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Viher PV, Seitz-Holland J, Schulz MS, Kensinger EA, Karmacharya S, Swisher T, Lyall AE, Makris N, Bouix S, Shenton ME, Kubicki M, Waldinger RJ. More organized white matter is associated with positivity bias in older adults. Brain Imaging Behav 2024; 18:555-565. [PMID: 38270836 PMCID: PMC11222031 DOI: 10.1007/s11682-024-00850-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/05/2024] [Indexed: 01/26/2024]
Abstract
On average, healthy older adults prefer positive over neutral or negative stimuli. This positivity bias is related to memory and attention processes and is linked to the function and structure of several interconnected brain areas. However, the relationship between the positivity bias and white matter integrity remains elusive. The present study examines how white matter organization relates to the degree of the positivity bias among older adults. We collected imaging and behavioral data from 25 individuals (12 females, 13 males, and a mean age of 77.32). Based on a functional memory task, we calculated a Pos-Neg score, reflecting the memory for positively valenced information over negative information, and a Pos-Neu score, reflecting the memory for positively valenced information over neutral information. Diffusion-weighted magnetic resonance imaging data were processed using Tract-Based Spatial Statistics. We performed two non-parametric permutation tests to correlate whole brain white matter integrity and the Pos-Neg and Pos-Neu scores while controlling for age, sex, and years of education. We observed a statistically significant positive association between the Pos-Neu score and white matter integrity in multiple brain connections, mostly frontal. The results did not remain significant when including verbal episodic memory as an additional covariate. Our study indicates that the positivity bias in memory in older adults is associated with more organized white matter in the connections of the frontal brain. While these frontal areas are critical for memory and executive processes and have been related to pathological aging, more extensive studies are needed to fully understand their role in the positivity bias and the potential for therapeutic interventions.
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Affiliation(s)
- Petra V Viher
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Marc S Schulz
- Department of Psychology, Bryn Mawr College, Bryn Mawr, PA, USA
| | | | - Sarina Karmacharya
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Talis Swisher
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Amanda E Lyall
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Sylvain Bouix
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Robert J Waldinger
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
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Hojjati SH, Babajani-Feremi A. Seeing beyond the symptoms: biomarkers and brain regions linked to cognitive decline in Alzheimer's disease. Front Aging Neurosci 2024; 16:1356656. [PMID: 38813532 PMCID: PMC11135344 DOI: 10.3389/fnagi.2024.1356656] [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: 12/15/2023] [Accepted: 04/08/2024] [Indexed: 05/31/2024] Open
Abstract
Objective Early Alzheimer's disease (AD) diagnosis remains challenging, necessitating specific biomarkers for timely detection. This study aimed to identify such biomarkers and explore their associations with cognitive decline. Methods A cohort of 1759 individuals across cognitive aging stages, including healthy controls (HC), mild cognitive impairment (MCI), and AD, was examined. Utilizing nine biomarkers from structural MRI (sMRI), diffusion tensor imaging (DTI), and positron emission tomography (PET), predictions were made for Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale Sum of Boxes (CDRSB), and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS). Biomarkers included four sMRI (e.g., average thickness [ATH]), four DTI (e.g., mean diffusivity [MD]), and one PET Amyloid-β (Aβ) measure. Ensemble regression tree (ERT) technique with bagging and random forest approaches were applied in four groups (HC/MCI, HC/AD, MCI/AD, and HC/MCI/AD). Results Aβ emerged as a robust predictor of cognitive scores, particularly in late-stage AD. Volumetric measures, notably ATH, consistently correlated with cognitive scores across early and late disease stages. Additionally, ADAS demonstrated links to various neuroimaging biomarkers in all subject groups, highlighting its efficacy in monitoring brain changes throughout disease progression. ERT identified key brain regions associated with cognitive scores, such as the right transverse temporal region for Aβ, left and right entorhinal cortex, left inferior temporal gyrus, and left middle temporal gyrus for ATH, and the left uncinate fasciculus for MD. Conclusion This study underscores the importance of an interdisciplinary approach in understanding AD mechanisms, offering potential contributions to early biomarker development.
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Affiliation(s)
- Seyed Hani Hojjati
- Department of Radiology, Weill Cornell Medicine, Brain Health Imaging Institute, New York, NY, United States
| | - Abbas Babajani-Feremi
- Department of Neurology, University of Florida, Gainesville, FL, United States
- Magnetoencephalography (MEG) Lab, The Norman Fixel Institute of Neurological Diseases, University of Florida Health, Gainesville, FL, United States
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3
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Zhong S, Lou J, Ma K, Shu Z, Chen L, Li C, Ye Q, Zhou L, Shen Y, Ye X, Zhang J. Disentangling in-vivo microstructural changes of white and gray matter in mild cognitive impairment and Alzheimer's disease: a systematic review and meta-analysis. Brain Imaging Behav 2023; 17:764-777. [PMID: 37752311 DOI: 10.1007/s11682-023-00805-2] [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] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
The microstructural characteristics of white and gray matter in mild cognitive impairment (MCI) and the early-stage of Alzheimer's disease (AD) remain unclear. This study aimed to systematically identify the microstructural damages of MCI/AD in studies using neurite orientation dispersion and density imaging (NODDI), and explore their correlations with cognitive performance. Multiple databases were searched for eligible studies. The 10 eligible NODDI studies were finally included. Patients with MCI/AD showed overall significant reductions in neurite density index (NDI) of specific white matter structures in bilateral hemispheres (left hemisphere: -0.40 [-0.53, -0.27], P < 0.001; right: -0.33 [-0.47, -0.19], P < 0.001), involving the bilateral superior longitudinal fasciculus (SLF), uncinate fasciculus (UF), the left posterior thalamic radiation (PTR), and the left cingulum. White matter regions exhibited significant increased orientation dispersion index (ODI) (left: 0.25 [0.02, 0.48], P < 0.05; right: 0.27 [0.07, 0.46], P < 0.05), including the left cingulum, the right UF, and the bilateral parahippocampal cingulum (PHC), and PTR. Additionally, the ODI of gray matter showed significant reduction in bilateral hippocampi (left: -0.97 [-1.42, -0.51], P < 0.001; right: -0.90 [-1.35, -0.45], P < 0.001). The cognitive performance in MCI/AD was significantly associated with NDI (r = 0.50, P < 0.001). Our findings highlight the microstructural changes in MCI/AD were characterized by decreased fiber orientation dispersion in the hippocampus, and decreased neurite density and increased fiber orientation dispersion in specific white matter tracts, including the cingulum, UF, and PTR. Moreover, the decreased NDI may indicate the declined cognitive level of MCI/AD patients.
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Affiliation(s)
- Shuchang Zhong
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jingjing Lou
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Ke Ma
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Lin Chen
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Chao Li
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Qing Ye
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Liang Zhou
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Ye Shen
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xiangming Ye
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jie Zhang
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
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Kumaresan K, Bengtsson S, Sami S, Clark A, Hummel T, Boardman J, High J, Sobhan R, Philpott C. A double-blinded randomised controlled trial of vitamin A drops to treat post-viral olfactory loss: study protocol for a proof-of-concept study for vitamin A nasal drops in post-viral olfactory loss (APOLLO). Pilot Feasibility Stud 2023; 9:174. [PMID: 37828592 PMCID: PMC10568902 DOI: 10.1186/s40814-023-01402-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Smell loss is a common problem with an estimated 5% of the population having no functioning sense of smell. Viral causes of smell loss are the second most common cause and the coronavirus (COVID-19) pandemic is estimated to have caused 20,000 more people this year to have a lasting loss of smell. Isolation, depression, anxiety, and risk of danger from hazards such as toxic gas and spoiled food are all negative impacts. It also affects appetite with weight loss/gain in two-thirds of those affected. Phantosmia or smell distortion can also occur making most foods seem unpalatable. Smell training has been tried with good results in the immediate post-viral phase. Evidence behind treatment with steroids has not shown to have proven effectiveness. With this, a key problem for patients and their clinicians is the lack of proven effective therapeutic treatment options. Based on previous studies, there is some evidence supporting the regenerative potential of retinoic acid, the metabolically active form of vitamin A in the regeneration of olfactory receptor neurons. It is based on this concept that we have chosen vitamin A as our study comparator. AIM To undertake a two-arm randomised trial of intranasally delivered vitamin A vs no intervention to determine proof of concept. METHODS/DESIGN The study will compare 10,000 IU once daily Vitamin A self-administered intranasal drops versus peanut oil drops (placebo) delivered over 12 weeks in patients with post-viral olfactory loss. Potentially eligible patients will be recruited from the Smell & Taste Clinic and via the charity Fifth Sense. They will be invited to attend the Brain Imaging Centre at the University of East Anglia on two occasions, 3 months apart. If they meet the eligibility criteria, they will be consented to enter the study and randomised to receive vitamin A drops or no treatment in a 2:1 ratio. MRI scanning will enable volumetric measurement of the OB and ROS; fMRI will then be conducted using an olfactometer to deliver pulsed odours-phenethylalcohol (rose-like) and hydrogen sulphide (rotten eggs). Participants will also perform a standard smell test at both visits as well as complete a quality-of-life questionnaire. Change in OB volume will be the primary outcome measure. DISCUSSION We expect the outputs of this study to enable a subsequent randomised controlled trial of Vitamin A versus placebo. With PPI input we will make the outputs publicly available using journals, conferences, and social media via Fifth Sense. We have already prepared a draft RCT proposal in partnership with the Norwich Clinical Trials Unit and plan to develop this further in light of the findings. TRIAL REGISTRATION ISRCTN registry 39523. Date of registration in the primary registry: 23rd February 2021.
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Affiliation(s)
- Kala Kumaresan
- Norwich Medical School, University of East Anglia, Norwich, UK
- Norfolk & Waveney ENT Service, James Paget University Hospital NHS Foundation Trust, Great Yarmouth, UK
| | - Sara Bengtsson
- School of Psychology, University of East Anglia, Norwich, UK
| | - Saber Sami
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Allan Clark
- Norwich Medical School, University of East Anglia, Norwich, UK
| | | | | | - Juliet High
- Norwich Clinical Trials Unit, University of East Anglia, Norwich, UK
| | - Rashed Sobhan
- Norwich Medical School, University of East Anglia, Norwich, UK
| | - Carl Philpott
- Norwich Medical School, University of East Anglia, Norwich, UK.
- Norfolk & Waveney ENT Service, James Paget University Hospital NHS Foundation Trust, Great Yarmouth, UK.
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Chen Y, Wang Y, Song Z, Fan Y, Gao T, Tang X. Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review. Ageing Res Rev 2023; 87:101911. [PMID: 36931328 DOI: 10.1016/j.arr.2023.101911] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 03/01/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023]
Abstract
Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.
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Affiliation(s)
- Yu Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yifei Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Zeyu Song
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tianxin Gao
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China; School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
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6
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Kamagata K, Andica C, Takabayashi K, Saito Y, Taoka T, Nozaki H, Kikuta J, Fujita S, Hagiwara A, Kamiya K, Wada A, Akashi T, Sano K, Nishizawa M, Hori M, Naganawa S, Aoki S. Association of MRI Indices of Glymphatic System With Amyloid Deposition and Cognition in Mild Cognitive Impairment and Alzheimer Disease. Neurology 2022; 99:e2648-e2660. [PMID: 36123122 PMCID: PMC9757870 DOI: 10.1212/wnl.0000000000201300] [Citation(s) in RCA: 80] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 08/12/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The glymphatic system is a whole-brain perivascular network, which promotes CSF/interstitial fluid exchange. Alterations to this system may play a pivotal role in amyloid β (Aβ) accumulation. However, its involvement in Alzheimer disease (AD) pathogenesis is not fully understood. Here, we investigated the changes in noninvasive MRI measurements related to the perivascular network in patients with mild cognitive impairment (MCI) and AD. Additionally, we explored the associations of MRI measures with neuropsychological score, PET standardized uptake value ratio (SUVR), and Aβ deposition. METHODS MRI measures, including perivascular space (PVS) volume fraction (PVSVF), fractional volume of free water in white matter (FW-WM), and index of diffusivity along the perivascular space (ALPS index) of patients with MCI, those with AD, and healthy controls from the Alzheimer's Disease Neuroimaging Initiative database were compared. MRI measures were also correlated with the levels of CSF biomarkers, PET SUVR, and cognitive score in the combined subcohort of patients with MCI and AD. Statistical analyses were performed with age, sex, years of education, and APOE status as confounding factors. RESULTS In total, 36 patients with AD, 44 patients with MCI, and 31 healthy controls were analyzed. Patients with AD had significantly higher total, WM, and basal ganglia PVSVF (Cohen d = 1.15-1.48; p < 0.001) and FW-WM (Cohen d = 0.73; p < 0.05) and a lower ALPS index (Cohen d = 0.63; p < 0.05) than healthy controls. Meanwhile, the MCI group only showed significantly higher total (Cohen d = 0.99; p < 0.05) and WM (Cohen d = 0.91; p < 0.05) PVSVF. Low ALPS index was associated with lower CSF Aβ42 (r s = 0.41, p fdr = 0.026), FDG-PET uptake (r s = 0.54, p fdr < 0.001), and worse multiple cognitive domain deficits. High FW-WM was also associated with lower CSF Aβ42 (r s = -0.47, p fdr = 0.021) and worse cognitive performances. DISCUSSION Our study indicates that changes in PVS-related MRI parameters occur in MCI and AD, possibly due to impairment of the glymphatic system. We also report the associations between MRI parameters and Aβ deposition, neuronal change, and cognitive impairment in AD.
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Affiliation(s)
- Koji Kamagata
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan.
| | - Christina Andica
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Kaito Takabayashi
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Yuya Saito
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Toshiaki Taoka
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Hayato Nozaki
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Junko Kikuta
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Shohei Fujita
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Akifumi Hagiwara
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Kouhei Kamiya
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Akihiko Wada
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Toshiaki Akashi
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Katsuhiro Sano
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Mitsuo Nishizawa
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Masaaki Hori
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Shinji Naganawa
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
| | - Shigeki Aoki
- From the Department of Radiology (Koji Kamagata, C.A., K.T., Y.S., H.N., J.K., S.F., A.H., A.W., T.A., K.S., M.N., S.A.), Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; Faculty of Health Data Science (C.A.), Juntendo University, Urayasu, Chiba, Department of Innovative Biomedical Visualization (iBMV) (T.T.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya; Department of Radiology (Kouhei Kamiya, M.H.), Toho University Omori Medical Center, Ota-ku, Tokyo; and Department of Radiology (S.N.), Nagoya University Graduate School of Medicine, Shouwa-ku, Nagoya, Japan
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7
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Kumar S, De Luca A, Leemans A, Saffari SE, Hartono S, Zailan FZ, Ng KP, Kandiah N. Topology of diffusion changes in corpus callosum in Alzheimer's disease: An exploratory case-control study. Front Neurol 2022; 13:1005406. [PMID: 36530616 PMCID: PMC9747939 DOI: 10.3389/fneur.2022.1005406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
AimThis study aims to assess the integrity of white matter in various segments of the corpus callosum in Alzheimer's disease (AD) by using metrics derived from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI) and white matter tract integrity model (WMTI) and compare these findings to healthy controls (HC).MethodsThe study was approved by the institutional ethics board. 12 AD patients and 12 HC formed the study population. All AD patients were recruited from a tertiary neurology memory clinic. A standardized battery of neuropsychological assessments was administered to the study participants by a trained rater. MRI scans were performed with a Philips Ingenia 3.0T scanner equipped with a 32-channel head coil. The protocol included a T1-weighted sequence, FLAIR and a dMRI acquisition. The dMRI scan included a total of 71 volumes, 8 at b = 0 s/mm2, 15 at b = 1,000 s/mm2 and 48 at b = 2,000 s/mm2. Diffusion data fit was performed using DKI REKINDLE and WMTI models.Results and discussionWe detected changes suggesting demyelination and axonal degeneration throughout the corpus callosum of patients with AD, most prominent in the mid-anterior and mid-posterior segments of CC. Axial kurtosis was the most significantly altered metric, being reduced in AD patients in almost all segments of corpus callosum. Reduced axial kurtosis in the CC segments correlated with poor cognition scores in AD patients in the visuospatial, language and attention domains.
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Affiliation(s)
- Sumeet Kumar
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Graduate Medical School, Singapore, Singapore
| | | | | | - Seyed Ehsan Saffari
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Septian Hartono
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Fatin Zahra Zailan
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Kok Pin Ng
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Nagaendran Kandiah
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- *Correspondence: Nagaendran Kandiah
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8
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Huang H, Ma X, Yue X, Kang S, Li Y, Rao Y, Feng Y, Wu J, Long W, Chen Y, Lyu W, Tan X, Qiu S. White Matter Characteristics of Damage Along Fiber Tracts in Patients with Type 2 Diabetes Mellitus. Clin Neuroradiol 2022; 33:327-341. [DOI: 10.1007/s00062-022-01213-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 08/06/2022] [Indexed: 11/03/2022]
Abstract
Abstract
Purpose
The white matter (WM) of the brain of type 2 diabetes mellitus (T2DM) patients is susceptible to neurodegenerative processes, but the specific types and positions of microstructural lesions along the fiber tracts remain unclear.
Methods
In this study 61 T2DM patients and 61 healthy controls were recruited and underwent diffusion spectrum imaging (DSI). The results were reconstructed with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). WM microstructural abnormalities were identified using tract-based spatial statistics (TBSS). Pointwise WM tract differences were detected through automatic fiber quantification (AFQ). The relationships between WM tract abnormalities and clinical characteristics were explored with partial correlation analysis.
Results
TBSS revealed widespread WM lesions in T2DM patients with decreased fractional anisotropy and axial diffusivity and an increased orientation dispersion index (ODI). The AFQ results showed microstructural abnormalities in T2DM patients in specific portions of the right superior longitudinal fasciculus (SLF), right arcuate fasciculus (ARC), left anterior thalamic radiation (ATR), and forceps major (FMA). In the right ARC of T2DM patients, an aberrant ODI was positively correlated with fasting insulin and insulin resistance, and an abnormal intracellular volume fraction was negatively correlated with fasting blood glucose. Additionally, negative associations were found between blood pressure and microstructural abnormalities in the right ARC, left ATR, and FMA in T2DM patients.
Conclusion
Using AFQ, together with DTI and NODDI, various kinds of microstructural alterations in the right SLF, right ARC, left ATR, and FMA can be accurately identified and may be associated with insulin and glucose status and blood pressure in T2DM patients.
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9
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Yang Y, Yang Y, Pan A, Xu Z, Wang L, Zhang Y, Nie K, Huang B. Identifying Depression in Parkinson's Disease by Using Combined Diffusion Tensor Imaging and Support Vector Machine. Front Neurol 2022; 13:878691. [PMID: 35795798 PMCID: PMC9251067 DOI: 10.3389/fneur.2022.878691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/20/2022] [Indexed: 12/02/2022] Open
Abstract
Objective To investigate white matter microstructural alterations in Parkinson's disease (PD) patients with depression using the whole-brain diffusion tensor imaging (DTI) method and to explore the DTI–based machine learning model in identifying depressed PD (dPD). Methods The DTI data were collected from 37 patients with dPD and 35 patients with non-depressed PD (ndPD), and 25 healthy control (HC) subjects were collected as the reference. An atlas-based analysis method was used to compare fractional anisotropy (FA) and mean diffusivity (MD) among the three groups. A support vector machine (SVM) was trained to examine the probability of discriminating between dPD and ndPD. Results As compared with ndPD, dPD group exhibited significantly decreased FA in the bilateral corticospinal tract, right cingulum (cingulate gyrus), left cingulum hippocampus, bilateral inferior longitudinal fasciculus, and bilateral superior longitudinal fasciculus, and increased MD in the right cingulum (cingulate gyrus) and left superior longitudinal fasciculus-temporal part. For discriminating between dPD and ndPD, the SVM model with DTI features exhibited an accuracy of 0.70 in the training set [area under the receiver operating characteristic curve (ROC) was 0.78] and an accuracy of 0.73 in the test set (area under the ROC was 0.71). Conclusion Depression in PD is associated with white matter microstructural alterations. The SVM machine learning model based on DTI parameters could be valuable for the individualized diagnosis of dPD.
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Affiliation(s)
- Yunjun Yang
- Department of Radiology, The First People's Hospital of Foshan, Foshan, China
| | - Yuelong Yang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Aizhen Pan
- Department of Radiology, The First People's Hospital of Foshan, Foshan, China
| | - Zhifeng Xu
- Department of Radiology, The First People's Hospital of Foshan, Foshan, China
| | - Lijuan Wang
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yuhu Zhang
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Kun Nie
- Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Biao Huang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- *Correspondence: Biao Huang
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10
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Jitsuishi T, Yamaguchi A. Searching for optimal machine learning model to classify mild cognitive impairment (MCI) subtypes using multimodal MRI data. Sci Rep 2022; 12:4284. [PMID: 35277565 PMCID: PMC8917197 DOI: 10.1038/s41598-022-08231-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 03/03/2022] [Indexed: 12/13/2022] Open
Abstract
The intervention at the stage of mild cognitive impairment (MCI) is promising for preventing Alzheimer's disease (AD). This study aims to search for the optimal machine learning (ML) model to classify early and late MCI (EMCI and LMCI) subtypes using multimodal MRI data. First, the tract-based spatial statistics (TBSS) analyses showed LMCI-related white matter changes in the Corpus Callosum. The ROI-based tractography addressed the connected cortical areas by affected callosal fibers. We then prepared two feature subsets for ML by measuring resting-state functional connectivity (TBSS-RSFC method) and graph theory metrics (TBSS-Graph method) in these cortical areas, respectively. We also prepared feature subsets of diffusion parameters in the regions of LMCI-related white matter alterations detected by TBSS analyses. Using these feature subsets, we trained and tested multiple ML models for EMCI/LMCI classification with cross-validation. Our results showed the ensemble ML model (AdaBoost) with feature subset of diffusion parameters achieved better performance of mean accuracy 70%. The useful brain regions for classification were those, including frontal, parietal lobe, Corpus Callosum, cingulate regions, insula, and thalamus regions. Our findings indicated the optimal ML model using diffusion parameters might be effective to distinguish LMCI from EMCI subjects at the prodromal stage of AD.
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Affiliation(s)
- Tatsuya Jitsuishi
- Department of Functional Anatomy, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Atsushi Yamaguchi
- Department of Functional Anatomy, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan.
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11
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He F, Zhang Y, Wu X, Li Y, Zhao J, Fang P, Fan L, Li C, Liu T, Wang J. Early Microstructure Changes of White Matter Fiber Bundles in Patients with Amnestic Mild Cognitive Impairment Predicts Progression of Mild Cognitive Impairment to Alzheimer's Disease. J Alzheimers Dis 2021; 84:179-192. [PMID: 34487042 DOI: 10.3233/jad-210495] [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: 11/15/2022]
Abstract
BACKGROUND Amnestic mild cognitive impairment (aMCI) is the transitional stage between normal aging and Alzheimer's disease (AD). Some aMCI patients will progress into AD eventually, whereas others will not. If the trajectory of aMCI can be predicted, it would enable early diagnosis and early therapy of AD. OBJECTIVE To explore the development trajectory of aMCI patients, we used diffusion tensor imaging to analyze the white matter microstructure changes of patients with different trajectories of aMCI. METHODS We included three groups of subjects:1) aMCI patients who convert to AD (MCI-P); 2) aMCI patients who remain in MCI status (MCI-S); 3) normal controls (NC). We analyzed the fractional anisotropy and mean diffusion rate of brain regions, and we adopted logistic binomial regression model to predicate the development trajectory of aMCI. RESULTS The fraction anisotropy value is significantly reduced, the mean diffusivity value is significantly increased in the two aMCI patient groups, and the MCI-P patients presented greater changes. Significant changes are mainly located in the cingulum, fornix, hippocampus, and uncinate fasciculus. These changed brain regions significantly correlated with the patient's Mini-Mental State Examination scores. CONCLUSION The study predicted the disease trajectory of different types of aMCI patients based on the characteristic values of the above-mentioned brain regions. The prediction accuracy rate can reach 90.2%, and the microstructure characteristics of the right cingulate band and the right hippocampus may have potential clinical application value to predict the disease trajectory.
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Affiliation(s)
- Fangmei He
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China.,National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong, P.R. China.,The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, P.R. China
| | - Yuchen Zhang
- Department of Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China
| | - Xiaofeng Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China.,National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong, P.R. China.,The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, P.R. China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China.,National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong, P.R. China.,The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, P.R. China
| | - Jie Zhao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China.,National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong, P.R. China.,The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, P.R. China
| | - Peng Fang
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, Shaanxi, P.R. China
| | - Liming Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China.,National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong, P.R. China.,The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, P.R. China
| | - Chenxi Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China.,National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong, P.R. China.,The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, P.R. China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China.,National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong, P.R. China.,The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, P.R. China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P.R. China.,National Engineering Research Center for Healthcare Devices, Guangzhou, Guangdong, P.R. China.,The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, P.R. China
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12
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Du J, Koch FC, Xia A, Jiang J, Crawford JD, Lam BCP, Thalamuthu A, Lee T, Kochan N, Fawns-Ritchie C, Brodaty H, Xu Q, Sachdev PS, Wen W. Difference in distribution functions: A new diffusion weighted imaging metric for estimating white matter integrity. Neuroimage 2021; 240:118381. [PMID: 34252528 DOI: 10.1016/j.neuroimage.2021.118381] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/27/2021] [Accepted: 07/08/2021] [Indexed: 11/19/2022] Open
Abstract
Diffusion weighted imaging (DWI) is a widely recognized neuroimaging technique to evaluate the microstructure of brain white matter. The objective of this study is to establish an improved automated DWI marker for estimating white matter integrity and investigating ageing related cognitive decline. The concept of Wasserstein distance was introduced to help establish a new measure: difference in distribution functions (DDF), which captures the difference of reshaping one's mean diffusivity (MD) distribution to a reference MD distribution. This new DWI measure was developed using a population-based cohort (n=19,369) from the UK Biobank. Validation was conducted using the data drawn from two independent cohorts: the Sydney Memory and Ageing Study, a community-dwelling sample (n=402), and the Renji Cerebral Small Vessel Disease Cohort Study (RCCS), which consisted of cerebral small vessel disease (CSVD) patients (n=171) and cognitively normal controls (NC) (n=43). DDF was associated with age across all three samples and better explained the variance of changes than other established DWI measures, such as fractional anisotropy, mean diffusivity and peak width of skeletonized mean diffusivity (PSMD). Significant correlations between DDF and cognition were found in the UK Biobank cohort and the MAS cohort. Binary logistic analysis and receiver operator characteristic curve analysis of RCCS demonstrated that DDF had higher sensitivity in distinguishing CSVD patients from NC than the other DWI measures. To demonstrate the flexibility of DDF, we calculated regional DDF which also showed significant correlation with age and cognition. DDF can be used as a marker for monitoring the white matter microstructural changes and ageing related cognitive decline in the elderly.
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Affiliation(s)
- Jing Du
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia.
| | - Forrest C Koch
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Aihua Xia
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - John D Crawford
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Ben C P Lam
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Teresa Lee
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia; Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, New South Wales 2031, Australia
| | - Nicole Kochan
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Chloe Fawns-Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Henry Brodaty
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia; Dementia Centre for Research Collaboration, School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Qun Xu
- Department of Health Manage Centre, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Perminder S Sachdev
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia; Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, New South Wales 2031, Australia
| | - Wei Wen
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia; Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, New South Wales 2031, Australia
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13
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Bunk S, Zuidema S, Koch K, Lautenbacher S, De Deyn PP, Kunz M. Pain processing in older adults with dementia-related cognitive impairment is associated with frontal neurodegeneration. Neurobiol Aging 2021; 106:139-152. [PMID: 34274699 DOI: 10.1016/j.neurobiolaging.2021.06.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/23/2021] [Accepted: 06/12/2021] [Indexed: 12/14/2022]
Abstract
Experimental pain research has shown that pain processing seems to be heightened in dementia. It is unclear which neuropathological changes underlie these alterations. This study examined whether differences in pressure pain sensitivity and endogenous pain inhibition (conditioned pain modulation (CPM)) between individuals with a dementia-related cognitive impairment (N=23) and healthy controls (N=35) are linked to dementia-related neurodegeneration. Pain was assessed via self-report ratings and by analyzing the facial expression of pain using the Facial Action Coding System. We found that cognitively impaired individuals show decreased CPM inhibition as assessed by facial responses compared to healthy controls, which was mediated by decreased gray matter volume in the medial orbitofrontal and anterior cingulate cortex in the patient group. This study confirms previous findings of intensified pain processing in dementia when pain is assessed using non-verbal responses. Our findings suggest that a loss of pain inhibitory functioning caused by structural changes in prefrontal areas might be one of the underlying mechanisms responsible for amplified pain responses in individuals with a dementia-related cognitive impairment.
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Affiliation(s)
- Steffie Bunk
- Department of General Practice and Elderly Care Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
| | - Sytse Zuidema
- Department of General Practice and Elderly Care Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Kathrin Koch
- Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Martinsried, Germany
| | | | - Peter P De Deyn
- Alzheimer Center Groningen, Department Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Miriam Kunz
- Department of General Practice and Elderly Care Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; Department of Medical Psychology and Sociology, University of Augsburg, Augsburg, Germany
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14
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Iriondo A, García-Sebastian M, Arrospide A, Arriba M, Aurtenetxe S, Barandiaran M, Clerigue M, Ecay-Torres M, Estanga A, Gabilondo A, Izagirre A, Saldias J, Tainta M, Villanua J, Blennow K, Zetterberg H, Mar J, Abad-García B, Dias IHK, Goñi FM, Martínez-Lage P. Cerebrospinal Fluid 7-Ketocholesterol Level is Associated with Amyloid-β42 and White Matter Microstructure in Cognitively Healthy Adults. J Alzheimers Dis 2021; 76:643-656. [PMID: 32538843 DOI: 10.3233/jad-200105] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Abnormal cholesterol metabolism changes the neuronal membrane and may promote amyloidogenesis. Oxysterols in cerebrospinal fluid (CSF) are related to Alzheimer's disease (AD) biomarkers in mild cognitive impairment and dementia. Cholesterol turnover is important for axonal and white matter (WM) microstructure maintenance. OBJECTIVE We aim to demonstrate that the association of oxysterols, AD biomarkers, and WM microstructure occurs early in asymptomatic individuals. METHODS We studied the association of inter-individual variability of CSF 24-hydroxycholesterol (24-OHC), 27-hydroxycholesterol (27-OHC), 7-ketocholesterol (7-KC), 7β-hydroxycholesterol (7β-OHC), amyloid-β42 (Aβ42), total-tau (t-tau), phosphorylated-tau (p-tau), neurofilament (NfL), and WM microstructure using diffusion tensor imaging, generalized linear models and moderation/mediation analyses in 153 healthy adults. RESULTS Higher 7-KC levels were related to lower Aβ42, indicative of greater AD pathology (p = 0.041) . Higher 7-KC levels were related to lower fractional anisotropy (FA) and higher mean (MD), axial (AxD), and radial (RD) diffusivity. 7-KC modulated the association between AxD and NfL in the corpus callosum splenium (B = 39.39, p = 0.017), genu (B = 68.64, p = 0.000), and fornix (B = 10.97, p = 0.000). Lower Aβ42 levels were associated to lower FA and higher MD, AxD, and RD in the fornix, corpus callosum, inferior longitudinal fasciculus, and hippocampus. The association between AxD and Aβ42 was moderated by 7K-C (p = 0.048). CONCLUSION This study adds clinical evidence to support the role of 7K-C on axonal integrity and the involvement of cholesterol metabolism in the Aβ42 generation process.
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Affiliation(s)
- Ane Iriondo
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
| | - Maite García-Sebastian
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
| | - Arantzazu Arrospide
- Gipuzkoa Primary Care - Integrated Health Care Organizations Research Unit, Alto Deba Integrated Health Care Organisation, Nafarroa Hiribidea, Arrasate, Gipuzkoa, Spain.,Biodonostia Health Research Institute, Paseo Doctor Begiristain, Donostia, San Sebastian, Spain
| | - Maria Arriba
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
| | - Sara Aurtenetxe
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
| | - Myriam Barandiaran
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
| | - Montserrat Clerigue
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
| | - Mirian Ecay-Torres
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
| | - Ainara Estanga
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
| | - Alazne Gabilondo
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
| | - Andrea Izagirre
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain.,Department of Nursing II, Faculty of Medicine and Nursing, University of the Basque Country (UPV/EHU), Spain
| | - Jon Saldias
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
| | - Mikel Tainta
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
| | - Jorge Villanua
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom.,UK Dementia Research Institute at UCL, London, United Kingdom
| | - Javier Mar
- Gipuzkoa Primary Care - Integrated Health Care Organizations Research Unit, Alto Deba Integrated Health Care Organisation, Nafarroa Hiribidea, Arrasate, Gipuzkoa, Spain.,Biodonostia Health Research Institute, Paseo Doctor Begiristain, Donostia, San Sebastian, Spain
| | - Beatriz Abad-García
- Central Analysis Service, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Bizkaia, Spain
| | - Irundika H K Dias
- Aston Medical Research Institute, Aston Medical School, Aston University, Birmingham, UK
| | - Felix M Goñi
- Departamento de Bioquímica, University of the Basque Country (UPV/EHU) and Instituto Biofisika (CSIC, UPV/EHU), Leioa, Bizkaia, Spain
| | - Pablo Martínez-Lage
- Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, Donostia-San Sebastian, Gipuzkoa, Spain
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15
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Lam J, Lee J, Liu CY, Lozano AM, Lee DJ. Deep Brain Stimulation for Alzheimer's Disease: Tackling Circuit Dysfunction. Neuromodulation 2020; 24:171-186. [PMID: 33377280 DOI: 10.1111/ner.13305] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/07/2020] [Accepted: 10/12/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVES Treatments for Alzheimer's disease are urgently needed given its enormous human and economic costs and disappointing results of clinical trials targeting the primary amyloid and tau pathology. On the other hand, deep brain stimulation (DBS) has demonstrated success in other neurological and psychiatric disorders leading to great interest in DBS as a treatment for Alzheimer's disease. MATERIALS AND METHODS We review the literature on 1) circuit dysfunction in Alzheimer's disease and 2) DBS for Alzheimer's disease. Human and animal studies are reviewed individually. RESULTS There is accumulating evidence of neural circuit dysfunction at the structural, functional, electrophysiological, and neurotransmitter level. Recent evidence from humans and animals indicate that DBS has the potential to restore circuit dysfunction in Alzheimer's disease, similarly to other movement and psychiatric disorders, and may even slow or reverse the underlying disease pathophysiology. CONCLUSIONS DBS is an intriguing potential treatment for Alzheimer's disease, targeting circuit dysfunction as a novel therapeutic target. However, further exploration of the basic disease pathology and underlying mechanisms of DBS is necessary to better understand how circuit dysfunction can be restored. Additionally, robust clinical data in the form of ongoing phase III clinical trials are needed to validate the efficacy of DBS as a viable treatment.
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Affiliation(s)
- Jordan Lam
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, 90033, USA.,Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, 90033, USA
| | - Justin Lee
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, 90033, USA.,Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, 90033, USA
| | - Charles Y Liu
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, 90033, USA.,Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, 90033, USA
| | - Andres M Lozano
- Division of Neurological Surgery, Department of Surgery, Toronto Western Hospital, University of Toronto, Toronto, ON, M5T 2S8, Canada
| | - Darrin J Lee
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, 90033, USA.,Department of Neurological Surgery, Keck School of Medicine of USC, Los Angeles, CA, 90033, USA
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