regional Development Fund (Radiomed & Biomed-Hub).
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Affiliation(s)
- Ekaterina Koshmanova
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Alexandre Berger
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- Institute of Neuroscience (IoNS), Université Catholique de Louvain (UCLouvain), Brussels, Belgium
- Synergia Medical SA, Mont-Saint-Guibert, Belgium
| | - Elise Beckers
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- Alzheimer Centre Limburg, School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Islay Campbell
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Nasrin Mortazavi
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Roya Sharifpour
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Ilenia Paparella
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Fermin Balda
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | | | - Christian Degueldre
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Eric Salmon
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- Neurology Department, Centre Hospitalier Universitaire de Liège, Liège, Belgium
- PsyNCog and
| | - Laurent Lamalle
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Christine Bastin
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- PsyNCog and
| | - Maxime Van Egroo
- Alzheimer Centre Limburg, School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Christophe Phillips
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- In Silico Medicine Unit, GIGA-Institute, ULiège, Liège, Belgium
| | - Pierre Maquet
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- Neurology Department, Centre Hospitalier Universitaire de Liège, Liège, Belgium
| | - Fabienne Collette
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
- PsyNCog and
| | - Vincenzo Muto
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Daphne Chylinski
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Heidi I.L. Jacobs
- Alzheimer Centre Limburg, School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Puneet Talwar
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Siya Sherif
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
| | - Gilles Vandewalle
- Sleep and Chronobiology Lab, GIGA-Institute, CRC-In Vivo Imaging Unit, University of Liège (ULiège), Liège, Belgium
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Mandal PK, Jindal K, Roy S, Arora Y, Sharma S, Joon S, Goel A, Ahasan Z, Maroon JC, Singh K, Sandal K, Tripathi M, Sharma P, Samkaria A, Gaur S, Shandilya S. SWADESH: a multimodal multi-disease brain imaging and neuropsychological database and data analytics platform. Front Neurol 2023; 14:1258116. [PMID: 37859652 PMCID: PMC10582723 DOI: 10.3389/fneur.2023.1258116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 09/15/2023] [Indexed: 10/21/2023] Open
Abstract
Multimodal neuroimaging data of various brain disorders provides valuable information to understand brain function in health and disease. Various neuroimaging-based databases have been developed that mainly consist of volumetric magnetic resonance imaging (MRI) data. We present the comprehensive web-based neuroimaging platform "SWADESH" for hosting multi-disease, multimodal neuroimaging, and neuropsychological data along with analytical pipelines. This novel initiative includes neurochemical and magnetic susceptibility data for healthy and diseased conditions, acquired using MR spectroscopy (MRS) and quantitative susceptibility mapping (QSM) respectively. The SWADESH architecture also provides a neuroimaging database which includes MRI, MRS, functional MRI (fMRI), diffusion weighted imaging (DWI), QSM, neuropsychological data and associated data analysis pipelines. Our final objective is to provide a master database of major brain disease states (neurodegenerative, neuropsychiatric, neurodevelopmental, and others) and to identify characteristic features and biomarkers associated with such disorders.
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Affiliation(s)
- Pravat K. Mandal
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
- Florey Institute of Neuroscience and Mental Health, Melbourne School of Medicine Campus, Melbourne, VIC, Australia
| | - Komal Jindal
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Saurav Roy
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Yashika Arora
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Shallu Sharma
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Shallu Joon
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Anshika Goel
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Zoheb Ahasan
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Joseph C. Maroon
- Department of Neurosurgery, University of Pittsburgh Medical School, Pittsburgh, PA, United States
| | - Kuldeep Singh
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Kanika Sandal
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Manjari Tripathi
- Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
| | - Pooja Sharma
- Medanta Institute of Education and Research, Medanta-The Medicity Hospital, Gurgaon, India
| | - Avantika Samkaria
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Shradha Gaur
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
| | - Sandhya Shandilya
- Neuroimaging and Neurospectroscopy (NINS) Laboratory, National Brain Research Centre, Gurgaon, India
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Suh PS, Jung W, Suh CH, Kim J, Oh J, Heo H, Shim WH, Lim JS, Lee JH, Kim HS, Kim SJ. Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg. Front Neurol 2023; 14:1221892. [PMID: 37719763 PMCID: PMC10503131 DOI: 10.3389/fneur.2023.1221892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 08/07/2023] [Indexed: 09/19/2023] Open
Abstract
Background and purpose To develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg. Materials and methods This retrospective study included 60 subjects [30 Alzheimer's disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference. Results The single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects. Conclusion Our deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV.
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Affiliation(s)
- Pae Sun Suh
- Department of Radiology, Asan Medical Center, Seoul, Republic of Korea
| | | | - Chong Hyun Suh
- Department of Radiology, Asan Medical Center, Seoul, Republic of Korea
| | | | - Jio Oh
- R&D Center, VUNO, Seoul, Republic of Korea
| | - Hwon Heo
- Department of Radiology, Asan Medical Center, Seoul, Republic of Korea
| | - Woo Hyun Shim
- Department of Radiology, Asan Medical Center, Seoul, Republic of Korea
| | - Jae-Sung Lim
- Department of Neurology, Asan Medical Center, College of Medicine, University of Ulsan, Ulsan, Republic of Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, College of Medicine, University of Ulsan, Ulsan, Republic of Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, Republic of Korea
| | - Sang Joon Kim
- Department of Radiology, Asan Medical Center, Seoul, Republic of Korea
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Zhang S, Sun H, Yang X, Wan X, Tan Q, Li S, Shao H, Su X, Yue Q, Gong Q. An MRI Study Combining Virtual Brain Grafting and Surface-Based Morphometry Analysis to Investigate Contralateral Alterations in Cortical Morphology in Patients With Diffuse Low-Grade Glioma. J Magn Reson Imaging 2023; 58:741-749. [PMID: 36524459 DOI: 10.1002/jmri.28562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/28/2022] [Accepted: 11/30/2022] [Indexed: 08/08/2023] Open
Abstract
BACKGROUND The human brain has ability to reorganize itself in response to glioma. However, the mechanism of cortical reorganization remains unclear. PURPOSE To investigate alterations in cortical thickness and local gyration index (LGI) in patients with unilateral frontal lobe diffuse low-grade glioma (DLGG). STUDY TYPE Retrospective. SUBJECTS Ninety-nine patients with histopathologically proven DLGG invading the left frontal lobe (LF; N = 56) or the right frontal lobe (RF; N = 43), and healthy controls (HC; N = 53). FIELD STRENGTH/SEQUENCE 3.0 T, 3D T1-weighted images and gadolinium enhanced T1-weighted images using magnetization-prepared rapid gradient echo sequence, T2-weighted images, and fluid-attenuated inversion recovery using turbo spin echo sequence. ASSESSMENT In patients with DLGG, virtual brain grafting combined with Freesurfer was utilized to enable automated cortical thickness and LGI calculation. In HC, standard FreeSurfer pipeline was applied to calculate these measures. Radiomic features were extracted from glioma using Pyradiomic software. STATISTICAL TESTS General linear model and Pearson's correlation analysis. A P value <0.05 was considered statistically significant. RESULTS For LF patients, there was significantly increased cortical thickness in the rostral middle frontal gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual and medial orbitofrontal (MOF) gyrus in contralateral hemisphere. For RF patients, there was significantly increased cortical thickness in the middle temporal, lateral occipital extending to isthmus cingulate gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual gyrus in the contralateral hemisphere. A negative association between four textural features of DLGG and LGI in the right MOF gyrus of LF group was found (r = -0.609, -0.442, -0.545, and -0.417, respectively). DATA CONCLUSION Cortical thickness compensation was shown in contralateral homotopic location and some distant contralateral regions. Additionally, there was decreased cortical thickness in the contralateral precentral gyrus and hypogyrification in contralateral lingual gyrus. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Simin Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, China
| | - Huaiqiang Sun
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Xibiao Yang
- Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xinyue Wan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - QiaoYue Tan
- Division of Radiation Physics, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, China
| | - Shuang Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, China
| | - Hanbin Shao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaorui Su
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, china
| | - Qiang Yue
- Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China
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de Oliveira CM, Leotti VB, Polita S, Anes M, Cappelli AH, Rocha AG, Ecco G, Bolzan G, Kersting N, Duarte JA, Saraiva-Pereira ML, Junior MCF, Rezende TJR, Jardim LB. The longitudinal progression of MRI changes in pre-ataxic carriers of SCA3/MJD. J Neurol 2023; 270:4276-4287. [PMID: 37193796 PMCID: PMC10187509 DOI: 10.1007/s00415-023-11763-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/02/2023] [Accepted: 05/03/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND The natural history of magnetic resonance imaging (MRI) in pre-ataxic stages of spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is not well known. We report cross-sectional and longitudinal data obtained at this stage. METHODS Baseline (follow-up) observations included 32 (17) pre-ataxic carriers (SARA < 3) and 20 (12) related controls. The mutation length was used to estimate the time to onset (TimeTo) of gait ataxia. Clinical scales and MRIs were performed at baseline and after a median (IQR) of 30 (7) months. Cerebellar volumetries (ACAPULCO), deep gray-matter (T1-Multiatlas), cortical thickness (FreeSurfer), cervical spinal cord area (SCT) and white matter (DTI-Multiatlas) were assessed. Baseline differences between groups were described; variables that presented a p < 0.1 after Bonferroni correction were assessed longitudinally, using TimeTo and study time. For TimeTo strategy, corrections for age, sex and intracranial volume were done with Z-score progression. A significance level of 5% was adopted. RESULTS SCT at C1 level distinguished pre-ataxic carriers from controls. DTI measures of the right inferior cerebellar peduncle (ICP), bilateral middle cerebellar peduncles (MCP) and bilateral medial lemniscus (ML), also distinguished pre-ataxic carriers from controls, and progressed over TimeTo, with effect sizes varying from 0.11 to 0.20, larger than those of the clinical scales. No MRI variable showed progression over study time. DISCUSSION DTI parameters of the right ICP, left MCP and right ML were the best biomarkers for the pre-ataxic stage of SCA3/MJD. TimeTo is an interesting timescale, since it captured the longitudinal worsening of these structures.
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Affiliation(s)
- Camila Maria de Oliveira
- Programa de Pós-Graduação em Ciências Médicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Vanessa Bielefeldt Leotti
- Departamento de Estatística, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Programa de Pós-Graduação em Epidemiologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Sandra Polita
- Serviço de Radiologia, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Mauricio Anes
- Serviço de Física Médica e Radioproteção, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Amanda Henz Cappelli
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Gabriela Ecco
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Gabriela Bolzan
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Nathalia Kersting
- Programa de Pós-Graduação em Ciências Médicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Juliana Avila Duarte
- Programa de Pós-Graduação em Ciências Médicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Serviço de Radiologia, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Maria-Luiza Saraiva-Pereira
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
- Serviço de Genética Médica, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Departamento de Bioquímica, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Marcondes Cavalcante França Junior
- Departamento de Neurologia, Faculdade de Ciências Médicas, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
- Neuroimaging Laboratory, Rua Vital Brasil, 89-99, Cidade Universitária "Zeferino Vaz", Campinas, SP, 13083-888, Brazil
| | - Thiago Junqueira Ribeiro Rezende
- Departamento de Neurologia, Faculdade de Ciências Médicas, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.
- Neuroimaging Laboratory, Rua Vital Brasil, 89-99, Cidade Universitária "Zeferino Vaz", Campinas, SP, 13083-888, Brazil.
- Medical Genetics Service, Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil.
| | - Laura Bannach Jardim
- Programa de Pós-Graduação em Ciências Médicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
- Centros de Pesquisa Clínica e Experimental, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
- Programa de Pós-Graduação em Genética e Biologia Molecular, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
- Serviço de Genética Médica, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.
- Departamento de Medicina Interna, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
- Medical Genetics Service, Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos 2350, Porto Alegre, 90035-003, Brazil.
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Doss DJ, Johnson GW, Narasimhan S, Shless JS, Jiang JW, González HFJ, Paulo DL, Lucas A, Davis KA, Chang C, Morgan VL, Constantinidis C, Dawant BM, Englot DJ. Deep Learning Segmentation of the Nucleus Basalis of Meynert on 3T MRI. AJNR Am J Neuroradiol 2023; 44:1020-1025. [PMID: 37562826 PMCID: PMC10494939 DOI: 10.3174/ajnr.a7950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 06/25/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND AND PURPOSE The nucleus basalis of Meynert is a key subcortical structure that is important in arousal and cognition and has been explored as a deep brain stimulation target but is difficult to study due to its small size, variability among patients, and lack of contrast on 3T MR imaging. Thus, our goal was to establish and evaluate a deep learning network for automatic, accurate, and patient-specific segmentations with 3T MR imaging. MATERIALS AND METHODS Patient-specific segmentations can be produced manually; however, the nucleus basalis of Meynert is difficult to accurately segment on 3T MR imaging, with 7T being preferred. Thus, paired 3T and 7T MR imaging data sets of 21 healthy subjects were obtained. A test data set of 6 subjects was completely withheld. The nucleus was expertly segmented on 7T, providing accurate labels for the paired 3T MR imaging. An external data set of 14 patients with temporal lobe epilepsy was used to test the model on brains with neurologic disorders. A 3D-Unet convolutional neural network was constructed, and a 5-fold cross-validation was performed. RESULTS The novel segmentation model demonstrated significantly improved Dice coefficients over the standard probabilistic atlas for both healthy subjects (mean, 0.68 [SD, 0.10] versus 0.45 [SD, 0.11], P = .002, t test) and patients (0.64 [SD, 0.10] versus 0.37 [SD, 0.22], P < .001). Additionally, the model demonstrated significantly decreased centroid distance in patients (1.18 [SD, 0.43] mm, 3.09 [SD, 2.56] mm, P = .007). CONCLUSIONS We developed the first model, to our knowledge, for automatic and accurate patient-specific segmentation of the nucleus basalis of Meynert. This model may enable further study into the nucleus, impacting new treatments such as deep brain stimulation.
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Affiliation(s)
- D J Doss
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
| | - G W Johnson
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
| | - S Narasimhan
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
- Department of Neurological Surgery (S.N., J.S.S., J.W.J., D.L.P., V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
| | - J S Shless
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Department of Neurological Surgery (S.N., J.S.S., J.W.J., D.L.P., V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
| | - J W Jiang
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Department of Neurological Surgery (S.N., J.S.S., J.W.J., D.L.P., V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
| | - H F J González
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
| | - D L Paulo
- Department of Neurological Surgery (S.N., J.S.S., J.W.J., D.L.P., V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
| | - A Lucas
- Department of Bioengineering (A.L.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - K A Davis
- Department of Neuroscience (K.A.D.), University of Pennsylvania, Philadelphia, Pennsylvania
- Center for Neuroengineering and Therapeutics (K.A.D.), University of Pennsylvania, Philadelphia, Pennsylvania
- Neurology (K.A.D.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - C Chang
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
- Department of Electrical and Computer Engineering (C. Chang, B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Department of Computer Science (C. Chang), Vanderbilt University, Nashville, Tennessee
| | - V L Morgan
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
- Department of Neurological Surgery (S.N., J.S.S., J.W.J., D.L.P., V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Neurology (V.L.M.), Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Radiological Sciences (V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
| | - C Constantinidis
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Department of Ophthalmology and Visual Sciences (C. Constantinidis), Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Neuroscience (C. Constantinidis), Vanderbilt University, Nashville, Tennessee
| | - B M Dawant
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
- Department of Electrical and Computer Engineering (C. Chang, B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
| | - D J Englot
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
- Department of Neurological Surgery (S.N., J.S.S., J.W.J., D.L.P., V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Electrical and Computer Engineering (C. Chang, B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Department of Radiological Sciences (V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
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Shin H, Jeong H, Ryu W, Lee G, Lee J, Kim D, Song IU, Chung YA, Lee S. Robotic transcranial magnetic stimulation in the treatment of depression: a pilot study. Sci Rep 2023; 13:14074. [PMID: 37640754 PMCID: PMC10462606 DOI: 10.1038/s41598-023-41044-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023] Open
Abstract
There has been an increasing demand for robotic coil positioning during repetitive transcranial magnetic stimulation (rTMS) treatment. Accurate coil positioning is crucial because rTMS generally targets specific brain regions for both research and clinical application with other reasons such as safety, consistency and reliability and individual variablity. Some previous studies have employed industrial robots or co-robots and showed they can more precisely stimulate the target cortical regions than traditional manual methods. In this study, we not only developed a custom-TMS robot for better TMS coil placement but also analyzed the therapeutic effects on depression. Treatment effects were evaluated by measuring regional cerebral blood flow (rCBF) using single-photon emission computed tomography and depression severity before and after rTMS for the two positioning methods. The rTMS preparation time with our robotic coil placement was reduced by 53% compared with that of the manual method. The position and orientation errors were also significantly reduced from 11.17 mm and 4.06° to 0.94 mm and 0.11°, respectively, confirming the superiority of robotic positioning. The results from clinical and neuroimaging assessments indicated comparable improvements in depression severity and rCBF in the left dorsolateral prefrontal cortex between the robotic and manual rTMS groups. A questionnaire was used to determine the patients' feelings about the robotic system, including the safety and preparation time. A high safety score indicated good acceptability of robotic rTMS at the clinical site.
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Affiliation(s)
- Hyunsoo Shin
- Department of Electrical and Electronic Engineering, Hanyang University, Ansan, 15588, Republic of Korea
| | - Hyeonseok Jeong
- Department of Radiology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 21431, Republic of Korea
- Department of Neurology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 21431, Republic of Korea
| | - Wooseok Ryu
- Tesollo Inc., Gwangmyeong, 14353, Republic of Korea
| | - Geunhu Lee
- Department of Electrical and Electronic Engineering, Hanyang University, Ansan, 15588, Republic of Korea
| | - Jaeho Lee
- Department of Electrical and Electronic Engineering, Hanyang University, Ansan, 15588, Republic of Korea
| | - Doyu Kim
- Department of Nuclear Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 21431, Republic of Korea
| | - In-Uk Song
- Department of Neurology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 21431, Republic of Korea
| | - Yong-An Chung
- Department of Nuclear Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 21431, Republic of Korea.
| | - Sungon Lee
- Department of Electrical and Electronic Engineering, Hanyang University, Ansan, 15588, Republic of Korea.
- Department of Robotics, Hanyang University, Ansan, 15588, Republic of Korea.
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Fortel I, Zhan L, Ajilore O, Wu Y, Mackin S, Leow A. Disrupted excitation-inhibition balance in cognitively normal individuals at risk of Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.21.554061. [PMID: 37662359 PMCID: PMC10473582 DOI: 10.1101/2023.08.21.554061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background Sex differences impact Alzheimer's disease (AD) neuropathology, but cell-to-network level dysfunctions in the prodromal phase are unclear. Alterations in hippocampal excitation-inhibition balance (EIB) have recently been linked to early AD pathology. Objective Examine how AD risk factors (age, APOE-ɛ4, amyloid-β) relate to hippocampal EIB in cognitively normal males and females using connectome-level measures. Methods Individuals from the OASIS-3 cohort (age 42-95) were studied (N = 437), with a subset aged 65+ undergoing neuropsychological testing (N = 231). Results In absence of AD risk factors (APOE-ɛ4/Aβ+), whole-brain EIB decreases with age more significantly in males than females (p = 0.021, β = -0.007). Regression modeling including APOE-ɛ4 allele carriers (Aβ-) yielded a significant positive AGE-by-APOE interaction in the right hippocampus for females only (p = 0.013, β = 0.014), persisting with inclusion of Aβ+ individuals (p = 0.012, β = 0.014). Partial correlation analyses of neuropsychological testing showed significant associations with EIB in females: positive correlations between right hippocampal EIB with categorical fluency and whole-brain EIB with the trail-making test (p < 0.05). Conclusion Sex differences in EIB emerge during normal aging and progresses differently with AD risk. Results suggest APOE-ɛ4 disrupts hippocampal balance more than amyloid in females. Increased excitation correlates positively with neuropsychological performance in the female group, suggesting a duality in terms of potential beneficial effects prior to cognitive impairment. This underscores the translational relevance of APOE-ɛ4 related hyperexcitation in females, potentially informing therapeutic targets or early interventions to mitigate AD progression in this vulnerable population.
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Affiliation(s)
- Igor Fortel
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL
| | - Yichao Wu
- Department of Math, Statistics and Computer Science, University of Illinois at Chicago, Chicago, IL
| | - Scott Mackin
- Department of Psychiatry, University of California - San Francisco, San Francisco, CA
| | - Alex Leow
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL
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Cackowski S, Barbier EL, Dojat M, Christen T. ImUnity: A generalizable VAE-GAN solution for multicenter MR image harmonization. Med Image Anal 2023; 88:102799. [PMID: 37245434 DOI: 10.1016/j.media.2023.102799] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 05/30/2023]
Abstract
ImUnity is an original 2.5D deep-learning model designed for efficient and flexible MR image harmonization. A VAE-GAN network, coupled with a confusion module and an optional biological preservation module, uses multiple 2D slices taken from different anatomical locations in each subject of the training database, as well as image contrast transformations for its training. It eventually generates 'corrected' MR images that can be used for various multi-center population studies. Using 3 open source databases (ABIDE, OASIS and SRPBS), which contain MR images from multiple acquisition scanner types or vendors and a large range of subjects ages, we show that ImUnity: (1) outperforms state-of-the-art methods in terms of quality of images generated using traveling subjects; (2) removes sites or scanner biases while improving patients classification; (3) harmonizes data coming from new sites or scanners without the need for an additional fine-tuning and (4) allows the selection of multiple MR reconstructed images according to the desired applications. Tested here on T1-weighted images, ImUnity could be used to harmonize other types of medical images.
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Affiliation(s)
- Stenzel Cackowski
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France.
| | - Emmanuel L Barbier
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France.
| | - Michel Dojat
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France.
| | - Thomas Christen
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France.
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Johnson GW, Doss DJ, Morgan VL, Paulo DL, Cai LY, Shless JS, Negi AS, Gummadavelli A, Kang H, Reddy SB, Naftel RP, Bick SK, Williams Roberson S, Dawant BM, Wallace MT, Englot DJ. The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence. Brain 2023; 146:2828-2845. [PMID: 36722219 PMCID: PMC10316780 DOI: 10.1093/brain/awad016] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 12/24/2022] [Accepted: 01/08/2023] [Indexed: 02/02/2023] Open
Abstract
Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.
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Affiliation(s)
- Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Derek J Doss
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Danika L Paulo
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Jared S Shless
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Aarushi S Negi
- Department of Neuroscience, Vanderbilt University, Nashville, TN 37232, USA
| | - Abhijeet Gummadavelli
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37232, USA
| | - Shilpa B Reddy
- Department of Pediatrics, Vanderbilt Children’s Hospital, Nashville, TN 37232, USA
| | - Robert P Naftel
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Sarah K Bick
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | - Benoit M Dawant
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
| | - Mark T Wallace
- Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN 37232, USA
- Department of Psychology, Vanderbilt University, Nashville, TN 37232, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University, Nashville, TN 37232, USA
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37232, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
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Begg A, Louey MGY, Pearce P, Bulluss K, Thevathasan W, McDermott HJ, Perera T. Evaluation of the PaCER Algorithm for Postoperative Subthalamic Nucleus Deep Brain Stimulation Electrode Localization . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083396 DOI: 10.1109/embc40787.2023.10340555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Deep Brain Stimulation (DBS) is an established therapy for many movement disorders. DBS entails electrical stimulation of precise brain structures using permanently implanted electrodes. Following implantation, locating the electrodes relative to the target brain structure assists patient outcome optimization. Here we evaluated an open-source automatic algorithm (PaCER) to localize individual electrodes on Computed Tomography imaging (co-registered to Magnetic Resonance Imaging). In a dataset of 111 participants, we found a modified version of the algorithm matched manual-markups with median error less than 0.191 mm (interquartile range 0.698 mm). Given the error is less than the voxel resolution (1 mm3) of the images, we conclude that the automatic algorithm is suitable for DBS electrode localizations.Clinical Relevance- Automated DBS electrode localization identifies the closest electrode to the target brain structure; allowing the neurologist to direct electrical stimulation to maximize patient outcomes. Further, if none of the electrodes are deemed suitable, localization will guide re-implantation.
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Tabassum M, Al Suman A, Russo C, Di Ieva A, Liu S. A Deep Learning Framework for Skull Stripping in Brain MRI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082786 DOI: 10.1109/embc40787.2023.10340846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Skull-stripping, an important pre-processing step in neuroimage computing, involves the automated removal of non-brain anatomy (such as the skull, eyes, and ears) from brain images to facilitate brain segmentation and analysis. Manual segmentation is still practiced, but it is time-consuming and highly dependent on the expertise of clinicians or image analysts. Prior studies have developed various skull-stripping algorithms that perform well on brains with mild or no structural abnormalities. Nonetheless, they were not able to address the issue for brains with significant morphological changes, such as those caused by brain tumors, particularly when the tumors are located near the skull's border. In such cases, a portion of the normal brain may be stripped, or the reverse may occur during skull stripping. To address this limitation, we propose to use a novel deep learning framework based on nnUNet for skull stripping in brain MRI. Two publicly available datasets were used to evaluate the proposed method, including a normal brain MRI dataset - The Neurofeedback Skull-stripped Repository (NFBS), and a brain tumor MRI dataset - The Cancer Genome Atlas (TCGA). The method proposed in the study performed better than six other current methods, namely BSE, ROBEX, UNet, SC-UNet, MV-UNet, and 3D U-Net. The proposed method achieved an average Dice coefficient of 0.9960, a sensitivity of 0.9999, and a specificity of 0.9996 on the NFBS dataset, and an average Dice coefficient of 0.9296, a sensitivity of 0.9288, a specificity of 0.9866 and an accuracy of 0.9762 on the TCGA brain tumor dataset.
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Berger A, Koshmanova E, Beckers E, Sharifpour R, Paparella I, Campbell I, Mortazavi N, Balda F, Yi YJ, Lamalle L, Dricot L, Phillips C, Jacobs HIL, Talwar P, El Tahry R, Sherif S, Vandewalle G. Structural and functional characterization of the locus coeruleus in young and late middle-aged individuals. FRONTIERS IN NEUROIMAGING 2023; 2:1207844. [PMID: 37554637 PMCID: PMC10406214 DOI: 10.3389/fnimg.2023.1207844] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/05/2023] [Indexed: 08/10/2023]
Abstract
INTRODUCTION The brainstem locus coeruleus (LC) influences a broad range of brain processes, including cognition. The so-called LC contrast is an accepted marker of the integrity of the LC that consists of a local hyperintensity on specific Magnetic Resonance Imaging (MRI) structural images. The small size of the LC has, however, rendered its functional characterization difficult in humans, including in aging. A full characterization of the structural and functional characteristics of the LC in healthy young and late middle-aged individuals is needed to determine the potential roles of the LC in different medical conditions. Here, we wanted to determine whether the activation of the LC in a mismatch negativity task changes in aging and whether the LC functional response was associated to the LC contrast. METHODS We used Ultra-High Field (UHF) 7-Tesla functional MRI (fMRI) to record brain response during an auditory oddball task in 53 healthy volunteers, including 34 younger (age: 22.15y ± 3.27; 29 women) and 19 late middle-aged (age: 61.05y ± 5.3; 14 women) individuals. RESULTS Whole-brain analyses confirmed brain responses in the typical cortical and subcortical regions previously associated with mismatch negativity. When focusing on the brainstem, we found a significant response in the rostral part of the LC probability mask generated based on individual LC images. Although bilateral, the activation was more extensive in the left LC. Individual LC activity was not significantly different between young and late middle-aged individuals. Importantly, while the LC contrast was higher in older individuals, the functional response of the LC was not significantly associated with its contrast. DISCUSSION These findings may suggest that the age-related alterations of the LC structural integrity may not be related to changes in its functional response. The results further suggest that LC responses may remain stable in healthy individuals aged 20 to 70.
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Affiliation(s)
- Alexandre Berger
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, Brussels, Belgium
- Synergia Medical SA, Mont-Saint-Guibert, Belgium
| | - Ekaterina Koshmanova
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Elise Beckers
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
- Alzheimer Centre Limburg, Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Roya Sharifpour
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Ilenia Paparella
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Islay Campbell
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Nasrin Mortazavi
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Fermin Balda
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Yeo-Jin Yi
- Institute of Cognitive Neurology and Dementia Research, Department of Natural Sciences, Faculty of Medicine, Otto-von-Guericke-University, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Laurent Lamalle
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Laurence Dricot
- Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, Brussels, Belgium
| | - Christophe Phillips
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Heidi I. L. Jacobs
- Alzheimer Centre Limburg, Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Puneet Talwar
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Riëm El Tahry
- Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, Brussels, Belgium
- Center for Refractory Epilepsy, Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
- Walloon Excellence in Life Sciences and Biotechnology (WELBIO) Department, WEL Research Institute, Wavre, Belgium
| | - Siya Sherif
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Gilles Vandewalle
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
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Heitkamp A, Madesta F, Amberg S, Wahaj S, Schröder T, Bechstein M, Meyer L, Broocks G, Hanning U, Gauer T, Werner R, Fiehler J, Gellißen S, Kniep HC. Discordant and Converting Receptor Expressions in Brain Metastases from Breast Cancer: MRI-Based Non-Invasive Receptor Status Tracking. Cancers (Basel) 2023; 15:cancers15112880. [PMID: 37296843 DOI: 10.3390/cancers15112880] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 05/15/2023] [Accepted: 05/18/2023] [Indexed: 06/12/2023] Open
Abstract
Discordance and conversion of receptor expressions in metastatic lesions and primary tumors is often observed in patients with brain metastases from breast cancer. Therefore, personalized therapy requires continuous monitoring of receptor expressions and dynamic adaptation of applied targeted treatment options. Radiological in vivo techniques may allow receptor status tracking at high frequencies at low risk and cost. The present study aims to investigate the potential of receptor status prediction through machine-learning-based analysis of radiomic MR image features. The analysis is based on 412 brain metastases samples from 106 patients acquired between 09/2007 and 09/2021. Inclusion criteria were as follows: diagnosed cerebral metastases from breast cancer; histopathology reports on progesterone (PR), estrogen (ER), and human epidermal growth factor 2 (HER2) receptor status; and availability of MR imaging data. In total, 3367 quantitative features of T1 contrast-enhanced, T1 non-enhanced, and FLAIR images and corresponding patient age were evaluated utilizing random forest algorithms. Feature importance was assessed using Gini impurity measures. Predictive performance was tested using 10 permuted 5-fold cross-validation sets employing the 30 most important features of each training set. Receiver operating characteristic areas under the curves of the validation sets were 0.82 (95% confidence interval [0.78; 0.85]) for ER+, 0.73 [0.69; 0.77] for PR+, and 0.74 [0.70; 0.78] for HER2+. Observations indicate that MR image features employed in a machine learning classifier could provide high discriminatory accuracy in predicting the receptor status of brain metastases from breast cancer.
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Affiliation(s)
- Alexander Heitkamp
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Frederic Madesta
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Sophia Amberg
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Schohla Wahaj
- Department of Radiotherapy and Radiation Oncology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Tanja Schröder
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Matthias Bechstein
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Lukas Meyer
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Gabriel Broocks
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Uta Hanning
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Tobias Gauer
- Department of Radiotherapy and Radiation Oncology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - René Werner
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Susanne Gellißen
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Helge C Kniep
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
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Chakrabarty S, Abidi SA, Mousa M, Mokkarala M, Hren I, Yadav D, Kelsey M, LaMontagne P, Wood J, Adams M, Su Y, Thorpe S, Chung C, Sotiras A, Marcus DS. Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO). JCO Clin Cancer Inform 2023; 7:e2200177. [PMID: 37146265 PMCID: PMC10281444 DOI: 10.1200/cci.22.00177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/25/2023] [Accepted: 03/06/2023] [Indexed: 05/07/2023] Open
Abstract
PURPOSE Efforts to use growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling, owing to data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements. MATERIALS AND METHODS Our end-to-end framework (1) classifies MRI sequences using an ensemble classifier, (2) preprocesses the data in a reproducible manner, (3) delineates tumor tissue subtypes using convolutional neural networks, and (4) extracts diverse radiomic features. Moreover, it is robust to missing sequences and adopts an expert-in-the-loop approach in which the segmentation results may be manually refined by radiologists. After the implementation of the framework in Docker containers, it was applied to two retrospective glioma data sets collected from the Washington University School of Medicine (WUSM; n = 384) and The University of Texas MD Anderson Cancer Center (MDA; n = 30), comprising preoperative MRI scans from patients with pathologically confirmed gliomas. RESULTS The scan-type classifier yielded an accuracy of >99%, correctly identifying sequences from 380 of 384 and 30 of 30 sessions from the WUSM and MDA data sets, respectively. Segmentation performance was quantified using the Dice Similarity Coefficient between the predicted and expert-refined tumor masks. The mean Dice scores were 0.882 (±0.244) and 0.977 (±0.04) for whole-tumor segmentation for WUSM and MDA, respectively. CONCLUSION This streamlined framework automatically curated, processed, and segmented raw MRI data of patients with varying grades of gliomas, enabling the curation of large-scale neuro-oncology data sets and demonstrating high potential for integration as an assistive tool in clinical practice.
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Affiliation(s)
- Satrajit Chakrabarty
- Department of Electrical and Systems Engineering, Washington University in St Louis, St Louis, MO
| | - Syed Amaan Abidi
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
| | - Mina Mousa
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
| | - Mahati Mokkarala
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
| | - Isabelle Hren
- Department of Computer Science & Engineering, Washington University in St Louis, St Louis, MO
| | - Divya Yadav
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Matthew Kelsey
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
| | - John Wood
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Michael Adams
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Yuzhuo Su
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Sherry Thorpe
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Caroline Chung
- Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
- Institute for Informatics, Washington University School of Medicine, St Louis, MO
| | - Daniel S. Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO
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Fama R, Müller-Oehring EM, Levine TF, Sullivan EV, Sassoon SA, Asok P, Brontë-Stewart HM, Poston KL, Pohl KM, Pfefferbaum A, Schulte T. Episodic memory deficit in HIV infection: common phenotype with Parkinson's disease, different neural substrates. Brain Struct Funct 2023; 228:845-858. [PMID: 37069296 PMCID: PMC10147801 DOI: 10.1007/s00429-023-02626-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 03/03/2023] [Indexed: 04/19/2023]
Abstract
Episodic memory deficits occur in people living with HIV (PLWH) and individuals with Parkinson's disease (PD). Given known effects of HIV and PD on frontolimbic systems, episodic memory deficits are often attributed to executive dysfunction. Although executive dysfunction, evidenced as retrieval deficits, is relevant to mnemonic deficits, learning deficits may also contribute. Here, the California Verbal Learning Test-II, administered to 42 PLWH, 41 PD participants, and 37 controls, assessed learning and retrieval using measures of free recall, cued recall, and recognition. Executive function was assessed with a composite score comprising Stroop Color-Word Reading and Backward Digit Spans. Neurostructural correlates were examined with MRI of frontal (precentral, superior, orbital, middle, inferior, supplemental motor, medial) and limbic (hippocampus, thalamus) volumes. HIV and PD groups were impaired relative to controls on learning and free and cued recall trials but did not differ on recognition or retention of learned material. In no case did executive functioning solely account for the observed mnemonic deficits or brain-performance relations. Critically, the shared learning and retrieval deficits in HIV and PD were related to different substrates of frontolimbic mnemonic neurocircuitry. Specifically, diminished learning and poorer free and cued recall were related to smaller orbitofrontal volume in PLWH but not PD, whereas diminished learning in PD but not PLWH was related to smaller frontal superior volume. In PD, poorer recognition correlated with smaller thalamic volume and poorer retention to hippocampal volume. Although memory deficits were similar, the neural correlates in HIV and PD suggest different pathogenic mechanisms.
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Affiliation(s)
- Rosemary Fama
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd, Stanford, CA, 94305, USA
- Neuroscience Program, Center for Health Sciences, Bioscience Division, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA
| | - Eva M Müller-Oehring
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd, Stanford, CA, 94305, USA.
- Neuroscience Program, Center for Health Sciences, Bioscience Division, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA.
| | - Taylor F Levine
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Edith V Sullivan
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd, Stanford, CA, 94305, USA
| | - Stephanie A Sassoon
- Neuroscience Program, Center for Health Sciences, Bioscience Division, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA
| | - Priya Asok
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd, Stanford, CA, 94305, USA
- Neuroscience Program, Center for Health Sciences, Bioscience Division, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA
| | - Helen M Brontë-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Kathleen L Poston
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Kilian M Pohl
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd, Stanford, CA, 94305, USA
- Neuroscience Program, Center for Health Sciences, Bioscience Division, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA
| | - Adolf Pfefferbaum
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd, Stanford, CA, 94305, USA
- Neuroscience Program, Center for Health Sciences, Bioscience Division, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA
| | - Tilman Schulte
- Neuroscience Program, Center for Health Sciences, Bioscience Division, SRI International, 333 Ravenswood Ave, Menlo Park, CA, 94025, USA
- Clinical Psychology, Palo Alto University, 1791 Arastradero Rd, Palo Alto, CA, 94304, USA
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Murty DVPS, Song S, Surampudi SG, Pessoa L. Threat and Reward Imminence Processing in the Human Brain. J Neurosci 2023; 43:2973-2987. [PMID: 36927571 PMCID: PMC10124955 DOI: 10.1523/jneurosci.1778-22.2023] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 03/03/2023] [Accepted: 03/12/2023] [Indexed: 03/18/2023] Open
Abstract
In the human brain, aversive and appetitive processing have been studied with controlled stimuli in rather static settings. In addition, the extent to which aversive-related and appetitive-related processing engage distinct or overlapping circuits remains poorly understood. Here, we sought to investigate the dynamics of aversive and appetitive processing while male and female participants engaged in comparable trials involving threat avoidance or reward seeking. A central goal was to characterize the temporal evolution of responses during periods of threat or reward imminence. For example, in the aversive domain, we predicted that the bed nucleus of the stria terminalis (BST), but not the amygdala, would exhibit anticipatory responses given the role of the former in anxious apprehension. We also predicted that the periaqueductal gray (PAG) would exhibit threat-proximity responses based on its involvement in proximal-threat processes, and that the ventral striatum would exhibit threat-imminence responses given its role in threat escape in rodents. Overall, we uncovered imminence-related temporally increasing ("ramping") responses in multiple brain regions, including the BST, PAG, and ventral striatum, subcortically, and dorsal anterior insula and anterior midcingulate, cortically. Whereas the ventral striatum generated anticipatory responses in the proximity of reward as expected, it also exhibited threat-related imminence responses. In fact, across multiple brain regions, we observed a main effect of arousal. In other words, we uncovered extensive temporally evolving, imminence-related processing in both the aversive and appetitive domain, suggesting that distributed brain circuits are dynamically engaged during the processing of biologically relevant information regardless of valence, findings further supported by network analysis.SIGNIFICANCE STATEMENT In the human brain, aversive and appetitive processing have been studied with controlled stimuli in rather static settings. Here, we sought to investigate the dynamics of aversive/appetitive processing while participants engaged in trials involving threat avoidance or reward seeking. A central goal was to characterize the temporal evolution of responses during periods of threat or reward imminence. We uncovered imminence-related temporally increasing ("ramping") responses in multiple brain regions, including the bed nucleus of the stria terminalis, periaqueductal gray, and ventral striatum, subcortically, and dorsal anterior insula and anterior midcingulate, cortically. Overall, we uncovered extensive temporally evolving, imminence-related processing in both the aversive and appetitive domain, suggesting that distributed brain circuits are dynamically engaged during the processing of biologically relevant information regardless of valence.
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Affiliation(s)
| | - Songtao Song
- Department of Psychology, University of Maryland, College Park, Maryland 20742
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, Maryland 20742
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Gangolli M, Wang WT, Gai ND, Pham DL, Butman JA. Simultaneous Acquisition of Diffusion Tensor and Dynamic Diffusion MRI. J Magn Reson Imaging 2023; 57:1079-1092. [PMID: 36056625 PMCID: PMC9981815 DOI: 10.1002/jmri.28407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Dynamic diffusion magnetic resonance imaging (ddMRI) metrics can assess transient microstructural alterations in tissue diffusivity but requires additional scan time hindering its clinical application. PURPOSE To determine whether a diffusion gradient table can simultaneously acquire data to estimate dynamic and diffusion tensor imaging (DTI) metrics. STUDY TYPE Prospective. SUBJECTS Seven healthy subjects, 39 epilepsy patients (15 female, 31 male, age ± 15). FIELD STRENGTH/SEQUENCE Two-dimensional diffusion MRI (b = 1000 s/mm2 ) at a field strength of 3 T. Sessions in healthy subjects-standard ddMRI (30 directions), standard DTI (15 and 30 directions), and nested cubes scans (15 and 30 directions). Sessions in epilepsy patients-two 30 direction (standard ddMRI, 10 nested cubes) or two 15 direction scans (standard DTI, 5 nested cubes). ASSESSMENT Fifteen direction DTI was repeated twice for within-session test-retest measurements in healthy subjects. Bland-Altman analysis computed bias and limits of agreement for DTI metrics using test-retest scans and standard 15 direction vs. 5 nested cubes scans. Intraclass correlation (ICC) analysis compared tensor metrics between 15 direction DTI scans (standard vs. 5 nested cubes) and the coefficients of variation (CoV) of trace and apparent diffusion coefficient (ADC) between 30 direction ddMRI scans (standard vs. 10 nested cubes). STATISTICAL TESTS Bland-Altman and ICC analysis using a P-value of 0.05 for statistical significance. RESULTS Correlations of mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were strong and significant in gray (ICC > 0.95) and white matter (ICC > 0.95) between standard vs. nested cubes DTI acquisitions. Correlation of white matter fractional anisotropy was also strong (ICC > 0.95) and significant. ICCs of the CoV of dynamic ADC measured using repeated cubes and nested cubes acquisitions were modest (ICC >0.60), but significant in gray matter. CONCLUSION A nested cubes diffusion gradient table produces tensor-based and dynamic diffusion measurements in a single acquisition. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Mihika Gangolli
- Center for Neuroscience and Regenerative Medicine
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc
| | - Wen-Tung Wang
- National Institutes of Health, Radiology and Imaging Sciences
| | - Neville D. Gai
- National Institutes of Health, National Heart Lung and Blood Institute
| | - Dzung L. Pham
- Center for Neuroscience and Regenerative Medicine
- Uniformed Services University, Radiology and Radiological Sciences
| | - John A. Butman
- Center for Neuroscience and Regenerative Medicine
- National Institutes of Health, Radiology and Imaging Sciences
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50
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Koshmanova E, Berger A, Beckers E, Campbell I, Mortazavi N, Sharifpour R, Paparella I, Balda F, Berthomier C, Degueldre C, Salmon E, Lamalle L, Bastin C, Egroo MV, Phillips C, Maquet P, Collette F, Muto V, Chylinski D, Jacobs HI, Talwar P, Sherif S, Vandewalle G. In vivo Locus Coeruleus activity while awake is associated with REM sleep quality in healthy older individuals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.10.527974. [PMID: 36993680 PMCID: PMC10054994 DOI: 10.1101/2023.02.10.527974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The locus coeruleus (LC) is the primary source of norepinephrine (NE) in the brain, and the LC-NE system is involved in regulating arousal and sleep. It plays key roles in the transition between sleep and wakefulness, and between slow wave sleep (SWS) and rapid eye movement sleep (REMS). However, it is not clear whether the LC activity during the day predicts sleep quality and sleep properties during the night, and how this varies as a function of age. Here, we used 7 Tesla functional Magnetic Resonance Imaging (7T fMRI), sleep electroencephalography (EEG) and a sleep questionnaire to test whether the LC activity during wakefulness was associated with sleep quality in 52 healthy younger (N=33; ~22y; 28 women) and older (N=19; ~61y; 14 women) individuals. We find that, in older, but not in younger participants, higher LC activity, as probed during an auditory mismatch negativity task, is associated with worse subjective sleep quality and with lower power over the EEG theta band during REMS (4-8Hz), which are two sleep parameters significantly correlated in our sample of older individuals. The results remain robust even when accounting for the age-related changes in the integrity of the LC. These findings suggest that the activity of the LC may contribute to the perception of the sleep quality and to an essential oscillatory mode of REMS, and that the LC may be an important target in the treatment of sleep disorders and age-related diseases.
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