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Peters-Founshtein G, Dafni-Merom A, Monsa R, Arzy S. Evidence for grid-cell-like activity in the time domain. Neuropsychologia 2024; 198:108878. [PMID: 38574806 DOI: 10.1016/j.neuropsychologia.2024.108878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 04/06/2024]
Abstract
The relation between the processing of space and time in the brain has been an enduring cross-disciplinary question. Grid cells have been recognized as a hallmark of the mammalian navigation system, with recent studies attesting to their involvement in the organization of conceptual knowledge in humans. To determine whether grid-cell-like representations support temporal processing, we asked subjects to mentally simulate changes in age and time-of-day, each constituting "trajectory" in an age-day space, while undergoing fMRI. We found that grid-cell-like representations supported trajecting across this age-day space. Furthermore, brain regions concurrently coding past-to-future orientation positively modulated the magnitude of grid-cell-like representation in the left entorhinal cortex. Finally, our findings suggest that temporal processing may be supported by spatially modulated systems, and that innate regularities of abstract domains may interface and alter grid-cell-like representations, similarly to spatial geometry.
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Affiliation(s)
- Gregory Peters-Founshtein
- The Computational Neuropsychiatry Lab, Department of Medical Neurobiology, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel; Department of Nuclear Medicine, Sheba Medical Center, Ramat-Gan, Israel.
| | - Amnon Dafni-Merom
- The Computational Neuropsychiatry Lab, Department of Medical Neurobiology, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rotem Monsa
- The Computational Neuropsychiatry Lab, Department of Medical Neurobiology, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Shahar Arzy
- The Computational Neuropsychiatry Lab, Department of Medical Neurobiology, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel; Department of Neurology, Hadassah Hebrew University Medical School, Jerusalem, Israel
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Brown C, Das S, Xie L, Nasrallah I, Detre J, Chen‐Plotkin A, Shaw L, McMillan C, Yushkevich P, Wolk D. Medial temporal lobe gray matter microstructure in preclinical Alzheimer's disease. Alzheimers Dement 2024; 20:4147-4158. [PMID: 38747539 PMCID: PMC11180947 DOI: 10.1002/alz.13832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/21/2024] [Accepted: 03/18/2024] [Indexed: 05/28/2024]
Abstract
INTRODUCTION Typical MRI measures of neurodegeneration have limited sensitivity in early disease stages. Diffusion MRI (dMRI) microstructural measures may allow for detection in preclinical stages. METHODS Participants had dMRI and either beta-amyloid PET or plasma biomarkers of Alzheimer's pathology within 18 months of MRI. Microstructure was measured in portions of the medial temporal lobe (MTL) with high neurofibrillary tangle (NFT) burden based on a previously developed post mortem 3D-map. Regressions examined relationships between microstructure and markers of Alzheimer's pathology in preclinical disease and then across disease stages. RESULTS There was higher isometric volume fraction in amyloid-positive compared to amyloid-negative cognitively unimpaired individuals in high tangle MTL regions. Similarly, plasma biomarkers and 18F-flortaucipir were associated with microstructural changes in preclinical disease. Additional microstructural effects were seen across disease stages. DISCUSSION Combining a post mortem atlas of NFT pathology with microstructural measures allows for detection of neurodegeneration in preclinical Alzheimer's disease. Highlights Typical markers of neurodegeneration are not sensitive in preclinical Alzheimer's. dMRI measured microstructure in regions with high NFT. Microstructural changes occur in medial temporal regions in preclinical disease. Microstructural changes occur in other typical Alzheimer's regions in later stages. Combining post mortem pathology atlases with in vivo MRI is a powerful framework.
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Affiliation(s)
- Christopher Brown
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sandhitsu Das
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Long Xie
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Siemens HealthineersDigital Technology and InnovationPrincetonNew JerseyUSA
| | - Ilya Nasrallah
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John Detre
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alice Chen‐Plotkin
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Leslie Shaw
- Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Corey McMillan
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Paul Yushkevich
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David Wolk
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Cousins KAQ, Phillips JS, Das SR, O'Brien K, Tropea TF, Chen‐Plotkin A, Shaw LM, Nasrallah IM, Mechanic‐Hamilton D, McMillan CT, Irwin DJ, Lee EB, Wolk DA. Pathologic and cognitive correlates of plasma biomarkers in neurodegenerative disease. Alzheimers Dement 2024; 20:3889-3905. [PMID: 38644682 PMCID: PMC11180939 DOI: 10.1002/alz.13777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 04/23/2024]
Abstract
INTRODUCTION We investigate pathological correlates of plasma phosphorylated tau 181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) across a clinically diverse spectrum of neurodegenerative disease, including normal cognition (NormCog) and impaired cognition (ImpCog). METHODS Participants were NormCog (n = 132) and ImpCog (n = 461), with confirmed β-amyloid (Aβ+/-) status (cerebrospinal fluid, positron emission tomography, autopsy) and single molecule array plasma measurements. Logistic regression and receiver operating characteristic (ROC) area under the curve (AUC) tested how combining plasma analytes discriminated Aβ+ from Aβ-. Survival analyses tested time to clinical dementia rating (global CDR) progression. RESULTS Multivariable models (p-tau+GFAP+NfL) had the best performance to detect Aβ+ in NormCog (ROCAUC = 0.87) and ImpCog (ROCAUC = 0.87). Survival analyses demonstrated that higher NfL best predicted faster CDR progression for both Aβ+ (hazard ratio [HR] = 2.94; p = 8.1e-06) and Aβ- individuals (HR = 3.11; p = 2.6e-09). DISCUSSION Combining plasma biomarkers can optimize detection of Alzheimer's disease (AD) pathology across cognitively normal and clinically diverse neurodegenerative disease. HIGHLIGHTS Participants were clinically heterogeneous, with autopsy- or biomarker-confirmed Aβ. Combining plasma p-tau181, GFAP, and NfL improved diagnostic accuracy for Aβ status. Diagnosis by plasma biomarkers is more accurate in amnestic AD than nonamnestic AD. Plasma analytes show independent associations with tau PET and post mortem Aβ/tau. Plasma NfL predicted longitudinal cognitive decline in both Aβ+ and Aβ- individuals.
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Affiliation(s)
- Katheryn A. Q. Cousins
- Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jeffrey S. Phillips
- Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sandhitsu R. Das
- Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kyra O'Brien
- Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Thomas F. Tropea
- Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alice Chen‐Plotkin
- Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ilya M. Nasrallah
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Dawn Mechanic‐Hamilton
- Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Corey T. McMillan
- Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David J. Irwin
- Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Edward B. Lee
- Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Li Y, Xie L, Khandelwal P, Wisse LEM, Brown CA, Prabhakaran K, Dylan Tisdall M, Mechanic-Hamilton D, Detre JA, Das SR, Wolk DA, Yushkevich PA. Automatic segmentation of medial temporal lobe subregions in multi-scanner, multi-modality MRI of variable quality. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.21.595190. [PMID: 38826413 PMCID: PMC11142184 DOI: 10.1101/2024.05.21.595190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background Volumetry of subregions in the medial temporal lobe (MTL) computed from automatic segmentation in MRI can track neurodegeneration in Alzheimer's disease. However, image quality may vary in MRI. Poor quality MR images can lead to unreliable segmentation of MTL subregions. Considering that different MRI contrast mechanisms and field strengths (jointly referred to as "modalities" here) offer distinct advantages in imaging different parts of the MTL, we developed a muti-modality segmentation model using both 7 tesla (7T) and 3 tesla (3T) structural MRI to obtain robust segmentation in poor-quality images. Method MRI modalities including 3T T1-weighted, 3T T2-weighted, 7T T1-weighted and 7T T2-weighted (7T-T2w) of 197 participants were collected from a longitudinal aging study at the Penn Alzheimer's Disease Research Center. Among them, 7T-T2w was used as the primary modality, and all other modalities were rigidly registered to the 7T-T2w. A model derived from nnU-Net took these registered modalities as input and outputted subregion segmentation in 7T-T2w space. 7T-T2w images most of which had high quality from 25 selected training participants were manually segmented to train the multi-modality model. Modality augmentation, which randomly replaced certain modalities with Gaussian noise, was applied during training to guide the model to extract information from all modalities. To compare our proposed model with a baseline single-modality model in the full dataset with mixed high/poor image quality, we evaluated the ability of derived volume/thickness measures to discriminate Amyloid+ mild cognitive impairment (A+MCI) and Amyloid- cognitively unimpaired (A-CU) groups, as well as the stability of these measurements in longitudinal data. Results The multi-modality model delivered good performance regardless of 7T-T2w quality, while the single-modality model under-segmented subregions in poor-quality images. The multi-modality model generally demonstrated stronger discrimination of A+MCI versus A-CU. Intra-class correlation and Bland-Altman plots demonstrate that the multi-modality model had higher longitudinal segmentation consistency in all subregions while the single-modality model had low consistency in poor-quality images. Conclusion The multi-modality MRI segmentation model provides an improved biomarker for neurodegeneration in the MTL that is robust to image quality. It also provides a framework for other studies which may benefit from multimodal imaging.
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Affiliation(s)
- Yue Li
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Long Xie
- Department of Digital Technology and Innovation, Siemens Healthineers, Princeton, USA
| | - Pulkit Khandelwal
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, USA
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | | | | | - M Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Dawn Mechanic-Hamilton
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
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Wuestefeld A, Binette AP, van Westen D, Strandberg O, Stomrud E, Mattsson-Carlgren N, Janelidze S, Smith R, Palmqvist S, Baumeister H, Berron D, Yushkevich PA, Hansson O, Spotorno N, Wisse LEM. Medial temporal lobe atrophy patterns in early- versus late-onset amnestic Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.21.594976. [PMID: 38826333 PMCID: PMC11142072 DOI: 10.1101/2024.05.21.594976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background The medial temporal lobe (MTL) is hypothesized to be relatively spared in early-onset Alzheimer's disease (EOAD). Yet, detailed examination of MTL subfield volumes and drivers of atrophy in amnestic EOAD is lacking. Methods BioFINDER-2 participants with memory impairment, abnormal amyloid-β status and tau-PET were included. Forty-one EOAD individuals aged ≤65 years and, as comparison, late-onset AD (LOAD, ≥70 years, n=154) and Aβ-negative cognitively unimpaired controls were included. MTL subregions and biomarkers of (co-)pathologies were measured. Results AD groups showed smaller MTL subregions compared to controls. Atrophy patterns were similar across AD groups, although LOAD showed thinner entorhinal cortices compared to EOAD. EOAD showed lower WMH compared to LOAD. No differences in MTL tau-PET or transactive response DNA binding protein 43-proxy positivity was found. Conclusions We found in vivo evidence for MTL atrophy in amnestic EOAD and overall similar levels to LOAD of MTL tau pathology and co-pathologies.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Danielle van Westen
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, 22242 Lund, Sweden
- Image and Function, Skåne University Hospital, 22242 Lund Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Department of Neurology, Skåne University Hospital, 22242 Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, 22184 Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia 19104, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 20502 Malmö, Sweden
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 22242 Lund, Sweden
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, 22242 Lund, Sweden
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Wuestefeld A, Baumeister H, Adams JN, de Flores R, Hodgetts CJ, Mazloum-Farzaghi N, Olsen RK, Puliyadi V, Tran TT, Bakker A, Canada KL, Dalton MA, Daugherty AM, La Joie R, Wang L, Bedard ML, Buendia E, Chung E, Denning A, Del Mar Arroyo-Jiménez M, Artacho-Pérula E, Irwin DJ, Ittyerah R, Lee EB, Lim S, Del Pilar Marcos-Rabal M, Iñiguez de Onzoño Martin MM, Lopez MM, de la Rosa Prieto C, Schuck T, Trotman W, Vela A, Yushkevich P, Amunts K, Augustinack JC, Ding SL, Insausti R, Kedo O, Berron D, Wisse LEM. Comparison of histological delineations of medial temporal lobe cortices by four independent neuroanatomy laboratories. Hippocampus 2024; 34:241-260. [PMID: 38415962 PMCID: PMC11039382 DOI: 10.1002/hipo.23602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 01/25/2024] [Accepted: 02/04/2024] [Indexed: 02/29/2024]
Abstract
The medial temporal lobe (MTL) cortex, located adjacent to the hippocampus, is crucial for memory and prone to the accumulation of certain neuropathologies such as Alzheimer's disease neurofibrillary tau tangles. The MTL cortex is composed of several subregions which differ in their functional and cytoarchitectonic features. As neuroanatomical schools rely on different cytoarchitectonic definitions of these subregions, it is unclear to what extent their delineations of MTL cortex subregions overlap. Here, we provide an overview of cytoarchitectonic definitions of the entorhinal and parahippocampal cortices as well as Brodmann areas (BA) 35 and 36, as provided by four neuroanatomists from different laboratories, aiming to identify the rationale for overlapping and diverging delineations. Nissl-stained series were acquired from the temporal lobes of three human specimens (two right and one left hemisphere). Slices (50 μm thick) were prepared perpendicular to the long axis of the hippocampus spanning the entire longitudinal extent of the MTL cortex. Four neuroanatomists annotated MTL cortex subregions on digitized slices spaced 5 mm apart (pixel size 0.4 μm at 20× magnification). Parcellations, terminology, and border placement were compared among neuroanatomists. Cytoarchitectonic features of each subregion are described in detail. Qualitative analysis of the annotations showed higher agreement in the definitions of the entorhinal cortex and BA35, while the definitions of BA36 and the parahippocampal cortex exhibited less overlap among neuroanatomists. The degree of overlap of cytoarchitectonic definitions was partially reflected in the neuroanatomists' agreement on the respective delineations. Lower agreement in annotations was observed in transitional zones between structures where seminal cytoarchitectonic features are expressed less saliently. The results highlight that definitions and parcellations of the MTL cortex differ among neuroanatomical schools and thereby increase understanding of why these differences may arise. This work sets a crucial foundation to further advance anatomically-informed neuroimaging research on the human MTL cortex.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Jenna N Adams
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, California, USA
| | - Robin de Flores
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen-Normandie University, GIP Cyceron, France
| | | | - Negar Mazloum-Farzaghi
- University of Toronto, Toronto, Ontario, Canada
- Rotman Research Institute, Toronto, Ontario, Canada
| | - Rosanna K Olsen
- University of Toronto, Toronto, Ontario, Canada
- Rotman Research Institute, Toronto, Ontario, Canada
| | - Vyash Puliyadi
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tammy T Tran
- Department of Psychology, Stanford University, Stanford, California, USA
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Kelsey L Canada
- Institute of Gerontology, Wayne State University, Detroit, Michigan, USA
| | | | - Ana M Daugherty
- Institute of Gerontology, Wayne State University, Detroit, Michigan, USA
- Department of Psychology, Wayne State University, Detroit, Michigan, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, California, USA
| | - Lei Wang
- The Ohio State University, Columbus, Ohio, USA
| | - Madigan L Bedard
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Esther Buendia
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain
| | - Eunice Chung
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Amanda Denning
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | | | - David J Irwin
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Edward B Lee
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sydney Lim
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | | | - Monica Munoz Lopez
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain
| | | | - Theresa Schuck
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Alicia Vela
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain
| | | | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | | | - Song-Lin Ding
- Allen Institute for Brain Science, Seattle, Washington, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain
| | - Olga Kedo
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
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Quek Y, Fung YL, Bourgeat P, Vogrin SJ, Collins SJ, Bowden SC. Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort. Brain Behav 2024; 14:e3505. [PMID: 38688879 PMCID: PMC11061200 DOI: 10.1002/brb3.3505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 05/02/2024] Open
Abstract
INTRODUCTION The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort. METHODS Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures. RESULTS Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone. CONCLUSION Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.
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Affiliation(s)
- Yi‐En Quek
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
| | - Yi Leng Fung
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
| | - Pierrick Bourgeat
- The Australian e‐Health Research CentreCSIRO Health and BiosecurityHerstonQueenslandAustralia
| | - Simon J. Vogrin
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
| | - Steven J. Collins
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
- Department of MedicineThe Royal Melbourne HospitalThe University of MelbourneParkvilleVictoriaAustralia
| | - Stephen C. Bowden
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
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8
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Trišins M, Zdanovskis N, Platkājis A, Šneidere K, Kostiks A, Karelis G, Stepens A. Brodmann Areas, V1 Atlas and Cognitive Impairment: Assessing Cortical Thickness for Cognitive Impairment Diagnostics. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:587. [PMID: 38674233 PMCID: PMC11052167 DOI: 10.3390/medicina60040587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/26/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024]
Abstract
Background and Objectives: Magnetic resonance imaging is vital for diagnosing cognitive decline. Brodmann areas (BA), distinct regions of the cerebral cortex categorized by cytoarchitectural variances, provide insights into cognitive function. This study aims to compare cortical thickness measurements across brain areas identified by BA mapping. We assessed these measurements among patients with and without cognitive impairment, and across groups categorized by cognitive performance levels using the Montreal Cognitive Assessment (MoCA) test. Materials and Methods: In this cross-sectional study, we included 64 patients who were divided in two ways: in two groups with (CI) or without (NCI) impaired cognitive function and in three groups with normal (NC), moderate (MPG) and low (LPG) cognitive performance according to MoCA scores. Scans with a 3T MRI scanner were carried out, and cortical thickness data was acquired using Freesurfer 7.2.0 software. Results: By analyzing differences between the NCI and CI groups cortical thickness of BA3a in left hemisphere (U = 241.000, p = 0.016), BA4a in right hemisphere (U = 269.000, p = 0.048) and BA28 in left hemisphere (U = 584.000, p = 0.005) showed significant differences. In the LPG, MPG and NC cortical thickness in BA3a in left hemisphere (H (2) = 6.268, p = 0.044), in V2 in right hemisphere (H (2) = 6.339, p = 0.042), in BA28 in left hemisphere (H (2) = 23.195, p < 0.001) and in BA28 in right hemisphere (H (2) = 10.015, p = 0.007) showed significant differences. Conclusions: Our study found that cortical thickness in specific Brodmann Areas-BA3a and BA28 in the left hemisphere, and BA4a in the right-differ significantly between NCI and CI groups. Significant differences were also observed in BA3a (left), V2 (right), and BA28 (both hemispheres) across LPG, MPG, NC groups. Despite a small sample size, these findings suggest cortical thickness measurements can serve as effective biomarkers for cognitive impairment diagnosis, warranting further validation with a larger cohort.
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Affiliation(s)
- Maksims Trišins
- Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia; (M.T.)
| | - Nauris Zdanovskis
- Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia; (M.T.)
- Department of Radiology, Riga East University Hospital, LV-1038 Riga, Latvia
- Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
| | - Ardis Platkājis
- Department of Radiology, Riga Stradins University, LV-1007 Riga, Latvia; (M.T.)
- Department of Radiology, Riga East University Hospital, LV-1038 Riga, Latvia
| | - Kristīne Šneidere
- Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
- Department of Health Psychology and Pedagogy, Riga Stradins University, LV-1007 Riga, Latvia
| | - Andrejs Kostiks
- Department of Neurology and Neurosurgery, Riga East University Hospital, LV-1038 Riga, Latvia (G.K.)
| | - Guntis Karelis
- Department of Neurology and Neurosurgery, Riga East University Hospital, LV-1038 Riga, Latvia (G.K.)
- Department of Infectology, Riga Stradins University, LV-1007 Riga, Latvia
| | - Ainārs Stepens
- Military Medicine Research and Study Centre, Riga Stradins University, LV-1007 Riga, Latvia
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9
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Phillips JS, Robinson JL, Cousins KAQ, Wolk DA, Lee EB, McMillan CT, Trojanowski JQ, Grossman M, Irwin DJ. Polypathologic Associations with Gray Matter Atrophy in Neurodegenerative Disease. J Neurosci 2024; 44:e0808232023. [PMID: 38050082 PMCID: PMC10860605 DOI: 10.1523/jneurosci.0808-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/01/2023] [Accepted: 10/26/2023] [Indexed: 12/06/2023] Open
Abstract
Mixed pathologies are common in neurodegenerative disease; however, antemortem imaging rarely captures copathologic effects on brain atrophy due to a lack of validated biomarkers for non-Alzheimer's pathologies. We leveraged a dataset comprising antemortem MRI and postmortem histopathology to assess polypathologic associations with atrophy in a clinically heterogeneous sample of 125 human dementia patients (41 female, 84 male) with T1-weighted MRI ≤ 5 years before death and postmortem ordinal ratings of amyloid-[Formula: see text], tau, TDP-43, and [Formula: see text]-synuclein. Regional volumes were related to pathology using linear mixed-effects models; approximately 25% of data were held out for testing. We contrasted a polypathologic model comprising independent factors for each proteinopathy with two alternatives: a model that attributed atrophy entirely to the protein(s) associated with the patient's primary diagnosis and a protein-agnostic model based on the sum of ordinal scores for all pathology types. Model fits were evaluated using log-likelihood and correlations between observed and fitted volume scores. Additionally, we performed exploratory analyses relating atrophy to gliosis, neuronal loss, and angiopathy. The polypathologic model provided superior fits in the training and testing datasets. Tau, TDP-43, and [Formula: see text]-synuclein burden were inversely associated with regional volumes, but amyloid-[Formula: see text] was not. Gliosis and neuronal loss explained residual variance in and mediated the effects of tau, TDP-43, and [Formula: see text]-synuclein on atrophy. Regional brain atrophy reflects not only the primary molecular pathology but also co-occurring proteinopathies; inflammatory immune responses may independently contribute to degeneration. Our findings underscore the importance of antemortem biomarkers for detecting mixed pathology.
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Affiliation(s)
- Jeffrey S Phillips
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - John L Robinson
- Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Katheryn A Q Cousins
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - David A Wolk
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Edward B Lee
- Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Corey T McMillan
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - John Q Trojanowski
- Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Murray Grossman
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - David J Irwin
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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10
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Wuestefeld A, Baumeister H, Adams JN, de Flores R, Hodgetts C, Mazloum-Farzaghi N, Olsen RK, Puliyadi V, Tran TT, Bakker A, Canada KL, Dalton MA, Daugherty AM, Joie RL, Wang L, Bedard M, Buendia E, Chung E, Denning A, Arroyo-Jiménez MDM, Artacho-Pérula E, Irwin DJ, Ittyerah R, Lee EB, Lim S, Marcos-Rabal MDP, Martin MMIDO, Lopez MM, Prieto CDLR, Schuck T, Trotman W, Vela A, Yushkevich P, Amunts K, Augustinack JC, Ding SL, Insausti R, Kedo O, Berron D, Wisse LEM. Comparison of histological delineations of medial temporal lobe cortices by four independent neuroanatomy laboratories. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.24.542054. [PMID: 37292729 PMCID: PMC10245880 DOI: 10.1101/2023.05.24.542054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The medial temporal lobe (MTL) cortex, located adjacent to the hippocampus, is crucial for memory and prone to the accumulation of certain neuropathologies such as Alzheimer's disease neurofibrillary tau tangles. The MTL cortex is composed of several subregions which differ in their functional and cytoarchitectonic features. As neuroanatomical schools rely on different cytoarchitectonic definitions of these subregions, it is unclear to what extent their delineations of MTL cortex subregions overlap. Here, we provide an overview of cytoarchitectonic definitions of the cortices that make up the parahippocampal gyrus (entorhinal and parahippocampal cortices) and the adjacent Brodmann areas (BA) 35 and 36, as provided by four neuroanatomists from different laboratories, aiming to identify the rationale for overlapping and diverging delineations. Nissl-stained series were acquired from the temporal lobes of three human specimens (two right and one left hemisphere). Slices (50 µm thick) were prepared perpendicular to the long axis of the hippocampus spanning the entire longitudinal extent of the MTL cortex. Four neuroanatomists annotated MTL cortex subregions on digitized (20X resolution) slices with 5 mm spacing. Parcellations, terminology, and border placement were compared among neuroanatomists. Cytoarchitectonic features of each subregion are described in detail. Qualitative analysis of the annotations showed higher agreement in the definitions of the entorhinal cortex and BA35, while definitions of BA36 and the parahippocampal cortex exhibited less overlap among neuroanatomists. The degree of overlap of cytoarchitectonic definitions was partially reflected in the neuroanatomists' agreement on the respective delineations. Lower agreement in annotations was observed in transitional zones between structures where seminal cytoarchitectonic features are expressed more gradually. The results highlight that definitions and parcellations of the MTL cortex differ among neuroanatomical schools and thereby increase understanding of why these differences may arise. This work sets a crucial foundation to further advance anatomically-informed human neuroimaging research on the MTL cortex.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Hannah Baumeister
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Jenna N Adams
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Robin de Flores
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain, Caen-Normandie University, Caen-Normandie, France
| | | | - Negar Mazloum-Farzaghi
- University of Toronto, Toronto, ON, Canada
- Rotman Research Institute, North York, ON, Canada
| | - Rosanna K Olsen
- University of Toronto, Toronto, ON, Canada
- Rotman Research Institute, North York, ON, Canada
| | - Vyash Puliyadi
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Tammy T Tran
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Arnold Bakker
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Kelsey L Canada
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | | | - Ana M Daugherty
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
- Department of Psychology, Wayne State University, Detroit, MI, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco USA
| | - Lei Wang
- The Ohio State University, Columbus, OH, USA
| | - Madigan Bedard
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Eunice Chung
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | - Edward B Lee
- University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Lim
- University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | - Alicia Vela
- University of Castilla-La Mancha, Albacete, Spain
| | | | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany
| | | | | | | | - Olga Kedo
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
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11
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Rani N, Alm KH, Corona-Long CA, Speck CL, Soldan A, Pettigrew C, Zhu Y, Albert M, Bakker A. Tau PET burden in Brodmann areas 35 and 36 is associated with individual differences in cognition in non-demented older adults. Front Aging Neurosci 2023; 15:1272946. [PMID: 38161595 PMCID: PMC10757623 DOI: 10.3389/fnagi.2023.1272946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/23/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction The accumulation of neurofibrillary tau tangles, a neuropathological hallmark of Alzheimer's disease (AD), occurs in medial temporal lobe (MTL) regions early in the disease process, with some of the earliest deposits localized to subregions of the entorhinal cortex. Although functional specialization of entorhinal cortex subregions has been reported, few studies have considered functional associations with localized tau accumulation. Methods In this study, stepwise linear regressions were used to examine the contributions of regional tau burden in specific MTL subregions, as measured by 18F-MK6240 PET, to individual variability in cognition. Dependent measures of interest included the Clinical Dementia Rating Sum of Boxes (CDR-SB), Mini Mental State Examination (MMSE), and composite scores of delayed episodic memory and language. Other model variables included age, sex, education, APOE4 status, and global amyloid burden, indexed by 11C-PiB. Results Tau burden in right Brodmann area 35 (BA35), left and right Brodmann area 36 (BA36), and age each uniquely contributed to the proportion of explained variance in CDR-SB scores, while right BA36 and age were also significant predictors of MMSE scores, and right BA36 was significantly associated with delayed episodic memory performance. Tau burden in both left and right BA36, along with education, uniquely contributed to the proportion of explained variance in language composite scores. Importantly, the addition of more inclusive ROIs, encompassing less granular segmentation of the entorhinal cortex, did not significantly contribute to explained variance in cognition across any of the models. Discussion These findings suggest that the ability to quantify tau burden in more refined MTL subregions may better account for individual differences in cognition, which may improve the identification of non-demented older adults who are on a trajectory of decline due to AD.
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Affiliation(s)
- Nisha Rani
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Kylie H. Alm
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Caitlin A. Corona-Long
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Caroline L. Speck
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Anja Soldan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Corinne Pettigrew
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Yuxin Zhu
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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12
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Lyu J, Bartlett PF, Nasrallah FA, Tang X. Toward hippocampal volume measures on ultra-high field magnetic resonance imaging: a comprehensive comparison study between deep learning and conventional approaches. Front Neurosci 2023; 17:1238646. [PMID: 38156266 PMCID: PMC10752989 DOI: 10.3389/fnins.2023.1238646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 12/04/2023] [Indexed: 12/30/2023] Open
Abstract
The hippocampus is a complex brain structure that plays an important role in various cognitive aspects such as memory, intelligence, executive function, and path integration. The volume of this highly plastic structure is identified as one of the most important biomarkers of specific neuropsychiatric and neurodegenerative diseases. It has also been extensively investigated in numerous aging studies. However, recent studies on aging show that the performance of conventional approaches in measuring the hippocampal volume is still far from satisfactory, especially in terms of delivering longitudinal measures from ultra-high field magnetic resonance images (MRIs), which can visualize more boundary details. The advancement of deep learning provides an alternative solution to measuring the hippocampal volume. In this work, we comprehensively compared a deep learning pipeline based on nnU-Net with several conventional approaches including Freesurfer, FSL and DARTEL, for automatically delivering hippocampal volumes: (1) Firstly, we evaluated the segmentation accuracy and precision on a public dataset through cross-validation. Results showed that the deep learning pipeline had the lowest mean (L = 1.5%, R = 1.7%) and the lowest standard deviation (L = 5.2%, R = 6.2%) in terms of volume percentage error. (2) Secondly, sub-millimeter MRIs of a group of healthy adults with test-retest 3T and 7T sessions were used to extensively assess the test-retest reliability. Results showed that the deep learning pipeline achieved very high intraclass correlation coefficients (L = 0.990, R = 0.986 for 7T; L = 0.985, R = 0.983 for 3T) and very small volume percentage differences (L = 1.2%, R = 0.9% for 7T; L = 1.3%, R = 1.3% for 3T). (3) Thirdly, a Bayesian linear mixed effect model was constructed with respect to the hippocampal volumes of two healthy adult datasets with longitudinal 7T scans and one disease-related longitudinal dataset. It was found that the deep learning pipeline detected both the subtle and disease-related changes over time with high sensitivity as well as the mild differences across subjects. Comparison results from the aforementioned three aspects showed that the deep learning pipeline significantly outperformed the conventional approaches by large margins. Results also showed that the deep learning pipeline can better accommodate longitudinal analysis purposes.
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Affiliation(s)
- Junyan Lyu
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia
| | - Perry F. Bartlett
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia
| | - Fatima A. Nasrallah
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia
| | - Xiaoying Tang
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
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13
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Gönner L, Baeuchl C, Glöckner F, Riedel P, Smolka MN, Li SC. Levodopa suppresses grid-like activity and impairs spatial learning in novel environments in healthy young adults. Cereb Cortex 2023; 33:11247-11256. [PMID: 37782941 PMCID: PMC10690865 DOI: 10.1093/cercor/bhad361] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 10/04/2023] Open
Abstract
Accumulated evidence from animal studies suggests a role for the neuromodulator dopamine in memory processes, particularly under conditions of novelty or reward. Our understanding of how dopaminergic modulation impacts spatial representations and spatial memory in humans remains limited. Recent evidence suggests age-specific regulation effects of dopamine pharmacology on activity in the medial temporal lobe, a key region for spatial memory. To which degree this modulation affects spatially patterned medial temporal representations remains unclear. We reanalyzed recent data from a pharmacological dopamine challenge during functional brain imaging combined with a virtual object-location memory paradigm to assess the effect of Levodopa, a dopamine precursor, on grid-like activity in the entorhinal cortex. We found that Levodopa impaired grid cell-like representations in a sample of young adults (n = 55, age = 26-35 years) in a novel environment, accompanied by reduced spatial memory performance. We observed no such impairment when Levodopa was delivered to participants who had prior experience with the task. These results are consistent with a role of dopamine in modulating the encoding of novel spatial experiences. Our results suggest that dopamine signaling may play a larger role in shaping ongoing spatial representations than previously thought.
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Affiliation(s)
- Lorenz Gönner
- Faculty of Psychology, Chair of Lifespan Developmental Neuroscience, TU Dresden, 01062 Dresden, Germany
- Department of Psychiatry, TU Dresden, 01307 Dresden, Germany
| | - Christian Baeuchl
- Faculty of Psychology, Chair of Lifespan Developmental Neuroscience, TU Dresden, 01062 Dresden, Germany
- Department of Psychiatry, TU Dresden, 01307 Dresden, Germany
| | - Franka Glöckner
- Faculty of Psychology, Chair of Lifespan Developmental Neuroscience, TU Dresden, 01062 Dresden, Germany
| | - Philipp Riedel
- Department of Psychiatry, TU Dresden, 01307 Dresden, Germany
| | | | - Shu-Chen Li
- Faculty of Psychology, Chair of Lifespan Developmental Neuroscience, TU Dresden, 01062 Dresden, Germany
- Centre for Tactile Internet With Human-in-the-Loop, TU Dresden, 01062 Dresden, Germany
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14
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Zhang Y, Li X, Ji Y, Ding H, Suo X, He X, Xie Y, Liang M, Zhang S, Yu C, Qin W. MRAβ: A multimodal MRI-derived amyloid-β biomarker for Alzheimer's disease. Hum Brain Mapp 2023; 44:5139-5152. [PMID: 37578386 PMCID: PMC10502620 DOI: 10.1002/hbm.26452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 04/30/2023] [Accepted: 08/01/2023] [Indexed: 08/15/2023] Open
Abstract
Florbetapir 18 F (AV45), a highly sensitive and specific positron emission tomographic (PET) molecular biomarker binding to the amyloid-β of Alzheimer's disease (AD), is constrained by radiation and cost. We sought to combat it by combining multimodal magnetic resonance imaging (MRI) images and a collaborative generative adversarial networks model (CollaGAN) to develop a multimodal MRI-derived Amyloid-β (MRAβ) biomarker. We collected multimodal MRI and PET AV45 data of 380 qualified participants from the ADNI dataset and 64 subjects from OASIS3 dataset. A five-fold cross-validation CollaGAN were applied to generate MRAβ. In the ADNI dataset, we found MRAβ could characterize the subject-level AV45 spatial variations in both AD and mild cognitive impairment (MCI). Voxel-wise two-sample t-tests demonstrated amyloid-β depositions identified by MRAβ in AD and MCI were significantly higher than healthy controls (HCs) in widespread cortices (p < .05, corrected) and were much similar to those by AV45 (r > .92, p < .001). Moreover, a 3D ResNet classifier demonstrated that MRAβ was comparable to AV45 in discriminating AD from HC in both the ADNI and OASIS3 datasets, and in discriminate MCI from HC in ADNI. Finally, we found MRAβ could mimic cortical hyper-AV45 in HCs who later converted to MCI (r = .79, p < .001) and was comparable to AV45 in discriminating them from stable HC (p > .05). In summary, our work illustrates that MRAβ synthesized by multimodal MRI could mimic the cerebral amyloid-β depositions like AV45 and lends credence to the feasibility of advancing MRI toward molecular-explainable biomarkers.
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Affiliation(s)
- Yu Zhang
- Department of Radiology and Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Xi Li
- Department of Radiology and Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
- Department of RadiologyFirst Clinical Medical College and First Hospital of Shanxi Medical UniversityTaiyuanShanxi ProvinceChina
| | - Yi Ji
- Department of Radiology and Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Hao Ding
- Department of Radiology and Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
- School of Medical ImagingTianjin Medical UniversityTianjinChina
| | - Xinjun Suo
- Department of Radiology and Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Xiaoxi He
- Department of Radiology and Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yingying Xie
- Department of Radiology and Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Meng Liang
- School of Medical ImagingTianjin Medical UniversityTianjinChina
| | - Shijie Zhang
- Department of PharmacologyTianjin Medical UniversityTianjinChina
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
- School of Medical ImagingTianjin Medical UniversityTianjinChina
| | - Wen Qin
- Department of Radiology and Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
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15
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Quek YE, Bourgeat P, Fung YL, Vogrin SJ, Collins SJ, Bowden SC. Validating ASHS-T1 automated entorhinal and transentorhinal cortical segmentation in Alzheimer's disease. Psychiatry Res Neuroimaging 2023; 335:111707. [PMID: 37639979 DOI: 10.1016/j.pscychresns.2023.111707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/25/2023] [Accepted: 08/09/2023] [Indexed: 08/31/2023]
Abstract
The current study aimed to validate entorhinal and transentorhinal cortical volumes measured by the automated segmentation tool Automatic Segmentation of Hippocampal Subfields (ASHS-T1). The study sample comprised 34 healthy controls (HCs), 37 individuals with amnestic mild cognitive impairment (aMCI), and 29 individuals with Alzheimer's disease (AD) dementia from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Entorhinal and transentorhinal cortical volumes were assessed using ASHS-T1, manual segmentation, as well as a widely used automated segmentation tool, FreeSurfer v6.0.1. Mean differences, intraclass correlation coefficients, and Bland-Altman plots were computed. ASHS-T1 tended to underestimate entorhinal and transentorhinal cortical volumes relative to manual segmentation and FreeSurfer. There was variable consistency and low agreement between ASHS-T1 and manual segmentation volumes. There was low-to-moderate consistency and low agreement between ASHS-T1 and FreeSurfer volumes. There was a trend toward higher consistency and agreement for the entorhinal cortex in the aMCI and AD groups compared to the HC group. Despite the differences in volume measurements, ASHS-T1 was sensitive to entorhinal and transentorhinal cortical atrophy in both early and late disease stages. Based on the current study, ASHS-T1 appears to be a promising tool for automated entorhinal and transentorhinal cortical volume measurement in individuals with likely underlying AD.
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Affiliation(s)
- Yi-En Quek
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia.
| | - Pierrick Bourgeat
- The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Brisbane, Queensland, Australia
| | - Yi Leng Fung
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia
| | - Simon J Vogrin
- Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia
| | - Steven J Collins
- Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia; Department of Medicine (RMH), The University of Melbourne, Parkville, Victoria, Australia
| | - Stephen C Bowden
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia; Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia
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16
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Hrybouski S, Das SR, Xie L, Wisse LEM, Kelley M, Lane J, Sherin M, DiCalogero M, Nasrallah I, Detre J, Yushkevich PA, Wolk DA. Aging and Alzheimer's disease have dissociable effects on local and regional medial temporal lobe connectivity. Brain Commun 2023; 5:fcad245. [PMID: 37767219 PMCID: PMC10521906 DOI: 10.1093/braincomms/fcad245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 08/06/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Functional disruption of the medial temporal lobe-dependent networks is thought to underlie episodic memory deficits in aging and Alzheimer's disease. Previous studies revealed that the anterior medial temporal lobe is more vulnerable to pathological and neurodegenerative processes in Alzheimer's disease. In contrast, cognitive and structural imaging literature indicates posterior, as opposed to anterior, medial temporal lobe vulnerability in normal aging. However, the extent to which Alzheimer's and aging-related pathological processes relate to functional disruption of the medial temporal lobe-dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity alterations in the medial temporal lobe and its immediate functional neighbourhood-the Anterior-Temporal and Posterior-Medial brain networks-in normal agers, individuals with preclinical Alzheimer's disease and patients with Mild Cognitive Impairment or mild dementia due to Alzheimer's disease. In the Anterior-Temporal network and in the perirhinal cortex, in particular, we observed an inverted 'U-shaped' relationship between functional connectivity and Alzheimer's stage. According to our results, the preclinical phase of Alzheimer's disease is characterized by increased functional connectivity between the perirhinal cortex and other regions of the medial temporal lobe, as well as between the anterior medial temporal lobe and its one-hop neighbours in the Anterior-Temporal system. This effect is no longer present in symptomatic Alzheimer's disease. Instead, patients with symptomatic Alzheimer's disease displayed reduced hippocampal connectivity within the medial temporal lobe as well as hypoconnectivity within the Posterior-Medial system. For normal aging, our results led to three main conclusions: (i) intra-network connectivity of both the Anterior-Temporal and Posterior-Medial networks declines with age; (ii) the anterior and posterior segments of the medial temporal lobe become increasingly decoupled from each other with advancing age; and (iii) the posterior subregions of the medial temporal lobe, especially the parahippocampal cortex, are more vulnerable to age-associated loss of function than their anterior counterparts. Together, the current results highlight evolving medial temporal lobe dysfunction in Alzheimer's disease and indicate different neurobiological mechanisms of the medial temporal lobe network disruption in aging versus Alzheimer's disease.
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Affiliation(s)
- Stanislau Hrybouski
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Diagnostic Radiology, Lund University, 221 00 Lund, Sweden
| | - Melissa Kelley
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jacqueline Lane
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Monica Sherin
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael DiCalogero
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ilya Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Detre
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
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17
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Wuestefeld A, Pichet Binette A, Berron D, Spotorno N, van Westen D, Stomrud E, Mattsson-Carlgren N, Strandberg O, Smith R, Palmqvist S, Glenn T, Moes S, Honer M, Arfanakis K, Barnes LL, Bennett DA, Schneider JA, Wisse LEM, Hansson O. Age-related and amyloid-beta-independent tau deposition and its downstream effects. Brain 2023; 146:3192-3205. [PMID: 37082959 PMCID: PMC10393402 DOI: 10.1093/brain/awad135] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 03/20/2023] [Accepted: 04/06/2023] [Indexed: 04/22/2023] Open
Abstract
Amyloid-β (Aβ) is hypothesized to facilitate the spread of tau pathology beyond the medial temporal lobe. However, there is evidence that, independently of Aβ, age-related tau pathology might be present outside of the medial temporal lobe. We therefore aimed to study age-related Aβ-independent tau deposition outside the medial temporal lobe in two large cohorts and to investigate potential downstream effects of this on cognition and structural measures. We included 545 cognitively unimpaired adults (40-92 years) from the BioFINDER-2 study (in vivo) and 639 (64-108 years) from the Rush Alzheimer's Disease Center cohorts (ex vivo). 18F-RO948- and 18F-flutemetamol-PET standardized uptake value ratios were calculated for regional tau and global/regional Aβ in vivo. Immunohistochemistry was used to estimate Aβ load and tangle density ex vivo. In vivo medial temporal lobe volumes (subiculum, cornu ammonis 1) and cortical thickness (entorhinal cortex, Brodmann area 35) were obtained using Automated Segmentation for Hippocampal Subfields packages. Thickness of early and late neocortical Alzheimer's disease regions was determined using FreeSurfer. Global cognition and episodic memory were estimated to quantify cognitive functioning. In vivo age-related tau deposition was observed in the medial temporal lobe and in frontal and parietal cortical regions, which was statistically significant when adjusting for Aβ. This was also observed in individuals with low Aβ load. Tau deposition was negatively associated with cortical volumes and thickness in temporal and parietal regions independently of Aβ. The associations between age and cortical volume or thickness were partially mediated via tau in regions with early Alzheimer's disease pathology, i.e. early tau and/or Aβ pathology (subiculum/Brodmann area 35/precuneus/posterior cingulate). Finally, the associations between age and cognition were partially mediated via tau in Brodmann area 35, even when including Aβ-PET as covariate. Results were validated in the ex vivo cohort showing age-related and Aβ-independent increases in tau aggregates in and outside the medial temporal lobe. Ex vivo age-cognition associations were mediated by medial and inferior temporal tau tangle density, while correcting for Aβ density. Taken together, our study provides support for primary age-related tauopathy even outside the medial temporal lobe in vivo and ex vivo, with downstream effects on structure and cognition. These results have implications for our understanding of the spreading of tau outside the medial temporal lobe, also in the context of Alzheimer's disease. Moreover, this study suggests the potential utility of tau-targeting treatments in primary age-related tauopathy, likely already in preclinical stages in individuals with low Aβ pathology.
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Affiliation(s)
- Anika Wuestefeld
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Danielle van Westen
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, SE-222 42 Lund, Sweden
- Image and Function, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Department of Neurology, Skåne University Hospital, SE-205 02 Malmö, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, 221 84 Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Department of Neurology, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
| | - Trevor Glenn
- Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Svenja Moes
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, CH-4070 Basel, Switzerland
| | - Michael Honer
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, CH-4070 Basel, Switzerland
| | - Konstantinos Arfanakis
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Lisa L Barnes
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Julie A Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Clinical Sciences, Lund University, SE-222 42 Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
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18
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André C, Kuhn E, Rehel S, Ourry V, Demeilliez-Servouin S, Palix C, Felisatti F, Champetier P, Dautricourt S, Yushkevich P, Vivien D, de La Sayette V, Chételat G, de Flores R, Rauchs G. Association of Sleep-Disordered Breathing and Medial Temporal Lobe Atrophy in Cognitively Unimpaired Amyloid-Positive Older Adults. Neurology 2023; 101:e370-e385. [PMID: 37258299 PMCID: PMC10435067 DOI: 10.1212/wnl.0000000000207421] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 04/03/2023] [Indexed: 06/02/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Sleep disordered breathing (SDB) has been related to amyloid deposition and an increased dementia risk. However, how SDB relates to medial temporal lobe neurodegeneration and subsequent episodic memory impairment is unclear. Our objective was to investigate the impact of amyloid positivity on the associations between SDB severity, medial temporal lobe subregions, and episodic memory performance in cognitively unimpaired older adults. METHODS Data were acquired between 2016 and 2020 in the context of the Age-Well randomized controlled trial of the Medit-Aging European project. Participants older than 65 years who were free of neurologic, psychiatric, or chronic medical diseases were recruited from the community. They completed a neuropsychological evaluation, in-home polysomnography, a Florbetapir PET, and an MRI, including a specific high-resolution assessment of the medial temporal lobe and hippocampal subfields. Multiple linear regressions were conducted to test interactions between amyloid status and SDB severity on the volume of MTL subregions, controlling for age, sex, education, and the ApoE4 status. Secondary analyses aimed at investigating the links between SDB, MTL subregional atrophy, and episodic memory performance at baseline and at a mean follow-up of 20.66 months in the whole cohort and in subgroups stratified according to amyloid status. RESULTS We included 122 cognitively intact community-dwelling older adults (mean age ± SD: 69.40 ± 3.85 years, 77 women, 26 Aβ+ individuals) in baseline analyses and 111 at follow-up. The apnea-hypopnea index interacted with entorhinal (β = -0.81, p < 0.001, pη2 = 0.19), whole hippocampal (β = -0.61, p < 0.001, pη2 = 0.10), subiculum (β = -0.56, p = 0.002, pη2 = 0.08), CA1 (β = -0.55, p = 0.002, pη2 = 0.08), and DG (β = -0.53, p = 0.003, pη2 = 0.08) volumes such that a higher sleep apnea severity was related to lower MTL subregion volumes in amyloid-positive individuals, but not in those who were amyloid negative. In the whole cohort, lower whole hippocampal (r = 0.27, p = 0.005) and CA1 (r = 0.28, p = 0.003) volumes at baseline were associated with worse episodic memory performance at follow-up. DISCUSSION Overall, we showed that SDB was associated with MTL atrophy in cognitively asymptomatic older adults engaged in the Alzheimer continuum, which may increase the risk of developing memory impairment over time. TRIAL REGISTRATION INFORMATION ClinicalTrials.gov Identifier: NCT02977819.
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Affiliation(s)
- Claire André
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Elizabeth Kuhn
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Stéphane Rehel
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Valentin Ourry
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Solène Demeilliez-Servouin
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Cassandre Palix
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Francesca Felisatti
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Pierre Champetier
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Sophie Dautricourt
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Paul Yushkevich
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Denis Vivien
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Vincent de La Sayette
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Gaël Chételat
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Robin de Flores
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France
| | - Géraldine Rauchs
- From the Normandie Univ (C.A., E.K., S.R., V.O., S.D.-S., C.P., F.F., P.C., S.D., D.V., G.C., R.F., G.R.), UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders" NEUROPRESAGE Team, Institut Blood and Brain @ Caen-Normandie, GIP Cyceron, France; Normandie Univ (C.A., S.R., V.O., P.C., V.L.S.), UNICAEN, PSL Université, EPHE, INSERM, CHU de Caen, GIP Cyceron, NIMH, France; Penn Image Computing and Science Laboratory (PICSL) (P.Y.), University of Pennsylvania, Philadelphia; Département de Recherche Clinique (D.V.), CHU Caen-Normandie, France; and Service de Neurologie (V.L.S.), CHU de Caen, France.
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19
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Wearn A, Raket LL, Collins DL, Spreng RN. Longitudinal changes in hippocampal texture from healthy aging to Alzheimer's disease. Brain Commun 2023; 5:fcad195. [PMID: 37465755 PMCID: PMC10351670 DOI: 10.1093/braincomms/fcad195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 05/09/2023] [Accepted: 07/04/2023] [Indexed: 07/20/2023] Open
Abstract
Early detection of Alzheimer's disease is essential to develop preventive treatment strategies. Detectible change in brain volume emerges relatively late in the pathogenic progression of disease, but microstructural changes caused by early neuropathology may cause subtle changes in the MR signal, quantifiable using texture analysis. Texture analysis quantifies spatial patterns in an image, such as smoothness, randomness and heterogeneity. We investigated whether the MRI texture of the hippocampus, an early site of Alzheimer's disease pathology, is sensitive to changes in brain microstructure before the onset of cognitive impairment. We also explored the longitudinal trajectories of hippocampal texture across the Alzheimer's continuum in relation to hippocampal volume and other biomarkers. Finally, we assessed the ability of texture to predict future cognitive decline, over and above hippocampal volume. Data were acquired from the Alzheimer's Disease Neuroimaging Initiative. Texture was calculated for bilateral hippocampi on 3T T1-weighted MRI scans. Two hundred and ninety-three texture features were reduced to five principal components that described 88% of total variance within cognitively unimpaired participants. We assessed cross-sectional differences in these texture components and hippocampal volume between four diagnostic groups: cognitively unimpaired amyloid-β- (n = 406); cognitively unimpaired amyloid-β+ (n = 213); mild cognitive impairment amyloid-β+ (n = 347); and Alzheimer's disease dementia amyloid-β+ (n = 202). To assess longitudinal texture change across the Alzheimer's continuum, we used a multivariate mixed-effects spline model to calculate a 'disease time' for all timepoints based on amyloid PET and cognitive scores. This was used as a scale on which to compare the trajectories of biomarkers, including volume and texture of the hippocampus. The trajectories were modelled in a subset of the data: cognitively unimpaired amyloid-β- (n = 345); cognitively unimpaired amyloid-β+ (n = 173); mild cognitive impairment amyloid-β+ (n = 301); and Alzheimer's disease dementia amyloid-β+ (n = 161). We identified a difference in texture component 4 at the earliest stage of Alzheimer's disease, between cognitively unimpaired amyloid-β- and cognitively unimpaired amyloid-β+ older adults (Cohen's d = 0.23, Padj = 0.014). Differences in additional texture components and hippocampal volume emerged later in the disease continuum alongside the onset of cognitive impairment (d = 0.30-1.22, Padj < 0.002). Longitudinal modelling of the texture trajectories revealed that, while most elements of texture developed over the course of the disease, noise reduced sensitivity for tracking individual textural change over time. Critically, however, texture provided additional information than was provided by volume alone to more accurately predict future cognitive change (d = 0.32-0.63, Padj < 0.0001). Our results support the use of texture as a measure of brain health, sensitive to Alzheimer's disease pathology, at a time when therapeutic intervention may be most effective.
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Affiliation(s)
- Alfie Wearn
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4
| | - Lars Lau Raket
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund SE-221 00, Sweden
- Novo Nordisk A/S, Søborg 2860, Denmark
| | - D Louis Collins
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada H3A 2B4
| | - R Nathan Spreng
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada H3A 2B4
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada H3A 2B4
- Departments of Psychology and Psychiatry, McGill University, Montreal, QC, Canada H3A 2B4
- Douglas Mental Health University Institute, Verdun, QC, Canada H4H 1R3
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20
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Henzen NA, Reinhardt J, Blatow M, Kressig RW, Krumm S. Excellent Interrater Reliability for Manual Segmentation of the Medial Perirhinal Cortex. Brain Sci 2023; 13:850. [PMID: 37371329 DOI: 10.3390/brainsci13060850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/15/2023] [Accepted: 05/18/2023] [Indexed: 06/29/2023] Open
Abstract
Objective: Evaluation of interrater reliability for manual segmentation of brain structures that are affected first by neurofibrillary tau pathology in Alzheimer's disease. Method: Medial perirhinal cortex, lateral perirhinal cortex, and entorhinal cortex were manually segmented by two raters on structural magnetic resonance images of 44 adults (20 men; mean age = 69.2 ± 10.4 years). Intraclass correlation coefficients (ICC) of cortical thickness and volumes were calculated. Results: Very high ICC values of manual segmentation for the cortical thickness of all regions (0.953-0.986) and consistently lower ICC values for volume estimates of the medial and lateral perirhinal cortex (0.705-0.874). Conclusions: The applied manual segmentation protocol allows different raters to achieve remarkably similar cortical thickness estimates for regions of the parahippocampal gyrus. In addition, the results suggest a preference for cortical thickness over volume as a reliable measure of atrophy, especially for regions affected by collateral sulcus variability (i.e., medial and lateral perirhinal cortex). The results provide a basis for future automated segmentation and collection of normative data.
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Affiliation(s)
- Nicolas A Henzen
- University Department of Geriatric Medicine FELIX PLATTER, 4055 Basel, Switzerland
- Faculty of Psychology, University of Basel, 4001 Basel, Switzerland
| | - Julia Reinhardt
- Division of Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Basel, University of Basel, 4031 Basel, Switzerland
- Department of Cardiology and Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, University of Basel, 4031 Basel, Switzerland
- Department of Orthopedic Surgery and Traumatology, University Hospital of Basel, University of Basel, 4031 Basel, Switzerland
| | - Maria Blatow
- Section of Neuroradiology, Department of Radiology and Nuclear Medicine, Neurocenter, Cantonal Hospital Lucerne, University of Lucerne, 6000 Lucerne, Switzerland
| | - Reto W Kressig
- University Department of Geriatric Medicine FELIX PLATTER, 4055 Basel, Switzerland
- Faculty of Medicine, University of Basel, 4056 Basel, Switzerland
| | - Sabine Krumm
- University Department of Geriatric Medicine FELIX PLATTER, 4055 Basel, Switzerland
- Faculty of Medicine, University of Basel, 4056 Basel, Switzerland
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21
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de Flores R, Demeilliez-Servouin S, Kuhn E, Chauveau L, Landeau B, Delcroix N, Gonneaud J, Vivien D, Chételat G. Respective influence of beta-amyloid and APOE ε4 genotype on medial temporal lobe subregions in cognitively unimpaired older adults. Neurobiol Dis 2023; 181:106127. [PMID: 37061167 DOI: 10.1016/j.nbd.2023.106127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 04/17/2023] Open
Abstract
Medial temporal lobe (MTL) subregions are differentially affected in Alzheimer's disease (AD), with a specific involvement of the entorhinal cortex (ERC), perirhinal cortex and hippocampal cornu ammonis (CA)1. While amyloid (Aβ) and APOEε4 are respectively the first molecular change and the main genetic risk factor in AD, their links with MTL atrophy remain relatively unclear. Our aim was to uncover these effects using baseline data from 130 participants included in the Age-Well study, for whom ultra-high-resolution structural MRI, amyloid-PET and APOEε4 genotype were available. No volume differences were observed between Aβ + (n = 24) and Aβ- (n = 103), nor between APOE4+ (n = 35) and APOE4- (n = 95) participants. However, our analyses showed that both Aβ and APOEε4 status interacted with age on CA1, which is known to be specifically atrophied in early AD. In addition, APOEε4 status moderated the effects of age on other subregions (subiculum, ERC), suggesting a more important contribution of APOEε4 than Aβ to MTL atrophy in cognitively unimpaired population. These results are crucial to develop MRI-based biomarkers to detect early AD.
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Affiliation(s)
- Robin de Flores
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen-Normandie University, GIP Cyceron, France.
| | - Solène Demeilliez-Servouin
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen-Normandie University, GIP Cyceron, France
| | - Elizabeth Kuhn
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen-Normandie University, GIP Cyceron, France
| | - Léa Chauveau
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen-Normandie University, GIP Cyceron, France
| | - Brigitte Landeau
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen-Normandie University, GIP Cyceron, France
| | | | - Julie Gonneaud
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen-Normandie University, GIP Cyceron, France
| | - Denis Vivien
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen-Normandie University, GIP Cyceron, France
| | - Gaël Chételat
- INSERM UMR-S U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen-Normandie University, GIP Cyceron, France
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22
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Xie L, Das SR, Wisse LEM, Ittyerah R, de Flores R, Shaw LM, Yushkevich PA, Wolk DA. Baseline structural MRI and plasma biomarkers predict longitudinal structural atrophy and cognitive decline in early Alzheimer's disease. Alzheimers Res Ther 2023; 15:79. [PMID: 37041649 PMCID: PMC10088234 DOI: 10.1186/s13195-023-01210-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 03/13/2023] [Indexed: 04/13/2023]
Abstract
BACKGROUND Crucial to the success of clinical trials targeting early Alzheimer's disease (AD) is recruiting participants who are more likely to progress over the course of the trials. We hypothesize that a combination of plasma and structural MRI biomarkers, which are less costly and non-invasive, is predictive of longitudinal progression measured by atrophy and cognitive decline in early AD, providing a practical alternative to PET or cerebrospinal fluid biomarkers. METHODS Longitudinal T1-weighted MRI, cognitive (memory-related test scores and clinical dementia rating scale), and plasma measurements of 245 cognitively normal (CN) and 361 mild cognitive impairment (MCI) patients from ADNI were included. Subjects were further divided into β-amyloid positive/negative (Aβ+/Aβ-)] subgroups. Baseline plasma (p-tau181 and neurofilament light chain) and MRI-based structural medial temporal lobe subregional measurements and their association with longitudinal measures of atrophy and cognitive decline were tested using stepwise linear mixed effect modeling in CN and MCI, as well as separately in the Aβ+/Aβ- subgroups. Receiver operating characteristic (ROC) analyses were performed to investigate the discriminative power of each model in separating fast and slow progressors (first and last terciles) of each longitudinal measurement. RESULTS A total of 245 CN (35.0% Aβ+) and 361 MCI (53.2% Aβ+) participants were included. In the CN and MCI groups, both baseline plasma and structural MRI biomarkers were included in most models. These relationships were maintained when limited to the Aβ+ and Aβ- subgroups, including Aβ- CN (normal aging). ROC analyses demonstrated reliable discriminative power in identifying fast from slow progressors in MCI [area under the curve (AUC): 0.78-0.93] and more modestly in CN (0.65-0.73). CONCLUSIONS The present data support the notion that plasma and MRI biomarkers, which are relatively easy to obtain, provide a prediction for the rate of future cognitive and neurodegenerative progression that may be particularly useful in clinical trial stratification and prognosis. Additionally, the effect in Aβ- CN indicates the potential use of these biomarkers in predicting a normal age-related decline.
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Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA.
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA
| | - Robin de Flores
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
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23
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Fel JT, Ellis CT, Turk-Browne NB. Automated and manual segmentation of the hippocampus in human infants. Dev Cogn Neurosci 2023; 60:101203. [PMID: 36791555 PMCID: PMC9957787 DOI: 10.1016/j.dcn.2023.101203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 01/25/2023] [Accepted: 01/25/2023] [Indexed: 01/30/2023] Open
Abstract
The hippocampus, critical for learning and memory, undergoes substantial changes early in life. Investigating the developmental trajectory of hippocampal structure and function requires an accurate method for segmenting this region from anatomical MRI scans. Although manual segmentation is regarded as the "gold standard" approach, it is laborious and subjective. This has fueled the pursuit of automated segmentation methods in adults. However, little is known about the reliability of these automated protocols in infants, particularly when anatomical scan quality is degraded by head motion or the use of shorter and quieter infant-friendly sequences. During a task-based fMRI protocol, we collected quiet T1-weighted anatomical scans from 42 sessions with awake infants aged 4-23 months. Two expert tracers first segmented the hippocampus in both hemispheres manually. The resulting inter-rater reliability (IRR) was only moderate, reflecting the difficulty of infant segmentation. We then used four protocols to predict these manual segmentations: average adult template, average infant template, FreeSurfer software, and Automated Segmentation of Hippocampal Subfields (ASHS) software. ASHS generated the most reliable hippocampal segmentations in infants, exceeding the manual IRR of experts. Automated methods thus provide robust hippocampal segmentations of noisy T1-weighted infant scans, opening new possibilities for interrogating early hippocampal development.
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Affiliation(s)
- J T Fel
- Department of Psychology, Yale University, New Haven, CT 06511, USA
| | - C T Ellis
- Department of Psychology, Stanford University, Stanford, CA 94305, USA
| | - N B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT 06511, USA; Wu Tsai Institute, Yale University, New Haven, CT 06511, USA.
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24
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Hrybouski S, Das SR, Xie L, Wisse LEM, Kelley M, Lane J, Sherin M, DiCalogero M, Nasrallah I, Detre JA, Yushkevich PA, Wolk DA. Aging and Alzheimer's Disease Have Dissociable Effects on Medial Temporal Lobe Connectivity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.18.23284749. [PMID: 36711782 PMCID: PMC9882834 DOI: 10.1101/2023.01.18.23284749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Functional disruption of the medial temporal lobe-dependent networks is thought to underlie episodic memory deficits in aging and Alzheimer's disease. Previous studies revealed that the anterior medial temporal lobe is more vulnerable to pathological and neurodegenerative processes in Alzheimer's disease. In contrast, cognitive and structural imaging literature indicates posterior, as opposed to anterior, medial temporal lobe vulnerability in normal aging. However, the extent to which Alzheimer's and aging-related pathological processes relate to functional disruption of the medial temporal lobe-dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity alterations in the medial temporal lobe and its immediate functional neighborhood - the Anterior-Temporal and Posterior-Medial brain networks - in normal agers, individuals with preclinical Alzheimer's disease, and patients with Mild Cognitive Impairment or mild dementia due to Alzheimer's disease. In the Anterior-Temporal network and in the perirhinal cortex, in particular, we observed an inverted 'U-shaped' relationship between functional connectivity and Alzheimer's stage. According to our results, the preclinical phase of Alzheimer's disease is characterized by increased functional connectivity between the perirhinal cortex and other regions of the medial temporal lobe, as well as between the anterior medial temporal lobe and its one-hop neighbors in the Anterior-Temporal system. This effect is no longer present in symptomatic Alzheimer's disease. Instead, patients with symptomatic Alzheimer's disease displayed reduced hippocampal connectivity within the medial temporal lobe as well as hypoconnectivity within the Posterior-Medial system. For normal aging, our results led to three main conclusions: (1) intra-network connectivity of both the Anterior-Temporal and Posterior-Medial networks declines with age; (2) the anterior and posterior segments of the medial temporal lobe become increasingly decoupled from each other with advancing age; and, (3) the posterior subregions of the medial temporal lobe, especially the parahippocampal cortex, are more vulnerable to age-associated loss of function than their anterior counterparts. Together, the current results highlight evolving medial temporal lobe dysfunction in Alzheimer's disease and indicate different neurobiological mechanisms of the medial temporal lobe network disruption in aging vs. Alzheimer's disease.
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25
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Loftus JR, Puri S, Meyers SP. Multimodality imaging of neurodegenerative disorders with a focus on multiparametric magnetic resonance and molecular imaging. Insights Imaging 2023; 14:8. [PMID: 36645560 PMCID: PMC9842851 DOI: 10.1186/s13244-022-01358-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 12/13/2022] [Indexed: 01/17/2023] Open
Abstract
Neurodegenerative diseases afflict a large number of persons worldwide, with the prevalence and incidence of dementia rapidly increasing. Despite their prevalence, clinical diagnosis of dementia syndromes remains imperfect with limited specificity. Conventional structural-based imaging techniques also lack the accuracy necessary for confident diagnosis. Multiparametric magnetic resonance imaging and molecular imaging provide the promise of improving specificity and sensitivity in the diagnosis of neurodegenerative disease as well as therapeutic monitoring of monoclonal antibody therapy. This educational review will briefly focus on the epidemiology, clinical presentation, and pathologic findings of common and uncommon neurodegenerative diseases. Imaging features of each disease spanning from conventional magnetic resonance sequences to advanced multiparametric methods such as resting-state functional magnetic resonance imaging and arterial spin labeling imaging will be described in detail. Additionally, the review will explore the findings of each diagnosis on molecular imaging including single-photon emission computed tomography and positron emission tomography with a variety of clinically used and experimental radiotracers. The literature and clinical cases provided demonstrate the power of advanced magnetic resonance imaging and molecular techniques in the diagnosis of neurodegenerative diseases and areas of future and ongoing research. With the advent of combined positron emission tomography/magnetic resonance imaging scanners, hybrid protocols utilizing both techniques are an attractive option for improving the evaluation of neurodegenerative diseases.
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Affiliation(s)
- James Ryan Loftus
- grid.412750.50000 0004 1936 9166Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642 USA
| | - Savita Puri
- grid.412750.50000 0004 1936 9166Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642 USA
| | - Steven P. Meyers
- grid.412750.50000 0004 1936 9166Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642 USA
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26
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Jarholm JA, Bjørnerud A, Dalaker TO, Akhavi MS, Kirsebom BE, Pålhaugen L, Nordengen K, Grøntvedt GR, Nakling A, Kalheim LF, Almdahl IS, Tecelão S, Fladby T, Selnes P. Medial Temporal Lobe Atrophy in Predementia Alzheimer's Disease: A Longitudinal Multi-Site Study Comparing Staging and A/T/N in a Clinical Research Cohort. J Alzheimers Dis 2023; 94:259-279. [PMID: 37248900 PMCID: PMC10657682 DOI: 10.3233/jad-221274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/22/2023] [Indexed: 05/31/2023]
Abstract
BACKGROUND Atrophy of the medial temporal lobe (MTL) is a biological characteristic of Alzheimer's disease (AD) and can be measured by segmentation of magnetic resonance images (MRI). OBJECTIVE To assess the clinical utility of automated volumetry in a cognitively well-defined and biomarker-classified multi-center longitudinal predementia cohort. METHODS We used Automatic Segmentation of Hippocampal Subfields (ASHS) to determine MTL morphometry from MRI. We harmonized scanner effects using the recently developed longitudinal ComBat. Subjects were classified according to the A/T/N system, and as normal controls (NC), subjective cognitive decline (SCD), or mild cognitive impairment (MCI). Positive or negative values of A, T, and N were determined by cerebrospinal fluid measurements of the Aβ42/40 ratio, phosphorylated and total tau. From 406 included subjects, longitudinal data was available for 206 subjects by stage, and 212 subjects by A/T/N. RESULTS Compared to A-/T-/N- at baseline, the entorhinal cortex, anterior and posterior hippocampus were smaller in A+/T+orN+. Compared to NC A- at baseline, these subregions were also smaller in MCI A+. Longitudinally, SCD A+ and MCI A+, and A+/T-/N- and A+/T+orN+, had significantly greater atrophy compared to controls in both anterior and posterior hippocampus. In the entorhinal and parahippocampal cortices, longitudinal atrophy was observed only in MCI A+ compared to NC A-, and in A+/T-/N- and A+/T+orN+ compared to A-/T-/N-. CONCLUSION We found MTL neurodegeneration largely consistent with existing models, suggesting that harmonized MRI volumetry may be used under conditions that are common in clinical multi-center cohorts.
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Affiliation(s)
- Jonas Alexander Jarholm
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Atle Bjørnerud
- Department of Physics, University of Oslo, Oslo, Norway
- Unit for Computational Radiology and Artificial Intelligence, Oslo University hospital, Oslo, Norway
- Department of Psychology, Faculty for Social Sciences, University of Oslo, Oslo, Norway
| | - Turi Olene Dalaker
- Department of Radiology, Stavanger Medical Imaging Laboratory, Stavanger University Hospital, Stavanger, Norway
| | - Mehdi Sadat Akhavi
- Department of Technology and Innovation, The Intervention Center, Oslo University Hospital, Oslo, Norway
| | - Bjørn Eivind Kirsebom
- Department of Neurology, University Hospital of North Norway, Tromso, Norway
- Department of Psychology, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromso, Norway
| | - Lene Pålhaugen
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kaja Nordengen
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Gøril Rolfseng Grøntvedt
- Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Arne Nakling
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Lisa F. Kalheim
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ina S. Almdahl
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sandra Tecelão
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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27
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Xie L, Wisse LEM, Wang J, Ravikumar S, Khandelwal P, Glenn T, Luther A, Lim S, Wolk DA, Yushkevich PA. Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation. Med Image Anal 2023; 83:102683. [PMID: 36379194 PMCID: PMC10009820 DOI: 10.1016/j.media.2022.102683] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 10/18/2022] [Accepted: 11/03/2022] [Indexed: 11/07/2022]
Abstract
Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting various anatomical structures in medical images but often suffer from relatively poor generalizability. Multi-atlas segmentation (MAS), while less accurate than DCNN in many applications, tends to generalize well to unseen datasets with different characteristics from the training dataset. Several groups have attempted to integrate the power of DCNN to learn complex data representations and the robustness of MAS to changes in image characteristics. However, these studies primarily focused on replacing individual components of MAS with DCNN models and reported marginal improvements in accuracy. In this study we describe and evaluate a 3D end-to-end hybrid MAS and DCNN segmentation pipeline, called Deep Label Fusion (DLF). The DLF pipeline consists of two main components with learnable weights, including a weighted voting subnet that mimics the MAS algorithm and a fine-tuning subnet that corrects residual segmentation errors to improve final segmentation accuracy. We evaluate DLF on five datasets that represent a diversity of anatomical structures (medial temporal lobe subregions and lumbar vertebrae) and imaging modalities (multi-modality, multi-field-strength MRI and Computational Tomography). These experiments show that DLF achieves comparable segmentation accuracy to nnU-Net (Isensee et al., 2020), the state-of-the-art DCNN pipeline, when evaluated on a dataset with similar characteristics to the training datasets, while outperforming nnU-Net on tasks that involve generalization to datasets with different characteristics (different MRI field strength or different patient population). DLF is also shown to consistently improve upon conventional MAS methods. In addition, a modality augmentation strategy tailored for multimodal imaging is proposed and demonstrated to be beneficial in improving the segmentation accuracy of learning-based methods, including DLF and DCNN, in missing data scenarios in test time as well as increasing the interpretability of the contribution of each individual modality.
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Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA.
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Jiancong Wang
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Sadhana Ravikumar
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Pulkit Khandelwal
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Trevor Glenn
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Anica Luther
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Sydney Lim
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
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28
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Dautricourt S, Gonneaud J, Landeau B, Calhoun VD, de Flores R, Poisnel G, Bougacha S, Ourry V, Touron E, Kuhn E, Demintz-King H, Marchant NL, Vivien D, de la Sayette V, Lutz A, Chételat G, Arenaza-Urquijo EM, Allais F, André C, Asselineau J, Bejanin A, Champetier P, Chételat G, Chocat A, Dautricourt S, de Flores R, Delarue M, Egret S, Felisatti F, Devouge EF, Frison E, Gonneaud J, Heidmann M, Tran TH, Kuhn E, le Du G, Landeau B, Lefranc V, Lutz A, Mezenge F, Moulinet I, Ourry V, Palix C, Paly L, Poisnel G, Quillard A, Rauchs G, Rehel S, Requier F, Touron E, Vivien D, Ware C, Lugo SB, Klimecki O, Vuilleumier P, Barnhofer T, Collette F, Salmon E, de la Sayette V, Delamillieure P, Batchelor M, Beaugonin A, Gheysen F, Demnitz-King H, Marchant N, Whitfield T, Schimmer C, Wirth M. Dynamic functional connectivity patterns associated with dementia risk. Alzheimers Res Ther 2022; 14:72. [PMID: 35606867 PMCID: PMC9128270 DOI: 10.1186/s13195-022-01006-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/06/2022] [Indexed: 12/03/2022]
Abstract
Background This study assesses the relationships between dynamic functional network connectivity (DFNC) and dementia risk. Methods DFNC of the default mode (DMN), salience (SN), and executive control networks was assessed in 127 cognitively unimpaired older adults. Stepwise regressions were performed with dementia risk and protective factors and biomarkers as predictors of DFNC. Results Associations were found between times spent in (i) a “weakly connected” state and lower self-reported engagement in early- and mid-life cognitive activity and higher LDL cholesterol; (ii) a “SN-negatively connected” state and higher blood pressure, higher depression score, and lower body mass index (BMI); (iii) a “strongly connected” state and higher self-reported engagement in early-life cognitive activity, Preclinical Alzheimer’s cognitive composite-5 score, and BMI; and (iv) a “DMN-negatively connected” state and higher self-reported engagement in early- and mid-life stimulating activities and lower LDL cholesterol and blood pressure. The lower number of state transitions was associated with lower brain perfusion. Conclusion DFNC states are differentially associated with dementia risk and could underlie reserve. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01006-7.
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Calandrelli R, Panfili M, Onofrj V, Tran HE, Piludu F, Guglielmi V, Colosimo C, Pilato F. Brain atrophy pattern in patients with mild cognitive impairment: MRI study. Transl Neurosci 2022; 13:335-348. [PMID: 36250040 PMCID: PMC9518661 DOI: 10.1515/tnsci-2022-0248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/21/2022] [Accepted: 09/02/2022] [Indexed: 11/21/2022] Open
Abstract
We evaluated the accuracy of the quantitative and semiquantitative analysis in detecting regional atrophy patterns and differentiating mild cognitive impairment patients who remain stable (aMCI-S) from patients who develop Alzheimer’s disease (aMCI-AD) at clinical follow-up. Baseline magnetic resonance imaging was used for quantitative and semiquantitative analysis using visual rating scales. Visual rating scores were related to gray matter thicknesses or volume measures of some structures belonging to the same brain regions. Receiver operating characteristic (ROC) analysis was performed to assess measures’ accuracy in differentiating aMCI-S from aMCI-AD. Comparing aMCI-S and aMCI-AD patients, significant differences were found for specific rating scales, for cortical thickness belonging to the middle temporal lobe (MTL), anterior temporal (AT), and fronto-insular (FI) regions, for gray matter volumes belonging to MTL and AT regions. ROC curve analysis showed that middle temporal atrophy, AT, and FI visual scales showed better diagnostic accuracy than quantitative measures also when thickness measures were combined with hippocampal volumes. Semiquantitative evaluation, performed by trained observers, is a fast and reliable tool in differentiating, at the early stage of disease, aMCI patients that remain stable from those patients that may progress to AD since visual rating scales may be informative both about early hippocampal volume loss and cortical thickness reduction.
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Affiliation(s)
- Rosalinda Calandrelli
- Dipartimento di Diagnostica per Immagini, Radioterapia, Oncologia ed Ematologia, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS , Largo A. Gemelli, 1 , 00168 Rome , Italy
| | - Marco Panfili
- Dipartimento di Diagnostica per Immagini, Radioterapia, Oncologia ed Ematologia, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS , Largo A. Gemelli, 1 , 00168 Rome , Italy
| | - Valeria Onofrj
- Department of Medical Imaging, Cliniques Universitaires Saint-Luc , Brussels , Belgium
| | - Huong Elena Tran
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS , Largo A. Gemelli, 1 , 00168 Rome , Italy
| | - Francesca Piludu
- Department of Radiology and Diagnostic Imaging, IRCCS Regina Elena National Cancer Institute , Via Elio Chianesi 53 , 00144 Rome , Italy
| | - Valeria Guglielmi
- Institute of Neurology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS , Largo A. Gemelli, 1 , 00168 Rome , Italy
| | - Cesare Colosimo
- Dipartimento di Diagnostica per Immagini, Radioterapia, Oncologia ed Ematologia, Institute of Radiology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS , Largo A. Gemelli, 1 , 00168 Rome , Italy
| | - Fabio Pilato
- Department of Medicine, Unit of Neurology, Neurophysiology, Neurobiology, Campus Bio-Medico University , Rome 00128 , Italy
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Perez SD, Phillips JS, Norise C, Kinney NG, Vaddi P, Halpin A, Rascovsky K, Irwin DJ, McMillan CT, Xie L, Wisse LE, Yushkevich PA, Kallogjeri D, Grossman M, Cousins KA. Neuropsychological and Neuroanatomical Features of Patients with Behavioral/Dysexecutive Variant Alzheimer’s disease (AD): A Comparison to Behavioral Variant Frontotemporal Dementia and Amnestic AD Groups. J Alzheimers Dis 2022; 89:641-658. [PMID: 35938245 PMCID: PMC10117623 DOI: 10.3233/jad-215728] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: An understudied variant of Alzheimer’s disease (AD), the behavioral/dysexecutive variant of AD (bvAD), is associated with progressive personality, behavior, and/or executive dysfunction and frontal atrophy. Objective: This study characterizes the neuropsychological and neuroanatomical features associated with bvAD by comparing it to behavioral variant frontotemporal dementia (bvFTD), amnestic AD (aAD), and subjects with normal cognition. Methods: Subjects included 16 bvAD, 67 bvFTD, and 18 aAD patients, and 26 healthy controls. Neuropsychological assessment and MRI data were compared between these groups. Results: Compared to bvFTD, bvAD showed more significant visuospatial impairments (Rey Figure copy and recall), more irritability (Neuropsychological Inventory), and equivalent verbal memory (Philadelphia Verbal Learning Test). Compared to aAD, bvAD indicated more executive dysfunction (F-letter fluency) and better visuospatial performance. Neuroimaging analysis found that bvAD showed cortical thinning relative to bvFTD posteriorly in left temporal-occipital regions; bvFTD had cortical thinning relative to bvAD in left inferior frontal cortex. bvAD had cortical thinning relative to aAD in prefrontal and anterior temporal regions. All patient groups had lower volumes than controls in both anterior and posterior hippocampus. However, bvAD patients had higher average volume than aAD patients in posterior hippocampus and higher volume than bvFTD patients in anterior hippocampus after adjustment for age and intracranial volume. Conclusion: Findings demonstrated that underlying pathology mediates disease presentation in bvAD and bvFTD.
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Affiliation(s)
- Sophia Dominguez Perez
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
| | - Jeffrey S. Phillips
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Catherine Norise
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nikolas G. Kinney
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Prerana Vaddi
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amy Halpin
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychology, University of Maine, Orono, ME, USA
| | - Katya Rascovsky
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David J. Irwin
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Disease Research, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Corey T. McMillan
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Long Xie
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Lab & Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura E.M. Wisse
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Lab & Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Paul A. Yushkevich
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Lab & Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dorina Kallogjeri
- Department of Otolaryngology, Washington University, St. Louis, MO, USA
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katheryn A.Q. Cousins
- Penn Frontotemporal Degeneration Center (FTDC), University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Wang D, Ehses P, Stöcker T, Stirnberg R. Reproducibility of rapid multi-parameter mapping at 3T and 7T with highly segmented and accelerated 3D-EPI. Magn Reson Med 2022; 88:2217-2232. [PMID: 35877781 DOI: 10.1002/mrm.29383] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/16/2022] [Accepted: 06/17/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE Quantitative multi-parameter mapping (MPM) has been shown to provide good longitudinal and cross-sectional reproducibility for clinical research. Unfortunately, acquisition times (TAs) are typically infeasible for routine scanning at high resolutions. METHODS A fast whole-brain MPM protocol based on interleaved multi-shot 3D-EPI with controlled aliasing (SC-EPI) at 3T and 7T is proposed and compared with MPM using a standard spoiled gradient echo (FLASH) sequence. Four parameters (R1 , PD, R 2 * $$ {R}_2^{\ast } $$ , and MTsat) were measured in less than 3 min at 1 mm isotropic resolution. Five subjects went through the same scanning sessions twice at each scanner. The intra-subject coefficient of variation (scan-rescan) (CoV) was estimated for each protocol and scanner to assess the longitudinal reproducibility. RESULTS At 3T, the CoV of SC-EPI ranged between 1.2%-4.8% for PD and R1 , 2.8%-10.6% for R 2 * $$ {R}_2^{\ast } $$ and MTsat, which was comparable with FLASH (0.6%-4.9% for PD and R1 , 2.6%-11.3% for R 2 * $$ {R}_2^{\ast } $$ and MTsat). At 7T, where the SC-EPI TA was reduced to ∼2 min, the CoV of SC-EPI (1.4%-10.6% for PD, R1 , and R 2 * $$ {R}_2^{\ast } $$ ) was 1.2-2.4 times larger than the CoV of FLASH (1.0%-15%) and MTsat showed much higher variability across subjects. The SC-EPI-MPM protocol at 3T showed high reproducibility and yielded stable quantitative maps at a clinically feasible resolution and scan time, whereas at 7T, MT saturation homogeneity needs to be improved. CONCLUSION SC-EPI-based MPM is feasible as an additional MRI modality in clinical or population studies where the parameters offer great potential as biomarkers.
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Affiliation(s)
- Difei Wang
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Philipp Ehses
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Tony Stöcker
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Physics and Astronomy, University of Bonn, Bonn, Germany
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Chau Loo Kung G, Chiu A, Davey Z, Mouchawar N, Carlson M, Moein Taghavi H, Martin D, Graber K, Razavi B, McNab J, Zeineh M. High-resolution hippocampal diffusion tensor imaging of mesial temporal sclerosis in refractory epilepsy. Epilepsia 2022; 63:2301-2311. [PMID: 35751514 DOI: 10.1111/epi.17330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE We explore the possibility of using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) to discern microstructural abnormalities in the hippocampus indicative of mesial temporal sclerosis (MTS) at the subfield level. METHODS We analyzed data from 57 patients with refractory epilepsy who previously underwent 3.0-T magnetic resonance imaging (MRI) including DTI as a standard part of presurgical workup. We collected information about each subject's seizure semiology, conventional electroencephalography (EEG), high-density EEG, positron emission tomography reports, surgical outcome, and available histopathological findings to assign a final diagnostic category. We also reviewed the radiology MRI report to determine the radiographic category. DTI- and NODDI-based metrics were obtained in the hippocampal subfields. RESULTS By examining diffusion characteristics among subfields in the final diagnostic categories, we found lower orientation dispersion indices and elevated axial diffusivity in the dentate gyrus in MTS compared to no MTS. By similarly examining among subfields in the different radiographic categories, we found all diffusion metrics were abnormal in the dentate gyrus and CA1. We finally examined whether diffusion imaging would better inform a radiographic diagnosis with respect to the final diagnosis, and found that dentate diffusivity suggested subtle changes that may help confirm a positive radiologic diagnosis. SIGNIFICANCE The results suggest that diffusion metric analysis at the subfield level, especially in dentate gyrus and CA1, maybe useful for clinical confirmation of MTS.
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Affiliation(s)
- Gustavo Chau Loo Kung
- Bioengineering Department, Stanford University, Stanford, California, USA.,Radiology Department, Stanford University, Stanford, California, USA
| | - Andrew Chiu
- Radiology Department, Stanford University, Stanford, California, USA
| | - Zach Davey
- Neurology and Neurological Sciences, Stanford Medicine, Stanford University, Stanford, California, USA
| | - Nicole Mouchawar
- Radiology Department, Stanford University, Stanford, California, USA
| | - Mackenzie Carlson
- Bioengineering Department, Stanford University, Stanford, California, USA.,Radiology Department, Stanford University, Stanford, California, USA
| | | | | | - Kevin Graber
- Neurology and Neurological Sciences, Stanford Medicine, Stanford University, Stanford, California, USA
| | - Babak Razavi
- Neurology and Neurological Sciences, Stanford Medicine, Stanford University, Stanford, California, USA
| | - Jennifer McNab
- Radiology Department, Stanford University, Stanford, California, USA
| | - Michael Zeineh
- Radiology Department, Stanford University, Stanford, California, USA
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33
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A CAD system design for Alzheimer's disease diagnosis using temporally consistent clustering and hybrid deep learning models. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103571] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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34
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de Flores R, Das SR, Xie L, Wisse LEM, Lyu X, Shah P, Yushkevich PA, Wolk DA. Medial Temporal Lobe Networks in Alzheimer's Disease: Structural and Molecular Vulnerabilities. J Neurosci 2022; 42:2131-2141. [PMID: 35086906 PMCID: PMC8916768 DOI: 10.1523/jneurosci.0949-21.2021] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 11/30/2021] [Accepted: 12/04/2021] [Indexed: 11/21/2022] Open
Abstract
The medial temporal lobe (MTL) is connected to the rest of the brain through two main networks: the anterior-temporal (AT) and the posterior-medial (PM) systems. Given the crucial role of the MTL and networks in the physiopathology of Alzheimer's disease (AD), the present study aimed at (1) investigating whether MTL atrophy propagates specifically within the AT and PM networks, and (2) evaluating the vulnerability of these networks to AD proteinopathies. To do that, we used neuroimaging data acquired in human male and female in three distinct cohorts: (1) resting-state functional MRI (rs-fMRI) from the aging brain cohort (ABC) to define the AT and PM networks (n = 68); (2) longitudinal structural MRI from Alzheimer's disease neuroimaging initiative (ADNI)GO/2 to highlight structural covariance patterns (n = 349); and (3) positron emission tomography (PET) data from ADNI3 to evaluate the networks' vulnerability to amyloid and tau (n = 186). Our results suggest that the atrophy of distinct MTL subregions propagates within the AT and PM networks in a dissociable manner. Brodmann area (BA)35 structurally covaried within the AT network while the parahippocampal cortex (PHC) covaried within the PM network. In addition, these networks are differentially associated with relative tau and amyloid burden, with higher tau levels in AT than in PM and higher amyloid levels in PM than in AT. Our results also suggest differences in the relative burden of tau species. The current results provide further support for the notion that two distinct MTL networks display differential alterations in the context of AD. These findings have important implications for disease spread and the cognitive manifestations of AD.SIGNIFICANCE STATEMENT The current study provides further support for the notion that two distinct medial temporal lobe (MTL) networks, i.e., anterior-temporal (AT) and the posterior-medial (PM), display differential alterations in the context of Alzheimer's disease (AD). Importantly, neurodegeneration appears to occur within these networks in a dissociable manner marked by their covariance patterns. In addition, the AT and PM networks are also differentially associated with relative tau and amyloid burden, and perhaps differences in the relative burden of tau species [e.g., neurofibriliary tangles (NFTs) vs tau in neuritic plaques]. These findings, in the context of a growing literature consistent with the present results, have important implications for disease spread and the cognitive manifestations of AD in light of the differential cognitive processes ascribed to them.
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Affiliation(s)
- Robin de Flores
- Department of Neurology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Université de Caen Normandie, Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche Scientifique (UMRS) Unité 1237, Caen 14000, France
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Department of Radiology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Department of Diagnostic Radiology, Lund University, Lund 22185, Sweden
| | - Xueying Lyu
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Preya Shah
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
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Woodworth DC, Sheikh-Bahaei N, Scambray KA, Phelan MJ, Perez-Rosendahl M, Corrada MM, Kawas CH, Sajjadi SA. Dementia is associated with medial temporal atrophy even after accounting for neuropathologies. Brain Commun 2022; 4:fcac052. [PMID: 35350552 PMCID: PMC8952251 DOI: 10.1093/braincomms/fcac052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/30/2021] [Accepted: 03/03/2022] [Indexed: 11/18/2022] Open
Abstract
Brain atrophy is associated with degenerative neuropathologies and the clinical status of dementia. Whether dementia is associated with atrophy independent of neuropathologies is not known. In this study, we examined the pattern of atrophy associated with dementia while accounting for the most common dementia-related neuropathologies. We used data from National Alzheimer's Coordinating Center (n = 129) and Alzheimer's Disease Neuroimaging Initiative (n = 47) participants with suitable in vivo 3D-T1w MRI and autopsy data. We determined dementia status at the visit closest to MRI. We examined the following dichotomized neuropathological variables: Alzheimer's disease neuropathology, hippocampal sclerosis, Lewy bodies, cerebral amyloid angiopathy and atherosclerosis. Voxel-based morphometry identified areas associated with dementia after accounting for neuropathologies. Identified regions of interest were further analysed. We used multiple linear regression models adjusted for neuropathologies and demographic variables. We also examined models with dementia and Clinical Dementia Rating sum of the boxes as the outcome and explored the potential mediating effect of medial temporal lobe structure volumes on the relationship between pathology and cognition. We found strong associations for dementia with volumes of the hippocampus, amygdala and parahippocampus (semi-partial correlations ≥ 0.28, P < 0.0001 for all regions in National Alzheimer's Coordinating Center; semi-partial correlations ≥ 0.35, P ≤ 0.01 for hippocampus and parahippocampus in Alzheimer's Disease Neuroimaging Initiative). Dementia status accounted for more unique variance in atrophy in these structures (∼8%) compared with neuropathological variables; the only exception was hippocampal sclerosis which accounted for more variance in hippocampal atrophy (10%). We also found that the volumes of the medial temporal lobe structures contributed towards explaining the variance in Clinical Dementia Rating sum of the boxes (ranging from 5% to 9%) independent of neuropathologies and partially mediated the association between Alzheimer's disease neuropathology and cognition. Even after accounting for the most common neuropathologies, dementia still had among the strongest associations with atrophy of medial temporal lobe structures. This suggests that atrophy of the medial temporal lobe is most related to the clinical status of dementia rather than Alzheimer's disease or other neuropathologies, with the potential exception of hippocampal sclerosis.
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Affiliation(s)
- Davis C. Woodworth
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - Nasim Sheikh-Bahaei
- Department of Radiology, University of Southern California, Los Angeles, CA, USA
| | - Kiana A. Scambray
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - Michael J. Phelan
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - Mari Perez-Rosendahl
- Department of Neurology, University of California, Irvine, CA, USA
- Department of Pathology and Laboratory Medicine, University of California, Irvine, CA, USA
| | - María M. Corrada
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
- Department of Epidemiology, University of California, Irvine, CA, USA
| | - Claudia H. Kawas
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - Seyed Ahmad Sajjadi
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
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Nordengen K, Pålhaugen L, Bettella F, Bahrami S, Selnes P, Jarholm J, Athanasiu L, Shadrin A, Andreassen OA, Fladby T. Phenotype‐informed polygenic risk scores are associated with worse outcome in individuals at risk of Alzheimer's disease. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2022; 14:e12350. [PMID: 35991219 PMCID: PMC9376972 DOI: 10.1002/dad2.12350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/30/2022] [Accepted: 07/07/2022] [Indexed: 11/11/2022]
Abstract
Introduction Patients with predementia Alzheimer's disease (AD) and at‐risk subjects are targets for promising disease‐modifying treatments, and improved polygenic risk scores (PRSs) could improve early‐stage case selection. Methods Phenotype‐informed PRSs were developed by selecting AD‐associated variants conditional on relevant inflammatory or cardiovascular traits. The primary outcome was longitudinal changes in measures of AD pathology, namely development of pathological amyloid deposition, medial temporal lobe atrophy, and cognitive decline in a prospective cohort study including 394 adults without AD dementia. Results High‐risk groups defined by phenotype‐informed AD PRSs had significantly steeper volume decline in medial temporal cortices, and the high‐risk group defined by the cardiovascular‐informed AD PRS had significantly increased hazard ratio of pathological amyloid deposition, compared to low‐risk groups. Discussion AD PRSs informed by inflammatory disorders or cardiovascular risk factors and diseases are associated with development of AD pathology markers and may improve identification of subjects at risk for progression of AD.
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Affiliation(s)
- Kaja Nordengen
- Department of Neurology Akershus University Hospital Lørenskog Norway
- Institute of Clinical Medicine University of Oslo Oslo Norway
| | - Lene Pålhaugen
- Department of Neurology Akershus University Hospital Lørenskog Norway
- Institute of Clinical Medicine University of Oslo Oslo Norway
| | - Francesco Bettella
- Norwegian Centre for Mental Disorders Research (NORMENT) Division of Mental Health and Addiction Oslo University Hospital Oslo Norway
| | - Shahram Bahrami
- Norwegian Centre for Mental Disorders Research (NORMENT) Division of Mental Health and Addiction Oslo University Hospital Oslo Norway
| | - Per Selnes
- Department of Neurology Akershus University Hospital Lørenskog Norway
- Institute of Clinical Medicine University of Oslo Oslo Norway
| | - Jonas Jarholm
- Department of Neurology Akershus University Hospital Lørenskog Norway
- Institute of Clinical Medicine University of Oslo Oslo Norway
| | - Lavinia Athanasiu
- Norwegian Centre for Mental Disorders Research (NORMENT) Division of Mental Health and Addiction Oslo University Hospital Oslo Norway
| | - Alexey Shadrin
- Norwegian Centre for Mental Disorders Research (NORMENT) Division of Mental Health and Addiction Oslo University Hospital Oslo Norway
| | - Ole A. Andreassen
- Institute of Clinical Medicine University of Oslo Oslo Norway
- Norwegian Centre for Mental Disorders Research (NORMENT) Division of Mental Health and Addiction Oslo University Hospital Oslo Norway
| | - Tormod Fladby
- Department of Neurology Akershus University Hospital Lørenskog Norway
- Institute of Clinical Medicine University of Oslo Oslo Norway
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37
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Zuroff L, Wisse LEM, Glenn T, Xie SX, Nasrallah IM, Habes M, Dubroff J, de Flores R, Xie L, Yushkevich P, Doshi J, Davatsikos C, Shaw LM, Tropea TF, Chen-Plotkin AS, Wolk DA, Das S, Mechanic-Hamilton D. Self- and Partner-Reported Subjective Memory Complaints: Association with Objective Cognitive Impairment and Risk of Decline. J Alzheimers Dis Rep 2022; 6:411-430. [PMID: 36072364 PMCID: PMC9397901 DOI: 10.3233/adr-220013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2022] [Indexed: 11/15/2022] Open
Abstract
Background Episodic memory decline is a hallmark of Alzheimer's disease (AD). Subjective memory complaints (SMCs) may represent one of the earliest signs of impending cognitive decline. The degree to which self- or partner-reported SMCs predict cognitive change remains unclear. Objective We aimed to evaluate the relationship between self- and partner-reported SMCs, objective cognitive performance, AD biomarkers, and risk of future decline in a well-characterized longitudinal memory center cohort. We also evaluated whether study partner characteristics influence reports of SMCs. Methods 758 participants and 690 study partners were recruited from the Penn Alzheimer's Disease Research Center Clinical Core. Participants included those with Normal Cognition, Mild Cognitive Impairment, and AD. SMCs were measured using the Prospective and Retrospective Memory Questionnaire (PRMQ), and were evaluated for their association with cognition, genetic, plasma, and neuroimaging biomarkers of AD, cognitive and functional decline, and diagnostic progression over an average of four years. Results We found that partner-reported SMCs were more consistent with cognitive test performance and increasing symptom severity than self-reported SMCs. Partner-reported SMCs showed stronger correlations with AD-associated brain atrophy, plasma biomarkers of neurodegeneration, and longitudinal cognitive and functional decline. A 10-point increase on baseline PRMQ increased the annual risk of diagnostic progression by approximately 70%. Study partner demographics and relationship to participants influenced reports of SMCs in AD participants only. Conclusion Partner-reported SMCs, using the PRMQ, have a stronger relationship with the neuroanatomic and cognitive changes associated with AD than patient-reported SMCs. Further work is needed to evaluate whether SMCs could be used to screen for future decline.
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Affiliation(s)
- Leah Zuroff
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Trevor Glenn
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sharon X. Xie
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M. Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA
| | - Jacob Dubroff
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Robin de Flores
- Université de Caen Normandie, INSERM UMRS U1237, Caen, France
| | - Long Xie
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jimit Doshi
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatsikos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas F. Tropea
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alice S. Chen-Plotkin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu Das
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dawn Mechanic-Hamilton
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Correspondence to: Dawn Mechanic-Hamilton, PCAM-2 South, 3400 Civic Center Boulevard, Philadelphia, PA 19104, USA. Tel.: +1 215 662 4516; E-mail:
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38
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Wisse LEM, Xie L, Das SR, De Flores R, Hansson O, Habes M, Doshi J, Davatzikos C, Yushkevich PA, Wolk DA. Tau pathology mediates age effects on medial temporal lobe structure. Neurobiol Aging 2022; 109:135-144. [PMID: 34740075 PMCID: PMC8800343 DOI: 10.1016/j.neurobiolaging.2021.09.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 09/14/2021] [Accepted: 09/15/2021] [Indexed: 01/03/2023]
Abstract
Hippocampal atrophy is endemic in 'normal aging' but it is unclear what factors drive age-related changes in medial temporal lobe (MTL) structural measures. We investigated cross-sectional (n = 191) and longitudinal (n = 164) MTL atrophy patterns in cognitively normal older adults from ADNI-GO/2 with no to low cerebral β-amyloid and assessed whether white matter hyperintensities (WMHs) and cerebrospinal fluid (CSF) phospho tau (p-tau) levels can explain age-related changes in the MTL. Age was significantly associated with hippocampal volumes and Brodmann Area (BA) 35 thickness, regions affected early by neurofibrillary tangle pathology, in the cross-sectional analysis and with anterior and/or posterior hippocampus, entorhinal cortex and BA35 in the longitudinal analysis. CSF p-tau was significantly associated with hippocampal volumes and atrophy rates. Mediation analyses showed that CSF p-tau levels partially mediated age effects on hippocampal atrophy rates. No significant associations were observed for WMHs. These findings point toward a role of tau pathology, potentially reflecting Primary Age-Related Tauopathy, in age-related MTL structural changes and suggests a potential role for tau-targeted interventions in age-associated neurodegeneration and memory decline.
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Affiliation(s)
- LEM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - L Xie
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - SR Das
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - R De Flores
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen, France
| | - O Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - M Habes
- Biggs Alzheimer’s Institute, UT Health, San Antonio, USA
| | - J Doshi
- Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - C Davatzikos
- Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - PA Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - DA Wolk
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
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Ourry V, Marchant NL, Schild AK, Coll-Padros N, Klimecki OM, Krolak-Salmon P, Goldet K, Reyrolle L, Bachelet R, Sannemann L, Meiberth D, Demnitz-King H, Whitfield T, Botton M, Lebahar J, Gonneaud J, de Flores R, Molinuevo JL, Jessen F, Vivien D, de la Sayette V, Valenzuela MJ, Rauchs G, Wirth M, Chételat G, Arenaza-Urquijo EM. Harmonisation and Between-Country Differences of the Lifetime of Experiences Questionnaire in Older Adults. Front Aging Neurosci 2021; 13:740005. [PMID: 34720992 PMCID: PMC8551756 DOI: 10.3389/fnagi.2021.740005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The Lifetime of Experiences Questionnaire (LEQ) assesses complex mental activity across the life-course and has been associated with brain and cognitive health. The different education systems and occupation classifications across countries represent a challenge for international comparisons. The objectives of this study were four-fold: to adapt and harmonise the LEQ across four European countries, assess its validity across countries, explore its association with brain and cognition and begin to investigate between-country differences in life-course mental activities. Method: The LEQ was administered to 359 cognitively unimpaired older adults (mean age and education: 71.2, 13.2 years) from IMAP and EU-funded Medit-Ageing projects. Education systems, classification of occupations and scoring guidelines were adapted to allow comparisons between France, Germany, Spain and United Kingdom. We assessed the LEQ's (i) concurrent validity with a similar instrument (cognitive activities questionnaire - CAQ) and its structural validity by testing the factors' structure across countries, (ii) we investigated its association with cognition and neuroimaging, and (iii) compared its scores between countries. Results: The LEQ showed moderate to strong positive associations with the CAQ and revealed a stable multidimensional structure across countries that was similar to the original LEQ. The LEQ was positively associated with global cognition. Between-country differences were observed in leisure activities across the life-course. Conclusions: The LEQ is a promising tool for assessing the multidimensional construct of cognitive reserve and can be used to measure socio-behavioural determinants of cognitive reserve in older adults across countries. Longitudinal studies are warranted to test further its clinical utility.
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Affiliation(s)
- Valentin Ourry
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen, France.,Normandie University, UNICAEN, PSL Université, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, NIMH, Caen, France
| | - Natalie L Marchant
- Division of Psychiatry, University College London, London, United Kingdom
| | - Ann-Katrin Schild
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Nina Coll-Padros
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clinic, IDIBAPS, Barcelona, Spain
| | - Olga M Klimecki
- Clinical Psychology and Behavioural Neuroscience, Technische Universität Dresden, Dresden, Germany
| | - Pierre Krolak-Salmon
- Clinical and Research Memory Center, Hospices Civils de Lyon, Université de Lyon, INSERM, Lyon, France
| | - Karine Goldet
- Hospices Civils de Lyon, Institut du Vieillissement, CRC Vieillissement-Cerveau-Fragilite, Lyon, France
| | - Leslie Reyrolle
- Hospices Civils de Lyon, Institut du Vieillissement, CRC Vieillissement-Cerveau-Fragilite, Lyon, France
| | - Romain Bachelet
- Hospices Civils de Lyon, Institut du Vieillissement, CRC Vieillissement-Cerveau-Fragilite, Lyon, France
| | - Lena Sannemann
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Dix Meiberth
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | | | - Tim Whitfield
- Division of Psychiatry, University College London, London, United Kingdom
| | - Maëlle Botton
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen, France
| | - Julie Lebahar
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen, France
| | - Julie Gonneaud
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen, France
| | - Robin de Flores
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen, France
| | - José Luis Molinuevo
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clinic, IDIBAPS, Barcelona, Spain
| | - Frank Jessen
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Denis Vivien
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen, France.,Département de Recherche Clinique, CHU Caen-Normandie, Caen, France
| | - Vincent de la Sayette
- Normandie University, UNICAEN, PSL Université, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, NIMH, Caen, France.,Service de Neurologie, CHU de Caen, Caen, France
| | - Michael J Valenzuela
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Skin2Neuron Pty Ltd., Sydney, NSW, Australia
| | - Géraldine Rauchs
- Normandie University, UNICAEN, PSL Université, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, NIMH, Caen, France
| | - Miranka Wirth
- German Centre for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Gaël Chételat
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen, France
| | - Eider M Arenaza-Urquijo
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Caen, France.,Barcelonabeta Brain Research Center, Fundación Pasqual Maragall, Barcelona, Spain
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40
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Chauveau L, Kuhn E, Palix C, Felisatti F, Ourry V, de La Sayette V, Chételat G, de Flores R. Medial Temporal Lobe Subregional Atrophy in Aging and Alzheimer's Disease: A Longitudinal Study. Front Aging Neurosci 2021; 13:750154. [PMID: 34720998 PMCID: PMC8554299 DOI: 10.3389/fnagi.2021.750154] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022] Open
Abstract
Medial temporal lobe (MTL) atrophy is a key feature of Alzheimer's disease (AD), however, it also occurs in typical aging. To enhance the clinical utility of this biomarker, we need to better understand the differential effects of age and AD by encompassing the full AD-continuum from cognitively unimpaired (CU) to dementia, including all MTL subregions with up-to-date approaches and using longitudinal designs to assess atrophy more sensitively. Age-related trajectories were estimated using the best-fitted polynomials in 209 CU adults (aged 19–85). Changes related to AD were investigated among amyloid-negative (Aβ−) (n = 46) and amyloid-positive (Aβ+) (n = 14) CU, Aβ+ patients with mild cognitive impairment (MCI) (n = 33) and AD (n = 31). Nineteen MCI-to-AD converters were also compared with 34 non-converters. Relationships with cognitive functioning were evaluated in 63 Aβ+ MCI and AD patients. All participants were followed up to 47 months. MTL subregions, namely, the anterior and posterior hippocampus (aHPC/pHPC), entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36 [as perirhinal cortex (PRC) substructures], and parahippocampal cortex (PHC), were segmented from a T1-weighted MRI using a new longitudinal pipeline (LASHiS). Statistical analyses were performed using mixed models. Adult lifespan models highlighted both linear (PRC, BA35, BA36, PHC) and nonlinear (HPC, aHPC, pHPC, ERC) trajectories. Group comparisons showed reduced baseline volumes and steeper volume declines over time for most of the MTL subregions in Aβ+ MCI and AD patients compared to Aβ− CU, but no differences between Aβ− and Aβ+ CU or between Aβ+ MCI and AD patients (except in ERC). Over time, MCI-to-AD converters exhibited a greater volume decline than non-converters in HPC, aHPC, and pHPC. Most of the MTL subregions were related to episodic memory performances but not to executive functioning or speed processing. Overall, these results emphasize the benefits of studying MTL subregions to distinguish age-related changes from AD. Interestingly, MTL subregions are unequally vulnerable to aging, and those displaying non-linear age-trajectories, while not damaged in preclinical AD (Aβ+ CU), were particularly affected from the prodromal stage (Aβ+ MCI). This volume decline in hippocampal substructures might also provide information regarding the conversion from MCI to AD-dementia. All together, these findings provide new insights into MTL alterations, which are crucial for AD-biomarkers definition.
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Affiliation(s)
- Léa Chauveau
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Elizabeth Kuhn
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Cassandre Palix
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | | | - Valentin Ourry
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France.,U1077 NIMH, Inserm, Caen-Normandie University, École Pratique des Hautes Études, Caen, France
| | - Vincent de La Sayette
- U1077 NIMH, Inserm, Caen-Normandie University, École Pratique des Hautes Études, Caen, France
| | - Gaël Chételat
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Robin de Flores
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
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41
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Dong M, Xie L, Das SR, Wang J, Wisse LEM, deFlores R, Wolk DA, Yushkevich PA. DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease. Neuroimage 2021; 243:118514. [PMID: 34450261 PMCID: PMC8604562 DOI: 10.1016/j.neuroimage.2021.118514] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 08/18/2021] [Accepted: 08/23/2021] [Indexed: 11/26/2022] Open
Abstract
Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestimation of hippocampal atrophy. For example, the deformation-based-morphometry method ALOHA (Das et al., 2012) finds an increase in hippocampal volume in a substantial proportion of longitudinal scan pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, unexpected, given that the hippocampal gray matter is lost with age and disease progression. We propose an alternative approach to quantify disease progression in the hippocampal region: to train a deep learning network (called DeepAtrophy) to infer temporal information from longitudinal scan pairs. The underlying assumption is that by learning to derive time-related information from scan pairs, the network implicitly learns to detect progressive changes that are related to aging and disease progression. Our network is trained using two categorical loss functions: one that measures the network's ability to correctly order two scans from the same subject, input in arbitrary order; and another that measures the ability to correctly infer the ratio of inter-scan intervals between two pairs of same-subject input scans. When applied to longitudinal MRI scan pairs from subjects unseen during training, DeepAtrophy achieves greater accuracy in scan temporal ordering and interscan interval inference tasks than ALOHA (88.5% vs. 75.5% and 81.1% vs. 75.0%, respectively). A scalar measure of time-related change in a subject level derived from DeepAtrophy is then examined as a biomarker of disease progression in the context of AD clinical trials. We find that this measure performs on par with ALOHA in discriminating groups of individuals at different stages of the AD continuum. Overall, our results suggest that using deep learning to infer temporal information from longitudinal MRI of the hippocampal region has good potential as a biomarker of disease progression, and hints that combining this approach with conventional deformation-based morphometry algorithms may lead to improved biomarkers in the future.
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Affiliation(s)
- Mengjin Dong
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States.
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Jiancong Wang
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Robin deFlores
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Institut National de la Santé et de la Recherche Médicale (INSERM), Caen, France
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States; Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
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42
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Yamashita K, Kuwashiro T, Ishikawa K, Furuya K, Harada S, Shin S, Wada N, Hirakawa C, Okada Y, Noguchi T. Right entorhinal cortical thickness is associated with Mini-Mental State Examination scores from multi-country datasets using MRI. Neuroradiology 2021; 64:279-288. [PMID: 34247261 DOI: 10.1007/s00234-021-02767-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 07/06/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE To discover common biomarkers correlating with the Mini-Mental State Examination (MMSE) scores from multi-country MRI datasets. METHODS The first dataset comprised 112 subjects (49 men, 63 women; range, 46-94 years) at the National Hospital Organization Kyushu Medical Center. A second dataset comprised 300 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (177 men, 123 women; range, 57-91 years). Three-dimensional T1-weighted MR images were collected from both datasets. In total, 14 deep gray matter volumes and 70 cortical thicknesses were obtained from MR images using FreeSurfer software. Total hippocampal volume and the ratio of hippocampus to cerebral volume were also calculated. Correlations between each variable and MMSE scores were assessed using Pearson's correlation coefficient. Parameters with moderate correlation coefficients (r > 0.3) from each dataset were determined as independent variables and evaluated using general linear model (GLM) analyses. RESULTS In Pearson's correlation coefficient, total and bilateral hippocampal volumes, right amygdala volume, and right entorhinal cortex (ERC) thickness showed moderate correlation coefficients (r > 0.3) with MMSE scores from the first dataset. The ADNI dataset showed moderate correlations with MMSE scores in more variables, including bilateral ERC thickness and hippocampal volume. GLM analysis revealed that right ERC thickness correlated significantly with MMSE score in both datasets. Cortical thicknesses of the left parahippocampal gyrus, left inferior parietal lobe, and right fusiform gyrus also significantly correlated with MMSE score in the ADNI dataset (p < 0.05). CONCLUSION A positive correlation between right ERC thickness and MMSE score was identified from multi-country datasets.
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Affiliation(s)
- Koji Yamashita
- Department of Radiology, Clinical Research Institute, National Hospital Organization Kyushu Medical Center, 1-8-1 Jigyohama, Chuo-ku, 810-0065, Fukuoka, Japan.
| | - Takahiro Kuwashiro
- Department of Cerebrovascular Medicine and Neurology, National Hospital Organization Kyushu Medical Center, 1-8-1 Jigyohama, Chuo-ku, 810-0065, Fukuoka, Japan
| | - Kensuke Ishikawa
- Department of Psychiatry, Clinical Research Institute, National Hospital Organization Kyushu Medical Center, 1-8-1 Jigyohama, Chuo-ku, 810-0065, Fukuoka, Japan
| | - Kiyomi Furuya
- Department of Radiology, Clinical Research Institute, National Hospital Organization Kyushu Medical Center, 1-8-1 Jigyohama, Chuo-ku, 810-0065, Fukuoka, Japan
| | - Shino Harada
- Department of Radiology, Clinical Research Institute, National Hospital Organization Kyushu Medical Center, 1-8-1 Jigyohama, Chuo-ku, 810-0065, Fukuoka, Japan
| | - Seitaro Shin
- Department of Radiology, Clinical Research Institute, National Hospital Organization Kyushu Medical Center, 1-8-1 Jigyohama, Chuo-ku, 810-0065, Fukuoka, Japan
| | - Noriaki Wada
- Department of Radiology, Clinical Research Institute, National Hospital Organization Kyushu Medical Center, 1-8-1 Jigyohama, Chuo-ku, 810-0065, Fukuoka, Japan
| | - Chika Hirakawa
- Department of Medical Technology, Division of Radiology, National Hospital Organization Kyushu Medical Center, 1-8-1 Jigyohama, Chuo-ku, Fukuoka, 810-0065, Japan
| | - Yasushi Okada
- Department of Cerebrovascular Medicine and Neurology, National Hospital Organization Kyushu Medical Center, 1-8-1 Jigyohama, Chuo-ku, 810-0065, Fukuoka, Japan
| | - Tomoyuki Noguchi
- Department of Radiology, Clinical Research Institute, National Hospital Organization Kyushu Medical Center, 1-8-1 Jigyohama, Chuo-ku, 810-0065, Fukuoka, Japan
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43
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Vockert N, Perosa V, Ziegler G, Schreiber F, Priester A, Spallazzi M, Garcia-Garcia B, Aruci M, Mattern H, Haghikia A, Düzel E, Schreiber S, Maass A. Hippocampal vascularization patterns exert local and distant effects on brain structure but not vascular pathology in old age. Brain Commun 2021; 3:fcab127. [PMID: 34222874 PMCID: PMC8249103 DOI: 10.1093/braincomms/fcab127] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 04/25/2021] [Accepted: 06/03/2021] [Indexed: 12/29/2022] Open
Abstract
The hippocampus within the medial temporal lobe is highly vulnerable to age-related pathology such as vascular disease. We examined hippocampal vascularization patterns by harnessing the ultra-high resolution of 7 Tesla magnetic resonance angiography. Dual-supply hemispheres with a contribution of the anterior choroidal artery to hippocampal blood supply were distinguished from single-supply ones with a sole dependence on the posterior cerebral artery. A recent study indicated that a dual vascular supply is related to preserved cognition and structural hippocampal integrity in old age and vascular disease. Here, we examined the regional specificity of these structural benefits at the level of medial temporal lobe sub-regions and hemispheres. In a cross-sectional study with an older cohort of 17 patients with cerebral small vessel disease (70.7 ± 9.0 years, 35.5% female) and 27 controls (71.1 ± 8.2 years, 44.4% female), we demonstrate that differences in grey matter volumes related to the hippocampal vascularization pattern were specifically observed in the anterior hippocampus and entorhinal cortex. These regions were especially bigger in dual-supply hemispheres, but also seemed to benefit from a contralateral dual supply. We further show that total grey matter volumes were greater in people with at least one dual-supply hemisphere, indicating that the hippocampal vascularization pattern has more far-reaching structural implications beyond the medial temporal lobe. A mediation analysis identified total grey matter as a mediator of differences in global cognition. However, our analyses on multiple neuroimaging markers for cerebral small vessel disease did not reveal any evidence that an augmented hippocampal vascularization conveys resistance nor resilience against vascular pathology. We propose that an augmented hippocampal vascularization might contribute to maintaining structural integrity in the brain and preserving cognition despite age-related degeneration. As such, the binary hippocampal vascularization pattern could have major implications for brain structure and function in ageing and dementia independent of vascular pathology, while presenting a simple framework with potential applicability to the clinical setting.
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Affiliation(s)
- Niklas Vockert
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
| | - Valentina Perosa
- Department of Neurology, Otto von Guericke University Magdeburg, 39120 Magdeburg, Germany
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, 39120 Magdeburg, Germany
| | - Frank Schreiber
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
- Department of Neurology, Otto von Guericke University Magdeburg, 39120 Magdeburg, Germany
| | - Anastasia Priester
- Department of Neuroradiology, University Hospital Heidelberg, 69120 Heidelberg, Germany
| | - Marco Spallazzi
- Department of Medicine and Surgery, Unit of Neurology, Azienda Ospedaliero- Universitaria, 43126 Parma, Italy
| | - Berta Garcia-Garcia
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
- Department of Neurology, Otto von Guericke University Magdeburg, 39120 Magdeburg, Germany
| | - Merita Aruci
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
| | - Hendrik Mattern
- Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, 39120 Magdeburg, Germany
| | - Aiden Haghikia
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
- Department of Neurology, Otto von Guericke University Magdeburg, 39120 Magdeburg, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
- Department of Neurology, Otto von Guericke University Magdeburg, 39120 Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University, 39120 Magdeburg, Germany
- Institute of Cognitive Neuroscience, University College London, London WC1N 3AZ, UK
- Center for Behavioral Brain Sciences (CBBS), 39106 Magdeburg, Germany
| | - Stefanie Schreiber
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
- Department of Neurology, Otto von Guericke University Magdeburg, 39120 Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), 39106 Magdeburg, Germany
| | - Anne Maass
- German Center for Neurodegenerative Diseases, 39120 Magdeburg, Germany
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44
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McCollum LE, Das SR, Xie L, de Flores R, Wang J, Xie SX, Wisse LEM, Yushkevich PA, Wolk DA. Oh brother, where art tau? Amyloid, neurodegeneration, and cognitive decline without elevated tau. Neuroimage Clin 2021; 31:102717. [PMID: 34119903 PMCID: PMC8207301 DOI: 10.1016/j.nicl.2021.102717] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 05/21/2021] [Accepted: 06/02/2021] [Indexed: 12/24/2022]
Abstract
Mild cognitive impairment (MCI) can be an early manifestation of Alzheimer's disease (AD) pathology, other pathologic entities [e.g., cerebrovascular disease, Lewy body disease, LATE (limbic-predominant age-related TDP-43 encephalopathy)], or mixed pathologies, with concomitant AD- and non-AD pathology being particularly common, albeit difficult to identify, in living MCI patients. The National Institute on Aging and Alzheimer's Association (NIA-AA) A/T/(N) [β-Amyloid/Tau/(Neurodegeneration)] AD research framework, which classifies research participants according to three binary biomarkers [β-amyloid (A+/A-), tau (T+/T-), and neurodegeneration (N+/N-)], provides an indirect means of identifying such cases. Individuals with A+T-(N+) MCI are thought to have both AD pathologic change, given the presence of β-amyloid, and non-AD pathophysiology, given neurodegeneration without tau, because in typical AD it is tau accumulation that is most tightly linked to neuronal injury and cognitive decline. Thus, in A+T-(N+) MCI (hereafter referred to as "mismatch MCI" for the tau-neurodegeneration mismatch), non-AD pathology is hypothesized to drive neurodegeneration and symptoms, because β-amyloid, in the absence of tau, likely reflects a preclinical stage of AD. We compared a group of individuals with mismatch MCI to groups with A+T+(N+) MCI (or "prodromal AD") and A-T-(N+) MCI (or "neurodegeneration-only MCI") on cross-sectional and longitudinal cognition and neuroimaging characteristics. β-amyloid and tau status were determined by CSF assays, while neurodegeneration status was based on hippocampal volume on MRI. Overall, mismatch MCI was less "AD-like" than prodromal AD and generally, with some exceptions, more closely resembled the neurodegeneration-only group. At baseline, mismatch MCI had less episodic memory loss compared to prodromal AD. Longitudinally, mismatch MCI declined more slowly than prodromal AD across all included cognitive domains, while mismatch MCI and neurodegeneration-only MCI declined at comparable rates. Prodromal AD had smaller baseline posterior hippocampal volume than mismatch MCI, and whole brain analyses demonstrated cortical thinning that was widespread in prodromal AD but largely restricted to the medial temporal lobes (MTLs) for the mismatch and neurodegeneration-only MCI groups. Longitudinally, mismatch MCI had slower rates of volume loss than prodromal AD throughout the MTLs. Differences in cross-sectional and longitudinal cognitive and neuroimaging measures between mismatch MCI and prodromal AD may reflect disparate underlying pathologic processes, with the mismatch group potentially being driven by non-AD pathologies on a background of largely preclinical AD. These findings suggest that β-amyloid status alone in MCI may not reveal the underlying driver of symptoms with important implications for enrollment in clinical trials and prognosis.
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Affiliation(s)
- Lauren E McCollum
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, University of Tennessee Graduate School of Medicine, Knoxville, TN, USA.
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA
| | - Long Xie
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA
| | - Robin de Flores
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA; INSERM UMR-S U1237, Université de Caen Normandie, Caen, Normandy, USA
| | - Jieqiong Wang
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sharon X Xie
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Laura E M Wisse
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA; Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Paul A Yushkevich
- Department of Radiology, Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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45
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Wisse LEM, de Flores R, Xie L, Das SR, McMillan CT, Trojanowski JQ, Grossman M, Lee EB, Irwin D, Yushkevich PA, Wolk DA. Pathological drivers of neurodegeneration in suspected non-Alzheimer's disease pathophysiology. ALZHEIMERS RESEARCH & THERAPY 2021; 13:100. [PMID: 33990226 PMCID: PMC8122549 DOI: 10.1186/s13195-021-00835-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 04/26/2021] [Indexed: 11/16/2022]
Abstract
Background Little is known about the heterogeneous etiology of suspected non-Alzheimer’s pathophysiology (SNAP), a group of subjects with neurodegeneration in the absence of β-amyloid. Using antemortem MRI and pathological data, we investigated the etiology of SNAP and the association of neurodegenerative pathologies with structural medial temporal lobe (MTL) measures in β-amyloid-negative subjects. Methods Subjects with antemortem MRI and autopsy data were selected from ADNI (n=63) and the University of Pennsylvania (n=156). Pathological diagnoses and semi-quantitative scores of MTL tau, neuritic plaques, α-synuclein, and TDP-43 pathology and MTL structural MRI measures from antemortem T1-weighted MRI scans were obtained. β-amyloid status (A+/A−) was determined by CERAD score and neurodegeneration status (N+/N−) by hippocampal volume. Results SNAP reflects a heterogeneous group of pathological diagnoses. In ADNI, SNAP (A−N+) had significantly more neuropathological diagnoses than A+N+. In the A− group, tau pathology was associated with hippocampal, entorhinal cortex, and Brodmann area 35 volume/thickness and TDP-43 pathology with hippocampal volume. Conclusion SNAP had a heterogeneous profile with more mixed pathologies than A+N+. Moreover, a role for TDP-43 and tau pathology in driving MTL neurodegeneration in the absence of β-amyloid was supported. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00835-2.
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Affiliation(s)
- L E M Wisse
- Department of Diagnostic Radiology, Lund University, Remissgatan 4, Room 14-520, 222 42, Lund, Sweden. .,Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA. .,Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA.
| | - R de Flores
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen, France
| | - L Xie
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA.,Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - S R Das
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - C T McMillan
- Penn FTD Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - J Q Trojanowski
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA
| | - M Grossman
- Penn FTD Center, Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - E B Lee
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA
| | - D Irwin
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA
| | - P A Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - D A Wolk
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
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46
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Zhang B, Lin L, Wu S. A Review of Brain Atrophy Subtypes Definition and Analysis for Alzheimer’s Disease Heterogeneity Studies. J Alzheimers Dis 2021; 80:1339-1352. [DOI: 10.3233/jad-201274] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Alzheimer’s disease (AD) is a heterogeneous disease with different subtypes. Studying AD subtypes from brain structure, neuropathology, and cognition are of great importance for AD heterogeneity research. Starting from the study of constructing AD subtypes based on the features of T1-weighted structural magnetic resonance imaging, this paper introduces the major connections between the subtype definition and analysis strategies, including brain region-based subtype definition, and their demographic, neuropathological, and neuropsychological characteristics. The advantages and existing problems are analyzed, and reasonable improvement schemes are prospected. Overall, this review offers a more comprehensive view in the field of atrophy subtype in AD, along with their advantages, challenges, and future prospects, and provide a basis for improving individualized AD diagnosis.
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Affiliation(s)
- Baiwen Zhang
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Lan Lin
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Shuicai Wu
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
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47
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Berron D, Vogel JW, Insel PS, Pereira JB, Xie L, Wisse LEM, Yushkevich PA, Palmqvist S, Mattsson-Carlgren N, Stomrud E, Smith R, Strandberg O, Hansson O. Early stages of tau pathology and its associations with functional connectivity, atrophy and memory. Brain 2021; 144:2771-2783. [PMID: 33725124 PMCID: PMC8557349 DOI: 10.1093/brain/awab114] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/15/2021] [Accepted: 03/04/2021] [Indexed: 11/12/2022] Open
Abstract
In Alzheimer's disease, postmortem studies have shown that the first cortical site where neurofibrillary tangles appear is the transentorhinal region, a subregion within the medial temporal lobe that largely overlaps with area 35, and the entorhinal cortex. Here we used tau-PET imaging to investigate the sequence of tau pathology progression within the human medial temporal lobe and across regions in the posterior-medial system. Our objective was to study how medial temporal tau is related to functional connectivity, regional atrophy, and memory performance. We included 215 β-amyloid negative cognitively unimpaired, 81 β-amyloid positive cognitively unimpaired and 87 β-amyloid positive individuals with mild cognitive impairment, who each underwent [18]F-RO948 tau and [18]F-flutemetamol amyloid PET imaging, structural T1-MRI and memory assessments as part of the Swedish BioFINDER-2 study. First, event-based modelling revealed that the entorhinal cortex and area 35 show the earliest signs of tau accumulation followed by the anterior and posterior hippocampus, area 36 and the parahippocampal cortex. In later stages, tau accumulation became abnormal in neocortical temporal and finally parietal brain regions. Second, in cognitively unimpaired individuals, increased tau load was related to local atrophy in the entorhinal cortex, area 35 and the anterior hippocampus and tau load in several anterior medial temporal lobe subregions was associated with distant atrophy of the posterior hippocampus. Tau load, but not atrophy, in these regions was associated with lower memory performance. Further, tau-related reductions in functional connectivity in critical networks between the medial temporal lobe and regions in the posterior-medial system were associated with this early memory impairment. Finally, in patients with mild cognitive impairment, the association of tau load in the hippocampus with memory performance was partially mediated by posterior hippocampal atrophy. In summary, our findings highlight the progression of tau pathology across medial temporal lobe subregions and its disease-stage specific association with memory performance. While tau pathology might affect memory performance in cognitively unimpaired individuals via reduced functional connectivity in critical medial temporal lobe-cortical networks, memory impairment in mild cognitively impaired patients is associated with posterior hippocampal atrophy.
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Affiliation(s)
- David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, 19104 Philadelphia, USA
| | - Philip S Insel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Department of Psychiatry and Behavioral Sciences, University of California, 94143 San Francisco, USA
| | - Joana B Pereira
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, 171 77 Stockholm, Sweden
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, 19104, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, 19104 Philadelphia, Pennsylvania, USA
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, 19104, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, 19104 Philadelphia, Pennsylvania, USA.,Department of Diagnostic Radiology, Lund University, 221 00 Lund, Sweden
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, 19104, Philadelphia, Pennsylvania, USA.,Department of Radiology, University of Pennsylvania, 19104 Philadelphia, Pennsylvania, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Memory Clinic, Skåne University Hospital, 205 02 Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Department of Neurology, Skåne University Hospital, 221 00 Lund, Sweden.,Wallenberg Center for Molecular Medicine, Lund University, 221 00 Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Memory Clinic, Skåne University Hospital, 205 02 Malmö, Sweden
| | - Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Department of Neurology, Skåne University Hospital, 221 00 Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, 223 62 Lund, Sweden.,Department of Psychiatry, University of Pennsylvania, 19104 Philadelphia, USA
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48
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van Oostveen WM, de Lange ECM. Imaging Techniques in Alzheimer's Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring. Int J Mol Sci 2021; 22:ijms22042110. [PMID: 33672696 PMCID: PMC7924338 DOI: 10.3390/ijms22042110] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting many individuals worldwide with no effective treatment to date. AD is characterized by the formation of senile plaques and neurofibrillary tangles, followed by neurodegeneration, which leads to cognitive decline and eventually death. INTRODUCTION In AD, pathological changes occur many years before disease onset. Since disease-modifying therapies may be the most beneficial in the early stages of AD, biomarkers for the early diagnosis and longitudinal monitoring of disease progression are essential. Multiple imaging techniques with associated biomarkers are used to identify and monitor AD. AIM In this review, we discuss the contemporary early diagnosis and longitudinal monitoring of AD with imaging techniques regarding their diagnostic utility, benefits and limitations. Additionally, novel techniques, applications and biomarkers for AD research are assessed. FINDINGS Reduced hippocampal volume is a biomarker for neurodegeneration, but atrophy is not an AD-specific measure. Hypometabolism in temporoparietal regions is seen as a biomarker for AD. However, glucose uptake reflects astrocyte function rather than neuronal function. Amyloid-β (Aβ) is the earliest hallmark of AD and can be measured with positron emission tomography (PET), but Aβ accumulation stagnates as disease progresses. Therefore, Aβ may not be a suitable biomarker for monitoring disease progression. The measurement of tau accumulation with PET radiotracers exhibited promising results in both early diagnosis and longitudinal monitoring, but large-scale validation of these radiotracers is required. The implementation of new processing techniques, applications of other imaging techniques and novel biomarkers can contribute to understanding AD and finding a cure. CONCLUSIONS Several biomarkers are proposed for the early diagnosis and longitudinal monitoring of AD with imaging techniques, but all these biomarkers have their limitations regarding specificity, reliability and sensitivity. Future perspectives. Future research should focus on expanding the employment of imaging techniques and identifying novel biomarkers that reflect AD pathology in the earliest stages.
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Affiliation(s)
- Wieke M. van Oostveen
- Faculty of Science, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands;
| | - Elizabeth C. M. de Lange
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
- Correspondence: ; Tel.: +31-71-527-6330
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49
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Wisse LEM, Chételat G, Daugherty AM, de Flores R, la Joie R, Mueller SG, Stark CEL, Wang L, Yushkevich PA, Berron D, Raz N, Bakker A, Olsen RK, Carr VA. Hippocampal subfield volumetry from structural isotropic 1 mm 3 MRI scans: A note of caution. Hum Brain Mapp 2021; 42:539-550. [PMID: 33058385 PMCID: PMC7775994 DOI: 10.1002/hbm.25234] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 09/01/2020] [Accepted: 09/29/2020] [Indexed: 01/05/2023] Open
Abstract
Spurred by availability of automatic segmentation software, in vivo MRI investigations of human hippocampal subfield volumes have proliferated in the recent years. However, a majority of these studies apply automatic segmentation to MRI scans with approximately 1 × 1 × 1 mm3 resolution, a resolution at which the internal structure of the hippocampus can rarely be visualized. Many of these studies have reported contradictory and often neurobiologically surprising results pertaining to the involvement of hippocampal subfields in normal brain function, aging, and disease. In this commentary, we first outline our concerns regarding the utility and validity of subfield segmentation on 1 × 1 × 1 mm3 MRI for volumetric studies, regardless of how images are segmented (i.e., manually or automatically). This image resolution is generally insufficient for visualizing the internal structure of the hippocampus, particularly the stratum radiatum lacunosum moleculare, which is crucial for valid and reliable subfield segmentation. Second, we discuss the fact that automatic methods that are employed most frequently to obtain hippocampal subfield volumes from 1 × 1 × 1 mm3 MRI have not been validated against manual segmentation on such images. For these reasons, we caution against using volumetric measurements of hippocampal subfields obtained from 1 × 1 × 1 mm3 images.
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Affiliation(s)
- Laura E. M. Wisse
- Diagnostic RadiologyLund UniversityLundSweden
- Penn Image Computing and Science Laboratory, Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Penn Memory Center, Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Gaël Chételat
- Université Normandie, InsermUniversité de Caen‐Normandie, Inserm UMR‐S U1237CaenFrance
| | - Ana M. Daugherty
- Department of PsychologyWayne State UniversityDetroitMichiganUSA
- Institute of GerontologyWayne State UniversityDetroitMichiganUSA
- Department of Psychiatry and Behavioral NeurosciencesWayne State UniversityDetroitMichiganUSA
| | - Robin de Flores
- Université Normandie, InsermUniversité de Caen‐Normandie, Inserm UMR‐S U1237CaenFrance
| | - Renaud la Joie
- Memory and Aging CenterUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Susanne G. Mueller
- Department of RadiologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Center for Imaging of Neurodegenerative DiseasesSan Francisco VA Medical CenterSan FranciscoCaliforniaUSA
| | - Craig E. L. Stark
- Department of Neurobiology and BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Lei Wang
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of RadiologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Paul A. Yushkevich
- Penn Image Computing and Science Laboratory, Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences MalmöLund UniversityLundSweden
| | - Naftali Raz
- Department of PsychologyWayne State UniversityDetroitMichiganUSA
- Institute of GerontologyWayne State UniversityDetroitMichiganUSA
- Center for Lifespan PsychologyMax Planck Institute for Human DevelopmentBerlinGermany
| | - Arnold Bakker
- Department of Psychiatry and Behavioral SciencesJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | | | - Valerie A. Carr
- Department of PsychologySan Jose State UniversitySan JoseCaliforniaUSA
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50
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Sanchez JS, Becker JA, Jacobs HIL, Hanseeuw BJ, Jiang S, Schultz AP, Properzi MJ, Katz SR, Beiser A, Satizabal CL, O'Donnell A, DeCarli C, Killiany R, El Fakhri G, Normandin MD, Gómez-Isla T, Quiroz YT, Rentz DM, Sperling RA, Seshadri S, Augustinack J, Price JC, Johnson KA. The cortical origin and initial spread of medial temporal tauopathy in Alzheimer's disease assessed with positron emission tomography. Sci Transl Med 2021; 13:eabc0655. [PMID: 33472953 PMCID: PMC7978042 DOI: 10.1126/scitranslmed.abc0655] [Citation(s) in RCA: 108] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 09/10/2020] [Indexed: 12/15/2022]
Abstract
Advances in molecular positron emission tomography (PET) have enabled anatomic tracking of brain pathology in longitudinal studies of normal aging and dementia, including assessment of the central model of Alzheimer's disease (AD) pathogenesis, according to which TAU pathology begins focally but expands catastrophically under the influence of amyloid-β (Aβ) pathology to mediate neurodegeneration and cognitive decline. Initial TAU deposition occurs many years before Aβ in a specific area of the medial temporal lobe. Building on recent work that enabled focus of molecular PET measurements on specific TAU-vulnerable convolutional temporal lobe anatomy, we applied an automated anatomic sampling method to quantify TAU PET signal in 443 adult participants from several observational studies of aging and AD, spanning a wide range of ages, Aβ burdens, and degrees of clinical impairment. We detected initial cortical emergence of tauopathy near the rhinal sulcus in clinically normal people and, in a subset with longitudinal 2-year follow-up data (n = 104), tracked Aβ-associated spread of TAU from this site first to nearby neocortex of the temporal lobe and then to extratemporal regions. Greater rate of TAU spread was associated with baseline measures of both global Aβ burden and medial temporal lobe TAU. These findings are consistent with clinicopathological correlation studies of Alzheimer's tauopathy and enable precise tracking of AD-related TAU progression for natural history studies and prevention therapeutic trials.
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Affiliation(s)
- Justin S Sanchez
- Massachusetts General Hospital, Boston, MA 02114, USA.
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - J Alex Becker
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - Heidi I L Jacobs
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
- School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, 6211 LK, Netherlands
| | - Bernard J Hanseeuw
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
- Université Catholique de Louvain, Brussels B-1348, Belgium
| | - Shu Jiang
- Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Aaron P Schultz
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael J Properzi
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Samantha R Katz
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - Alexa Beiser
- Boston University School of Medicine, Boston, MA 02118, USA
- Boston University School of Public Health, Boston, MA 02118, USA
- Framingham Heart Study, Framingham, MA 01702, USA
| | - Claudia L Satizabal
- Boston University School of Medicine, Boston, MA 02118, USA
- Framingham Heart Study, Framingham, MA 01702, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 78229, USA
| | - Adrienne O'Donnell
- Boston University School of Public Health, Boston, MA 02118, USA
- Framingham Heart Study, Framingham, MA 01702, USA
| | | | - Ron Killiany
- Boston University School of Medicine, Boston, MA 02118, USA
- Boston University School of Public Health, Boston, MA 02118, USA
| | - Georges El Fakhri
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - Marc D Normandin
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - Teresa Gómez-Isla
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Yakeel T Quiroz
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Grupo de Neurociencias, Universidad de Antioquia, Antioquia 050010, Colombia
| | - Dorene M Rentz
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Reisa A Sperling
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Sudha Seshadri
- Boston University School of Medicine, Boston, MA 02118, USA
- Framingham Heart Study, Framingham, MA 01702, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX 78229, USA
| | - Jean Augustinack
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Julie C Price
- Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Keith A Johnson
- Massachusetts General Hospital, Boston, MA 02114, USA.
- Harvard Medical School, Boston, MA 02115, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
- Brigham and Women's Hospital, Boston, MA 02115, USA
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