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Chen AM, Gajdošík M, Ahmed W, Ahn S, Babb JS, Blessing EM, Boutajangout A, de Leon MJ, Debure L, Gaggi N, Gajdošík M, George A, Ghuman M, Glodzik L, Harvey P, Juchem C, Marsh K, Peralta R, Rusinek H, Sheriff S, Vedvyas A, Wisniewski T, Zheng H, Osorio R, Kirov II. Retrospective analysis of Braak stage- and APOE4 allele-dependent associations between MR spectroscopy and markers of tau and neurodegeneration in cognitively unimpaired elderly. Neuroimage 2024; 297:120742. [PMID: 39029606 DOI: 10.1016/j.neuroimage.2024.120742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/28/2024] [Accepted: 07/16/2024] [Indexed: 07/21/2024] Open
Abstract
PURPOSE The pathological hallmarks of Alzheimer's disease (AD), amyloid, tau, and associated neurodegeneration, are present in the cortical gray matter (GM) years before symptom onset, and at significantly greater levels in carriers of the apolipoprotein E4 (APOE4) allele. Their respective biomarkers, A/T/N, have been found to correlate with aspects of brain biochemistry, measured with magnetic resonance spectroscopy (MRS), indicating a potential for MRS to augment the A/T/N framework for staging and prediction of AD. Unfortunately, the relationships between MRS and A/T/N biomarkers are unclear, largely due to a lack of studies examining them in the context of the spatial and temporal model of T/N progression. Advanced MRS acquisition and post-processing approaches have enabled us to address this knowledge gap and test the hypotheses, that glutamate-plus-glutamine (Glx) and N-acetyl-aspartate (NAA), metabolites reflecting synaptic and neuronal health, respectively, measured from regions on the Braak stage continuum, correlate with: (i) cerebrospinal fluid (CSF) p-tau181 level (T), and (ii) hippocampal volume or cortical thickness of parietal lobe GM (N). We hypothesized that these correlations will be moderated by Braak stage and APOE4 genotype. METHODS We conducted a retrospective imaging study of 34 cognitively unimpaired elderly individuals who received APOE4 genotyping and lumbar puncture from pre-existing prospective studies at the NYU Grossman School of Medicine between October 2014 and January 2019. Subjects returned for their imaging exam between April 2018 and February 2020. Metabolites were measured from the left hippocampus (Braak II) using a single-voxel semi-adiabatic localization by adiabatic selective refocusing sequence; and from the bilateral posterior cingulate cortex (PCC; Braak IV), bilateral precuneus (Braak V), and bilateral precentral gyrus (Braak VI) using a multi-voxel echo-planar spectroscopic imaging sequence. Pearson and Spearman correlations were used to examine the relationships between absolute levels of choline, creatine, myo-inositol, Glx, and NAA and CSF p-tau181, and between these metabolites and hippocampal volume or parietal cortical thicknesses. Covariates included age, sex, years of education, Fazekas score, and months between CSF collection and MRI exam. RESULTS There was a direct correlation between hippocampal Glx and CSF p-tau181 in APOE4 carriers (Pearson's r = 0.76, p = 0.02), but not after adjusting for covariates. In the entire cohort, there was a direct correlation between hippocampal NAA and hippocampal volume (Spearman's r = 0.55, p = 0.001), even after adjusting for age and Fazekas score (Spearman's r = 0.48, p = 0.006). This relationship was observed only in APOE4 carriers (Pearson's r = 0.66, p = 0.017), and was also retained after adjustment (Pearson's r = 0.76, p = 0.008; metabolite-by-carrier interaction p = 0.03). There were no findings in the PCC, nor in the negative control (late Braak stage) regions of the precuneus and precentral gyrus. CONCLUSIONS Our findings are in line with the spatially- and temporally-resolved Braak staging model of pathological severity in which the hippocampus is affected earlier than the PCC. The correlations, between MRS markers of synaptic and neuronal health and, respectively, T and N pathology, were found exclusively within APOE4 carriers, suggesting a connection with AD pathological change, rather than with normal aging. We therefore conclude that MRS has the potential to augment early A/T/N staging, with the hippocampus serving as a more sensitive MRS target compared to the PCC.
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Affiliation(s)
- Anna M Chen
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI(2)R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA; Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, USA
| | - Martin Gajdošík
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI(2)R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA; Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Wajiha Ahmed
- Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Sinyeob Ahn
- Siemens Medical Solutions USA Inc., Malvern, PA, USA
| | - James S Babb
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI(2)R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Esther M Blessing
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA; Healthy Brain Aging and Sleep Center, NYU Langone Health, New York, NY, USA
| | - Allal Boutajangout
- Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA; Department of Neuroscience and Physiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Mony J de Leon
- Retired Director, Center for Brain Health, Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA; Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Ludovic Debure
- Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Naomi Gaggi
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA; Healthy Brain Aging and Sleep Center, NYU Langone Health, New York, NY, USA
| | - Mia Gajdošík
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI(2)R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Ajax George
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI(2)R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Mobeena Ghuman
- Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Lidia Glodzik
- Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Patrick Harvey
- Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Christoph Juchem
- Department of Biomedical Engineering, Columbia University, New York, NY, USA; Department of Radiology, Columbia University, New York, NY, USA
| | - Karyn Marsh
- Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Rosemary Peralta
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI(2)R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Henry Rusinek
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI(2)R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Sulaiman Sheriff
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Alok Vedvyas
- Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Thomas Wisniewski
- Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA; Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA; Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
| | - Helena Zheng
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI(2)R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Ricardo Osorio
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA; Healthy Brain Aging and Sleep Center, NYU Langone Health, New York, NY, USA.
| | - Ivan I Kirov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA; Center for Advanced Imaging Innovation and Research (CAI(2)R), Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA; Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, USA; Center for Cognitive Neurology, Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA; Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA.
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2
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Lu H, Li J. MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters. J Cent Nerv Syst Dis 2024; 16:11795735241266556. [PMID: 39049837 PMCID: PMC11268046 DOI: 10.1177/11795735241266556] [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: 01/10/2024] [Accepted: 06/02/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Brain age model, including estimated brain age and brain-predicted age difference (brain-PAD), has shown great potentials for serving as imaging markers for monitoring normal ageing, as well as for identifying the individuals in the pre-diagnostic phase of neurodegenerative diseases. PURPOSE This study aimed to investigate the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion. METHODS Pre-trained brain age model was constructed using the structural magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) project (N = 609). The tested brain age model was built using the baseline, 1-year and 3-year follow-up MRI data from normal ageing (NA) adults (n = 32) and MCI converters (n = 22) drew from the Open Access Series of Imaging Studies (OASIS-2). The quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. Brain age models were calculated based on the individual's morphometric features using the support vector machine (SVM) algorithm. RESULTS With comparable chronological age, MCI converters showed significant increased TIV-based (Baseline: P = 0.021; 1-year follow-up: P = 0.037; 3-year follow-up: P = 0.001) and left GMV-based brain age than NA adults at all time points. Higher brain-PAD scores were associated with worse global cognition. Acceptable classification performance of TIV-based (AUC = 0.698) and left GMV-based brain age (AUC = 0.703) was found, which could differentiate the MCI converters from NA adults at the baseline. CONCLUSIONS This is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.
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Affiliation(s)
- Hanna Lu
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jing Li
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China
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Murai SA, Mano T, Sanes JN, Watanabe T. Atypical intrinsic neural timescale in the left angular gyrus in Alzheimer's disease. Brain Commun 2024; 6:fcae199. [PMID: 38993284 PMCID: PMC11227993 DOI: 10.1093/braincomms/fcae199] [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: 10/15/2023] [Revised: 04/18/2024] [Accepted: 06/07/2024] [Indexed: 07/13/2024] Open
Abstract
Alzheimer's disease is characterized by cognitive impairment and progressive brain atrophy. Recent human neuroimaging studies reported atypical anatomical and functional changes in some regions in the default mode network in patients with Alzheimer's disease, but which brain area of the default mode network is the key region whose atrophy disturbs the entire network activity and consequently contributes to the symptoms of the disease remains unidentified. Here, in this case-control study, we aimed to identify crucial neural regions that mediated the phenotype of Alzheimer's disease, and as such, we examined the intrinsic neural timescales-a functional metric to evaluate the capacity to integrate diverse neural information-and grey matter volume of the regions in the default mode network using resting-state functional MRI images and structural MRI data obtained from individuals with Alzheimer's disease and cognitively typical people. After confirming the atypically short neural timescale of the entire default mode network in Alzheimer's disease and its link with the symptoms of the disease, we found that the shortened neural timescale of the default mode network was associated with the aberrantly short neural timescale of the left angular gyrus. Moreover, we revealed that the shortened neural timescale of the angular gyrus was correlated with the atypically reduced grey matter volume of this parietal region. Furthermore, we identified an association between the neural structure, brain function and symptoms and proposed a model in which the reduced grey matter volume of the left angular gyrus shortened the intrinsic neural time of the region, which then destabilized the entire neural timescale of the default mode network and resultantly contributed to cognitive decline in Alzheimer's disease. These findings highlight the key role of the left angular gyrus in the anatomical and functional aetiology of Alzheimer's disease.
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Affiliation(s)
- Shota A Murai
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, Bunkyo City, Tokyo 113-0033, Japan
| | - Tatsuo Mano
- Department of Degenerative Neurological Diseases, National Center of Neurology and Psychiatry, Tokyo 187-8551, Japan
| | - Jerome N Sanes
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
- Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA
- Center for Neurorestoration and Neurotechnology, Veterans Affairs Providence Healthcare System, Providence, RI 02908, USA
| | - Takamitsu Watanabe
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study, Bunkyo City, Tokyo 113-0033, Japan
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Recht G, Hou J, Buddenbaum C, Cheng H, Newman SD, Saykin AJ, Kawata K. Multiparameter cortical surface morphology in former amateur contact sport athletes. Cereb Cortex 2024; 34:bhae301. [PMID: 39077916 DOI: 10.1093/cercor/bhae301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/29/2024] [Accepted: 07/08/2024] [Indexed: 07/31/2024] Open
Abstract
The lifetime effects of repetitive head impacts have captured considerable public and scientific interest over the past decade, yet a knowledge gap persists in our understanding of midlife neurological well-being, particularly in amateur level athletes. This study aimed to identify the effects of lifetime exposure to sports-related head impacts on brain morphology in retired, amateur athletes. This cross-sectional study comprised of 37 former amateur contact sports athletes and 21 age- and sex-matched noncontact athletes. High-resolution anatomical, T1 scans were analyzed for the cortical morphology, including cortical thickness, sulcal depth, and sulcal curvature, and cognitive function was assessed using the Dementia Rating Scale-2. Despite no group differences in cognitive functions, the contact group exhibited significant cortical thinning particularly in the bilateral frontotemporal regions and medial brain regions, such as the cingulate cortex and precuneus, compared to the noncontact group. Deepened sulcal depth and increased sulcal curvature across all four lobes of the brain were also notable in the contact group. These data suggest that brain morphology of middle-aged former amateur contact athletes differs from that of noncontact athletes and that lifetime exposure to repetitive head impacts may be associated with neuroanatomical changes.
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Affiliation(s)
- Grace Recht
- Department of Kinesiology, Indiana University School of Public Health-Bloomington, 1025 E. 10th Street, Bloomington, IN 47405, United States
| | - Jiancheng Hou
- Department of Kinesiology, Indiana University School of Public Health-Bloomington, 1025 E. 10th Street, Bloomington, IN 47405, United States
- Research Center for Cross-Straits Cultural Development, Fujian Normal University, Cangshan Campus, No. 8 Shangshan Road, Cangshan District, Fuzhou, Fujian 350007, China
| | - Claire Buddenbaum
- Department of Kinesiology, Indiana University School of Public Health-Bloomington, 1025 E. 10th Street, Bloomington, IN 47405, United States
| | - Hu Cheng
- Department of Psychological and Brain Sciences, College of Arts and Sciences, Indiana University, 1101 E. 10th Street, Bloomington, IN 47405, United States
- Program in Neuroscience, The College of Arts and Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN 47405, United States
| | - Sharlene D Newman
- Alabama Life Research Institute, College of Arts & Sciences, University of Alabama, 211 Peter Bryce Blvd., Tuscaloosa, AL 35401, United States
| | - Andrew J Saykin
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, 355 West 16th Street, Indianapolis, IN 46202, United States
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 University Blvd, Indianapolis, IN 46202, United States
| | - Keisuke Kawata
- Department of Kinesiology, Indiana University School of Public Health-Bloomington, 1025 E. 10th Street, Bloomington, IN 47405, United States
- Program in Neuroscience, The College of Arts and Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN 47405, United States
- Department of Pediatrics, Indiana University School of Medicine, 1130 W Michigan St, Indianapolis, IN 46202, United States
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Younes K, Smith V, Johns E, Carlson ML, Winer J, He Z, Henderson VW, Greicius MD, Young CB, Mormino EC. Temporal tau asymmetry spectrum influences divergent behavior and language patterns in Alzheimer's disease. Brain Behav Immun 2024; 119:807-817. [PMID: 38710339 DOI: 10.1016/j.bbi.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/31/2024] [Accepted: 05/02/2024] [Indexed: 05/08/2024] Open
Abstract
Understanding the psychiatric symptoms of Alzheimer s disease (AD) is crucial for advancing precision medicine and therapeutic strategies. The relationship between AD behavioral symptoms and asymmetry in spatial tau PET patterns is not well-known. Braak tau progression implicates the temporal lobes early. However, the clinical and pathological implications of temporal tau laterality remain unexplored. This cross-sectional study investigated the correlation between temporal tau PET asymmetry and behavior assessed using the neuropsychiatric inventory and composite scores for memory, executive function, and language, using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. In the entire cohort, continuous right and left temporal tau contributions to behavior and cognition were evaluated, controlling for age, sex, education, and tau burden on the contralateral side. Additionally, a temporal tau laterality index was calculated to define "asymmetry-extreme" groups (individuals with laterality indices greater than two standard deviations from the mean). 695 individuals (age = 73.9 ± 7.6 years, 372 (53.5 %) females) were included, comprising 281 (40%) cognitively unimpaired (CU) amyloid negative, 185 (27%) CU amyloid positive, and 229 (33%) impaired (CI) amyloid positive participants. In the full cohort analysis, right temporal tau was associated with worse behavior (B = 8.14, p-value = 0.007), and left temporal tau was associated with worse language (B = 1.4, p-value < 0.001). Categorization into asymmetry-extreme groups revealed 20 right- and 27 left-asymmetric participants. Within these extreme groups, there was additional heterogeneity along the anterior-posterior dimension. Asymmetrical tau burden is associated with distinct behavioral and cognitive profiles. Wide multi-cultural implementation of social cognition measures is needed to understand right-sided asymmetry in AD.
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Affiliation(s)
- Kyan Younes
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA.
| | - Viktorija Smith
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA
| | - Emily Johns
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA
| | - Mackenzie L Carlson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA
| | - Joseph Winer
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA; Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Victor W Henderson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA; Department of Epidemiology and Population Health, Stanford University, USA
| | - Michael D Greicius
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA
| | - Christina B Young
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA
| | - Elizabeth C Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, USA; Wu Tsai Neuroscience Institute, Stanford, CA, USA
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Panigrahi P, Das S, Chakrabarti S. CCADD: An online webserver for Alzheimer's disease detection from brain MRI. Comput Biol Med 2024; 177:108622. [PMID: 38781645 DOI: 10.1016/j.compbiomed.2024.108622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/26/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024]
Abstract
Alzheimer's disease (AD) imposes a growing burden on public health due to its impact on memory, cognition, behavior, and social skills. Early detection using non-invasive brain magnetic resonance images (MRI) is vital for disease management. We introduce CCADD (Corpus Callosum-based Alzheimer's Disease Detection), a user-friendly webserver that automatically identifies and segments the corpus callosum (CC) region from brain MRI slices. Extracted shape and size-based features of CC are fed into Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) classifiers to predict AD or Mild Cognitive Impairment (MCI). Exhaustive benchmarking on ADNI data reveals high prediction accuracies for different AD severity levels. CCADD empowers clinicians and researchers for AD detection. This server is available at: http://www.hpppi.iicb.res.in/add.
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Affiliation(s)
- Priyanka Panigrahi
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), TRUE Campus, Kolkata, 700091, West Bengal, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, Uttar Pradesh, India
| | - Subhrangshu Das
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), TRUE Campus, Kolkata, 700091, West Bengal, India.
| | - Saikat Chakrabarti
- Structural Biology and Bioinformatics Division, Council for Scientific and Industrial Research (CSIR) - Indian Institute of Chemical Biology (IICB), TRUE Campus, Kolkata, 700091, West Bengal, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, Uttar Pradesh, India.
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7
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Witt ST, Brown A, Gravelsins L, Engström M, Classon E, Lykke N, Åvall-Lundqvist E, Theodorsson E, Ernerudh J, Kjölhede P, Einstein G. Gray matter volume in women with the BRCA mutation with and without ovarian removal: evidence for increased risk of late-life Alzheimer's disease or dementia. Menopause 2024; 31:608-616. [PMID: 38688467 DOI: 10.1097/gme.0000000000002361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
OBJECTIVE Ovarian removal prior to spontaneous/natural menopause (SM) is associated with increased risk of late life dementias including Alzheimer's disease. This increased risk may be related to the sudden and early loss of endogenous estradiol. Women with breast cancer gene mutations (BRCAm) are counseled to undergo oophorectomy prior to SM to significantly reduce their risk of developing breast, ovarian, and cervical cancers. There is limited evidence of the neurological effects of ovarian removal prior to the age of SM showing women without the BRCAm had cortical thinning in medial temporal lobe structures. A second study in women with BRCAm and bilateral salpingo-oophorectomy (BSO) noted changes in cognition. METHODS The present, cross-sectional study examined whole-brain differences in gray matter (GM) volume using high-resolution, quantitative magnetic resonance imaging in women with BRCAm and intact ovaries (BRCA-preBSO [study cohort with BRCA mutation prior to oophorectomy]; n = 9) and after surgery with (BSO + estradiol-based therapy [ERT]; n = 10) and without (BSO; n = 10) postsurgical estradiol hormone therapy compared with age-matched women (age-matched controls; n = 10) with their ovaries. RESULTS The BRCA-preBSO and BSO groups showed significantly lower GM volume in the left medial temporal and frontal lobe structures. BSO + ERT exhibited few areas of lower GM volume compared with age-matched controls. Novel to this study, we also observed that all three BRCAm groups exhibited significantly higher GM volume compared with age-matched controls, suggesting continued plasticity. CONCLUSIONS The present study provides evidence, through lower GM volume, to support both the possibility that the BRCAm, alone, and early life BSO may play a role in increasing the risk for late-life dementia. At least for BRCAm with BSO, postsurgical ERT seems to ameliorate GM losses.
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Affiliation(s)
| | - Alana Brown
- Psychology, University of Toronto, Toronto, ON, Canada
| | | | | | - Elisabet Classon
- Department of Acute Internal Medicine and Geriatrics, and Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine, Linköping University, Linköping, Sweden
| | - Nina Lykke
- Thematic Studies, Linköping University, Sweden
| | - Elisabeth Åvall-Lundqvist
- Department of Oncology in Linköping and Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Elvar Theodorsson
- Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Jan Ernerudh
- Department of Clinical Immunology and Transfusion Medicine, and Department of Biomedical and Clinical Sciences, Linköping University, Sweden
| | - Preben Kjölhede
- Department of Obstetrics and Gynecology and Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
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Karim SMS, Fahad MS, Rathore RS. Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning. Front Neuroinform 2024; 18:1384720. [PMID: 38957548 PMCID: PMC11217540 DOI: 10.3389/fninf.2024.1384720] [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: 02/10/2024] [Accepted: 05/17/2024] [Indexed: 07/04/2024] Open
Abstract
Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.
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Affiliation(s)
- S. M. Shayez Karim
- Department of Bioinformatics, Central University of South Bihar, Bihar, India
| | - Md Shah Fahad
- Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, India
| | - R. S. Rathore
- Department of Bioinformatics, Central University of South Bihar, Bihar, India
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Rajagopal SK, Beltz AM, Hampstead BM, Polk TA. Estimating individual trajectories of structural and cognitive decline in mild cognitive impairment for early prediction of progression to dementia of the Alzheimer's type. Sci Rep 2024; 14:12906. [PMID: 38839800 PMCID: PMC11153588 DOI: 10.1038/s41598-024-63301-7] [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: 12/27/2023] [Accepted: 05/27/2024] [Indexed: 06/07/2024] Open
Abstract
Only a third of individuals with mild cognitive impairment (MCI) progress to dementia of the Alzheimer's type (DAT). Identifying biomarkers that distinguish individuals with MCI who will progress to DAT (MCI-Converters) from those who will not (MCI-Non-Converters) remains a key challenge in the field. In our study, we evaluate whether the individual rates of loss of volumes of the Hippocampus and entorhinal cortex (EC) with age in the MCI stage can predict progression to DAT. Using data from 758 MCI patients in the Alzheimer's Disease Neuroimaging Database, we employ Linear Mixed Effects (LME) models to estimate individual trajectories of regional brain volume loss over 12 years on average. Our approach involves three key analyses: (1) mapping age-related volume loss trajectories in MCI-Converters and Non-Converters, (2) using logistic regression to predict progression to DAT based on individual rates of hippocampal and EC volume loss, and (3) examining the relationship between individual estimates of these volumetric changes and cognitive decline across different cognitive functions-episodic memory, visuospatial processing, and executive function. We find that the loss of Hippocampal volume is significantly more rapid in MCI-Converters than Non-Converters, but find no such difference in EC volumes. We also find that the rate of hippocampal volume loss in the MCI stage is a significant predictor of conversion to DAT, while the rate of volume loss in the EC and other additional regions is not. Finally, individual estimates of rates of regional volume loss in both the Hippocampus and EC, and other additional regions, correlate strongly with individual rates of cognitive decline. Across all analyses, we find significant individual variation in the initial volumes and the rates of changes in volume with age in individuals with MCI. This study highlights the importance of personalized approaches in predicting AD progression, offering insights for future research and intervention strategies.
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Affiliation(s)
| | - Adriene M Beltz
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Benjamin M Hampstead
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
- VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
| | - Thad A Polk
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
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10
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Mateos MJ, Bribiesca E, Guzmán-Arenas A, Aguilar W, Marquez-Flores JA. 3D Tortuosity computation as a shape descriptor and its application to brain structure analysis. BMC Med Imaging 2024; 24:130. [PMID: 38834987 DOI: 10.1186/s12880-024-01312-6] [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: 02/14/2023] [Accepted: 05/27/2024] [Indexed: 06/06/2024] Open
Abstract
In this study, we propose a novel method for quantifying tortuosity in 3D voxelized objects. As a shape characteristic, tortuosity has been widely recognized as a valuable feature in image analysis, particularly in the field of medical imaging. Our proposed method extends the two-dimensional approach of the Slope Chain Code (SCC) which creates a one-dimensional representation of curves. The utility of 3D tortuosity ( τ 3 D ) as a shape descriptor was investigated by characterizing brain structures. The results of the τ 3 D computation on the central sulcus and the main lobes revealed significant differences between Alzheimer's disease (AD) patients and control subjects, suggesting its potential as a biomarker for AD. We found a p < 0.05 for the left central sulcus and the four brain lobes.
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Affiliation(s)
- Maria-Julieta Mateos
- Graduate Program in Computer Science and Engineering, Universidad Nacional Autónoma de México, Mexico City, México.
| | - Ernesto Bribiesca
- Institute of Research in Applied Mathematics and Systems (IIMAS), Universidad Nacional Autónoma de México, Circuito Escolar 3000, Ciudad Universitaria, 04510, Coyoacán, Mexico City, México
| | - Adolfo Guzmán-Arenas
- Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City, México
| | - Wendy Aguilar
- Institute of Research in Applied Mathematics and Systems (IIMAS), Universidad Nacional Autónoma de México, Circuito Escolar 3000, Ciudad Universitaria, 04510, Coyoacán, Mexico City, México
| | - Jorge A Marquez-Flores
- Instituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Universidad Nacional Autónoma de México, Circuito Exterior S/N, Ciudad Universitaria, 04510, Mexico City, México
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11
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Weiss J, Beydoun MA, Beydoun HA, Georgescu MF, Hu YH, Noren Hooten N, Banerjee S, Launer LJ, Evans MK, Zonderman AB. Pathways explaining racial/ethnic and socio-economic disparities in brain white matter integrity outcomes in the UK Biobank study. SSM Popul Health 2024; 26:101655. [PMID: 38562403 PMCID: PMC10982559 DOI: 10.1016/j.ssmph.2024.101655] [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: 12/09/2023] [Revised: 02/14/2024] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
Pathways explaining racial/ethnic and socio-economic status (SES) disparities in white matter integrity (WMI) reflecting brain health, remain underexplored, particularly in the UK population. We examined racial/ethnic and SES disparities in diffusion tensor brain magnetic resonance imaging (dMRI) markers, namely global and tract-specific mean fractional anisotropy (FA), and tested total, direct and indirect effects through lifestyle, health-related and cognition factors using a structural equations modeling approach among 36,184 UK Biobank participants aged 40-70 y at baseline assessment (47% men). Multiple linear regression models were conducted, testing independent associations of race/ethnicity, socio-economic and other downstream factors in relation to global mean FA, while stratifying by Alzheimer's Disease polygenic Risk Score (AD PRS) tertiles. Race (Non-White vs. White) and lower SES predicted poorer WMI (i.e. lower global mean FA) at follow-up, with racial/ethnic disparities in FAmean involving multiple pathways and SES playing a central role in those pathways. Mediational patterns differed across tract-specific FA outcomes, with SES-FAmean total effect being partially mediated (41% of total effect = indirect effect). Furthermore, the association of poor cognition with FAmean was markedly stronger in the two uppermost AD PRS tertiles compared to the lower tertile (T2 and T3: β±SE: -0.0009 ± 0.0001 vs. T1: β±SE: -0.0005 ± 0.0001, P < 0.001), independently of potentially confounding factors. Race and lower SES were generally important determinants of adverse WMI outcomes, with partial mediation of socio-economic disparities in global mean FA through lifestyle, health-related and cognition factors. The association of poor cognition with lower global mean FA was stronger at higher AD polygenic risk.
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Affiliation(s)
- Jordan Weiss
- Stanford Center on Longevity, Stanford University, Stanford, CA, USA
| | - May A. Beydoun
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Hind A. Beydoun
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Michael F. Georgescu
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Yi-Han Hu
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Nicole Noren Hooten
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Sri Banerjee
- Public Health Doctoral Programs, Walden University, Minneapolis, MN, USA
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Michele K. Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Alan B. Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
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12
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Romascano D, Rebsamen M, Radojewski P, Blattner T, McKinley R, Wiest R, Rummel C. Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods. Neuroimage Clin 2024; 43:103624. [PMID: 38823248 PMCID: PMC11168488 DOI: 10.1016/j.nicl.2024.103624] [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: 02/19/2024] [Revised: 05/21/2024] [Accepted: 05/25/2024] [Indexed: 06/03/2024]
Abstract
Over the past decades, morphometric analysis of brain MRI has contributed substantially to the understanding of healthy brain structure, development and aging as well as to improved characterisation of disease related pathologies. Certified commercial tools based on normative modeling of these metrics are meanwhile available for diagnostic purposes, but they are cost intensive and their clinical evaluation is still in its infancy. Here we have compared the performance of "ScanOMetrics", an open-source research-level tool for detection of statistical anomalies in individual MRI scans, depending on whether it is operated on the output of FreeSurfer or of the deep learning based brain morphometry tool DL + DiReCT. When applied to the public OASIS3 dataset, containing patients with Alzheimer's disease (AD) and healthy controls (HC), cortical thickness anomalies in patient scans were mainly detected in regions that are known as predilection areas of cortical atrophy in AD, regardless of the software used for extraction of the metrics. By contrast, anomaly detections in HCs were up to twenty-fold reduced and spatially unspecific using both DL + DiReCT and FreeSurfer. Progression of the atrophy pattern with clinical dementia rating (CDR) was clearly observable with both methods. DL + DiReCT provided results in less than 25 min, more than 15 times faster than FreeSurfer. This difference in computation time might be relevant when considering application of this or similar methodology as diagnostic decision support for neuroradiologists.
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Affiliation(s)
- David Romascano
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland; Danish Research Center for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Timo Blattner
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland
| | - Richard McKinley
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, University Hospital Bern, CH-3010 Bern, Switzerland; European Campus Rottal-Inn, Technische Hochschule Deggendorf, Max-Breiherr-Straße 32, D-84347 Pfarrkirchen, Germany.
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13
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Kim T, Shu H, Jia Q, de Leon MJ. DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2024; 238:946-954. [PMID: 38741695 PMCID: PMC11090200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Voxel-based multiple testing is widely used in neuroimaging data analysis. Traditional false discovery rate (FDR) control methods often ignore the spatial dependence among the voxel-based tests and thus suffer from substantial loss of testing power. While recent spatial FDR control methods have emerged, their validity and optimality remain questionable when handling the complex spatial dependencies of the brain. Concurrently, deep learning methods have revolutionized image segmentation, a task closely related to voxel-based multiple testing. In this paper, we propose DeepFDR, a novel spatial FDR control method that leverages unsupervised deep learning-based image segmentation to address the voxel-based multiple testing problem. Numerical studies, including comprehensive simulations and Alzheimer's disease FDG-PET image analysis, demonstrate DeepFDR's superiority over existing methods. DeepFDR not only excels in FDR control and effectively diminishes the false nondiscovery rate, but also boasts exceptional computational efficiency highly suited for tackling large-scale neuroimaging data.
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Affiliation(s)
- Taehyo Kim
- Department of Biostatistics, School of Global Public Health, New York University
| | - Hai Shu
- Department of Biostatistics, School of Global Public Health, New York University
| | - Qiran Jia
- Department of Biostatistics, School of Global Public Health, New York University
- Department of Population and Public Health Sciences, University of Southern California
| | - Mony J. de Leon
- Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine
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14
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Huang Y, Cao C, Dai S, Deng H, Su L, Zheng JS. Magnetoencephalography-derived oscillatory microstate patterns across lifespan: the Cambridge centre for ageing and neuroscience cohort. Brain Commun 2024; 6:fcae150. [PMID: 38745970 PMCID: PMC11091929 DOI: 10.1093/braincomms/fcae150] [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: 08/16/2023] [Revised: 03/01/2024] [Accepted: 04/26/2024] [Indexed: 05/16/2024] Open
Abstract
The aging brain represents the primary risk factor for many neurodegenerative disorders. Whole-brain oscillations may contribute novel early biomarkers of aging. Here, we investigated the dynamic oscillatory neural activities across lifespan (from 18 to 88 years) using resting Magnetoencephalography (MEG) in a large cohort of 624 individuals. Our aim was to examine the patterns of oscillation microstates during the aging process. By using a machine-learning algorithm, we identify four typical clusters of microstate patterns across different age groups and different frequency bands: left-to-right topographic MS1, right-to-left topographic MS2, anterior-posterior MS3 and fronto-central MS4. We observed a decreased alpha duration and an increased alpha occurrence for sensory-related microstate patterns (MS1 & MS2). Accordingly, theta and beta changes from MS1 & MS2 may be related to motor decline that increased with age. Furthermore, voluntary 'top-down' saliency/attention networks may be reflected by the increased MS3 & MS4 alpha occurrence and complementary beta activities. The findings of this study advance our knowledge of how the aging brain shows dysfunctions in neural state transitions. By leveraging the identified microstate patterns, this study provides new insights into predicting healthy aging and the potential neuropsychiatric cognitive decline.
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Affiliation(s)
- Yujing Huang
- Zhejiang Key Laboratory of Multi-Omics in Infection and Immunity, Center for Infectious Disease Research, School of Medicine, Westlake University, Hangzhou 310024, Zhejiang Province, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang Province, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang Province, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
| | - Chenglong Cao
- Department of Neurosurgery, The First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, Anhui, China
| | - Shenyi Dai
- Department of Economics and Management, China Jiliang University, Hangzhou 310024, Zhejiang Province, China
- Hangzhou iNeuro Technology Co., LTD, Hangzhou 310024, Zhejiang Province, China
| | - Hu Deng
- Peking University Huilongguan Clinical Medical School, Beijing Huilongguan Hospital, Beijing 100096, China
| | - Li Su
- Department of Psychiatry, University of Cambridge, Cambridge CB20SZ, United Kingdom
- Neuroscience Institute, University of Sheffield, Sheffield, South Yorkshire S102HQ, United Kingdom
| | - Ju-Sheng Zheng
- Zhejiang Key Laboratory of Multi-Omics in Infection and Immunity, Center for Infectious Disease Research, School of Medicine, Westlake University, Hangzhou 310024, Zhejiang Province, China
- Research Center for Industries of the Future, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang Province, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang Province, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
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15
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Chan DC, Kim C, Kang RY, Kuhn MK, Beidler LM, Zhang N, Proctor EA. Cytokine expression patterns predict suppression of vulnerable neural circuits in a mouse model of Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.17.585383. [PMID: 38559177 PMCID: PMC10979954 DOI: 10.1101/2024.03.17.585383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Alzheimer's disease is a neurodegenerative disorder characterized by progressive amyloid plaque accumulation, tau tangle formation, neuroimmune dysregulation, synapse an neuron loss, and changes in neural circuit activation that lead to cognitive decline and dementia. Early molecular and cellular disease-instigating events occur 20 or more years prior to presentation of symptoms, making them difficult to study, and for many years amyloid-β, the aggregating peptide seeding amyloid plaques, was thought to be the toxic factor responsible for cognitive deficit. However, strategies targeting amyloid-β aggregation and deposition have largely failed to produce safe and effective therapies, and amyloid plaque levels poorly correlate with cognitive outcomes. However, a role still exists for amyloid-β in the variation in an individual's immune response to early, soluble forms of aggregates, and the downstream consequences of this immune response for aberrant cellular behaviors and creation of a detrimental tissue environment that harms neuron health and causes changes in neural circuit activation. Here, we perform functional magnetic resonance imaging of awake, unanesthetized Alzheimer's disease mice to map changes in functional connectivity over the course of disease progression, in comparison to wild-type littermates. In these same individual animals, we spatiotemporally profile the immune milieu by measuring cytokines, chemokines, and growth factors across various brain regions and over the course of disease progression from pre-pathology through established cognitive deficit. We identify specific signatures of immune activation predicting hyperactivity followed by suppression of intra- and then inter-regional functional connectivity in multiple disease-relevant brain regions, following the pattern of spread of amyloid pathology.
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Affiliation(s)
- Dennis C Chan
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neurotechnology in Mental Health Research, Pennsylvania State University, University Park, PA, USA
| | - ChaeMin Kim
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Rachel Y Kang
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Madison K Kuhn
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
| | - Lynne M Beidler
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neurotechnology in Mental Health Research, Pennsylvania State University, University Park, PA, USA
| | - Elizabeth A Proctor
- Department of Neurosurgery, Penn State College of Medicine, Hershey, PA, USA
- Department of Pharmacology, Penn State College of Medicine, Hershey, PA, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
- Center for Neural Engineering, Pennsylvania State University, University Park, PA, USA
- Department of Engineering Science & Mechanics, Pennsylvania State University, University Park, PA, USA
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16
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Cao T, Pang JC, Segal A, Chen Y, Aquino KM, Breakspear M, Fornito A. Mode-based morphometry: A multiscale approach to mapping human neuroanatomy. Hum Brain Mapp 2024; 45:e26640. [PMID: 38445545 PMCID: PMC10915742 DOI: 10.1002/hbm.26640] [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: 05/30/2023] [Revised: 02/06/2024] [Accepted: 02/18/2024] [Indexed: 03/07/2024] Open
Abstract
Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes-eigenmodes-of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.
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Affiliation(s)
- Trang Cao
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| | - James C. Pang
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| | - Ashlea Segal
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| | - Yu‐Chi Chen
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
| | - Kevin M. Aquino
- School of PhysicsUniversity of SydneyCamperdownNew South WalesAustralia
| | - Michael Breakspear
- School of Psychological SciencesUniversity of NewcastleCallaghanNew South WalesAustralia
| | - Alex Fornito
- The Turner Institute for Brain and Mental HealthSchool of Psychological Sciences, and Monash Biomedical Imaging, Monash UniversityClaytonVictoriaAustralia
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17
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Kim JS, Han JW, Oh DJ, Suh SW, Kwon MJ, Park J, Jo S, Kim JH, Kim KW. Effects of sleep quality on diurnal variation of brain volume in older adults: A retrospective cross-sectional study. Neuroimage 2024; 288:120533. [PMID: 38340880 DOI: 10.1016/j.neuroimage.2024.120533] [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: 10/25/2023] [Revised: 02/02/2024] [Accepted: 02/07/2024] [Indexed: 02/12/2024] Open
Abstract
AIM Brain volume is influenced by several factors that can change throughout the day. In addition, most of these factors are influenced by sleep quality. This study investigated diurnal variation in brain volume and its relation to overnight sleep quality. METHODS We enrolled 1,003 healthy Koreans without any psychiatric disorders aged 60 years or older. We assessed sleep quality and average wake time using the Pittsburgh Sleep Quality Index, and divided sleep quality into good, moderate, and poor groups. We estimated the whole and regional brain volumes from three-dimensional T1-weighted brain MRI scans. We divided the interval between average wake-up time and MRI acquisition time (INT) into tertile groups: short (INT1), medium (INT2), and long (INT3). RESULTS Whole and regional brain volumes showed no significance with respect to INT. However, the `interaction between INT and sleep quality showed significance for whole brain, cerebral gray matter, and cerebrospinal fluid volumes (p < .05). The INT2 group showed significantly lower volumes of whole brain, whole gray matter, cerebral gray matter, cortical gray matter, subcortical gray matter, and cerebrospinal fluid than the INT1 and INT3 groups only in the individuals with good sleep quality. CONCLUSION Human brain volume changes significantly within a day associated with overnight sleep in the individuals with good sleep quality.
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Affiliation(s)
- Jun Sung Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Ji Won Han
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea; Department of Psychiatry, Seoul National University, College of Medicine, Seoul, South Korea
| | - Dae Jong Oh
- Department of Psychiatry, Seoul National University, College of Medicine, Seoul, South Korea; Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul Korea
| | - Seung Wan Suh
- Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Min Jeong Kwon
- Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - Jieun Park
- Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - Sungman Jo
- Department of Health Science and Technology, Graduate school of convergence science and technology, Seoul National University, Seoul, South Korea
| | - Jae Hyoung Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam, South Korea
| | - Ki Woong Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea; Department of Psychiatry, Seoul National University, College of Medicine, Seoul, South Korea; Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences, Seoul, South Korea; Department of Health Science and Technology, Graduate school of convergence science and technology, Seoul National University, Seoul, South Korea.
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18
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Crane PK, Groot C, Ossenkoppele R, Mukherjee S, Choi S, Lee M, Scollard P, Gibbons LE, Sanders RE, Trittschuh E, Saykin AJ, Mez J, Nakano C, Donald CM, Sohi H, Risacher S. Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns. Alzheimers Dement 2024; 20:1739-1752. [PMID: 38093529 PMCID: PMC10984445 DOI: 10.1002/alz.13567] [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: 07/03/2023] [Revised: 10/16/2023] [Accepted: 11/03/2023] [Indexed: 03/03/2024]
Abstract
INTRODUCTION We sought to determine structural magnetic resonance imaging (MRI) characteristics across subgroups defined based on relative cognitive domain impairments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to compare cognitively defined to imaging-defined subgroups. METHODS We used data from 584 people with Alzheimer's disease (AD) (461 amyloid positive, 123 unknown amyloid status) and 118 amyloid-negative controls. We used voxel-based morphometry to compare gray matter volume (GMV) for each group compared to controls and to AD-Memory. RESULTS There was pronounced bilateral lower medial temporal lobe atrophy with relative cortical sparing for AD-Memory, lower left hemisphere GMV for AD-Language, anterior lower GMV for AD-Executive, and posterior lower GMV for AD-Visuospatial. Formal asymmetry comparisons showed substantially more asymmetry in the AD-Language group than any other group (p = 1.15 × 10-10 ). For overlap between imaging-defined and cognitively defined subgroups, AD-Memory matched up with an imaging-defined limbic predominant group. DISCUSSION MRI findings differ across cognitively defined AD subgroups.
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Affiliation(s)
- Paul K. Crane
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Colin Groot
- Clinical Memory Research UnitLund UniversityLundSweden
- Alzheimer centerAmsterdam UMC ‐ VU Medical CenterAmsterdamNetherlands
| | - Rik Ossenkoppele
- Clinical Memory Research UnitLund UniversityLundSweden
- Alzheimer centerAmsterdam UMC ‐ VU Medical CenterAmsterdamNetherlands
| | | | - Seo‐Eun Choi
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Michael Lee
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Phoebe Scollard
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Laura E. Gibbons
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | | | - Emily Trittschuh
- Department of Psychiatry and Behavioral SciencesUniversity of Washington, and Geriatrics ResearchEducation, and Clinical CenterVA Puget Sound Health Care SystemSeattleUSA
| | - Andrew J. Saykin
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisUSA
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisUSA
| | - Jesse Mez
- Department of NeurologyBoston UniversityBostonMassachusettsUSA
| | - Connie Nakano
- Department of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | | | - Harkirat Sohi
- Department of Biomedical Informatics and Medical EducationUniversity of WashingtonSeattleUSA
- Now Pacific Northwest National LaboratoryRichlandUSA
| | | | - Shannon Risacher
- Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisUSA
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisUSA
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19
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Jauregi Zinkunegi A, Bruno D, Betthauser TJ, Koscik RL, Asthana S, Chin NA, Hermann BP, Johnson SC, Mueller KD. A comparison of story-recall metrics to predict hippocampal volume in older adults with and without cognitive impairment. Clin Neuropsychol 2024; 38:453-470. [PMID: 37349970 PMCID: PMC10739621 DOI: 10.1080/13854046.2023.2223389] [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: 02/06/2023] [Accepted: 06/05/2023] [Indexed: 06/24/2023]
Abstract
Objective: Process-based scores of episodic memory tests, such as the recency ratio (Rr), have been found to compare favourably to, or to be better than, most conventional or "traditional" scores employed to estimate memory ability in older individuals (Bock et al., 2021; Bruno et al., 2019). We explored the relationship between process-based scores and hippocampal volume in older adults, while comparing process-based to traditional story recall-derived scores, to examine potential differences in their predictive abilities. Methods: We analysed data from 355 participants extracted from the WRAP and WADRC databases, who were classified as cognitively unimpaired, or exhibited mild cognitive impairment (MCI) or dementia. Story Recall was measured with the Logical Memory Test (LMT) from the Weschler Memory Scale Revised, collected within twelve months of the magnetic resonance imaging scan. Linear regression analyses were conducted with left or right hippocampal volume (HV) as outcomes separately, and with Rr, Total ratio, Immediate LMT, or Delayed LMT scores as predictors, along with covariates. Results: Higher Rr and Tr scores significantly predicted lower left and right HV, while Tr showed the best model fit of all, as indicated by AIC. Traditional scores, Immediate LMT and Delayed LMT, were significantly associated with left and right HV, but were outperformed by both process-based scores for left HV, and by Tr for right HV. Conclusions: Current findings show the direct relationship between hippocampal volume and all the LMT scores examined here, and that process-based scores outperform traditional scores as markers of hippocampal volume.
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Affiliation(s)
| | - Davide Bruno
- School of Psychology, Liverpool John Moores University, UK
| | - Tobey J. Betthauser
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
| | - Rebecca Langhough Koscik
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
| | - Sanjay Asthana
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Veterans Hospital, Madison, WI, USA
| | - Nathaniel A. Chin
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
| | - Bruce P. Hermann
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
- Department of Neurology, University of Wisconsin – Madison, Madison, WI, USA
| | - Sterling C. Johnson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Veterans Hospital, Madison, WI, USA
| | - Kimberly D. Mueller
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI, USA
- Department of Communication Sciences and Disorders, University of Wisconsin-Madison, Madison, WI, USA
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20
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Matthiesen ST, Sieg M, Andersen SS, Amanzio M, Finnerup NB, Jensen TS, Gottrup H, Vase L. Placebo analgesia and nocebo hyperalgesia in patients with Alzheimer disease and healthy participants. Pain 2024; 165:440-449. [PMID: 37703397 DOI: 10.1097/j.pain.0000000000003035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 05/23/2023] [Indexed: 09/15/2023]
Abstract
ABSTRACT The role of placebo analgesia and nocebo hyperalgesia in patients with Alzheimer disease (AD) is largely unknown, with only few studies in the area. Therefore, this study aims to investigate to which extent placebo analgesia and nocebo hyperalgesia effects are present in patients experiencing mild-to-moderate AD. Twenty-one patients with AD (test population) and 26 healthy participants (HP; design validation) were exposed to thermal pain stimulation on 3 test days: Lidocaine condition (open/hidden lidocaine administration), capsaicin condition (open/hidden capsaicin administration), and natural history (no treatment), in a randomized, within-subject design. Open lidocaine and open capsaicin were accompanied by verbal suggestions for pain relief and pain increase, respectively. Expected pain and actual pain intensity were measured on a numerical rating scale (0-10). Placebo and nocebo effects were calculated as pain differences in open-hidden lidocaine and capsaicin, respectively, controlled for no treatment. Healthy participants obtained a placebo effect ( P = 0.01) and a trend for a nocebo effect ( P = 0.07). Patients with AD did not obtain a placebo effect ( P = 0.44) nor a significant nocebo effect ( P = 0.86). Healthy participants expected lower and higher pain with open vs hidden lidocaine and capsaicin, respectively ( P < 0.001). The same expectation effects were seen in patients with AD (open vs hidden lidocaine, P = 0.008; open vs hidden capsaicin, P < 0.001). With a well-controlled experimental setting, this study suggests that patients with AD may not experience placebo analgesia effects. Nocebo hyperalgesia effects in patients with AD needs further research. These findings may have implications for the conduction of clinical trials and the treatment of patients with AD in clinical practice.
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Affiliation(s)
- Susan Tomczak Matthiesen
- Division for Psychology and Neuroscience, Department of Psychology and Behavioral Science, School of Business and Social Sciences, Aarhus University, Denmark
| | - Mette Sieg
- Division for Psychology and Neuroscience, Department of Psychology and Behavioral Science, School of Business and Social Sciences, Aarhus University, Denmark
| | - Stephanie Skøtt Andersen
- Division for Psychology and Neuroscience, Department of Psychology and Behavioral Science, School of Business and Social Sciences, Aarhus University, Denmark
| | | | - Nanna Brix Finnerup
- Department of Clinical Medicine, Danish Pain Research Center, Aarhus University, Denmark
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | | | - Hanne Gottrup
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Lene Vase
- Division for Psychology and Neuroscience, Department of Psychology and Behavioral Science, School of Business and Social Sciences, Aarhus University, Denmark
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21
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Feng Y, Murphy MC, Hojo E, Li F, Roberts N. Magnetic Resonance Elastography in the Study of Neurodegenerative Diseases. J Magn Reson Imaging 2024; 59:82-96. [PMID: 37084171 DOI: 10.1002/jmri.28747] [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: 02/16/2023] [Revised: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 04/22/2023] Open
Abstract
Neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) present a major health burden to society. Changes in brain structure and cognition are generally only observed at the late stage of the disease. Although advanced magnetic resonance imaging (MRI) techniques such as diffusion imaging may allow identification of biomarkers at earlier stages of neurodegeneration, early diagnosis is still challenging. Magnetic resonance elastography (MRE) is a noninvasive MRI technique for studying the mechanical properties of tissues by measuring the wave propagation induced in the tissues using a purpose-built actuator. Here, we present a systematic review of preclinical and clinical studies in which MRE has been applied to study neurodegenerative diseases. Actuator systems for data acquisition, inversion algorithms for data analysis, and sample demographics are described and tissue stiffness measures obtained for the whole brain and internal structures are summarized. A total of six animal studies and eight human studies have been published. The animal studies refer to 123 experimental animals (68 AD and 55 PD) and 121 wild-type animals, while the human studies refer to 142 patients with neurodegenerative disease (including 56 AD and 17 PD) and 166 controls. The animal studies are consistent in the reporting of decreased stiffness of the hippocampal region in AD mice. However, in terms of disease progression, although consistent decreases in either storage modulus or shear modulus magnitude are reported for whole brain, there is variation in the results reported for the hippocampal region. The clinical studies are consistent in reports of a significant decrease in either whole brain storage modulus or shear modulus magnitude, in both AD and PD and with different brain structures affected in different neurodegenerative diseases. MRE studies of neurodegenerative diseases are still in their infancy, and in future it will be interesting to investigate potential relationships between brain mechanical properties and clinical measures, which may help elucidate the mechanisms underlying onset and progression of neurodegenerative diseases. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yuan Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Department of Radiology, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
- National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, China
| | - Matthew C Murphy
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Emi Hojo
- Centre for Reproductive Health (CRH), School of Clinical Sciences, University of Edinburgh, Edinburgh, UK
| | - Fei Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Neil Roberts
- Centre for Reproductive Health (CRH), School of Clinical Sciences, University of Edinburgh, Edinburgh, UK
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22
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Daniel E, Deng F, Patel SK, Sedrak MS, Kim H, Razavi M, Sun C, Root JC, Ahles TA, Dale W, Chen BT. Brain white matter microstructural changes in chemotherapy-treated older long-term breast cancer survivors. Cancer Med 2024; 13:e6881. [PMID: 38152038 PMCID: PMC10807556 DOI: 10.1002/cam4.6881] [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: 07/06/2023] [Revised: 12/11/2023] [Accepted: 12/17/2023] [Indexed: 12/29/2023] Open
Abstract
PURPOSE To assess white matter microstructural changes in older long-term breast cancer survivors 5-15 years post-chemotherapy treatment. METHODS Breast cancer survivors aged 65 years or older who underwent chemotherapy (C+) and who did not undergo chemotherapy (C-) and age- and sex-matched healthy controls (HC) were enrolled at time point 1 (TP1) and followed for 2 years for time point 2 (TP2). All participants underwent brain MRI with diffusion tensor images and neuropsychological (NP) testing with the NIH Toolbox Cognition Battery. Tract-based spatial statistics (TBSS) analysis was performed on the diffusion tensor images to assess white matter microstructural changes with the fractional anisotropy (FA) parameter. RESULTS There were significant longitudinal alterations in FA within the C+ group over time. The C+ group showed diminished FA in the body and genu of corpus callosum, anterior corona radiate, and external capsule on both the whole brain and region of interest (ROI) based analyses after p < 0.05 family-wise error (FWE) correction. However, there were no significant group differences between the groups at TP1. Additionally, at TP1, a positive correlation (R = 0.58, p = 0.04) was observed between the FA value of the anterior corona radiata and the crystallized composite score in the C+ group. CONCLUSIONS Brain white matter microstructural alterations may be the underlying neural correlates of cognitive changes in older breast cancer survivors who had chemotherapy treatment years ago.
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Affiliation(s)
- Ebenezer Daniel
- Department of Diagnostic RadiologyCity of Hope National Medical CenterDuarteCAUSA
| | - Frank Deng
- Department of Diagnostic RadiologyCity of Hope National Medical CenterDuarteCAUSA
| | - Sunita K. Patel
- Department of Population ScienceCity of Hope National Medical CenterDuarteCAUSA
| | - Mina S. Sedrak
- Department of Medical OncologyCity of Hope National Medical CenterDuarteCAUSA
| | - Heeyoung Kim
- Center for Cancer and AgingCity of Hope National Medical CenterDuarteCAUSA
| | - Marianne Razavi
- Department of Supportive Care MedicineCity of Hope National Medical CenterDuarteCAUSA
| | - Can‐Lan Sun
- Center for Cancer and AgingCity of Hope National Medical CenterDuarteCAUSA
| | - James C. Root
- Neurocognitive Research LabMemorial Sloan Kettering Cancer CenterNew YorkNYUSA
| | - Tim A. Ahles
- Neurocognitive Research LabMemorial Sloan Kettering Cancer CenterNew YorkNYUSA
| | - William Dale
- Center for Cancer and AgingCity of Hope National Medical CenterDuarteCAUSA
- Department of Supportive Care MedicineCity of Hope National Medical CenterDuarteCAUSA
| | - Bihong T. Chen
- Department of Diagnostic RadiologyCity of Hope National Medical CenterDuarteCAUSA
- Center for Cancer and AgingCity of Hope National Medical CenterDuarteCAUSA
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23
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Zarifkar AH, Zarifkar A, Safaei S. Different paradigms of transcranial electrical stimulation induce structural changes in the CA1 region of the hippocampus in a rat model of Alzheimer's disease. Neurosci Lett 2024; 818:137570. [PMID: 38000774 DOI: 10.1016/j.neulet.2023.137570] [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: 10/07/2023] [Revised: 11/15/2023] [Accepted: 11/21/2023] [Indexed: 11/26/2023]
Abstract
One of the prominent sign of Alzheimer's disease (AD) is structural changes in the hippocampus. Recently, the new methods used to treat this disease is transcranial electrical stimulation (tES). This study evaluated the effect of four primary standards of tES, including tDCS, tACS, tRNS, and tPCS on beta-amyloid 25-35 (Aβ25-35)-induced structural changes in the CA1 region of hippocampus in male rats. For this purpose, rats weighing 250-275 g were selected, the cannula was embedded reciprocally into the hippocampi. Aβ25-35 (5 μg/ 2.5 ml/ day) was infused reciprocally for four continuous days.Then, animals were then given tES for 6 days.Subsequently, structural changes in the hippocampal CA1 were evaluated using the stereological method. Aβ25-35 resulted in loss of neurons (P < 0.01) and decreased hippocampal volume (P < 0.05). However, the administration of tES paradigms prevented these changes. The results proposed that through the improvement of hippocampal cell number and volume, tES paradigms can retain efficiency in remediating structural impairments in AD. From this, it can be concluded that other tES paradigms besides tDCS can also be considered for the treatment of AD.
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Affiliation(s)
- Amir Hossein Zarifkar
- Department of Physiology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; Cellular and Molecular Biology Research Center, Larestan University of Medical Sciences, Larestan, Iran.
| | - Asadollah Zarifkar
- Department of Physiology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Sepideh Safaei
- Gerash Amir-al-Momenin Medical and Educational Center, Gerash University of Medical Sciences, Gerash, Iran
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24
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Brown JA, Lee AJ, Fernhoff K, Pistone T, Pasquini L, Wise AB, Staffaroni AM, Luisa Mandelli M, Lee SE, Boxer AL, Rankin KP, Rabinovici GD, Luisa Gorno Tempini M, Rosen HJ, Kramer JH, Miller BL, Seeley WW. Functional network collapse in neurodegenerative disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.01.569654. [PMID: 38106054 PMCID: PMC10723363 DOI: 10.1101/2023.12.01.569654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Cognitive and behavioral deficits in Alzheimer's disease (AD) and frontotemporal dementia (FTD) result from brain atrophy and altered functional connectivity. However, it is unclear how atrophy relates to functional connectivity disruptions across dementia subtypes and stages. We addressed this question using structural and functional MRI from 221 patients with AD (n=82), behavioral variant FTD (n=41), corticobasal syndrome (n=27), nonfluent (n=34) and semantic (n=37) variant primary progressive aphasia, and 100 cognitively normal individuals. Using partial least squares regression, we identified three principal structure-function components. The first component showed overall atrophy correlating with primary cortical hypo-connectivity and subcortical/association cortical hyper-connectivity. Components two and three linked focal syndrome-specific atrophy to peri-lesional hypo-connectivity and distal hyper-connectivity. Structural and functional component scores predicted global and domain-specific cognitive deficits. Anatomically, functional connectivity changes reflected alterations in specific brain activity gradients. Eigenmode analysis identified temporal phase and amplitude collapse as an explanation for atrophy-driven functional connectivity changes.
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Affiliation(s)
- Jesse A. Brown
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Alex J. Lee
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Kristen Fernhoff
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Taylor Pistone
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Lorenzo Pasquini
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Amy B. Wise
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Adam M. Staffaroni
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Maria Luisa Mandelli
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Suzee E. Lee
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Adam L. Boxer
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Katherine P. Rankin
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Gil D. Rabinovici
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Maria Luisa Gorno Tempini
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Howard J. Rosen
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Joel H. Kramer
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Bruce L. Miller
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - William W. Seeley
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
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25
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Rubido N, Riedel G, Vuksanović V. Genetic basis of anatomical asymmetry and aberrant dynamic functional networks in Alzheimer's disease. Brain Commun 2023; 6:fcad320. [PMID: 38173803 PMCID: PMC10763534 DOI: 10.1093/braincomms/fcad320] [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: 05/09/2023] [Revised: 10/14/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Genetic associations with macroscopic brain networks can provide insights into healthy and aberrant cortical connectivity in disease. However, associations specific to dynamic functional connectivity in Alzheimer's disease are still largely unexplored. Understanding the association between gene expression in the brain and functional networks may provide useful information about the molecular processes underlying variations in impaired brain function. Given the potential of dynamic functional connectivity to uncover brain states associated with Alzheimer's disease, it is interesting to ask: How does gene expression associated with Alzheimer's disease map onto the dynamic functional brain connectivity? If genetic variants associated with neurodegenerative processes involved in Alzheimer's disease are to be correlated with brain function, it is essential to generate such a map. Here, we investigate how the relation between gene expression in the brain and dynamic functional connectivity arises from nodal interactions, quantified by their role in network centrality (i.e. the drivers of the metastability), and the principal component of genetic co-expression across the brain. Our analyses include genetic variations associated with Alzheimer's disease and also genetic variants expressed within the cholinergic brain pathways. Our findings show that contrasts in metastability of functional networks between Alzheimer's and healthy individuals can in part be explained by the two combinations of genetic co-variations in the brain with the confidence interval between 72% and 92%. The highly central nodes, driving the brain aberrant metastable dynamics in Alzheimer's disease, highly correlate with the magnitude of variations from two combinations of genes expressed in the brain. These nodes include mainly the white matter, parietal and occipital brain regions, each of which (or their combinations) are involved in impaired cognitive function in Alzheimer's disease. In addition, our results provide evidence of the role of genetic associations across brain regions in asymmetric changes in ageing. We validated our findings on the same cohort using alternative brain parcellation methods. This work demonstrates how genetic variations underpin aberrant dynamic functional connectivity in Alzheimer's disease.
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Affiliation(s)
- Nicolás Rubido
- Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, UK
| | - Gernot Riedel
- Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
| | - Vesna Vuksanović
- Health Data Science, Swansea University Medical School, Swansea University, Swansea SA2 8PP, UK
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26
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Littlejohn J, Blackburn DJ, Venneri A. Testing central auditory processing abilities in older adults with and without dementia using the consonant-vowel dichotic listening task. FRONTIERS IN DEMENTIA 2023; 2:1207546. [PMID: 39081992 PMCID: PMC11285700 DOI: 10.3389/frdem.2023.1207546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 10/30/2023] [Indexed: 08/02/2024]
Abstract
Background Hearing loss and dementia are linked, although the roles of peripheral and central auditory dysfunction are not well defined. Many behavioral measures of hearing are confounded by the overlapping cognitive functions required to perform the tests. Objective To collect pilot data to identify how central auditory function, measured using a dichotic listening test that indexes both auditory and cognitive components under different attentional conditions, differs among people with mild cognitive impairment (MCI), dementia and controls, and how performance relates to neuropsychological results. Method Fifty-eight participants (17 MCI, 11 dementia and 30 controls) undertook hearing screening, the Bergen consonant-vowel dichotic listening paradigm, and a short battery of neuropsychological tests chosen to index attention and executive control. Dichotic listening was assessed under three attentional conditions (non-forced, forced right ear and forced left) amongst older adults with normal cognitive function, MCI and dementia. Results We report two main findings: (a) The expected right ear advantage under non-forced conditions, was seen in controls and patients with dementia but not in people with MCI, who showed equal numbers of correct responses from both ears (i.e., a lack of asymmetry); (b) Performance under forced attentional conditions was significantly associated with disease progression (i.e., control > MCI > dementia) and performance on the cognitive tasks. Conclusion The reduction in asymmetry on dichotic listening tasks may be a marker of MCI and reflect underlying compensatory mechanisms. Use of this test could aid stratification of patients with memory disorders. Whether abnormalities could predict dementia onset needs longitudinal investigation in a larger sample.
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Affiliation(s)
- Jenna Littlejohn
- Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
- Manchester Centre for Audiology and Deafness, University of Manchester, Manchester, United Kingdom
| | - Daniel J. Blackburn
- Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
| | - Annalena Venneri
- Department of Life Sciences, Brunel University London, London, United Kingdom
- Department of Medicine and Surgery, University of Parma, Parma, Italy
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27
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Cabrera-Martín MN, Nespral P, Valles-Salgado M, Bascuñana P, Delgado-Alonso C, Delgado-Álvarez A, Fernández-Romero L, López-Carbonero JI, Díez-Cirarda M, Gil-Moreno MJ, Matías-Guiu J, Matias-Guiu JA. FDG-PET-based neural correlates of Addenbrooke's cognitive examination III scores in Alzheimer's disease and frontotemporal degeneration. Front Psychol 2023; 14:1273608. [PMID: 38034292 PMCID: PMC10687370 DOI: 10.3389/fpsyg.2023.1273608] [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: 08/06/2023] [Accepted: 11/02/2023] [Indexed: 12/02/2023] Open
Abstract
Introduction The Addenbrooke's Cognitive Examination III (ACE-III) is a brief test useful for neuropsychological assessment. Several studies have validated the test for the diagnosis of Alzheimer's disease (AD) and frontotemporal dementia (FTD). In this study, we aimed to examine the metabolic correlates associated with the performance of ACE-III in AD and behavioral variant FTD. Methods We enrolled 300 participants in a cross-sectional study, including 180 patients with AD, 60 with behavioral FTD (bvFTD), and 60 controls. An 18F-Fluorodeoxyglucose positron emission tomography study was performed in all cases. Correlation between the ACE-III and its domains (attention, memory, fluency, language, and visuospatial) with the brain metabolism was estimated. Results The ACE-III showed distinct neural correlates in bvFTD and AD, effectively capturing the most relevant regions involved in these disorders. Neural correlates differed for each domain, especially in the case of bvFTD. Lower ACE-III scores were associated with more advanced stages in both disorders. The ACE-III exhibited high discrimination between bvFTD vs. HC, and between AD vs. HC. Additionally, it was sensitive to detect hypometabolism in brain regions associated with bvFTD and AD. Conclusion Our study contributes to the knowledge of the brain regions associated with ACE-III, thereby facilitating its interpretation, and highlighting its suitability for screening and monitoring. This study provides further validation of ACE-III in the context of AD and FTD.
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Affiliation(s)
- María Nieves Cabrera-Martín
- Department of Neurology, San Carlos Institute for Health Research (IdISSC), Universidad Complutense, Madrid, Spain
| | - Pedro Nespral
- Department of Neurology, San Carlos Institute for Health Research (IdISSC), Universidad Complutense, Madrid, Spain
| | - Maria Valles-Salgado
- Department of Nuclear Medicine, San Carlos Institute for Health Research (IdISSC), Universidad Complutense, Madrid, Spain
| | - Pablo Bascuñana
- Department of Neurology, San Carlos Institute for Health Research (IdISSC), Universidad Complutense, Madrid, Spain
| | - Cristina Delgado-Alonso
- Department of Nuclear Medicine, San Carlos Institute for Health Research (IdISSC), Universidad Complutense, Madrid, Spain
| | - Alfonso Delgado-Álvarez
- Department of Nuclear Medicine, San Carlos Institute for Health Research (IdISSC), Universidad Complutense, Madrid, Spain
| | - Lucía Fernández-Romero
- Department of Nuclear Medicine, San Carlos Institute for Health Research (IdISSC), Universidad Complutense, Madrid, Spain
| | - Juan Ignacio López-Carbonero
- Department of Nuclear Medicine, San Carlos Institute for Health Research (IdISSC), Universidad Complutense, Madrid, Spain
| | - María Díez-Cirarda
- Department of Nuclear Medicine, San Carlos Institute for Health Research (IdISSC), Universidad Complutense, Madrid, Spain
| | - María José Gil-Moreno
- Department of Nuclear Medicine, San Carlos Institute for Health Research (IdISSC), Universidad Complutense, Madrid, Spain
| | - Jorge Matías-Guiu
- Department of Nuclear Medicine, San Carlos Institute for Health Research (IdISSC), Universidad Complutense, Madrid, Spain
| | - Jordi A. Matias-Guiu
- Department of Nuclear Medicine, San Carlos Institute for Health Research (IdISSC), Universidad Complutense, Madrid, Spain
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Younes K, Smith V, Johns E, Carlson ML, Winer J, He Z, Henderson VW, Greicius MD, Young CB, Mormino EC. Temporal tau asymmetry spectrum influences divergent behavior and language patterns in Alzheimer`s disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.10.23296836. [PMID: 37986964 PMCID: PMC10659470 DOI: 10.1101/2023.11.10.23296836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Understanding psychiatric symptoms in Alzheimer`s disease (AD) is crucial for advancing precision medicine and therapeutic strategies. The relationship between AD behavioral symptoms and asymmetry in spatial tau PET patterns is unknown. Braak tau progression implicates the temporal lobes early. However, the clinical and pathological implications of temporal tau laterality remain unexplored. This cross-sectional study investigated the correlation between temporal tau PET asymmetry and behavior assessed using the neuropsychiatric inventory, and composite scores for memory, executive function, and language; using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. In the entire cohort, continuous right and left temporal tau contributions to behavior and cognition were evaluated controlling for age, sex, education, and tau burden on the contralateral side. Additionally, a temporal tau laterality index was calculated to define "asymmetry-extreme" groups (individuals with laterality indices greater than two standard deviations from the mean). 858 individuals (age=73.9±7.7 years, 434(50%) females) were included, comprising 438 cognitively unimpaired (CU) (53.4%) and 420 impaired (CI) participants (48.9%). In the full cohort analysis, right temporal tau was associated with worse behavior (B(SE)=7.19 (2.9), p-value=0.01) and left temporal tau was associated with worse language (B(SE)=1.4(0.2), p-value<0.0001). Categorization into asymmetry-extreme groups revealed 20 right- and 27 left-asymmetric participants. Within these extreme groups, four patterns of tau PET uptake were observed: anterior temporal, typical AD, typical AD with frontal involvement, and posterior. Asymmetrical tau burden is associated with distinct behavioral and cognitive profiles. Behavioral and socioemotional measures are needed to understand right-sided asymmetry in AD.
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Affiliation(s)
- Kyan Younes
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Viktorija Smith
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Emily Johns
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Mackenzie L. Carlson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Joseph Winer
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California
| | - Victor W. Henderson
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
- Department of Epidemiology and Population Health, Stanford University
| | - Michael D. Greicius
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Christina B. Young
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
| | - Elizabeth C. Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine
- Wu Tsai Neuroscience Institute, Stanford, CA, USA
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Totsune T, Baba T, Sugimura Y, Oizumi H, Tanaka H, Takahashi T, Yoshioka M, Nagamatsu KI, Takeda A. Nuclear Imaging Data-Driven Classification of Parkinson's Disease. Mov Disord 2023; 38:2053-2063. [PMID: 37638533 DOI: 10.1002/mds.29582] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/23/2023] [Accepted: 07/31/2023] [Indexed: 08/29/2023] Open
Abstract
BACKGROUND Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder characterized by motor and nonmotor symptoms. Several features have prognostic importance and have been used as key indicators for identifying clinical subtypes. However, the symptom-based classification approach has limitations with respect to the stability of the obtained subtypes. OBJECTIVES The purpose of this study was to identify subtypes of PD using nuclear imaging biomarkers targeting the cardiac sympathetic nervous and nigro-striatal systems and to compare patterns of cortical morphological change among obtained subtypes. METHODS We performed unbiased hierarchical cluster analysis using 123 I-metaiodobenzylguanidine cardiac scintigraphy and 123 I-N-(3-fluoropropyl)-2β-carbomethoxy-3β-(4-iodophenyl) nortropane single photon emission computed tomography data for 56 patients with PD. We compared clinical characteristics and the patterns of cortical atrophy in the obtained clusters. RESULTS Three clusters were identified and showed distinct characteristics in onset ages and dopamine-replacement therapy and deep brain stimulation requirements. According to the characteristics, clusters were classified into two subtypes, namely, "cardio-cortical impairment (CC)" and "dopaminergic-dominant dysfunction (DD)" subtype. The three clusters were named according to subtype and time since onset in which 14 patients were classified as "early DD," 25 as "advanced DD," and 17 as "early CC." Compared with the early DD subtype, the early CC subtype showed parietal-dominant diffuse cortical atrophy and the advanced DD subtype showed left-side predominant mild cortical atrophy. CONCLUSIONS Nuclear imaging biomarker-based classification can be used to identify clinically and pathologically relevant PD subtypes with distinct disease trajectories. © 2023 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Tomoko Totsune
- Department of Neurology, National Hospital Organization Sendai-Nishitaga Hospital, Sendai, Japan
- Department of Aging Research and Geriatric Medicine, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Toru Baba
- Department of Neurology, National Hospital Organization Sendai-Nishitaga Hospital, Sendai, Japan
| | - Yoko Sugimura
- Department of Neurology, National Hospital Organization Sendai-Nishitaga Hospital, Sendai, Japan
- Department of Cognitive & Motor Aging, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Hideki Oizumi
- Department of Neurology, National Hospital Organization Sendai-Nishitaga Hospital, Sendai, Japan
| | - Hiroyasu Tanaka
- Department of Neurology, National Hospital Organization Sendai-Nishitaga Hospital, Sendai, Japan
| | - Toshiaki Takahashi
- Department of Neurology, National Hospital Organization Sendai-Nishitaga Hospital, Sendai, Japan
| | - Masaru Yoshioka
- Department of Neurology, National Hospital Organization Sendai-Nishitaga Hospital, Sendai, Japan
| | - Ken-Ichi Nagamatsu
- Department of Neurosurgery, National Hospital Organization Sendai-Nishitaga Hospital, Sendai, Japan
| | - Atsushi Takeda
- Department of Neurology, National Hospital Organization Sendai-Nishitaga Hospital, Sendai, Japan
- Department of Cognitive & Motor Aging, Tohoku University Graduate School of Medicine, Sendai, Japan
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Huang Y, Zhang T, Zhang S, Zhang W, Yang L, Zhu D, Liu T, Jiang X, Han J, Guo L. Genetic Influence on Gyral Peaks. Neuroimage 2023; 280:120344. [PMID: 37619794 DOI: 10.1016/j.neuroimage.2023.120344] [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: 05/10/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.
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Affiliation(s)
- Ying Huang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China; School of Information and Technology, Northwest University, Xi'an 710127, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China.
| | - Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Weihan Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Li Yang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Dajiang Zhu
- Computer Science & Engineering, University of Texas at Arlington, TX 76010, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
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Johansson L, Guo X, Sacuiu S, Fässberg MM, Kern S, Zettergren A, Skoog I. Longstanding smoking associated with frontal brain lobe atrophy: a 32-year follow-up study in women. BMJ Open 2023; 13:e072803. [PMID: 37802622 PMCID: PMC10565256 DOI: 10.1136/bmjopen-2023-072803] [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: 02/24/2023] [Accepted: 08/18/2023] [Indexed: 10/10/2023] Open
Abstract
OBJECTIVE To examine the association between midlife tobacco smoking and late-life brain atrophy and white matter lesions. METHODS The study includes 369 women from the Prospective Population Study of Women in Gothenburg, Sweden. Cigarette smoking was reported at baseline 1968 (mean age=44 years) and at follow-up in 1974-1975 and 1980-1981. CT of the brain was conducted 32 years after baseline examination (mean age=76 years) to evaluate cortical atrophy and white matter lesions. Multiple logistic regressions estimated associations between midlife smoking and late-life brain lesions. The final analyses were adjusted for alcohol consumption and several other covariates. RESULTS Smoking in 1968-1969 (adjusted OR 1.85; 95% CI 1.12 to 3.04), in 1974-1975 (OR 2.37; 95% CI 1.39 to 4.04) and in 1980-1981 (OR 2.47; 95% CI 1.41 to 4.33) were associated with late-life frontal lobe atrophy (2000-2001). The strongest association was observed in women who reported smoking at all three midlife examinations (OR 2.63; 95% CI 1.44 to 4.78) and in those with more frequent alcohol consumption (OR 6.02; 95% CI 1.74 to 20.84). Smoking in 1980-1981 was also associated with late-life parietal lobe atrophy (OR 1.99; 95% CI 1.10 to 3.58). There were no associations between smoking and atrophy in the temporal or occipital lobe, or with white matter lesions. CONCLUSION Longstanding tobacco smoking was mainly associated with atrophy in the frontal lobe cortex. A long-term stimulation of nicotine receptors in the frontal neural pathway might be harmful for targeted brain cell.
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Affiliation(s)
- Lena Johansson
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
- Department of Addiction and Dependency, Sahlgrenska University Hospital, Sahlgrenska universitetssjukhuset, Goteborg, Sweden
- Institute of Health and Care Sciences at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Xinxin Guo
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
| | - Simona Sacuiu
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
| | - Madeleine Mellqvist Fässberg
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
| | - Silke Kern
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
| | - Anna Zettergren
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
| | - Ingmar Skoog
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AgeCap), Institute of Neuroscience and Physiology, University of Gothenburg, Goteborg, Sweden
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Wright AL, Konen LM, Mockett BG, Morris GP, Singh A, Burbano LE, Milham L, Hoang M, Zinn R, Chesworth R, Tan RP, Royle GA, Clark I, Petrou S, Abraham WC, Vissel B. The Q/R editing site of AMPA receptor GluA2 subunit acts as an epigenetic switch regulating dendritic spines, neurodegeneration and cognitive deficits in Alzheimer's disease. Mol Neurodegener 2023; 18:65. [PMID: 37759260 PMCID: PMC10537207 DOI: 10.1186/s13024-023-00632-5] [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: 01/11/2023] [Accepted: 06/03/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND RNA editing at the Q/R site of GluA2 occurs with ~99% efficiency in the healthy brain, so that the majority of AMPARs contain GluA2(R) instead of the exonically encoded GluA2(Q). Reduced Q/R site editing infcreases AMPA receptor calcium permeability and leads to dendritic spine loss, neurodegeneration, seizures and learning impairments. Furthermore, GluA2 Q/R site editing is impaired in Alzheimer's disease (AD), raising the possibility that unedited GluA2(Q)-containing AMPARs contribute to synapse loss and neurodegeneration in AD. If true, then inhibiting expression of unedited GluA2(Q), while maintaining expression of GluA2(R), may be a novel strategy of preventing synapse loss and neurodegeneration in AD. METHODS We engineered mice with the 'edited' arginine codon (CGG) in place of the unedited glutamine codon (CAG) at position 607 of the Gria2 gene. We crossbred this line with the J20 mouse model of AD and conducted anatomical, electrophysiological and behavioural assays to determine the impact of eliminating unedited GluA2(Q) expression on AD-related phenotypes. RESULTS Eliminating unedited GluA2(Q) expression in AD mice prevented dendritic spine loss and hippocampal CA1 neurodegeneration as well as improved working and reference memory in the radial arm maze. These phenotypes were improved independently of Aβ pathology and ongoing seizure susceptibility. Surprisingly, our data also revealed increased spine density in non-AD mice with exonically encoded GluA2(R) as compared to their wild-type littermates, suggesting an unexpected and previously unknown role for unedited GluA2(Q) in regulating dendritic spines. CONCLUSION The Q/R editing site of the AMPA receptor subunit GluA2 may act as an epigenetic switch that regulates dendritic spines, neurodegeneration and memory deficits in AD.
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Affiliation(s)
- Amanda L Wright
- St Vincent's Clinical School, St Vincent's Hospital Sydney, Faculty of Medicine, University of New South Wales, Darlinghurst, NSW, 2010, Australia
- School of Rural Medicine, Charles Sturt University, Orange, NSW, 2800, Australia
| | - Lyndsey M Konen
- Centre for Neuroscience and Regenerative Medicine, St Vincent's Centre for Applied Medical Research, St Vincent's Hospital Sydney, Darlinghurst, NSW, 2010, Australia
| | - Bruce G Mockett
- Department of Psychology, Brain Health Research Centre, Brain Research New Zealand, University of Otago, Box 56, Dunedin, 9054, New Zealand
| | - Gary P Morris
- Centre for Neuroscience and Regenerative Medicine, St Vincent's Centre for Applied Medical Research, St Vincent's Hospital Sydney, Darlinghurst, NSW, 2010, Australia
- Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS, 7005, Australia
| | - Anurag Singh
- Department of Psychology, Brain Health Research Centre, Brain Research New Zealand, University of Otago, Box 56, Dunedin, 9054, New Zealand
| | - Lisseth Estefania Burbano
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC, 3010, Australia
- Department of Anatomy and Neuroscience, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Luke Milham
- St Vincent's Clinical School, St Vincent's Hospital Sydney, Faculty of Medicine, University of New South Wales, Darlinghurst, NSW, 2010, Australia
- Centre for Neuroscience and Regenerative Medicine, St Vincent's Centre for Applied Medical Research, St Vincent's Hospital Sydney, Darlinghurst, NSW, 2010, Australia
| | - Monica Hoang
- School of Pharmacy, University of Waterloo, Kitchener, ON, N2G 1C5, Canada
| | - Raphael Zinn
- Centre for Neuroscience and Regenerative Medicine, St Vincent's Centre for Applied Medical Research, St Vincent's Hospital Sydney, Darlinghurst, NSW, 2010, Australia
| | - Rose Chesworth
- School of Medicine, Western Sydney University, Campbelltown, NSW, 2560, Australia
| | - Richard P Tan
- Chronic Diseases, School of Medical Sciences, Faculty of Health and Medicine, University of Sydney, Sydney, NSW, 2050, Australia
- Charles Perkins Centre, University of Sydney, Sydney, NSW, 2006, Australia
| | - Gordon A Royle
- Middlemore Hospital, Counties Manukau DHB, Otahuhu, Auckland, 1062, New Zealand
- Faculty of Medical and Health Sciences, University of Auckland, Grafton, Auckland, 1023, New Zealand
| | - Ian Clark
- Research School of Biology, Australian National University, Canberra, ACT, 0200, Australia
| | - Steven Petrou
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC, 3010, Australia
- Department of Anatomy and Neuroscience, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Wickliffe C Abraham
- Department of Psychology, Brain Health Research Centre, Brain Research New Zealand, University of Otago, Box 56, Dunedin, 9054, New Zealand
| | - Bryce Vissel
- St Vincent's Clinical School, St Vincent's Hospital Sydney, Faculty of Medicine, University of New South Wales, Darlinghurst, NSW, 2010, Australia.
- Centre for Neuroscience and Regenerative Medicine, St Vincent's Centre for Applied Medical Research, St Vincent's Hospital Sydney, Darlinghurst, NSW, 2010, Australia.
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Baxter LC, Limback-Stokin M, Patten KJ, Arreola AC, Locke DE, Hu L, Zhou Y, Caselli RJ. Hippocampal connectivity and memory decline in cognitively intact APOE ε4 carriers. Alzheimers Dement 2023; 19:3806-3814. [PMID: 36906845 PMCID: PMC11105018 DOI: 10.1002/alz.13023] [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: 08/24/2022] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 03/13/2023]
Abstract
INTRODUCTION Resting-state functional magnetic resonance imaging (fMRI) graph theory may help detect subtle functional connectivity changes affecting memory prior to impairment. METHODS Cognitively normal apolipoprotein E (APOE) ε4 carriers/noncarriers underwent longitudinal cognitive assessment and one-time MRI. The relationship of left/right hippocampal connectivity and memory trajectory were compared between carriers/noncarriers. RESULTS Steepness of verbal memory decline correlated with decreased connectivity in the left hippocampus, only among APOE ε4 carriers. Right hippocampal metrics were not correlated with memory and there were no significant correlations in the noncarriers. Verbal memory decline correlated with left hippocampal volume loss for both carriers and noncarriers, with no other significant volumetric findings. DISCUSSION Findings support early hippocampal dysfunction in intact carriers, the AD disconnection hypothesis, and left hippocampal dysfunction earlier than the right. Combining lateralized graph theoretical metrics with a sensitive measure of memory trajectory allowed for detection of early-stage changes in APOE ε4 carriers before symptoms of mild cognitive impairment are present. HIGHLIGHTS Graph theory connectivity detects preclinical hippocampal changes in APOE ε4 carriers. The AD disconnection hypothesis was supported in unimpaired APOE ε4 carriers. Hippocampal dysfunction starts asymmetrically on the left.
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Affiliation(s)
- Leslie C. Baxter
- Department of Psychiatry and Psychology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | | | - K. Jakob Patten
- Department of Speech and Hearing Sciences, Tempe, Arizona, 85281 USA
| | | | - Dona E.C. Locke
- Department of Psychiatry and Psychology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Leland Hu
- Department of Radiology, Mayo Clinic Arizona, Phoenix, Arizona, 85054 USA
| | - Yuxiang Zhou
- Department of Medical Physics, Mayo Clinic Arizona, Phoenix, Arizona, 85054 USA
| | - Richard J. Caselli
- Department of Neurology, Mayo Clinic Arizona, Phoenix, Arizona, 85259 USA
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Cao G, Zhang M, Wang Y, Zhang J, Han Y, Xu X, Huang J, Kang G. End-to-end automatic pathology localization for Alzheimer's disease diagnosis using structural MRI. Comput Biol Med 2023; 163:107110. [PMID: 37321102 DOI: 10.1016/j.compbiomed.2023.107110] [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: 01/25/2023] [Revised: 05/18/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
Structural magnetic resonance imaging (sMRI) is an essential part of the clinical assessment of patients at risk of Alzheimer dementia. One key challenge in sMRI-based computer-aided dementia diagnosis is to localize local pathological regions for discriminative feature learning. Existing solutions predominantly depend on generating saliency maps for pathology localization and handle the localization task independently of the dementia diagnosis task, leading to a complex multi-stage training pipeline that is hard to optimize with weakly-supervised sMRI-level annotations. In this work, we aim to simplify the pathology localization task and construct an end-to-end automatic localization framework (AutoLoc) for Alzheimer's disease diagnosis. To this end, we first present an efficient pathology localization paradigm that directly predicts the coordinate of the most disease-related region in each sMRI slice. Then, we approximate the non-differentiable patch-cropping operation with the bilinear interpolation technique, which eliminates the barrier to gradient backpropagation and thus enables the joint optimization of localization and diagnosis tasks. Extensive experiments on commonly used ADNI and AIBL datasets demonstrate the superiority of our method. Especially, we achieve 93.38% and 81.12% accuracy on Alzheimer's disease classification and mild cognitive impairment conversion prediction tasks, respectively. Several important brain regions, such as rostral hippocampus and globus pallidus, are identified to be highly associated with Alzheimer's disease.
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Affiliation(s)
- Gongpeng Cao
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Manli Zhang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Yiping Wang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Jing Zhang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Xin Xu
- Department of Neurosurgery, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Jinguo Huang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
| | - Guixia Kang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
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He S, Qiu S, Pan M, Palavicini JP, Wang H, Li X, Bhattacharjee A, Barannikov S, Bieniek KF, Dupree JL, Han X. Central nervous system sulfatide deficiency as a causal factor for bladder disorder in Alzheimer's disease. Clin Transl Med 2023; 13:e1332. [PMID: 37478300 PMCID: PMC10361545 DOI: 10.1002/ctm2.1332] [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: 04/14/2023] [Revised: 07/02/2023] [Accepted: 07/10/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Despite being a brain disorder, Alzheimer's disease (AD) is often accompanied by peripheral organ dysregulations (e.g., loss of bladder control in late-stage AD), which highly rely on spinal cord coordination. However, the causal factor(s) for peripheral organ dysregulation in AD remain elusive. METHODS The central nervous system (CNS) is enriched in lipids. We applied quantitative shotgun lipidomics to determine lipid profiles of human AD spinal cord tissues. Additionally, a CNS sulfatide (ST)-deficient mouse model was used to study the lipidome, transcriptome and peripheral organ phenotypes of ST loss. RESULTS We observed marked myelin lipid reduction in the spinal cord of AD subjects versus cognitively normal individuals. Among which, levels of ST, a myelin-enriched lipid class, were strongly and negatively associated with the severity of AD. A CNS myelin-specific ST-deficient mouse model was used to further identify the causes and consequences of spinal cord lipidome changes. Interestingly, ST deficiency led to spinal cord lipidome and transcriptome profiles highly resembling those observed in AD, characterized by decline of multiple myelin-enriched lipid classes and enhanced inflammatory responses, respectively. These changes significantly disrupted spinal cord function and led to substantial enlargement of urinary bladder in ST-deficient mice. CONCLUSIONS Our study identified CNS ST deficiency as a causal factor for AD-like lipid dysregulation, inflammation response and ultimately the development of bladder disorders. Targeting to maintain ST levels may serve as a promising strategy for the prevention and treatment of AD-related peripheral disorders.
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Affiliation(s)
- Sijia He
- Barshop Institute for Longevity and Aging StudiesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Shulan Qiu
- Barshop Institute for Longevity and Aging StudiesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Meixia Pan
- Barshop Institute for Longevity and Aging StudiesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Juan P. Palavicini
- Barshop Institute for Longevity and Aging StudiesUniversity of Texas Health San AntonioSan AntonioTexasUSA
- Division of DiabetesDepartment of MedicineUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Hu Wang
- Barshop Institute for Longevity and Aging StudiesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Xin Li
- Barshop Institute for Longevity and Aging StudiesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Anindita Bhattacharjee
- Barshop Institute for Longevity and Aging StudiesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Savannah Barannikov
- Department of PathologyGlenn Biggs Institute for Alzheimer's and Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Kevin F. Bieniek
- Department of PathologyGlenn Biggs Institute for Alzheimer's and Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Jeffrey L. Dupree
- Department of Anatomy and NeurobiologyVirginia Commonwealth UniversityRichmondVirginiaUSA
- Research DivisionMcGuire Veterans Affairs Medical CenterRichmondVirginiaUSA
| | - Xianlin Han
- Barshop Institute for Longevity and Aging StudiesUniversity of Texas Health San AntonioSan AntonioTexasUSA
- Division of DiabetesDepartment of MedicineUniversity of Texas Health San AntonioSan AntonioTexasUSA
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Kress GT, Popa ES, Thompson PM, Bookheimer SY, Thomopoulos SI, Ching CRK, Zheng H, Hirsh DA, Merrill DA, Panos SE, Raji CA, Siddarth P, Bramen JE. Preliminary validation of a structural magnetic resonance imaging metric for tracking dementia-related neurodegeneration and future decline. Neuroimage Clin 2023; 39:103458. [PMID: 37421927 PMCID: PMC10338152 DOI: 10.1016/j.nicl.2023.103458] [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: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 07/10/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an "AD-NeuroScore," that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1-91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses. Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics. In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.
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Affiliation(s)
- Gavin T Kress
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Emily S Popa
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Susan Y Bookheimer
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Hong Zheng
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Daniel A Hirsh
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA.
| | - David A Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; Department of Translational Neurosciences and Neurotherapeutics, Providence Saint John's Cancer Institute, Santa Monica, CA 90404, USA; UCLA Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Stella E Panos
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA
| | - Cyrus A Raji
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Prabha Siddarth
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; UCLA Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Jennifer E Bramen
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA.
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Tian YE, Di Biase MA, Mosley PE, Lupton MK, Xia Y, Fripp J, Breakspear M, Cropley V, Zalesky A. Evaluation of Brain-Body Health in Individuals With Common Neuropsychiatric Disorders. JAMA Psychiatry 2023; 80:567-576. [PMID: 37099313 PMCID: PMC10134046 DOI: 10.1001/jamapsychiatry.2023.0791] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/15/2023] [Indexed: 04/27/2023]
Abstract
Importance Physical health and chronic medical comorbidities are underestimated, inadequately treated, and often overlooked in psychiatry. A multiorgan, systemwide characterization of brain and body health in neuropsychiatric disorders may enable systematic evaluation of brain-body health status in patients and potentially identify new therapeutic targets. Objective To evaluate the health status of the brain and 7 body systems across common neuropsychiatric disorders. Design, Setting, and Participants Brain imaging phenotypes, physiological measures, and blood- and urine-based markers were harmonized across multiple population-based neuroimaging biobanks in the US, UK, and Australia, including UK Biobank; Australian Schizophrenia Research Bank; Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing; Alzheimer's Disease Neuroimaging Initiative; Prospective Imaging Study of Ageing; Human Connectome Project-Young Adult; and Human Connectome Project-Aging. Cross-sectional data acquired between March 2006 and December 2020 were used to study organ health. Data were analyzed from October 18, 2021, to July 21, 2022. Adults aged 18 to 95 years with a lifetime diagnosis of 1 or more common neuropsychiatric disorders, including schizophrenia, bipolar disorder, depression, generalized anxiety disorder, and a healthy comparison group were included. Main Outcomes and Measures Deviations from normative reference ranges for composite health scores indexing the health and function of the brain and 7 body systems. Secondary outcomes included accuracy of classifying diagnoses (disease vs control) and differentiating between diagnoses (disease vs disease), measured using the area under the receiver operating characteristic curve (AUC). Results There were 85 748 participants with preselected neuropsychiatric disorders (36 324 male) and 87 420 healthy control individuals (40 560 male) included in this study. Body health, especially scores indexing metabolic, hepatic, and immune health, deviated from normative reference ranges for all 4 neuropsychiatric disorders studied. Poor body health was a more pronounced illness manifestation compared to brain changes in schizophrenia (AUC for body = 0.81 [95% CI, 0.79-0.82]; AUC for brain = 0.79 [95% CI, 0.79-0.79]), bipolar disorder (AUC for body = 0.67 [95% CI, 0.67-0.68]; AUC for brain = 0.58 [95% CI, 0.57-0.58]), depression (AUC for body = 0.67 [95% CI, 0.67-0.68]; AUC for brain = 0.58 [95% CI, 0.58-0.58]), and anxiety (AUC for body = 0.63 [95% CI, 0.63-0.63]; AUC for brain = 0.57 [95% CI, 0.57-0.58]). However, brain health enabled more accurate differentiation between distinct neuropsychiatric diagnoses than body health (schizophrenia-other: mean AUC for body = 0.70 [95% CI, 0.70-0.71] and mean AUC for brain = 0.79 [95% CI, 0.79-0.80]; bipolar disorder-other: mean AUC for body = 0.60 [95% CI, 0.59-0.60] and mean AUC for brain = 0.65 [95% CI, 0.65-0.65]; depression-other: mean AUC for body = 0.61 [95% CI, 0.60-0.63] and mean AUC for brain = 0.65 [95% CI, 0.65-0.66]; anxiety-other: mean AUC for body = 0.63 [95% CI, 0.62-0.63] and mean AUC for brain = 0.66 [95% CI, 0.65-0.66). Conclusions and Relevance In this cross-sectional study, neuropsychiatric disorders shared a substantial and largely overlapping imprint of poor body health. Routinely monitoring body health and integrated physical and mental health care may help reduce the adverse effect of physical comorbidity in people with mental illness.
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Affiliation(s)
- Ye Ella Tian
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, Melbourne Medical School, the University of Melbourne, Melbourne, Victoria, Australia
| | - Maria A. Di Biase
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, Melbourne Medical School, the University of Melbourne, Melbourne, Victoria, Australia
| | - Philip E. Mosley
- Clinical Brain Networks Group, Queensland Institute of Medical Research Berghofer Medical Institute, Brisbane, Queensland, Australia
- Queensland Brain Institute, Brisbane, Queensland, Australia
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation Health and Biosecurity, Brisbane, Queensland, Australia
| | - Michelle K. Lupton
- Queensland Institute of Medical Research Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ying Xia
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation Health and Biosecurity, Brisbane, Queensland, Australia
| | - Jurgen Fripp
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation Health and Biosecurity, Brisbane, Queensland, Australia
| | - Michael Breakspear
- Discipline of Psychiatry, College of Health, Medicine and Wellbeing, the University of Newcastle, Newcastle, New South Wales, Australia
- School of Psychological Sciences, College of Engineering, Science and Environment, the University of Newcastle, Newcastle, New South Wales, Australia
| | - Vanessa Cropley
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, Melbourne Medical School, the University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew Zalesky
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, Melbourne Medical School, the University of Melbourne, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, Faculty of Engineering and Information Technology, the University of Melbourne, Melbourne, Victoria, Australia
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Huang Y, Shan Y, Qin W, Zhao G. Apolipoprotein E ε4 accelerates the longitudinal cerebral atrophy in open access series of imaging studies-3 elders without dementia at enrollment. Front Aging Neurosci 2023; 15:1158579. [PMID: 37323144 PMCID: PMC10265507 DOI: 10.3389/fnagi.2023.1158579] [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: 02/04/2023] [Accepted: 05/03/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction Early studies have reported that APOE is strongly associated with brain atrophy and cognitive decline among healthy elders and Alzheimer's disease (AD). However, previous research has not directly outlined the modulation of APOE on the trajectory of cerebral atrophy with aging during the conversion from cognitive normal (CN) to dementia (CN2D). Methods This study tried to elucidate this issue from a voxel-wise whole-brain perspective based on 416 qualified participants from a longitudinal OASIS-3 neuroimaging cohort. A voxel-wise linear mixed-effects model was applied for detecting cerebrum regions whose nonlinear atrophic trajectories were driven by AD conversion and to elucidate the effect of APOE variants on the cerebral atrophic trajectories during the process. Results We found that CN2D participants had faster quadratically accelerated atrophy in bilateral hippocampi than persistent CN. Moreover, APOE ε4 carriers had faster-accelerated atrophy in the left hippocampus than ε4 noncarriers in both CN2D and persistent CN, and CN2D ε4 carriers an noncarriers presented a faster atrophic speed than CN ε4 carriers. These findings could be replicated in a sub-sample with a tough match in demographic information. Discussion Our findings filled the gap that APOE ε4 accelerates hippocampal atrophy and the conversion from normal cognition to dementia.
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Affiliation(s)
- Yuda Huang
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Yongzhi Shan
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Wen Qin
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Guoguang Zhao
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, China
- Clinical Research Center for Epilepsy Capital Medical University, Beijing, China
- Beijing Municipal Geriatric Medical Research Center, Beijing, China
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Gutteridge DS, Segal A, McNeil JJ, Beilin L, Brodtmann A, Chowdhury EK, Egan GF, Ernst ME, Hussain SM, Reid CM, Robb CE, Ryan J, Woods RL, Keage HA, Jamadar S. The relationship between long-term blood pressure variability and cortical thickness in older adults. Neurobiol Aging 2023; 129:157-167. [PMID: 37331246 DOI: 10.1016/j.neurobiolaging.2023.05.011] [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: 01/17/2023] [Revised: 05/02/2023] [Accepted: 05/17/2023] [Indexed: 06/20/2023]
Abstract
High blood pressure variability (BPV) is a risk factor for cognitive decline and dementia, but its association with cortical thickness is not well understood. Here we use a topographical approach, to assess links between long-term BPV and cortical thickness in 478 (54% men at baseline) community dwelling older adults (70-88 years) from the ASPirin in Reducing Events in the Elderly NEURO sub-study. BPV was measured as average real variability, based on annual visits across three years. Higher diastolic BPV was significantly associated with reduced cortical thickness in multiple areas, including temporal (banks of the superior temporal sulcus), parietal (supramarginal gyrus, post-central gyrus), and posterior frontal areas (pre-central gyrus, caudal middle frontal gyrus), while controlling for mean BP. Higher diastolic BPV was associated with faster progression of cortical thinning across the three years. Diastolic BPV is an important predictor of cortical thickness, and trajectory of cortical thickness, independent of mean blood pressure. This finding suggests an important biological link in the relationship between BPV and cognitive decline in older age.
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Affiliation(s)
- D S Gutteridge
- Cognitive Ageing and Impairment Neuroscience Laboratory (CAIN), University of South Australia, Adelaide, South Australia, Australia.
| | - A Segal
- Turner Institute for Brain & Mental Health, Monash University, Melbourne, Victoria, Australia
| | - J J McNeil
- School of Public Health & Preventative Medicine, Monash University, Melbourne, Victoria, Australia
| | - L Beilin
- School of Medicine, Royal Perth Hospital Unit, University of Western Australia, Perth, Western Australia, Australia
| | - A Brodtmann
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - E K Chowdhury
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - G F Egan
- Turner Institute for Brain & Mental Health, Monash University, Melbourne, Victoria, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - M E Ernst
- Department of Family Medicine, Carver College of Medicine. The University of Iowa, Iowa City, IA, USA; Department of Pharmacy Practice and Science, College of Pharmacy, Carver College of Medicine. The University of Iowa, Iowa City, IA, USA
| | - S M Hussain
- School of Public Health & Preventative Medicine, Monash University, Melbourne, Victoria, Australia; Department of Medical Education, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
| | - C M Reid
- School of Public Health & Preventative Medicine, Monash University, Melbourne, Victoria, Australia; School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - C E Robb
- School of Public Health & Preventative Medicine, Monash University, Melbourne, Victoria, Australia
| | - J Ryan
- School of Public Health & Preventative Medicine, Monash University, Melbourne, Victoria, Australia
| | - R L Woods
- School of Public Health & Preventative Medicine, Monash University, Melbourne, Victoria, Australia
| | - H A Keage
- Cognitive Ageing and Impairment Neuroscience Laboratory (CAIN), University of South Australia, Adelaide, South Australia, Australia
| | - S Jamadar
- Turner Institute for Brain & Mental Health, Monash University, Melbourne, Victoria, Australia; Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
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Lin H, Pan T, Wang M, Ge J, Lu J, Ju Z, Chen K, Zhang H, Guan Y, Zhao Q, Shan B, Nie B, Zuo C, Wu P. Metabolic Asymmetry Relates to Clinical Characteristics and Brain Network Abnormalities in Alzheimer's Disease. J Alzheimers Dis 2023:JAD221258. [PMID: 37182878 DOI: 10.3233/jad-221258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Metabolic asymmetry has been observed in Alzheimer's disease (AD), but different studies have inconsistent viewpoints. OBJECTIVE To analyze the asymmetry of cerebral glucose metabolism in AD and investigate its clinical significance and potential metabolic network abnormalities. METHODS Standardized uptake value ratios (SUVRs) were obtained from 18F-FDG positron emission tomography (PET) images of all participants, and the asymmetry indices (AIs) were calculated according to the SUVRs. AD group was divided into left/right-dominant or bilateral symmetric hypometabolism (AD-L/AD-R or AD-BI) when more than half of the AIs of the 20 regions of interest (ROIs) were < -2SD, >2SD, or between±1SD. Differences in clinical features among the three AD groups were compared, and the abnormal network characteristics underlying metabolic asymmetry were explored. RESULTS In AD group, the proportions of AD-L, AD-R, and AD-BI were 28.4%, 17.9%, and 18.5%, respectively. AD-L/AD-R groups had younger age of onset and faster rate of cognitive decline than AD-BI group (p < 0.05). The absolute values of AIs in half of the 20 ROIs became higher at follow-up than at baseline (p < 0.05). Compared with those in AD-BI group, metabolic connection strength of network, global efficiency, cluster coefficient, degree centrality and local efficiency were lower, but shortest path length was longer in AD-L and AD-R groups (p < 0.05). CONCLUSION Asymmetric and symmetric hypometabolism may represent different clinical subtypes of AD, which may provide a clue for future studies on the heterogeneity of AD and help to optimize the design of clinical trials.
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Affiliation(s)
- Huamei Lin
- Deparment of Nuclear Medicine / PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Tingting Pan
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High EnergyPhysics, Chinese Academy of Sciences, Beijing, China
| | - Min Wang
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Jingjie Ge
- Deparment of Nuclear Medicine / PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiaying Lu
- Deparment of Nuclear Medicine / PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zizhao Ju
- Deparment of Nuclear Medicine / PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Keliang Chen
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Huiwei Zhang
- Deparment of Nuclear Medicine / PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yihui Guan
- Deparment of Nuclear Medicine / PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Qianhua Zhao
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Baoci Shan
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High EnergyPhysics, Chinese Academy of Sciences, Beijing, China
| | - Binbin Nie
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High EnergyPhysics, Chinese Academy of Sciences, Beijing, China
| | - Chuantao Zuo
- Deparment of Nuclear Medicine / PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- Deparment of Nuclear Medicine / PET Center, Huashan Hospital, Fudan University, Shanghai, China
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
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Molecular and neural roles of sodium-glucose cotransporter 2 inhibitors in alleviating neurocognitive impairment in diabetic mice. Psychopharmacology (Berl) 2023; 240:983-1000. [PMID: 36869919 PMCID: PMC10006050 DOI: 10.1007/s00213-023-06341-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 02/10/2023] [Indexed: 03/05/2023]
Abstract
Diabetes causes a variety of molecular changes in the brain, making it a real risk factor for the development of cognitive dysfunction. Complex pathogenesis and clinical heterogeneity of cognitive impairment makes the efficacy of current drugs limited. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) gained our attention as drugs with potential beneficial effects on the CNS. In the present study, these drugs ameliorated the cognitive impairment associated with diabetes. Moreover, we verified whether SGLT2i can mediate the degradation of amyloid precursor protein (APP) and modulation of gene expression (Bdnf, Snca, App) involved in the control of neuronal proliferation and memory. The results of our research proved the participation of SGLT2i in the multifactorial process of neuroprotection. SGLT2i attenuate the neurocognitive impairment through the restoration of neurotrophin levels, modulation of neuroinflammatory signaling, and gene expression of Snca, Bdnf, and App in the brain of diabetic mice. The targeting of the above-mentioned genes is currently seen as one of the most promising and developed therapeutic strategies for diseases associated with cognitive dysfunction. The results of this work could form the basis of a future administration of SGLT2i in diabetics with neurocognitive impairment.
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Hollenbenders Y, Pobiruchin M, Reichenbach A. Two Routes to Alzheimer's Disease Based on Differential Structural Changes in Key Brain Regions. J Alzheimers Dis 2023; 92:1399-1412. [PMID: 36911937 DOI: 10.3233/jad-221061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder with homogenous disease patterns. Neuropathological changes precede symptoms by up to two decades making neuroimaging biomarkers a prime candidate for early diagnosis, prognosis, and patient stratification. OBJECTIVE The goal of the study was to discern intermediate AD stages and their precursors based on neuroanatomical features for stratifying patients on their progression through different stages. METHODS Data include grey matter features from 14 brain regions extracted from longitudinal structural MRI and cognitive data obtained from 1,017 healthy controls and AD patients of ADNI. AD progression was modeled with a Hidden Markov Model, whose hidden states signify disease stages derived from the neuroanatomical data. To tie the progression in brain atrophy to a behavioral marker, we analyzed the ADAS-cog sub-scores in the stages. RESULTS The optimal model consists of eight states with differentiable neuroanatomical features, forming two routes crossing once at a very early point and merging at the final state. The cortical route is characterized by early and sustained atrophy in cortical regions. The limbic route is characterized by early decrease in limbic regions. Cognitive differences between the two routes are most noticeable in the memory domain with subjects from the limbic route experiencing stronger memory impairments. CONCLUSION Our findings corroborate that more than one pattern of grey matter deterioration with several discernable stages can be identified in the progression of AD. These neuroanatomical subtypes are behaviorally meaningful and provide a door into early diagnosis of AD and prognosis of the disease's progression.
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Affiliation(s)
- Yasmin Hollenbenders
- Medical Faculty Heidelberg, Heidelberg University, Germany.,Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,Center for Machine Learning, Heilbronn University of Applied Sciences, Germany
| | - Monika Pobiruchin
- Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,GECKO Institute for Medicine, Informatics and Economics, Heilbronn University of Applied Sciences, Germany
| | - Alexandra Reichenbach
- Medical Faculty Heidelberg, Heidelberg University, Germany.,Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,Center for Machine Learning, Heilbronn University of Applied Sciences, Germany
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Kang YJ, Xue Y, Shin JH, Cho H. Human mini-brains for reconstituting central nervous system disorders. LAB ON A CHIP 2023; 23:964-981. [PMID: 36644973 DOI: 10.1039/d2lc00897a] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Neurological disorders in the central nervous system (CNS) are progressive and irreversible diseases leading to devastating impacts on patients' life as they cause cognitive impairment, dementia, and even loss of essential body functions. The development of effective medicines curing CNS disorders is, however, one of the most ambitious challenges due to the extremely complex functions and structures of the human brain. In this regard, there are unmet needs to develop simplified but physiopathologically-relevant brain models. Recent advances in the microfluidic techniques allow multicellular culture forming miniaturized 3D human brains by aligning parts of brain regions with specific cells serving suitable functions. In this review, we overview designs and strategies of microfluidics-based human mini-brains for reconstituting CNS disorders, particularly Alzheimer's disease (AD), Parkinson's disease (PD), traumatic brain injury (TBI), vascular dementia (VD), and environmental risk factor-driven dementia (ERFD). Afterward, the applications of the mini-brains in the area of medical science are introduced in terms of the clarification of pathogenic mechanisms and identification of promising biomarkers. We also present expanded model systems ranging from the CNS to CNS-connecting organ axes to study the entry pathways of pathological risk factors into the brain. Lastly, the advantages and potential challenges of current model systems are addressed with future perspectives.
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Affiliation(s)
- You Jung Kang
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, Republic of Korea.
- Department of Biophysics, Sungkyunkwan University, Suwon, Republic of Korea
| | - Yingqi Xue
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, Republic of Korea.
- Department of Biophysics, Sungkyunkwan University, Suwon, Republic of Korea
| | - Jae Hee Shin
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, Republic of Korea.
- Department of Biophysics, Sungkyunkwan University, Suwon, Republic of Korea
| | - Hansang Cho
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, Republic of Korea.
- Department of Biophysics, Sungkyunkwan University, Suwon, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
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Cao T, Pang JC, Segal A, Chen YC, Aquino KM, Breakspear M, Fornito A. Mode-based morphometry: A multiscale approach to mapping human neuroanatomy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.26.529328. [PMID: 36909539 PMCID: PMC10002616 DOI: 10.1101/2023.02.26.529328] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes--eigenmodes--of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.
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Affiliation(s)
- Trang Cao
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| | - James C Pang
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| | - Ashlea Segal
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| | - Yu-Chi Chen
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
| | - Kevin M Aquino
- School of Physics, University of Sydney, Physics Rd, Camperdown NSW 2006, Australia
| | - Michael Breakspear
- School of Psychological Sciences, University of Newcastle, University Dr, Callaghan NSW 2308, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, 762-772 Blackburn Rd, Clayton VIC 3168, Australia
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45
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Li H, Guan Q, Huang R, Lei M, Luo YJ, Zhang Z, Tao W. Altered functional coupling between the cerebellum and cerebrum in patients with amnestic mild cognitive impairment. Cereb Cortex 2023; 33:2061-2074. [PMID: 36857720 DOI: 10.1093/cercor/bhac193] [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: 02/10/2022] [Revised: 04/19/2022] [Accepted: 04/20/2022] [Indexed: 11/14/2022] Open
Abstract
Cognitive processing relies on the functional coupling between the cerebrum and cerebellum. However, it remains unclear how the 2 collaborate in amnestic mild cognitive impairment (aMCI) patients. With functional magnetic resonance imaging techniques, we compared cerebrocerebellar functional connectivity during the resting state (rsFC) between the aMCI and healthy control (HC) groups. Additionally, we distinguished coupling between functionally corresponding and noncorresponding areas across the cerebrum and cerebellum. The results demonstrated decreased rsFC between both functionally corresponding and noncorresponding areas, suggesting distributed deficits of cerebrocerebellar connections in aMCI patients. Increased rsFC was also observed, which were between functionally noncorresponding areas. Moreover, the increased rsFC was positively correlated with attentional scores in the aMCI group, and this effect was absent in the HC group, supporting that there exists a compensatory mechanism in patients. The current study contributes to illustrating how the cerebellum adjusts its coupling with the cerebrum in individuals with cognitive impairment.
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Affiliation(s)
- Hehui Li
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, P.R. China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, P.R. China
| | - Rong Huang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, P.R. China
| | - Mengmeng Lei
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, P.R. China
| | - Yue-Jia Luo
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, P.R. China.,State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing 100875, P.R. China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing 100875, P.R. China
| | - Wuhai Tao
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, 3688 Nanhai Avenue, Nanshan District, Shenzhen 518060, P.R. China
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46
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Zheng X, Cawood J, Hayre C, Wang S. Computer assisted diagnosis of Alzheimer's disease using statistical likelihood-ratio test. PLoS One 2023; 18:e0279574. [PMID: 36800393 PMCID: PMC9937475 DOI: 10.1371/journal.pone.0279574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 12/11/2022] [Indexed: 02/18/2023] Open
Abstract
The purpose of this work is to present a computer assisted diagnostic tool for radiologists in their diagnosis of Alzheimer's disease. A statistical likelihood-ratio procedure from signal detection theory was implemented in the detection of Alzheimer's disease. The probability density functions of the likelihood ratio were constructed by using medial temporal lobe (MTL) volumes of patients with Alzheimer's disease (AD) and normal controls (NC). The volumes of MTL as well as other anatomical regions of the brains were calculated by the FreeSurfer software using T1 weighted MRI images. The MRI images of AD and NC were downloaded from the database of Alzheimer's disease neuroimaging initiative (ADNI). A separate dataset of minimal interval resonance imaging in Alzheimer's disease (MIRIAD) was used for diagnostic testing. A sensitivity of 89.1% and specificity of 87.0% were achieved for the MIRIAD dataset which are better than the 85% sensitivity and specificity achieved by the best radiologists without input of other patient information.
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Affiliation(s)
- Xiaoming Zheng
- Medical Radiation Science, School of Dentistry and Medical Sciences, Charles Sturt University, Wagga Wagga, NSW, Australia
- * E-mail:
| | - Justin Cawood
- Medical Radiation Science, School of Dentistry and Medical Sciences, Charles Sturt University, Wagga Wagga, NSW, Australia
| | - Chris Hayre
- Medical Radiation Science, School of Dentistry and Medical Sciences, Charles Sturt University, Wagga Wagga, NSW, Australia
| | - Shaoyu Wang
- Biomedical Sciences, School of Dentistry and Medical Sciences, Charles Sturt University, Wagga Wagga, NSW, Australia
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47
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Makdissi S, Parsons BD, Di Cara F. Towards early detection of neurodegenerative diseases: A gut feeling. Front Cell Dev Biol 2023; 11:1087091. [PMID: 36824371 PMCID: PMC9941184 DOI: 10.3389/fcell.2023.1087091] [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: 11/01/2022] [Accepted: 01/20/2023] [Indexed: 02/10/2023] Open
Abstract
The gastrointestinal tract communicates with the nervous system through a bidirectional network of signaling pathways called the gut-brain axis, which consists of multiple connections, including the enteric nervous system, the vagus nerve, the immune system, endocrine signals, the microbiota, and its metabolites. Alteration of communications in the gut-brain axis is emerging as an overlooked cause of neuroinflammation. Neuroinflammation is a common feature of the pathogenic mechanisms involved in various neurodegenerative diseases (NDs) that are incurable and debilitating conditions resulting in progressive degeneration and death of neurons, such as in Alzheimer and Parkinson diseases. NDs are a leading cause of global death and disability, and the incidences are expected to increase in the following decades if prevention strategies and successful treatment remain elusive. To date, the etiology of NDs is unclear due to the complexity of the mechanisms of diseases involving genetic and environmental factors, including diet and microbiota. Emerging evidence suggests that changes in diet, alteration of the microbiota, and deregulation of metabolism in the intestinal epithelium influence the inflammatory status of the neurons linked to disease insurgence and progression. This review will describe the leading players of the so-called diet-microbiota-gut-brain (DMGB) axis in the context of NDs. We will report recent findings from studies in model organisms such as rodents and fruit flies that support the role of diets, commensals, and intestinal epithelial functions as an overlooked primary regulator of brain health. We will finish discussing the pivotal role of metabolisms of cellular organelles such as mitochondria and peroxisomes in maintaining the DMGB axis and how alteration of the latter can be used as early disease makers and novel therapeutic targets.
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Affiliation(s)
- Stephanie Makdissi
- Dalhousie University, Department of Microbiology and Immunology, Halifax, NS, Canada
- IWK Health Centre, Department of Pediatrics, Halifax, Canada
| | - Brendon D. Parsons
- Dalhousie University, Department of Microbiology and Immunology, Halifax, NS, Canada
| | - Francesca Di Cara
- Dalhousie University, Department of Microbiology and Immunology, Halifax, NS, Canada
- IWK Health Centre, Department of Pediatrics, Halifax, Canada
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48
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Milani A, Morawski M, Bechmann I. Inter-hemispherical comparison of tau-pathology in the human temporal lobe. Ann Anat 2023; 246:152042. [PMID: 36592871 DOI: 10.1016/j.aanat.2022.152042] [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: 10/13/2022] [Revised: 11/24/2022] [Accepted: 11/29/2022] [Indexed: 12/31/2022]
Abstract
Alzheimer's disease (AD) is characterized histopathologically by hyperphosphorylated and aggregated Tau and amyloid plaques. While the latter appear in a less stringent way throughout the brain in the course of the disease, the former evolve in a highly predictable pattern as described by Braak and Braak (1991). It is, however, not clear if this pattern develops simultaneously in both hemispheres. In this study, we therefore compared Tau-pathology of both hemispheres of the same individual in 36 consecutive brain donations as they arrived in our brain bank. 26 exhibited little differences, in eight cases left hemisphere was clearly more affected and in two cases the right hemisphere. Thus, cases with evident interhemispheric Tau-pathology do exist and interhemispheric comparison in such cases may help to identify driving forces in the progression of AD.
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Affiliation(s)
| | - Markus Morawski
- Paul-Flechsig-Institute of Brain Research, Universität Leipzig, Leipzig, Germany
| | - Ingo Bechmann
- Institute of Anatomy, Universität Leipzig, Leipzig, Germany.
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49
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Daniel E, Deng F, Patel SK, Sedrak MS, Kim H, Razavi M, Sun CL, Root JC, Ahles TA, Dale W, Chen BT. Cortical thinning in chemotherapy-treated older long-term breast cancer survivors. Brain Imaging Behav 2023; 17:66-76. [PMID: 36369620 PMCID: PMC10156471 DOI: 10.1007/s11682-022-00743-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/30/2022] [Indexed: 11/13/2022]
Abstract
Cognitive decline is an increasing issue for cancer survivors, especially for older adults, as chemotherapy affects brain structure and function. The purpose of this single center study was to evaluate alterations in cortical thickness and cognition in older long-term survivors of breast cancer who had been treated with chemotherapy years ago. In this prospective cohort study, we enrolled 3 groups of women aged ≥ 65 years with a history of stage I-III breast cancer who had received adjuvant chemotherapy 5 to 15 years ago (chemotherapy group, C +), age-matched women with breast cancer but no chemotherapy (no-chemotherapy group, C-) and healthy controls (HC). All participants underwent brain magnetic resonance imaging and neuropsychological testing with the NIH Toolbox Cognition Battery at time point 1 (TP1) and again at 2 years after enrollment (time point 2 (TP2)). At TP1, there were no significant differences in cortical thickness among the 3 groups. Longitudinally, the C + group showed cortical thinning in the fusiform gyrus (p = 0.006, effect size (d) = -0.60 [ -1.86, -0.66]), pars triangularis (p = 0.026, effect size (d) = -0.43 [-1.68, -0.82]), and inferior temporal lobe (p = 0.026, effect size (d) = -0.38 [-1.62, -0.31]) of the left hemisphere. The C + group also showed decreases in neuropsychological scores such as the total composite score (p = 0.01, effect size (d) = -3.9726 [-0.9656, -6.9796], fluid composite score (p = 0.03, effect size (d) = -4.438 [-0.406, -8.47], and picture vocabulary score (p = 0.04, effect size (d) = -3.7499 [-0.0617, -7.438]. Our results showed that cortical thickness could be a candidate neuroimaging biomarker for cancer-related cognitive impairment and accelerated aging in older long-term cancer survivors.
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Affiliation(s)
- Ebenezer Daniel
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Frank Deng
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Sunita K Patel
- Department of Population Science, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Mina S Sedrak
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Heeyoung Kim
- Center for Cancer and Aging, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, 91010, USA
| | - Marianne Razavi
- Department of Supportive Care Medicine, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Can-Lan Sun
- Center for Cancer and Aging, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, 91010, USA
| | - James C Root
- Neurocognitive Research Lab, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tim A Ahles
- Neurocognitive Research Lab, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - William Dale
- Center for Cancer and Aging, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, 91010, USA.,Department of Supportive Care Medicine, City of Hope National Medical Center, Duarte, CA, 91010, USA
| | - Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, 91010, USA. .,Center for Cancer and Aging, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, 91010, USA.
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50
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Lin CT, Ghosh S, Hinkley LB, Dale CL, Souza ACS, Sabes JH, Hess CP, Adams ME, Cheung SW, Nagarajan SS. Multi-tasking deep network for tinnitus classification and severity prediction from multimodal structural MR images. J Neural Eng 2023; 20. [PMID: 36595270 DOI: 10.1088/1741-2552/acab33] [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: 07/13/2022] [Accepted: 12/13/2022] [Indexed: 12/15/2022]
Abstract
Objective:Subjective tinnitus is an auditory phantom perceptual disorder without an objective biomarker. Fast and efficient diagnostic tools will advance clinical practice by detecting or confirming the condition, tracking change in severity, and monitoring treatment response. Motivated by evidence of subtle anatomical, morphological, or functional information in magnetic resonance images of the brain, we examine data-driven machine learning methods for joint tinnitus classification (tinnitus or no tinnitus) and tinnitus severity prediction.Approach:We propose a deep multi-task multimodal framework for tinnitus classification and severity prediction using structural MRI (sMRI) data. To leverage complementary information multimodal neuroimaging data, we integrate two modalities of three-dimensional sMRI-T1 weighted (T1w) and T2 weighted (T2w) images. To explore the key components in the MR images that drove task performance, we segment both T1w and T2w images into three different components-cerebrospinal fluid, grey matter and white matter, and evaluate performance of each segmented image.Main results:Results demonstrate that our multimodal framework capitalizes on the information across both modalities (T1w and T2w) for the joint task of tinnitus classification and severity prediction.Significance:Our model outperforms existing learning-based and conventional methods in terms of accuracy, sensitivity, specificity, and negative predictive value.
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Affiliation(s)
- Chieh-Te Lin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, CA 94143, United States of America
| | - Sanjay Ghosh
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, CA 94143, United States of America
| | - Leighton B Hinkley
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, CA 94143, United States of America
| | - Corby L Dale
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, CA 94143, United States of America
| | - Ana C S Souza
- Department of Telecommunication and Mechatronics Engineering, Federal University of Sao Joao del-Rei, Praca Frei Orlando, 170, Sao Joao del Rei 36307, MG, Brazil
| | - Jennifer H Sabes
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, 2380 Sutter St., San Francisco, CA 94115, United States of America
| | - Christopher P Hess
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, CA 94143, United States of America
| | - Meredith E Adams
- Department of Otolaryngology-Head and Neck Surgery, University of Minnesota, Phillips Wangensteen Building, 516 Delaware St., Minneapolis, MN 55455, United States of America
| | - Steven W Cheung
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, 2380 Sutter St., San Francisco, CA 94115, United States of America.,Surgical Services, Veterans Affairs, 4150 Clement St., San Francisco, CA 94121, United States of America
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, CA 94143, United States of America.,Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, 2380 Sutter St., San Francisco, CA 94115, United States of America.,Surgical Services, Veterans Affairs, 4150 Clement St., San Francisco, CA 94121, United States of America
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