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Denis C, Dabbs K, Nair VA, Mathis J, Almane DN, Lakshmanan A, Nencka A, Birn RM, Conant L, Humphries C, Felton E, Raghavan M, DeYoe EA, Binder JR, Hermann B, Prabhakaran V, Bendlin BB, Meyerand ME, Boly M, Struck AF. T1-/T2-weighted ratio reveals no alterations to gray matter myelination in temporal lobe epilepsy. Ann Clin Transl Neurol 2023; 10:2149-2154. [PMID: 37872734 PMCID: PMC10647008 DOI: 10.1002/acn3.51653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/29/2022] [Accepted: 06/09/2022] [Indexed: 10/25/2023] Open
Abstract
Short-range functional connectivity in the limbic network is increased in patients with temporal lobe epilepsy (TLE), and recent studies have shown that cortical myelin content correlates with fMRI connectivity. We thus hypothesized that myelin may increase progressively in the epileptic network. We compared T1w/T2w gray matter myelin maps between TLE patients and age-matched controls and assessed relationships between myelin and aging. While both TLE patients and healthy controls exhibited increased T1w/T2w intensity with age, we found no evidence for significant group-level aberrations in overall myelin content or myelin changes through time in TLE.
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Affiliation(s)
- Colin Denis
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Kevin Dabbs
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Veena A. Nair
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Jedidiah Mathis
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Dace N. Almane
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | | | - Andrew Nencka
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Rasmus M. Birn
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of PsychiatryUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Lisa Conant
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Colin Humphries
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Elizabeth Felton
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Manoj Raghavan
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Edgar A. DeYoe
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Jeffrey R. Binder
- Department of NeurologyMedical College of WisconsinMilwaukeeWisconsinUSA
- Department of BiophysicsMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Bruce Hermann
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Vivek Prabhakaran
- Department of RadiologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Barbara B. Bendlin
- Department of MedicineUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Mary E. Meyerand
- Department of Medical PhysicsUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Mélanie Boly
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- Department of PsychiatryUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
| | - Aaron F. Struck
- Department of NeurologyUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
- William S. Middleton Veterans Administration HospitalMadisonWisconsinUSA
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2
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Panigrahy A, Schmithorst V, Ceschin R, Lee V, Beluk N, Wallace J, Wheaton O, Chenevert T, Qiu D, Lee JN, Nencka A, Gagoski B, Berman JI, Yuan W, Macgowan C, Coatsworth J, Fleysher L, Cannistraci C, Sleeper LA, Hoskoppal A, Silversides C, Radhakrishnan R, Markham L, Rhodes JF, Dugan LM, Brown N, Ermis P, Fuller S, Cotts TB, Rodriguez FH, Lindsay I, Beers S, Aizenstein H, Bellinger DC, Newburger JW, Umfleet LG, Cohen S, Zaidi A, Gurvitz M. Design and Harmonization Approach for the Multi-Institutional Neurocognitive Discovery Study (MINDS) of Adult Congenital Heart Disease (ACHD) Neuroimaging Ancillary Study: A Technical Note. J Cardiovasc Dev Dis 2023; 10:381. [PMID: 37754810 PMCID: PMC10532244 DOI: 10.3390/jcdd10090381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 09/28/2023] Open
Abstract
Dramatic advances in the management of congenital heart disease (CHD) have improved survival to adulthood from less than 10% in the 1960s to over 90% in the current era, such that adult CHD (ACHD) patients now outnumber their pediatric counterparts. ACHD patients demonstrate domain-specific neurocognitive deficits associated with reduced quality of life that include deficits in educational attainment and social interaction. Our hypothesis is that ACHD patients exhibit vascular brain injury and structural/physiological brain alterations that are predictive of specific neurocognitive deficits modified by behavioral and environmental enrichment proxies of cognitive reserve (e.g., level of education and lifestyle/social habits). This technical note describes an ancillary study to the National Heart, Lung, and Blood Institute (NHLBI)-funded Pediatric Heart Network (PHN) "Multi-Institutional Neurocognitive Discovery Study (MINDS) in Adult Congenital Heart Disease (ACHD)". Leveraging clinical, neuropsychological, and biospecimen data from the parent study, our study will provide structural-physiological correlates of neurocognitive outcomes, representing the first multi-center neuroimaging initiative to be performed in ACHD patients. Limitations of the study include recruitment challenges inherent to an ancillary study, implantable cardiac devices, and harmonization of neuroimaging biomarkers. Results from this research will help shape the care of ACHD patients and further our understanding of the interplay between brain injury and cognitive reserve.
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Affiliation(s)
- Ashok Panigrahy
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
- Department of Pediatric Radiology, Children’s Hospital of Pittsburgh of UPMC, 45th Str., Penn Ave., Pittsburgh, PA 15201, USA
| | - Vanessa Schmithorst
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Rafael Ceschin
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Vince Lee
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Nancy Beluk
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Julia Wallace
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Olivia Wheaton
- HealthCore Inc., 480 Pleasant Str., Watertown, MA 02472, USA;
| | - Thomas Chenevert
- Department of Radiology, Michigan Medicine University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA;
- Congenital Heart Center, C. S. Mott Children’s Hospital, 1540 E Hospital Dr., Ann Arbor, MI 48109, USA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, Emory School of Medicine, 1364 Clifton Rd., Atlanta, GA 30322, USA;
| | - James N Lee
- Department of Radiology, The University of Utah, 50 2030 E, Salt Lake City, UT 84112, USA;
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, 9200 W Wisconsin Ave., Milwaukee, WI 53226, USA;
| | - Borjan Gagoski
- Department of Radiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA;
| | - Jeffrey I. Berman
- Department of Radiology, Children’s Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA 19104, USA;
| | - Weihong Yuan
- Department of Radiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH 45229, USA;
- Department of Radiology, University of Cincinnati College of Medicine, 3230 Eden Ave., Cincinnati, OH 45267, USA
| | - Christopher Macgowan
- Department of Medical Biophysics, University of Toronto, 101 College Str. Suite 15-701, Toronto, ON M5G 1L7, Canada;
- The Hospital for Sick Children Division of Translational Medicine, 555 University Ave., Toronto, ON M5G 1X8, Canada
| | - James Coatsworth
- Department of Radiology, Medical University of South Carolina, 171 Ashley Ave., Room 372, Charleston, SC 29425, USA;
| | - Lazar Fleysher
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave., New York, NY 10029, USA; (L.F.); (C.C.); (A.Z.)
| | - Christopher Cannistraci
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave., New York, NY 10029, USA; (L.F.); (C.C.); (A.Z.)
| | - Lynn A. Sleeper
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA; (L.A.S.); (J.W.N.); (M.G.)
| | - Arvind Hoskoppal
- Department of Radiology, UPMC Children’s Hospital of Pittsburgh, 4401 Penn Ave. Floor 2, Pittsburgh, PA 15224, USA; (V.S.); (R.C.); (V.L.); (N.B.); (J.W.); (A.H.)
| | - Candice Silversides
- Department of Cardiology, University of Toronto, C. David Naylor Building, 6 Queen’s Park Crescent West, Third Floor, Toronto, ON M5S 3H2, Canada;
| | - Rupa Radhakrishnan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 University Blvd., Indianapolis, IN 46202, USA;
| | - Larry Markham
- Department of Cardiology, University of Indiana School of Medicine, 545 Barnhill Dr., Indianapolis, IN 46202, USA;
| | - John F. Rhodes
- Department of Cardiology, Medical University of South Carolina, 96 Jonathan Lucas Str. Ste. 601, MSC 617, Charleston, SC 29425, USA;
| | - Lauryn M. Dugan
- Department of Cardiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH 45229, USA; (L.M.D.); (N.B.)
| | - Nicole Brown
- Department of Cardiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave., Cincinnati, OH 45229, USA; (L.M.D.); (N.B.)
| | - Peter Ermis
- Department of Radiology, Texas Children’s Hospital, Houston, TX 77030, USA; (P.E.); (S.F.)
| | - Stephanie Fuller
- Department of Radiology, Texas Children’s Hospital, Houston, TX 77030, USA; (P.E.); (S.F.)
| | - Timothy Brett Cotts
- Departments of Internal Medicine and Pediatrics, Michigan Medicine University of Michigan, 1500 E Medical Center Dr., Ann Arbor, MI 48109, USA;
| | - Fred Henry Rodriguez
- Department of Cardiology, Emory School of Medicine, 100 Woodruff Circle, Atlanta, GA 30322, USA;
| | - Ian Lindsay
- Department of Cardiology, The University of Utah, 95 S 2000 E, Salt Lake City, UT 84112, USA;
| | - Sue Beers
- Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O’Hara Str., Pittsburgh, PA 15213, USA; (S.B.); (H.A.)
| | - Howard Aizenstein
- Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O’Hara Str., Pittsburgh, PA 15213, USA; (S.B.); (H.A.)
| | - David C. Bellinger
- Cardiac Neurodevelopmental Program, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA;
| | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA; (L.A.S.); (J.W.N.); (M.G.)
| | - Laura Glass Umfleet
- Department of Neuropsychology, Medical College of Wisconsin, 9200 W Wisconsin Ave., Milwaukee, WI 53226, USA;
| | - Scott Cohen
- Heart and Vascular Center, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA;
| | - Ali Zaidi
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, 1470 Madison Ave., New York, NY 10029, USA; (L.F.); (C.C.); (A.Z.)
| | - Michelle Gurvitz
- Department of Cardiology, Boston Children’s Hospital, 300 Longwood Ave., Boston, MA 02115, USA; (L.A.S.); (J.W.N.); (M.G.)
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3
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Reiter K, Butts AM, Janecek JK, Correro AN, Nencka A, Agarwal M, Franczak M, Glass Umfleet L. Relationship between cognitive reserve, brain volume, and neuropsychological performance in amnestic and nonamnestic MCI. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 2023; 30:940-956. [PMID: 36573001 DOI: 10.1080/13825585.2022.2161462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 12/16/2022] [Indexed: 12/28/2022]
Abstract
Cognitive Reserve (CR) is a theoretical construct that influences the onset and course of cognitive and structural changes that occur with aging and mild cognitive impairment (MCI). There is a paucity of research that examines the relationship of CR and brain volumes in amnestic (aMCI) and nonamnestic (naMCI) separately. This study is a retrospective chart review of MCI patients who underwent neuropsychological evaluation and brain MRI with NeuroReader™ (NR). NR is an FDA-cleared software that standardizes MRI volumes to a control sample. Classifications of aMCI and naMCI were based on Petersen criteria. CR was measured as education, occupation, and word reading. Data analysis included bivariate correlations between CR, neuropsychological test scores, and NR-brain volumes by MCI subtype. The Benjamini-Hochberg method corrected for multiple comparisons. The sample included 91 participants with aMCI and 41 with naMCI. Within naMCI, positive correlations were observed between CR and whole brain volume, total gray matter, bifrontal, left parietal, left occipital, and bilateral cerebellum. Within aMCI, no significant correlations were observed between CR and brain volumes. Positive correlations with CR were observed in language, attention, and visual learning in both aMCI and naMCI groups. The current study adds to the minimal literature on CR and naMCI. Results revealed that CR is associated with volumetrics in naMCI only, though cognitive findings were similar in both MCI groups. Possible explanations include heterogeneous disease pathologies, disease stage, or a differential influence of CR on volumetrics in MCI. Additional longitudinal and biomarker studies will better elucidate this relationship.
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Affiliation(s)
- K Reiter
- Cleveland Clinic, Neurological Institute, Cleveland, OH, USA
| | - A M Butts
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - J K Janecek
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - A N Correro
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - A Nencka
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - M Agarwal
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - M Franczak
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - L Glass Umfleet
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
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4
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Pommy J, Conant L, Butts AM, Nencka A, Wang Y, Franczak M, Glass-Umfleet L. A graph theoretic approach to neurodegeneration: five data-driven neuropsychological subtypes in mild cognitive impairment. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 2023; 30:903-922. [PMID: 36648118 DOI: 10.1080/13825585.2022.2163973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 12/26/2022] [Indexed: 01/18/2023]
Abstract
Mild cognitive Impairment (MCI) is notoriously heterogenous in terms of clinical presentation, neuroimaging correlates, and subsequent progression. Predicting who will progress to dementia, which type of dementia, and over what timeframe is challenging. Previous work has attempted to identify MCI subtypes using neuropsychological measures in an effort to address this challenge; however, there is no consensus on approach, which may account for some of the variability. Using a hierarchical community detection approach, we examined cognitive subtypes within an MCI sample (from the Alzheimer's Disease Neuroimaging Initiative [ADNI] study). We then examined whether these subtypes were related to biomarkers (e.g., cortical volumes, fluorodeoxyglucose (FDG)-positron emission tomography (PET) hypometabolism) or clinical progression. We identified five communities (i.e., cognitive subtypes) within the MCI sample: 1) predominantly memory impairment, 2) predominantly language impairment, 3) cognitively normal, 4) multidomain, with notable executive dysfunction, 5) multidomain, with notable processing speed impairment. Community membership was significantly associated with 1) cortical volume in the hippocampus, entorhinal cortex, and fusiform cortex; 2) FDG PET hypometabolism in the posterior cingulate, angular gyrus, and inferior/middle temporal gyrus; and 3) conversion to dementia at follow up. Overall, community detection as an approach appears a viable method for identifying unique cognitive subtypes in a neurodegenerative sample that were linked to several meaningful biomarkers and modestly with progression at one year follow up.
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Affiliation(s)
- Jessica Pommy
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
| | - L Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
| | - A M Butts
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
| | - A Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, United States
| | - Y Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, United States
| | - M Franczak
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
| | - L Glass-Umfleet
- Department of Neurology, Medical College of Wisconsin, Milwaukee, United States
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Garcia-Ramos C, Adluru N, Chu DY, Nair V, Adluru A, Nencka A, Maganti R, Mathis J, Conant LL, Alexander AL, Prabhakaran V, Binder JR, Meyerand ME, Hermann B, Struck AF. Multi-shell connectome DWI-based graph theory measures for the prediction of temporal lobe epilepsy and cognition. Cereb Cortex 2023; 33:8056-8065. [PMID: 37067514 PMCID: PMC10267614 DOI: 10.1093/cercor/bhad098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/28/2023] [Accepted: 03/02/2023] [Indexed: 04/18/2023] Open
Abstract
Temporal lobe epilepsy (TLE) is the most common epilepsy syndrome that empirically represents a network disorder, which makes graph theory (GT) a practical approach to understand it. Multi-shell diffusion-weighted imaging (DWI) was obtained from 89 TLE and 50 controls. GT measures extracted from harmonized DWI matrices were used as factors in a support vector machine (SVM) analysis to discriminate between groups, and in a k-means algorithm to find intrinsic structural phenotypes within TLE. SVM was able to predict group membership (mean accuracy = 0.70, area under the curve (AUC) = 0.747, Brier score (BS) = 0.264) using 10-fold cross-validation. In addition, k-means clustering identified 2 TLE clusters: 1 similar to controls, and 1 dissimilar. Clusters were significantly different in their distribution of cognitive phenotypes, with the Dissimilar cluster containing the majority of TLE with cognitive impairment (χ2 = 6.641, P = 0.036). In addition, cluster membership showed significant correlations between GT measures and clinical variables. Given that SVM classification seemed driven by the Dissimilar cluster, SVM analysis was repeated to classify Dissimilar versus Similar + Controls with a mean accuracy of 0.91 (AUC = 0.957, BS = 0.189). Altogether, the pattern of results shows that GT measures based on connectome DWI could be significant factors in the search for clinical and neurobehavioral biomarkers in TLE.
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Affiliation(s)
- Camille Garcia-Ramos
- Department of Neurology, University of Wisconsin-Madison, Medical Foundation Centennial Building, 1685 Highland Ave, Madison, WI 53705-2281, United States
| | - Nagesh Adluru
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, United States
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI 53705, United States
| | - Daniel Y Chu
- Department of Neurology, University of Wisconsin-Madison, Medical Foundation Centennial Building, 1685 Highland Ave, Madison, WI 53705-2281, United States
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, United States
| | - Veena Nair
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, United States
| | - Anusha Adluru
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, United States
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, 9200 W. Wisconsin Ave. Milwaukee, WI 53226, United States
| | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, Medical Foundation Centennial Building, 1685 Highland Ave, Madison, WI 53705-2281, United States
| | - Jedidiah Mathis
- Department of Neurology, Medical College of Wisconsin, 9200 W. Wisconsin Ave. Milwaukee, WI 53226, United States
| | - Lisa L Conant
- Department of Neurology, Medical College of Wisconsin, 9200 W. Wisconsin Ave. Milwaukee, WI 53226, United States
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Ave, Madison, WI 53705, United States
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave, Rm 1005, Madison, WI 53705-2275, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53792, United States
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, 9200 W. Wisconsin Ave. Milwaukee, WI 53226, United States
| | - Mary E Meyerand
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Ave, Rm 1005, Madison, WI 53705-2275, United States
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, Medical Foundation Centennial Building, 1685 Highland Ave, Madison, WI 53705-2281, United States
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Medical Foundation Centennial Building, 1685 Highland Ave, Madison, WI 53705-2281, United States
- William S. Middleton VA Hospital, 2500 Overlook Terrace, Madison, WI 53705, United States
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Duenweg SR, Brehler M, Bobholz SA, Lowman AK, Winiarz A, Kyereme F, Nencka A, Iczkowski KA, LaViolette PS. Comparison of a machine and deep learning model for automated tumor annotation on digitized whole slide prostate cancer histology. PLoS One 2023; 18:e0278084. [PMID: 36928230 PMCID: PMC10019669 DOI: 10.1371/journal.pone.0278084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/04/2023] [Indexed: 03/18/2023] Open
Abstract
One in eight men will be affected by prostate cancer (PCa) in their lives. While the current clinical standard prognostic marker for PCa is the Gleason score, it is subject to inter-reviewer variability. This study compares two machine learning methods for discriminating between cancerous regions on digitized histology from 47 PCa patients. Whole-slide images were annotated by a GU fellowship-trained pathologist for each Gleason pattern. High-resolution tiles were extracted from annotated and unlabeled tissue. Patients were separated into a training set of 31 patients (Cohort A, n = 9345 tiles) and a testing cohort of 16 patients (Cohort B, n = 4375 tiles). Tiles from Cohort A were used to train a ResNet model, and glands from these tiles were segmented to calculate pathomic features to train a bagged ensemble model to discriminate tumors as (1) cancer and noncancer, (2) high- and low-grade cancer from noncancer, and (3) all Gleason patterns. The outputs of these models were compared to ground-truth pathologist annotations. The ensemble and ResNet models had overall accuracies of 89% and 88%, respectively, at predicting cancer from noncancer. The ResNet model was additionally able to differentiate Gleason patterns on data from Cohort B while the ensemble model was not. Our results suggest that quantitative pathomic features calculated from PCa histology can distinguish regions of cancer; however, texture features captured by deep learning frameworks better differentiate unique Gleason patterns.
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Affiliation(s)
- Savannah R Duenweg
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Michael Brehler
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Samuel A Bobholz
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Allison K Lowman
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Aleksandra Winiarz
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Fitzgerald Kyereme
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Kenneth A Iczkowski
- Department of Pathology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Peter S LaViolette
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
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7
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Franczak S, Pommy J, Minor G, Zolliecoffer C, Bhalla M, Agarwal M, Nencka A, Wang Y, Klein A, O’Neill D, Henry J, Umfleet G. Detecting Primary Progressive Aphasia Atrophy Patterns: A Comparison of Visual Assessment and Quantitative Neuroimaging Techniques. J Alzheimers Dis Rep 2022; 6:493-501. [PMID: 36186726 PMCID: PMC9484148 DOI: 10.3233/adr-220036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/13/2022] [Indexed: 11/15/2022] Open
Abstract
Background: There are now clinically available automated MRI analysis software programs that compare brain volumes of patients to a normative sample and provide z-score data for various brain regions. These programs have yet to be validated in primary progressive aphasia (PPA). Objective: To address this gap in the literature, we examined Neuroreadertrademark z-scores in PPA, relative to visual MRI assessment. We predicted that Neuroreadertrademark 1) would be more sensitive for detecting left > right atrophy in the cortical lobar regions in logopenic variant PPA clinical phenotype (lvPPA), and 2) would distinguish lvPPA (n = 11) from amnestic mild cognitive impairment (aMCI; n = 12). Methods: lvPPA or aMCI patients who underwent MRI with Neuroreadertrademark were included in this study. Two neuroradiologists rated 10 regions. Neuroreadertrademark lobar z-scores for those 10 regions, as well as a hippocampal asymmetry metric, were included in analyses. Results: Cohen’s Kappa coefficients were significant in 10 of the 28 computations (k = 0.351 to 0.593, p≤0.029). Neuroradiologists agreed 0% of the time that left asymmetry was present across regions. No significant differences emerged between aMCI and lvPPA in Neuroreadertrademark z-scores across left or right frontal, temporal, or parietal regions (ps > 0.10). There were significantly lower z-scores in the left compared to right for the hippocampus, as well as parietal, occipital, and temporal cortices in lvPPA. Conclusion: Overall, our results indicated moderate to low interrater reliability, and raters never agreed that left asymmetry was present. While lower z-scores in the left hemisphere regions emerged in lvPPA, Neuroreadertrademark failed to differentiate lvPPA from aMCI.
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Affiliation(s)
- Stephanie Franczak
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Jessica Pommy
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Greta Minor
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Manav Bhalla
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Mohit Agarwal
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Andrew Klein
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Darren O’Neill
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Jude Henry
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Glass Umfleet
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
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Murphy SA, Furger R, Kurpad SN, Arpinar VE, Nencka A, Koch K, Budde MD. Filtered Diffusion-Weighted MRI of the Human Cervical Spinal Cord: Feasibility and Application to Traumatic Spinal Cord Injury. AJNR Am J Neuroradiol 2021; 42:2101-2106. [PMID: 34620590 DOI: 10.3174/ajnr.a7295] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 07/07/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE In traumatic spinal cord injury, DTI is sensitive to injury but is unable to differentiate multiple pathologies. Axonal damage is a central feature of the underlying cord injury, but prominent edema confounds its detection. The purpose of this study was to examine a filtered DWI technique in patients with acute spinal cord injury. MATERIALS AND METHODS The MR imaging protocol was first evaluated in a cohort of healthy subjects at 3T (n = 3). Subsequently, patients with acute cervical spinal cord injury (n = 8) underwent filtered DWI concurrent with their acute clinical MR imaging examination <24 hours postinjury at 1.5T. DTI was obtained with 25 directions at a b-value of 800 s/mm2. Filtered DWI used spinal cord-optimized diffusion-weighting along 26 directions with a "filter" b-value of 2000 s/mm2 and a "probe" maximum b-value of 1000 s/mm2. Parallel diffusivity metrics obtained from DTI and filtered DWI were compared. RESULTS The high-strength diffusion-weighting perpendicular to the cord suppressed signals from tissues outside of the spinal cord, including muscle and CSF. The parallel ADC acquired from filtered DWI at the level of injury relative to the most cranial region showed a greater decrease (38.71%) compared with the decrease in axial diffusivity acquired by DTI (17.68%). CONCLUSIONS The results demonstrated that filtered DWI is feasible in the acute setting of spinal cord injury and reveals spinal cord diffusion characteristics not evident with conventional DTI.
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Affiliation(s)
- S A Murphy
- From the Department of Neurosurgery (S.A.M., R.F., S.N.K., M.D.B.)
| | - R Furger
- From the Department of Neurosurgery (S.A.M., R.F., S.N.K., M.D.B.)
- Center for Neurotrauma Research (R.F., S.N.K., M.D.B.)
| | - S N Kurpad
- From the Department of Neurosurgery (S.A.M., R.F., S.N.K., M.D.B.)
- Center for Neurotrauma Research (R.F., S.N.K., M.D.B.)
| | - V E Arpinar
- Center for Imaging Research (V.E.A., A.N., K.K.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - A Nencka
- Center for Imaging Research (V.E.A., A.N., K.K.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - K Koch
- Center for Imaging Research (V.E.A., A.N., K.K.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - M D Budde
- From the Department of Neurosurgery (S.A.M., R.F., S.N.K., M.D.B.)
- Center for Neurotrauma Research (R.F., S.N.K., M.D.B.)
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9
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Chaarani B, Hahn S, Allgaier N, Adise S, Owens MM, Juliano AC, Yuan DK, Loso H, Ivanciu A, Albaugh MD, Dumas J, Mackey S, Laurent J, Ivanova M, Hagler DJ, Cornejo MD, Hatton S, Agrawal A, Aguinaldo L, Ahonen L, Aklin W, Anokhin AP, Arroyo J, Avenevoli S, Babcock D, Bagot K, Baker FC, Banich MT, Barch DM, Bartsch H, Baskin-Sommers A, Bjork JM, Blachman-Demner D, Bloch M, Bogdan R, Bookheimer SY, Breslin F, Brown S, Calabro FJ, Calhoun V, Casey BJ, Chang L, Clark DB, Cloak C, Constable RT, Constable K, Corley R, Cottler LB, Coxe S, Dagher RK, Dale AM, Dapretto M, Delcarmen-Wiggins R, Dick AS, Do EK, Dosenbach NUF, Dowling GJ, Edwards S, Ernst TM, Fair DA, Fan CC, Feczko E, Feldstein-Ewing SW, Florsheim P, Foxe JJ, Freedman EG, Friedman NP, Friedman-Hill S, Fuemmeler BF, Galvan A, Gee DG, Giedd J, Glantz M, Glaser P, Godino J, Gonzalez M, Gonzalez R, Grant S, Gray KM, Haist F, Harms MP, Hawes S, Heath AC, Heeringa S, Heitzeg MM, Hermosillo R, Herting MM, Hettema JM, Hewitt JK, Heyser C, Hoffman E, Howlett K, Huber RS, Huestis MA, Hyde LW, Iacono WG, Infante MA, Irfanoglu O, Isaiah A, Iyengar S, Jacobus J, James R, Jean-Francois B, Jernigan T, Karcher NR, Kaufman A, Kelley B, Kit B, Ksinan A, Kuperman J, Laird AR, Larson C, LeBlanc K, Lessov-Schlagger C, Lever N, Lewis DA, Lisdahl K, Little AR, Lopez M, Luciana M, Luna B, Madden PA, Maes HH, Makowski C, Marshall AT, Mason MJ, Matochik J, McCandliss BD, McGlade E, Montoya I, Morgan G, Morris A, Mulford C, Murray P, Nagel BJ, Neale MC, Neigh G, Nencka A, Noronha A, Nixon SJ, Palmer CE, Pariyadath V, Paulus MP, Pelham WE, Pfefferbaum D, Pierpaoli C, Prescot A, Prouty D, Puttler LI, Rajapaske N, Rapuano KM, Reeves G, Renshaw PF, Riedel MC, Rojas P, de la Rosa M, Rosenberg MD, Ross MJ, Sanchez M, Schirda C, Schloesser D, Schulenberg J, Sher KJ, Sheth C, Shilling PD, Simmons WK, Sowell ER, Speer N, Spittel M, Squeglia LM, Sripada C, Steinberg J, Striley C, Sutherland MT, Tanabe J, Tapert SF, Thompson W, Tomko RL, Uban KA, Vrieze S, Wade NE, Watts R, Weiss S, Wiens BA, Williams OD, Wilbur A, Wing D, Wolff-Hughes D, Yang R, Yurgelun-Todd DA, Zucker RA, Potter A, Garavan HP. Baseline brain function in the preadolescents of the ABCD Study. Nat Neurosci 2021; 24:1176-1186. [PMID: 34099922 PMCID: PMC8947197 DOI: 10.1038/s41593-021-00867-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 04/30/2021] [Indexed: 02/05/2023]
Abstract
The Adolescent Brain Cognitive Development (ABCD) Study® is a 10-year longitudinal study of children recruited at ages 9 and 10. A battery of neuroimaging tasks are administered biennially to track neurodevelopment and identify individual differences in brain function. This study reports activation patterns from functional MRI (fMRI) tasks completed at baseline, which were designed to measure cognitive impulse control with a stop signal task (SST; N = 5,547), reward anticipation and receipt with a monetary incentive delay (MID) task (N = 6,657) and working memory and emotion reactivity with an emotional N-back (EN-back) task (N = 6,009). Further, we report the spatial reproducibility of activation patterns by assessing between-group vertex/voxelwise correlations of blood oxygen level-dependent (BOLD) activation. Analyses reveal robust brain activations that are consistent with the published literature, vary across fMRI tasks/contrasts and slightly correlate with individual behavioral performance on the tasks. These results establish the preadolescent brain function baseline, guide interpretation of cross-sectional analyses and will enable the investigation of longitudinal changes during adolescent development.
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Affiliation(s)
- B Chaarani
- Department of Psychiatry, University of Vermont, Burlington, VT, USA.
| | - S Hahn
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - N Allgaier
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - S Adise
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - M M Owens
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - A C Juliano
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - D K Yuan
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - H Loso
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - A Ivanciu
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - M D Albaugh
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - J Dumas
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - S Mackey
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - J Laurent
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - M Ivanova
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - D J Hagler
- University of California, San Diego, La Jolla, CA, USA
| | - M D Cornejo
- Institute of Physics UC, Pontificia Universidad Catolica de Chile, Pontificia, Chile
| | - S Hatton
- University of California, San Diego, La Jolla, CA, USA
| | - A Agrawal
- Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | - L Aguinaldo
- University of California, San Diego, La Jolla, CA, USA
| | - L Ahonen
- University of Pittsburgh, Pittsburgh, PA, USA
| | - W Aklin
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - A P Anokhin
- Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | - J Arroyo
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - S Avenevoli
- National Institute of Mental Health, Bethesda, MD, USA
| | - D Babcock
- National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - K Bagot
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - F C Baker
- SRI International, Menlo Park, CA, USA
| | - M T Banich
- University of Colorado, Boulder, CO, USA
| | - D M Barch
- Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | - H Bartsch
- Haukeland University Hospital, Bergen, Norway
| | | | - J M Bjork
- Virginia Commonwealth University, Richmond, VA, USA
| | - D Blachman-Demner
- NIH Office of Behavioral and Social Sciences Research, Bethesda, MD, USA
| | - M Bloch
- National Cancer Institute, Bethesda, MD, USA
| | - R Bogdan
- Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | | | - F Breslin
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - S Brown
- University of California, San Diego, La Jolla, CA, USA
| | - F J Calabro
- University of Pittsburgh, Pittsburgh, PA, USA
| | - V Calhoun
- University of Colorado, Boulder, CO, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | | | - L Chang
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - D B Clark
- University of Pittsburgh, Pittsburgh, PA, USA
| | - C Cloak
- University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - K Constable
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - R Corley
- University of Colorado, Boulder, CO, USA
| | | | - S Coxe
- Florida International University, Miami, FL, USA
| | - R K Dagher
- National Institute on Minority Health and Health Disparities, Bethesda, MD, USA
| | - A M Dale
- University of California, San Diego, La Jolla, CA, USA
| | - M Dapretto
- University of California, Los Angeles, CA, USA
| | | | - A S Dick
- Florida International University, Miami, FL, USA
| | - E K Do
- Virginia Commonwealth University, Richmond, VA, USA
| | - N U F Dosenbach
- Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | - G J Dowling
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - S Edwards
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - T M Ernst
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - D A Fair
- Oregon Health & Science University, Portland, OR, USA
| | - C C Fan
- Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | - E Feczko
- Oregon Health & Science University, Portland, OR, USA
| | | | | | - J J Foxe
- University of Rochester, Rochester, NY, USA
| | | | | | | | | | - A Galvan
- University of California, Los Angeles, CA, USA
| | - D G Gee
- Yale University, New Haven, CT, USA
| | - J Giedd
- University of California, San Diego, La Jolla, CA, USA
| | - M Glantz
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - P Glaser
- Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | - J Godino
- University of California, San Diego, La Jolla, CA, USA
| | - M Gonzalez
- Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - R Gonzalez
- Florida International University, Miami, FL, USA
| | - S Grant
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - K M Gray
- Medical University of South Carolina, Charleston, SC, USA
| | - F Haist
- University of California, San Diego, La Jolla, CA, USA
| | - M P Harms
- Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | - S Hawes
- Florida International University, Miami, FL, USA
| | - A C Heath
- University of California, San Diego, La Jolla, CA, USA
| | - S Heeringa
- University of Michigan, Ann Arbor, MI, USA
| | | | - R Hermosillo
- Oregon Health & Science University, Portland, OR, USA
| | - M M Herting
- University of Southern California, Los Angeles, CA, USA
| | - J M Hettema
- Virginia Commonwealth University, Richmond, VA, USA
| | - J K Hewitt
- University of Colorado, Boulder, CO, USA
| | - C Heyser
- University of California, San Diego, La Jolla, CA, USA
| | - E Hoffman
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - K Howlett
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - R S Huber
- University of Utah, Salt Lake City, UT, USA
| | - M A Huestis
- Thomas Jefferson University, Philadelphia, PA, USA
| | - L W Hyde
- University of Michigan, Ann Arbor, MI, USA
| | - W G Iacono
- University of Minnesota, Minneapolis, MN, USA
| | - M A Infante
- University of California, San Diego, La Jolla, CA, USA
| | - O Irfanoglu
- National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, USA
| | - A Isaiah
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - S Iyengar
- National Endowment for the Arts, Washington DC, USA
| | - J Jacobus
- University of California, San Diego, La Jolla, CA, USA
| | - R James
- Virginia Commonwealth University, Richmond, VA, USA
| | - B Jean-Francois
- National Institute on Minority Health and Health Disparities, Bethesda, MD, USA
| | - T Jernigan
- University of California, San Diego, La Jolla, CA, USA
| | - N R Karcher
- Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | - A Kaufman
- National Cancer Institute, Bethesda, MD, USA
| | - B Kelley
- National Institute of Justice, Washington DC, USA
| | - B Kit
- National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - A Ksinan
- Virginia Commonwealth University, Richmond, VA, USA
| | - J Kuperman
- University of California, San Diego, La Jolla, CA, USA
| | - A R Laird
- Florida International University, Miami, FL, USA
| | - C Larson
- University of Wisconsin, Milwaukee, WI, USA
| | - K LeBlanc
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - C Lessov-Schlagger
- Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | - N Lever
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - D A Lewis
- University of Pittsburgh, Pittsburgh, PA, USA
| | - K Lisdahl
- University of Wisconsin, Milwaukee, WI, USA
| | - A R Little
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - M Lopez
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - M Luciana
- University of Minnesota, Minneapolis, MN, USA
| | - B Luna
- University of Pittsburgh, Pittsburgh, PA, USA
| | - P A Madden
- Department of Psychiatry, Washington University in Saint Louis, St. Louis, MO, USA
| | - H H Maes
- Virginia Commonwealth University, Richmond, VA, USA
| | - C Makowski
- University of California, San Diego, La Jolla, CA, USA
| | - A T Marshall
- Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - M J Mason
- University of Tennessee, Knoxville, TN, USA
| | - J Matochik
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | | | - E McGlade
- University of Utah, Salt Lake City, UT, USA
| | - I Montoya
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - G Morgan
- National Cancer Institute, Bethesda, MD, USA
| | - A Morris
- Oklahoma State University, Stillwater, OK, USA
| | - C Mulford
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - P Murray
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - B J Nagel
- Oregon Health & Science University, Portland, OR, USA
| | - M C Neale
- Virginia Commonwealth University, Richmond, VA, USA
| | - G Neigh
- Virginia Commonwealth University, Richmond, VA, USA
| | - A Nencka
- Medical College of Wisconsin, Milwaukee, WI, USA
| | - A Noronha
- National Institute on Alcohol Abuse and Alcoholism, Bethesda, MD, USA
| | - S J Nixon
- University of Florida, Gainesville, FL, USA
| | - C E Palmer
- University of California, San Diego, La Jolla, CA, USA
| | - V Pariyadath
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - M P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - W E Pelham
- Florida International University, Miami, FL, USA
| | | | - C Pierpaoli
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA
| | - A Prescot
- University of Utah, Salt Lake City, UT, USA
| | - D Prouty
- SRI International, Menlo Park, CA, USA
| | | | - N Rajapaske
- National Institute on Minority Health and Health Disparities, Bethesda, MD, USA
| | | | - G Reeves
- University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - M C Riedel
- Florida International University, Miami, FL, USA
| | - P Rojas
- Florida International University, Miami, FL, USA
| | - M de la Rosa
- Florida International University, Miami, FL, USA
| | | | - M J Ross
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - M Sanchez
- Florida International University, Miami, FL, USA
| | - C Schirda
- University of Pittsburgh, Pittsburgh, PA, USA
| | - D Schloesser
- NIH Office of Behavioral and Social Sciences Research, Bethesda, MD, USA
| | | | - K J Sher
- University of Missouri, Columbia, MO, USA
| | - C Sheth
- University of Utah, Salt Lake City, UT, USA
| | - P D Shilling
- University of California, San Diego, La Jolla, CA, USA
| | - W K Simmons
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - E R Sowell
- Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - N Speer
- University of Colorado, Boulder, CO, USA
| | - M Spittel
- NIH Office of Behavioral and Social Sciences Research, Bethesda, MD, USA
| | - L M Squeglia
- Medical University of South Carolina, Charleston, SC, USA
| | - C Sripada
- University of Michigan, Ann Arbor, MI, USA
| | - J Steinberg
- Virginia Commonwealth University, Richmond, VA, USA
| | - C Striley
- University of Florida, Gainesville, FL, USA
| | | | - J Tanabe
- University of Colorado, Boulder, CO, USA
| | - S F Tapert
- University of California, San Diego, La Jolla, CA, USA
| | - W Thompson
- University of California, San Diego, La Jolla, CA, USA
| | - R L Tomko
- Medical University of South Carolina, Charleston, SC, USA
| | - K A Uban
- University of California, Irvine, CA, USA
| | - S Vrieze
- University of Minnesota, Minneapolis, MN, USA
| | - N E Wade
- University of California, San Diego, La Jolla, CA, USA
| | - R Watts
- Yale University, New Haven, CT, USA
| | - S Weiss
- National Institute on Drug Abuse, Bethesda, MD, USA
| | - B A Wiens
- University of Florida, Gainesville, FL, USA
| | - O D Williams
- Florida International University, Miami, FL, USA
| | - A Wilbur
- SRI International, Menlo Park, CA, USA
| | - D Wing
- University of California, San Diego, La Jolla, CA, USA
| | - D Wolff-Hughes
- NIH Office of Behavioral and Social Sciences Research, Bethesda, MD, USA
| | - R Yang
- University of California, San Diego, La Jolla, CA, USA
| | | | - R A Zucker
- University of Michigan, Ann Arbor, MI, USA
| | - A Potter
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - H P Garavan
- Department of Psychiatry, University of Vermont, Burlington, VT, USA.
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10
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Struck AF, Boly M, Hwang G, Nair V, Mathis J, Nencka A, Conant LL, DeYoe EA, Ragahavan M, Prabhakaran V, Binder JR, Meyerand ME, Hermann BP. Regional and global resting-state functional MR connectivity in temporal lobe epilepsy: Results from the Epilepsy Connectome Project. Epilepsy Behav 2021; 117:107841. [PMID: 33611101 PMCID: PMC8035304 DOI: 10.1016/j.yebeh.2021.107841] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 01/20/2021] [Accepted: 02/01/2021] [Indexed: 12/28/2022]
Abstract
Temporal lobe epilepsy (TLE) has been conceptualized as focal disease with a discrete neurobiological focus and can respond well to targeted resection or ablation. In contrast, the neuro-cognitive deficits resulting from TLE can be widespread involving regions beyond the primary epileptic network. We hypothesize that this seemingly paradoxical findings can be explained by differences in connectivity between the primary epileptic region which is hyper-connected and its secondary influence on global connectome organization. This hypothesis is tested using regional and global graph theory metrics where we anticipate that regional mesial-temporal hyperconnectivity will be found and correlate with seizure frequency while global networks will be disorganized and be more closely associated with neuro-cognitive deficits. Resting-state fMRI was used to examine temporal lobe regional connectivity and global functional connectivity from 102 patients with TLE and 55 controls. Connectivity matrices were calculated for subcortical volumes and cortical parcellations. Graph theory metrics (global clustering coefficient (GCC), degree, closeness) were compared between groups and in relation to neuropsychological profiles and disease covariates using permutation testing and causal analysis. In TLE there was a decrease in GCC (p = 0.0345) associated with a worse neuropsychological profile (p = 0.0134). There was increased connectivity in the left hippocampus/amygdala (degree p = 0.0103, closeness p = 0.0104) and a decrease in connectivity in the right lateral temporal lobe (degree p = 0.0186, closeness p = 0.0122). A ratio between the hippocampus/amygdala and lateral temporal lobe-temporal lobe connectivity ratio (TLCR) revealed differences between TLE and controls for closeness (left p = 0.00149, right p = 0.0494) and for degree on left p = 0.00169; with trend on right p = 0.0567. Causal analysis suggested that "Epilepsy Activity" (seizure frequency, anti-seizure medications) was associated with increase in TLCR but not in GCC, while cognitive decline was associated with decreased GCC. These findings support the hypothesis that in TLE there is hyperconnectivity in the hippocampus/amygdala and hypoconnectivity in the lateral temporal lobe associated with "Epilepsy Activity." While, global connectome disorganization was associated with worse neuropsychological phenotype.
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Affiliation(s)
- Aaron F Struck
- University of Wisconsin-Madison, Department of Neurology, United States; William S. Middleton Veterans Administration Hospital, Madison, WI, United States.
| | - Melanie Boly
- University of Wisconsin-Madison, Department of Neurology
| | - Gyujoon Hwang
- University of Wisconsin-Madison, Department of Medical Physics
| | - Veena Nair
- University of Wisconsin-Madison, Department of Radiology
| | | | - Andrew Nencka
- Medical College of Wisconsin, Department of Radiology
| | - Lisa L Conant
- Medical College of Wisconsin, Department of Neurology
| | - Edgar A DeYoe
- Medical College of Wisconsin, Department of Radiology
| | | | | | | | - Mary E Meyerand
- University of Wisconsin-Madison, Department of Medical Physics
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11
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McGarry SD, Bukowy JD, Iczkowski KA, Lowman AK, Brehler M, Bobholz S, Nencka A, Barrington A, Jacobsohn K, Unteriner J, Duvnjak P, Griffin M, Hohenwalter M, Keuter T, Huang W, Antic T, Paner G, Palangmonthip W, Banerjee A, LaViolette PS. Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancer. J Med Imaging (Bellingham) 2020; 7:054501. [PMID: 32923510 PMCID: PMC7479263 DOI: 10.1117/1.jmi.7.5.054501] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 08/20/2020] [Indexed: 12/19/2022] Open
Abstract
Purpose: Our study predictively maps epithelium density in magnetic resonance imaging (MRI) space while varying the ground truth labels provided by five pathologists to quantify the downstream effects of interobserver variability. Approach: Clinical imaging and postsurgical tissue from 48 recruited prospective patients were used in our study. Tissue was sliced to match the MRI orientation and whole-mount slides were stained and digitized. Data from 28 patients ( n = 33 slides) were sent to five pathologists to be annotated. Slides from the remaining 20 patients ( n = 123 slides) were annotated by one of the five pathologists. Interpathologist variability was measured using Krippendorff's alpha. Pathologist-specific radiopathomic mapping models were trained using a partial least-squares regression using MRI values to predict epithelium density, a known marker for disease severity. An analysis of variance characterized intermodel means difference in epithelium density. A consensus model was created and evaluated using a receiver operator characteristic classifying high grade versus low grade and benign, and was statistically compared to apparent diffusion coefficient (ADC). Results: Interobserver variability ranged from low to acceptable agreement (0.31 to 0.69). There was a statistically significant difference in mean predicted epithelium density values ( p < 0.001 ) between the five models. The consensus model outperformed ADC (areas under the curve = 0.80 and 0.71, respectively, p < 0.05 ). Conclusion: We demonstrate that radiopathomic maps of epithelium density are sensitive to the pathologist annotating the dataset; however, it is unclear if these differences are clinically significant. The consensus model produced the best maps, matched the performance of the best individual model, and outperformed ADC.
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Affiliation(s)
- Sean D McGarry
- Medical College of Wisconsin, Department of Biophysics, Milwaukee, Wisconsin, United States
| | - John D Bukowy
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Kenneth A Iczkowski
- Medical College of Wisconsin, Department of Pathology, Milwaukee, Wisconsin, United States
| | - Allison K Lowman
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Michael Brehler
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Samuel Bobholz
- Medical College of Wisconsin, Department of Biophysics, Milwaukee, Wisconsin, United States
| | - Andrew Nencka
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Alex Barrington
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Kenneth Jacobsohn
- Medical College of Wisconsin, Department of Urological Surgery, Milwaukee, Wisconsin, United States
| | - Jackson Unteriner
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Petar Duvnjak
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Michael Griffin
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Mark Hohenwalter
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Tucker Keuter
- Medical College of Wisconsin, Department of Biostatistics, Milwaukee, Wisconsin, United States
| | - Wei Huang
- University of Wisconsin-Madison, Department of Pathology, Madison, Wisconsin, United States
| | - Tatjana Antic
- University of Chicago, Department of Pathology, Chicago, Illinois, United States
| | - Gladell Paner
- University of Chicago, Department of Pathology, Chicago, Illinois, United States
| | - Watchareepohn Palangmonthip
- Medical College of Wisconsin, Department of Pathology, Milwaukee, Wisconsin, United States.,Chiang Mai University, Department of Pathology, Faculty of Medicine, Chiang Mai, Thailand
| | - Anjishnu Banerjee
- Medical College of Wisconsin, Department of Biostatistics, Milwaukee, Wisconsin, United States
| | - Peter S LaViolette
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States.,Medical College of Wisconsin, Department of Biomedical Engineering, Milwaukee, Wisconsin, United States
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12
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Rivera Bonet CN, Hwang G, Hermann B, Struck AF, J Cook C, A Nair V, Mathis J, Allen L, Almane DN, Arkush K, Birn R, Conant LL, DeYoe EA, Felton E, Maganti R, Nencka A, Raghavan M, Shah U, Sosa VN, Ustine C, Prabhakaran V, Binder JR, Meyerand ME. Neuroticism in temporal lobe epilepsy is associated with altered limbic-frontal lobe resting-state functional connectivity. Epilepsy Behav 2020; 110:107172. [PMID: 32554180 PMCID: PMC7483612 DOI: 10.1016/j.yebeh.2020.107172] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 05/11/2020] [Accepted: 05/11/2020] [Indexed: 11/18/2022]
Abstract
Neuroticism, a core personality trait characterized by a tendency towards experiencing negative affect, has been reported to be higher in people with temporal lobe epilepsy (TLE) compared with healthy individuals. Neuroticism is a known predictor of depression and anxiety, which also occur more frequently in people with TLE. The purpose of this study was to identify abnormalities in whole-brain resting-state functional connectivity in relation to neuroticism in people with TLE and to determine the degree of unique versus shared patterns of abnormal connectivity in relation to elevated symptoms of depression and anxiety. Ninety-three individuals with TLE (55 females) and 40 healthy controls (18 females) from the Epilepsy Connectome Project (ECP) completed measures of neuroticism, depression, and anxiety, which were all significantly higher in people with TLE compared with controls. Resting-state functional connectivity was compared between controls and groups with TLE with high and low neuroticism using analysis of variance (ANOVA) and t-test. In secondary analyses, the same analytics were performed using measures of depression and anxiety and the unique variance in resting-state connectivity associated with neuroticism independent of symptoms of depression and anxiety identified. Increased neuroticism was significantly associated with hyposynchrony between the right hippocampus and Brodmann area (BA) 9 (region of prefrontal cortex (PFC)) (p < 0.005), representing a unique relationship independent of symptoms of depression and anxiety. Hyposynchrony of connection between the right hippocampus and BA47 (anterior frontal operculum) was associated with high neuroticism and with higher depression and anxiety scores (p < 0.05), making it a shared abnormal connection for the three measures. In conclusion, increased neuroticism exhibits both unique and shared patterns of abnormal functional connectivity with depression and anxiety symptoms between regions of the mesial temporal and frontal lobe.
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Affiliation(s)
| | - Gyujoon Hwang
- Department of Medical Physics, University of Wisconsin-Madison, United States of America
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Cole J Cook
- Department of Medical Physics, University of Wisconsin-Madison, United States of America
| | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, United States of America
| | - Jedidiah Mathis
- Department of Radiology Froedtert & Medical College of Wisconsin, United States of America
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, United States of America
| | - Dace N Almane
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Karina Arkush
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, United States of America
| | - Rasmus Birn
- Neuroscience Training Program, University of Wisconsin-Madison, United States of America; Department of Medical Physics, University of Wisconsin-Madison, United States of America; Department of Psychiatry, University of Wisconsin-Madison, United States of America
| | - Lisa L Conant
- Department of Neurology, Medical College of Wisconsin, United States of America
| | - Edgar A DeYoe
- Department of Radiology Froedtert & Medical College of Wisconsin, United States of America; Department of Biophysics, Medical College of Wisconsin, United States of America
| | - Elizabeth Felton
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, United States of America
| | - Andrew Nencka
- Department of Radiology Froedtert & Medical College of Wisconsin, United States of America
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, United States of America
| | - Umang Shah
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, United States of America
| | - Veronica N Sosa
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, United States of America
| | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, United States of America
| | - Vivek Prabhakaran
- Neuroscience Training Program, University of Wisconsin-Madison, United States of America; Department of Neurology, University of Wisconsin-Madison, United States of America; Department of Radiology, University of Wisconsin-Madison, United States of America
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, United States of America; Department of Biophysics, Medical College of Wisconsin, United States of America
| | - Mary E Meyerand
- Neuroscience Training Program, University of Wisconsin-Madison, United States of America; Department of Medical Physics, University of Wisconsin-Madison, United States of America; Department of Radiology, University of Wisconsin-Madison, United States of America
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13
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Muftuler LT, Arpinar VE, Koch K, Bhave S, Yang B, Kaushik S, Banerjee S, Nencka A. Optimization of hyperparameters for SMS reconstruction. Magn Reson Imaging 2020; 73:91-103. [PMID: 32835848 DOI: 10.1016/j.mri.2020.08.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/30/2020] [Accepted: 08/17/2020] [Indexed: 11/30/2022]
Abstract
PURPOSE Simultaneous multi-slice (SMS) imaging accelerates MRI data acquisition by exciting multiple image slices with a single radiofrequency pulse. Overlapping slices encoded in acquired signal are separated using a mathematical model, which requires estimation of image reconstruction kernels using calibration data. Several parameters used in SMS reconstruction impact the quality and fidelity of final images. Therefore, finding an optimal set of reconstruction parameters is critical to ensure that accelerated acquisition does not significantly degrade resulting image quality. METHODS Gradient-echo echo planar imaging data were acquired with a range of SMS acceleration factors from a cohort of five volunteers with no known neurological pathology. Images were collected using two available phased-array head coils (a 48-channel array and a reduced diameter 32-channel array) that support SMS. Data from these coils were identically reconstructed offline using a range of coil compression factors and reconstruction kernel parameters. A hybrid space (k-x), externally-calibrated coil-by-coil slice unaliasing approach was used for image reconstruction. The image quality of the resulting reconstructed SMS images was assessed by evaluating correlations with identical echo-planar reference data acquired without SMS. A finger tapping functional MRI (fMRI) experiment was also performed and group analysis results were compared between data sets reconstructed with different coil compression levels. RESULTS Between the two RF coils tested in this study, the 32-channel coil with smaller dimensions clearly outperformed the larger 48-channel coil in our experiments. Generally, a large calibration region (144-192 samples) and small kernel sizes (2-4 samples) in ky direction improved image quality. Use of regularization in the kernel fitting procedure had a notable impact on the fidelity of reconstructed images and a regularization value 0.0001 provided good image quality. With optimal selection of other hyperparameters in the hybrid space SMS unaliasing algorithm, coil compression caused small reduction in correlation between single-band and SMS unaliased images. Similarly, group analysis of fMRI results did not show a significant influence of coil compression on resulting image quality. CONCLUSIONS This study demonstrated that the hyperparameters used in SMS reconstruction need to be fine-tuned once the experimental factors such as the RF receive coil and SMS factor have been determined. A cursory evaluation of SMS reconstruction hyperparameter values is therefore recommended before conducting a full-scale quantitative study using SMS technologies.
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Affiliation(s)
- L Tugan Muftuler
- Department of Neurosurgery, Medical College of Wisconsin, 8701 Watertown Plk Rd, Milwaukee, WI 53226, USA.
| | - Volkan Emre Arpinar
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plk Rd, Milwaukee, WI 53226, USA; Center for Imaging Research, Medical College of Wisconsin, 8701 Watertown Plk Rd, Milwaukee, WI 53226, USA
| | - Kevin Koch
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plk Rd, Milwaukee, WI 53226, USA; Center for Imaging Research, Medical College of Wisconsin, 8701 Watertown Plk Rd, Milwaukee, WI 53226, USA
| | - Sampada Bhave
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plk Rd, Milwaukee, WI 53226, USA; Center for Imaging Research, Medical College of Wisconsin, 8701 Watertown Plk Rd, Milwaukee, WI 53226, USA
| | - Baolian Yang
- GE Healthcare, 3000 N Grandview Blvd, Waukesha, WI 53188, USA
| | - Sivaram Kaushik
- GE Healthcare, 3000 N Grandview Blvd, Waukesha, WI 53188, USA
| | | | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plk Rd, Milwaukee, WI 53226, USA; Center for Imaging Research, Medical College of Wisconsin, 8701 Watertown Plk Rd, Milwaukee, WI 53226, USA
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14
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Hermann B, Conant LL, Cook CJ, Hwang G, Garcia-Ramos C, Dabbs K, Nair VA, Mathis J, Bonet CNR, Allen L, Almane DN, Arkush K, Birn R, DeYoe EA, Felton E, Maganti R, Nencka A, Raghavan M, Shah U, Sosa VN, Struck AF, Ustine C, Reyes A, Kaestner E, McDonald C, Prabhakaran V, Binder JR, Meyerand ME. Network, clinical and sociodemographic features of cognitive phenotypes in temporal lobe epilepsy. Neuroimage Clin 2020; 27:102341. [PMID: 32707534 PMCID: PMC7381697 DOI: 10.1016/j.nicl.2020.102341] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 06/10/2020] [Accepted: 07/03/2020] [Indexed: 01/14/2023]
Abstract
This study explored the taxonomy of cognitive impairment within temporal lobe epilepsy and characterized the sociodemographic, clinical and neurobiological correlates of identified cognitive phenotypes. 111 temporal lobe epilepsy patients and 83 controls (mean ages 33 and 39, 57% and 61% female, respectively) from the Epilepsy Connectome Project underwent neuropsychological assessment, clinical interview, and high resolution 3T structural and resting-state functional MRI. A comprehensive neuropsychological test battery was reduced to core cognitive domains (language, memory, executive, visuospatial, motor speed) which were then subjected to cluster analysis. The resulting cognitive subgroups were compared in regard to sociodemographic and clinical epilepsy characteristics as well as variations in brain structure and functional connectivity. Three cognitive subgroups were identified (intact, language/memory/executive function impairment, generalized impairment) which differed significantly, in a systematic fashion, across multiple features. The generalized impairment group was characterized by an earlier age at medication initiation (P < 0.05), fewer patient (P < 0.001) and parental years of education (P < 0.05), greater racial diversity (P < 0.05), and greater number of lifetime generalized seizures (P < 0.001). The three groups also differed in an orderly manner across total intracranial (P < 0.001) and bilateral cerebellar cortex volumes (P < 0.01), and rate of bilateral hippocampal atrophy (P < 0.014), but minimally in regional measures of cortical volume or thickness. In contrast, large-scale patterns of cortical-subcortical covariance networks revealed significant differences across groups in global and local measures of community structure and distribution of hubs. Resting-state fMRI revealed stepwise anomalies as a function of cluster membership, with the most abnormal patterns of connectivity evident in the generalized impairment group and no significant differences from controls in the cognitively intact group. Overall, the distinct underlying cognitive phenotypes of temporal lobe epilepsy harbor systematic relationships with clinical, sociodemographic and neuroimaging correlates. Cognitive phenotype variations in patient and familial education and ethnicity, with linked variations in total intracranial volume, raise the question of an early and persisting socioeconomic-status related neurodevelopmental impact, with additional contributions of clinical epilepsy factors (e.g., lifetime generalized seizures). The neuroimaging features of cognitive phenotype membership are most notable for disrupted large scale cortical-subcortical networks and patterns of functional connectivity with bilateral hippocampal and cerebellar atrophy. The cognitive taxonomy of temporal lobe epilepsy appears influenced by features that reflect the combined influence of socioeconomic, neurodevelopmental and neurobiological risk factors.
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Affiliation(s)
- Bruce Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Lisa L Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Cole J Cook
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Gyujoon Hwang
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Camille Garcia-Ramos
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Kevin Dabbs
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Veena A Nair
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Jedidiah Mathis
- Department of Radiology Froedtert & Medical College of Wisconsin, Milwaukee, WI, USA
| | - Charlene N Rivera Bonet
- Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Dace N Almane
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Karina Arkush
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, Milwaukee, WI, USA
| | - Rasmus Birn
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Edgar A DeYoe
- Department of Radiology Froedtert & Medical College of Wisconsin, Milwaukee, WI, USA; Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Elizabeth Felton
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Rama Maganti
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Andrew Nencka
- Department of Radiology Froedtert & Medical College of Wisconsin, Milwaukee, WI, USA
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Umang Shah
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, Milwaukee, WI, USA
| | - Veronica N Sosa
- Neuroscience Innovation Institute, Aurora St. Luke's Medical Center, Milwaukee, WI, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Anny Reyes
- Department of Psychiatry, University of California-San Diego, La Jolla, CA, USA
| | - Erik Kaestner
- Department of Psychiatry, University of California-San Diego, La Jolla, CA, USA
| | - Carrie McDonald
- Department of Psychiatry, University of California-San Diego, La Jolla, CA, USA
| | - Vivek Prabhakaran
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA; Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Mary E Meyerand
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
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15
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Hwang G, Hermann B, Nair VA, Conant LL, Dabbs K, Mathis J, Cook CJ, Rivera-Bonet CN, Mohanty R, Zhao G, Almane DN, Nencka A, Felton E, Struck AF, Birn R, Maganti R, Humphries CJ, Raghavan M, DeYoe EA, Bendlin BB, Prabhakaran V, Binder JR, Meyerand ME. Brain aging in temporal lobe epilepsy: Chronological, structural, and functional. Neuroimage Clin 2020; 25:102183. [PMID: 32058319 PMCID: PMC7016276 DOI: 10.1016/j.nicl.2020.102183] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 12/03/2019] [Accepted: 01/13/2020] [Indexed: 10/26/2022]
Abstract
The association of epilepsy with structural brain changes and cognitive abnormalities in midlife has raised concern regarding the possibility of future accelerated brain and cognitive aging and increased risk of later life neurocognitive disorders. To address this issue we examined age-related processes in both structural and functional neuroimaging among individuals with temporal lobe epilepsy (TLE, N = 104) who were participants in the Epilepsy Connectome Project (ECP). Support vector regression (SVR) models were trained from 151 healthy controls and used to predict TLE patients' brain ages. It was found that TLE patients on average have both older structural (+6.6 years) and functional (+8.3 years) brain ages compared to healthy controls. Accelerated functional brain age (functional - chronological age) was mildly correlated (corrected P = 0.07) with complex partial seizure frequency and the number of anti-epileptic drug intake. Functional brain age was a significant correlate of declining cognition (fluid abilities) and partially mediated chronological age-fluid cognition relationships. Chronological age was the only positive predictor of crystallized cognition. Accelerated aging is evident not only in the structural brains of patients with TLE, but also in their functional brains. Understanding the causes of accelerated brain aging in TLE will be clinically important in order to potentially prevent or mitigate their cognitive deficits.
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Affiliation(s)
- Gyujoon Hwang
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.
| | - Bruce Hermann
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Veena A Nair
- Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Lisa L Conant
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Kevin Dabbs
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jed Mathis
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Cole J Cook
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Rosaleena Mohanty
- Electrical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Gengyan Zhao
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Dace N Almane
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrew Nencka
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Aaron F Struck
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Rasmus Birn
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Rama Maganti
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Manoj Raghavan
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Edgar A DeYoe
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Vivek Prabhakaran
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Mary E Meyerand
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
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16
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Cook CJ, Hwang G, Mathis J, Nair VA, Conant LL, Allen L, Almane DN, Birn R, DeYoe EA, Felton E, Forseth C, Humphries CJ, Kraegel P, Nencka A, Nwoke O, Raghavan M, Rivera-Bonet C, Rozman M, Tellapragada N, Ustine C, Ward BD, Struck A, Maganti R, Hermann B, Prabhakaran V, Binder JR, Meyerand ME. Effective Connectivity Within the Default Mode Network in Left Temporal Lobe Epilepsy: Findings from the Epilepsy Connectome Project. Brain Connect 2019; 9:174-183. [PMID: 30398367 DOI: 10.1089/brain.2018.0600] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The Epilepsy Connectome Project examines the differences in connectomes between temporal lobe epilepsy (TLE) patients and healthy controls. Using these data, the effective connectivity of the default mode network (DMN) in patients with left TLE compared with healthy controls was investigated using spectral dynamic causal modeling (spDCM) of resting-state functional magnetic resonance imaging data. Group comparisons were made using two parametric empirical Bayes (PEB) models. The first level of each PEB model consisted of each participant's spDCM. Two different second-level models were constructed: the first comparing effective connectivity of the groups directly and the second using the Rey Auditory Verbal Learning Test (RAVLT) delayed free recall index as a covariate at the second level to assess effective connectivity controlling for the poor memory performance of left TLE patients. After an automated search over the nested parameter space and thresholding parameters at 95% posterior probability, both models revealed numerous connections in the DMN, which lead to inhibition of the left hippocampal formation. Left hippocampal formation inhibition may be an inherent result of the left temporal epileptogenic focus as memory differences were controlled for in one model and the same connections remained. An excitatory connection from the posterior cingulate cortex to the medial prefrontal cortex was found to be concomitant with left hippocampal formation inhibition in TLE patients when including RAVLT delayed free recall at the second level.
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Affiliation(s)
- Cole J Cook
- 1 Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Gyujoon Hwang
- 1 Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jedidiah Mathis
- 2 Department of Radiology, Froedtert and Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Veena A Nair
- 3 Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | - Lisa L Conant
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Linda Allen
- 5 Department of Neurology, Froedtert Hospital, Milwaukee, Wisconsin
| | - Dace N Almane
- 6 Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rasmus Birn
- 1 Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin.,7 Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
| | - Edgar A DeYoe
- 2 Department of Radiology, Froedtert and Medical College of Wisconsin, Milwaukee, Wisconsin.,8 Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Felton
- 6 Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Courtney Forseth
- 6 Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Colin J Humphries
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Peter Kraegel
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Andrew Nencka
- 8 Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Onyekachi Nwoke
- 9 School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin
| | - Manoj Raghavan
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Charlene Rivera-Bonet
- 10 Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin
| | - Megan Rozman
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Neelima Tellapragada
- 3 Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin
| | - Candida Ustine
- 6 Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - B Douglas Ward
- 8 Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Aaron Struck
- 5 Department of Neurology, Froedtert Hospital, Milwaukee, Wisconsin
| | - Rama Maganti
- 5 Department of Neurology, Froedtert Hospital, Milwaukee, Wisconsin
| | - Bruce Hermann
- 6 Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Vivek Prabhakaran
- 3 Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin.,6 Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin.,10 Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jeffrey R Binder
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin.,7 Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
| | - Mary E Meyerand
- 1 Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin.,3 Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin.,11 Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
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17
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Bukowy JD, Foss H, McGarry SD, Lowman A, Hurrell S, Ickzkowski KA, Banerjee A, Barrington A, Dayton A, Unteriner J, Jacobsohn K, See W, Nevalanian M, Nencka A, Ethridge T, Jarrard D, LaViolette P. Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural network. FASEB J 2019. [DOI: 10.1096/fasebj.2019.33.1_supplement.lb12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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18
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Hwang G, Dabbs K, Conant L, Nair VA, Mathis J, Almane DN, Nencka A, Birn R, Humphries C, Raghavan M, DeYoe EA, Struck AF, Maganti R, Binder JR, Meyerand E, Prabhakaran V, Hermann B. Cognitive slowing and its underlying neurobiology in temporal lobe epilepsy. Cortex 2019; 117:41-52. [PMID: 30927560 DOI: 10.1016/j.cortex.2019.02.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 12/06/2018] [Accepted: 02/23/2019] [Indexed: 11/17/2022]
Abstract
Cognitive slowing is a known but comparatively under-investigated neuropsychological complication of the epilepsies in relation to other known cognitive comorbidities such as memory, executive function and language. Here we focus on a novel metric of processing speed, characterize its relative salience compared to other cognitive difficulties in epilepsy, and explore its underlying neurobiological correlates. Research participants included 55 patients with temporal lobe epilepsy (TLE) and 58 healthy controls from the Epilepsy Connectome Project (ECP) who were administered a battery of tests yielding 14 neuropsychological measures, including selected tests from the NIH Toolbox-Cognitive Battery, and underwent 3T MRI and resting state fMRI. TLE patients exhibited a pattern of generalized cognitive impairment with very few lateralized abnormalities. Using the neuropsychological measures, machine learning (Support Vector Machine binary classification model) classified the TLE and control groups with 74% accuracy with processing speed (NIH Toolbox Pattern Comparison Processing Speed Test) the best predictor. In TLE, slower processing speed was associated predominantly with decreased local gyrification in regions including the rostral and caudal middle frontal gyrus, inferior precentral cortex, insula, inferior parietal cortex (angular and supramarginal gyri), lateral occipital cortex, rostral anterior cingulate, and medial orbital frontal regions, as well as three small regions of the temporal lobe. Slower processing speed was also associated with decreased connectivity between the primary visual cortices in both hemispheres and the left supplementary motor area, as well as between the right parieto-occipital sulcus and right middle insular area. Overall, slowed processing speed is an important cognitive comorbidity of TLE associated with altered brain structure and connectivity.
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Affiliation(s)
- Gyujoon Hwang
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Kevin Dabbs
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Lisa Conant
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Veena A Nair
- Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jed Mathis
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Dace N Almane
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrew Nencka
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Rasmus Birn
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Manoj Raghavan
- Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Edgar A DeYoe
- Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Aaron F Struck
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | - Rama Maganti
- Neurology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Elizabeth Meyerand
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Vivek Prabhakaran
- Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Radiology, University of Wisconsin-Madison, Madison, WI, USA; Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - Bruce Hermann
- Neurology, University of Wisconsin-Madison, Madison, WI, USA.
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19
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Hwang G, Nair VA, Mathis J, Cook CJ, Mohanty R, Zhao G, Tellapragada N, Ustine C, Nwoke OO, Rivera-Bonet C, Rozman M, Allen L, Forseth C, Almane DN, Kraegel P, Nencka A, Felton E, Struck AF, Birn R, Maganti R, Conant LL, Humphries CJ, Hermann B, Raghavan M, DeYoe EA, Binder JR, Meyerand E, Prabhakaran V. Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning. Brain Connect 2019; 9:184-193. [PMID: 30803273 PMCID: PMC6484357 DOI: 10.1089/brain.2018.0601] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and includes 20 min of resting-state functional magnetic resonance imaging acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with the FreeSurfer subcortical regions to generate resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF measures. Seven different frequency ranges such as Slow-5 (0.01-0.027 Hz) and Slow-4 (0.027-0.073 Hz) were selected to compute these measures. The goal was to train machine learning classification models to discriminate TLE patients from healthy controls, and to determine which combination of the resting state measure and frequency range produced the best classification model. The samples included age- and gender-matched groups of 60 TLE patients and 59 healthy controls. Three traditional machine learning models were trained: support vector machine, linear discriminant analysis, and naive Bayes classifier. The highest classification accuracy was obtained using RSFC measures in the Slow-4 + 5 band (0.01-0.073 Hz) as features. Leave-one-out cross-validation accuracies were ∼83%, with receiver operating characteristic area-under-the-curve reaching close to 90%. Increased connectivity from right area posterior 9-46v in TLE patients contributed to the high accuracies. With increased sample sizes in the near future, better machine learning models will be trained not only to aid the diagnosis of TLE, but also as a tool to understand this brain disorder.
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Affiliation(s)
- Gyujoon Hwang
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Veena A. Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jed Mathis
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Cole J. Cook
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rosaleena Mohanty
- Department of Electrical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Gengyan Zhao
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | | | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | | | - Megan Rozman
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Courtney Forseth
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Dace N. Almane
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Peter Kraegel
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Felton
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Aaron F. Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rasmus Birn
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Lisa L. Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Colin J. Humphries
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Edgar A. DeYoe
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jeffrey R. Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Meyerand
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Vivek Prabhakaran
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
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Moonnoh Lee R, Espana L, Prost R, Nencka A, Koch K, Bradley Meier T. Quantitative analysis of neurometabolites in adolescents with persistent symptoms following concussion using magnetic resonance spectroscopy. Neurology 2018. [DOI: 10.1212/01.wnl.0000550675.62726.83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Jesmanowicz A, Nencka A, Hyde JS. Direct radiofrequency phase control in MRI by digital waveform playback at the Larmor frequency. Magn Reson Med 2015; 71:846-52. [PMID: 23468035 DOI: 10.1002/mrm.24713] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
PURPOSE A scalable multiband and multichannel digital magnetic resonance imaging system has been developed with the goal of reducing the time needed for acquisition of a single volume of gradient-recalled echo-planar images of the brain. METHODS Transmit pulses are created by an offline computer equipped with a Pentek excitation card (PCIe model 78621) that was built around the Texas Instruments D/A converter (DAC5688). RESULTS The spectral purity of pulses made in this way surpasses the quality of pulses made by the standard modulators of the scanner, even when using the same pulse-creation algorithm. There is no need to mix reference waveforms with the magnetic resonance imaging signal to obtain inter-k-space coherency for different repetitions. The key was the use of a system clock to create the Larmor frequency used for pulse formation. The 3- and 4-fold slice accelerations were tested using phantoms as well as functional and resting-state magnetic resonance imaging of the human brain. CONCLUSION Synthesizers with limited modulation-time steps should be replaced not only because of the improved spectral quality of radiofrequency pulses but also for the exceptional coherence of pulses at different slice-selection frequencies.
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Affiliation(s)
- Andrzej Jesmanowicz
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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Babaei A, Ward BD, Siwiec RM, Ahmad S, Kern M, Nencka A, Li SJ, Shaker R. Functional connectivity of the cortical swallowing network in humans. Neuroimage 2013; 76:33-44. [PMID: 23416253 DOI: 10.1016/j.neuroimage.2013.01.037] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2012] [Revised: 12/20/2012] [Accepted: 01/18/2013] [Indexed: 11/18/2022] Open
Abstract
INTRODUCTION Coherent fluctuations of blood oxygenation level dependent (BOLD) signal have been referred to as "functional connectivity" (FC). Our aim was to systematically characterize FC of underlying neural network involved in swallowing, and to evaluate its reproducibility and modulation during rest or task performance. METHODS Activated seed regions within known areas of the cortical swallowing network (CSN) were independently identified in 16 healthy volunteers. Subjects swallowed using a paradigm driven protocol, and the data analyzed using an event-related technique. Then, in the same 16 volunteers, resting and active state data were obtained for 540 s in three conditions: 1) swallowing task; 2) control visual task; and 3) resting state; all scans were performed twice. Data was preprocessed according to standard FC pipeline. We determined the correlation coefficient values of member regions of the CSN across the three aforementioned conditions and compared between two sessions using linear regression. Average FC matrices across conditions were then compared. RESULTS Swallow activated twenty-two positive BOLD and eighteen negative BOLD regions distributed bilaterally within cingulate, insula, sensorimotor cortex, prefrontal and parietal cortices. We found that: 1) Positive BOLD regions were highly connected to each other during all test conditions while negative BOLD regions were tightly connected among themselves; 2) Positive and negative BOLD regions were anti-correlated at rest and during task performance; 3) Across all three test conditions, FC among the regions was reproducible (r>0.96, p<10(-5)); and 4) The FC of sensorimotor region to other regions of the CSN increased during swallowing scan. CONCLUSIONS 1) Swallow activated cortical substrates maintain a consistent pattern of functional connectivity; 2) FC of sensorimotor region is significantly higher during swallow scan than that observed during a non-swallow visual task or at rest.
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Affiliation(s)
- Arash Babaei
- Gastroenterology and Hepatology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Babaei A, Ward BD, Ahmad S, Patel A, Nencka A, Li SJ, Hyde J, Shaker R. Reproducibility of swallow-induced cortical BOLD positive and negative fMRI activity. Am J Physiol Gastrointest Liver Physiol 2012; 303:G600-9. [PMID: 22766854 PMCID: PMC3468557 DOI: 10.1152/ajpgi.00167.2012] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Functional MRI (fMRI) studies have demonstrated that a number of brain regions (cingulate, insula, prefrontal, and sensory/motor cortices) display blood oxygen level-dependent (BOLD) positive activity during swallow. Negative BOLD activations and reproducibility of these activations have not been systematically studied. The aim of our study was to investigate the reproducibility of swallow-related cortical positive and negative BOLD activity across different fMRI sessions. We studied 16 healthy volunteers utilizing an fMRI event-related analysis. Individual analysis using a general linear model was used to remove undesirable signal changes correlated with motion, white matter, and cerebrospinal fluid. The group analysis used a mixed-effects multilevel model to identify active cortical regions. The volume and magnitude of a BOLD signal within each cluster was compared between the two study sessions. All subjects showed significant clustered BOLD activity within the known areas of cortical swallowing network across both sessions. The cross-correlation coefficient of percent fMRI signal change and the number of activated voxels across both positive and negative BOLD networks were similar between the two studies (r ≥ 0.87, P < 0.0001). Swallow-associated negative BOLD activity was comparable to the well-defined "default-mode" network, and positive BOLD activity had noticeable overlap with the previously described "task-positive" network. Swallow activates two parallel cortical networks. These include a positive and a negative BOLD network, respectively, correlated and anticorrelated with swallow stimulus. Group cortical activity maps, as well as extent and amplitude of activity induced by volitional swallowing in the cortical swallowing network, are reproducible between study sessions.
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Affiliation(s)
- Arash Babaei
- 1Gastroenterology and Hepatology, Department of Medicine, and
| | - B. Douglas Ward
- 2Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Shahryar Ahmad
- 1Gastroenterology and Hepatology, Department of Medicine, and
| | - Anna Patel
- 1Gastroenterology and Hepatology, Department of Medicine, and
| | - Andrew Nencka
- 2Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Shi-Jiang Li
- 2Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - James Hyde
- 2Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Reza Shaker
- 1Gastroenterology and Hepatology, Department of Medicine, and
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