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Asp I, Cawley-Bennett ATJ, Frascino JC, Golshan S, Bondi MW, Smith CN. News event memory in amnestic and non-amnestic MCI, heritable risk for dementia, and subjective memory complaints. Neuropsychologia 2024; 199:108887. [PMID: 38621578 DOI: 10.1016/j.neuropsychologia.2024.108887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 04/09/2024] [Accepted: 04/09/2024] [Indexed: 04/17/2024]
Abstract
Robust and sensitive clinical measures are needed for more accurate and earlier detection of Alzheimer's disease (AD), for staging preclinical AD, and for gauging the efficacy of treatments. Mild impairment on episodic memory tests is thought to indicate a cognitive risk of developing AD and mild cognitive impairment (MCI), considered to be a transitional stage between normal aging and AD. Novel tests of semantic memory, such as memory for news events, are also impaired early on but have received little clinical attention even though they may provide a novel way to assess cognitive risk for AD. We examined memory for news events in older adults with normal cognition (NC, N = 34), amnestic MCI (aMCI, N = 27), or non-aMCI (N = 10) using the Retrograde Memory News Events Test (RM-NET). We asked if news event memory was sensitive to 1) aMCI and also non-aMCI, which has rarely been examined, 2) genetic risk for dementia (positive family history of any type of dementia, presence of an APOE-4 allele, or polygenic risk for AD), and 3) subjective memory functioning judgments about the past. We found that both MCI subgroups exhibited impaired RM-NET Lifespan accuracy scores together with temporally-limited retrograde amnesia. For the aMCI group amnesia extended back 45 years prior to testing, but not beyond that time frame. The extent of retrograde amnesia could not be reliably estimated in the small non-aMCI group. The effect sizes of having MCI on the RM-NET were medium for the non-aMCI group and large for the aMCI group, whereas the effect sizes of participant characteristics on RM-NET accuracy scores were small. For the combined MCI group (N = 37), news event memory was significantly related to positive family history of dementia but was not related to the more specific genetic markers of AD risk. For the NC group, news event memory was not related to any measure of genetic risk. Objective measures of past memory from the RM-NET were not related to subjective memory judgements about the present or the recent past in either group. By contrast, when individuals subjectively compared their present versus past memory abilities, there was a significant association between this judgment and objective measures of the past from the RM-NET (direct association for the NC group and inverse for the MCI group). The RM-NET holds significant promise for early identification of those with cognitive and genetic risk factors for AD and non-AD dementias.
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Affiliation(s)
- Isabel Asp
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA
| | | | - Jennifer C Frascino
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, CA, USA
| | - Shahrokh Golshan
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, CA, USA
| | - Mark W Bondi
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, CA, USA
| | - Christine N Smith
- San Diego Veterans Affairs Medical Center, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, CA, USA; Center for the Neurobiology of Learning and Memory, University of California Irvine, CA, USA.
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Zhang B, Xu M, Wu Q, Ye S, Zhang Y, Li Z. Definition and analysis of gray matter atrophy subtypes in mild cognitive impairment based on data-driven methods. Front Aging Neurosci 2024; 16:1328301. [PMID: 38894849 PMCID: PMC11183285 DOI: 10.3389/fnagi.2024.1328301] [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: 10/26/2023] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Introduction Mild cognitive impairment (MCI) is an important stage in Alzheimer's disease (AD) research, focusing on early pathogenic factors and mechanisms. Examining MCI patient subtypes and identifying their cognitive and neuropathological patterns as the disease progresses can enhance our understanding of the heterogeneous disease progression in the early stages of AD. However, few studies have thoroughly analyzed the subtypes of MCI, such as the cortical atrophy, and disease development characteristics of each subtype. Methods In this study, 396 individuals with MCI, 228 cognitive normal (CN) participants, and 192 AD patients were selected from ADNI database, and a semi-supervised mixture expert algorithm (MOE) with multiple classification boundaries was constructed to define AD subtypes. Moreover, the subtypes of MCI were obtained by using the multivariate linear boundary mapping of support vector machine (SVM). Then, the gray matter atrophy regions and severity of each MCI subtype were analyzed and the features of each subtype in demography, pathology, cognition, and disease progression were explored combining the longitudinal data collected for 2 years and analyzed important factors that cause conversion of MCI were analyzed. Results Three MCI subtypes were defined by MOE algorithm, and the three subtypes exhibited their own features in cortical atrophy. Nearly one-third of patients diagnosed with MCI have almost no significant difference in cerebral cortex from the normal aging population, and their conversion rate to AD are the lowest. The subtype characterized by severe atrophy in temporal lobe and frontal lobe have a faster decline rate in many cognitive manifestations than the subtype featured with diffuse atrophy in the whole cortex. APOE ε4 is an important factor that cause the conversion of MCI to AD. Conclusion It was proved through the data-driven method that MCI collected by ADNI baseline presented different subtype features. The characteristics and disease development trajectories among subtypes can help to improve the prediction of clinical progress in the future and also provide necessary clues to solve the classification accuracy of MCI.
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Affiliation(s)
- Baiwen Zhang
- Institute of Information and Artificial Intelligence Technology, Beijing Academy of Science and Technology, Beijing, China
| | - Meng Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Qing Wu
- Institute of Information and Artificial Intelligence Technology, Beijing Academy of Science and Technology, Beijing, China
| | - Sicheng Ye
- International College, Beijing University of Posts and Telecommunications, Beijing, China
| | - Ying Zhang
- Institute of Information and Artificial Intelligence Technology, Beijing Academy of Science and Technology, Beijing, China
| | - Zufei Li
- Department of Otorhinolaryngology, Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
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Ly MT, Adler J, Ton Loy AF, Edmonds EC, Bondi MW, Delano-Wood L. Comparing neuropsychological, typical, and ADNI criteria for the diagnosis of mild cognitive impairment in Vietnam-era veterans. J Int Neuropsychol Soc 2024; 30:439-447. [PMID: 38263745 DOI: 10.1017/s135561772301144x] [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] [Indexed: 01/25/2024]
Abstract
OBJECTIVE Neuropsychological criteria for mild cognitive impairment (MCI) more accurately predict progression to Alzheimer's disease (AD) and are more strongly associated with AD biomarkers and neuroimaging profiles than ADNI criteria. However, research to date has been conducted in relatively healthy samples with few comorbidities. Given that history of traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD) are risk factors for AD and common in Veterans, we compared neuropsychological, typical (Petersen/Winblad), and ADNI criteria for MCI in Vietnam-era Veterans with histories of TBI or PTSD. METHOD 267 Veterans (mean age = 69.8) from the DOD-ADNI study were evaluated for MCI using neuropsychological, typical, and ADNI criteria. Linear regressions adjusting for age and education assessed associations between MCI status and AD biomarker levels (cerebrospinal fluid [CSF] p-tau181, t-tau, and Aβ42) by diagnostic criteria. Logistic regressions adjusting for age and education assessed the effects of TBI severity and PTSD symptom severity simultaneously on MCI classification by each criteria. RESULTS Agreement between criteria was poor. Neuropsychological criteria identified more Veterans with MCI than typical or ADNI criteria, and were associated with higher CSF p-tau181 and t-tau. Typical and ADNI criteria were not associated with CSF biomarkers. PTSD symptom severity predicted MCI diagnosis by neuropsychological and ADNI criteria. History of moderate/severe TBI predicted MCI by typical and ADNI criteria. CONCLUSIONS MCI diagnosis using sensitive neuropsychological criteria is more strongly associated with AD biomarkers than conventional diagnostic methods. MCI diagnostics in Veterans would benefit from incorporation of comprehensive neuropsychological methods and consideration of the impact of PTSD.
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Affiliation(s)
- Monica T Ly
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego Health, La Jolla, CA, USA
| | - Jennifer Adler
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego Health, La Jolla, CA, USA
| | - Adan F Ton Loy
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Emily C Edmonds
- Banner Alzheimer's Institute, Tucson, AZ, USA
- Departments of Neurology and Psychology, University of Arizona, Tucson, AZ, USA
| | - Mark W Bondi
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego Health, La Jolla, CA, USA
| | - Lisa Delano-Wood
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego Health, La Jolla, CA, USA
- Center for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
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Edmonds EC, Thomas KR, Rapcsak SZ, Lindemer SL, Delano‐Wood L, Salmon DP, Bondi MW. Data-driven classification of cognitively normal and mild cognitive impairment subtypes predicts progression in the NACC dataset. Alzheimers Dement 2024; 20:3442-3454. [PMID: 38574399 PMCID: PMC11095435 DOI: 10.1002/alz.13793] [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: 06/14/2023] [Revised: 10/20/2023] [Accepted: 02/23/2024] [Indexed: 04/06/2024]
Abstract
INTRODUCTION Data-driven neuropsychological methods can identify mild cognitive impairment (MCI) subtypes with stronger associations to dementia risk factors than conventional diagnostic methods. METHODS Cluster analysis used neuropsychological data from participants without dementia (mean age = 71.6 years) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (n = 26,255) and the "normal cognition" subsample (n = 16,005). Survival analyses examined MCI or dementia progression. RESULTS Five clusters were identified: "Optimal" cognitively normal (oCN; 13.2%), "Typical" CN (tCN; 28.0%), Amnestic MCI (aMCI; 25.3%), Mixed MCI-Mild (mMCI-Mild; 20.4%), and Mixed MCI-Severe (mMCI-Severe; 13.0%). Progression to dementia differed across clusters (oCN < tCN < aMCI < mMCI-Mild < mMCI-Severe). Cluster analysis identified more MCI cases than consensus diagnosis. In the "normal cognition" subsample, five clusters emerged: High-All Domains (High-All; 16.7%), Low-Attention/Working Memory (Low-WM; 22.1%), Low-Memory (36.3%), Amnestic MCI (16.7%), and Non-amnestic MCI (naMCI; 8.3%), with differing progression rates (High-All < Low-WM = Low-Memory < aMCI < naMCI). DISCUSSION Our data-driven methods outperformed consensus diagnosis by providing more precise information about progression risk and revealing heterogeneity in cognition and progression risk within the NACC "normal cognition" group.
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Affiliation(s)
- Emily C. Edmonds
- Banner Alzheimer's InstituteTucsonArizonaUSA
- Departments of Neurology and PsychologyUniversity of ArizonaTucsonArizonaUSA
| | - Kelsey R. Thomas
- Research Service, Veterans Affairs San Diego Healthcare SystemSan DiegoCaliforniaUSA
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Steven Z. Rapcsak
- Banner Alzheimer's InstituteTucsonArizonaUSA
- Departments of Neurology and PsychologyUniversity of ArizonaTucsonArizonaUSA
- Department of Speech, Language, & Hearing SciencesUniversity of ArizonaTucsonArizonaUSA
| | | | - Lisa Delano‐Wood
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Psychology Service, Veterans Affairs San Diego Healthcare SystemSan DiegoCaliforniaUSA
| | - David P. Salmon
- Department of NeurosciencesUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Mark W. Bondi
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Psychology Service, Veterans Affairs San Diego Healthcare SystemSan DiegoCaliforniaUSA
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Kokubun K, Nemoto K, Yamakawa Y. Continuous inhalation of essential oil increases gray matter volume. Brain Res Bull 2024; 208:110896. [PMID: 38331299 DOI: 10.1016/j.brainresbull.2024.110896] [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: 10/31/2023] [Revised: 12/22/2023] [Accepted: 02/05/2024] [Indexed: 02/10/2024]
Abstract
Research into the health benefits of scents is on the rise. However, little is known about the effects of continuous inhalation, such as wearing scents on clothing, on brain structure. Therefore, in this study, an intervention study was conducted on a total of 50 healthy female people, 28 in the intervention group and 22 in the control group, asking them to wear a designated rose scent on their clothes for a month. The effect of continuous inhalation of essential oil on the gray matter of the brain was measured by calculating changes in brain images of participants taken before and after the intervention using Magnetic Resonance Imaging (MRI). The results showed that the intervention increased the gray matter volume (GMV) of the whole brain and posterior cingulate cortex (PCC) subregion. On the other hand, the GMV of the amygdala and orbitofrontal cortex (OFC) did not change. This study is the first to show that continuous scent inhalation changes brain structure.
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Affiliation(s)
- Keisuke Kokubun
- Open Innovation Institute, Kyoto University, Kyoto, Japan; Graduate School of Management, Kyoto University, Kyoto, Japan.
| | - Kiyotaka Nemoto
- Department of Psychiatry, Institute of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Yoshinori Yamakawa
- Open Innovation Institute, Kyoto University, Kyoto, Japan; Graduate School of Management, Kyoto University, Kyoto, Japan; Institute of Innovative Research, Tokyo Institute of Technology, Meguro, Tokyo, Japan; ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan), Chiyoda, Tokyo, Japan; Office for Academic and Industrial Innovation, Kobe University, Kobe, Japan; Brain Impact, Kyoto, Japan
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Thomas KR, Clark AL, Weigand AJ, Edwards L, Durazo AA, Membreno R, Luu B, Rantins P, Ly MT, Rotblatt LJ, Bangen KJ, Jak AJ. Cognition and Amyloid-β in Older Veterans: Characterization and Longitudinal Outcomes of Data-Derived Phenotypes. J Alzheimers Dis 2024; 99:417-427. [PMID: 38669550 DOI: 10.3233/jad-240077] [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] [Indexed: 04/28/2024]
Abstract
Background Within older Veterans, multiple factors may contribute to cognitive difficulties. Beyond Alzheimer's disease (AD), psychiatric (e.g., PTSD) and health comorbidities (e.g., TBI) may also impact cognition. Objective This study aimed to derive subgroups based on objective cognition, subjective cognitive decline (SCD), and amyloid burden, and then compare subgroups on clinical characteristics, biomarkers, and longitudinal change in functioning and global cognition. Methods Cluster analysis of neuropsychological measures, SCD, and amyloid PET was conducted on 228 predominately male Vietnam-Era Veterans from the Department of Defense-Alzheimer's Disease Neuroimaging Initiative. Cluster-derived subgroups were compared on baseline characteristics as well as 1-year changes in everyday functioning and global cognition. Results The cluster analysis identified 3 groups. Group 1 (n = 128) had average-to-above average cognition with low amyloid burden. Group 2 (n = 72) had the lowest memory and language, highest SCD, and average amyloid burden; they also had the most severe PTSD, pain, and worst sleep quality. Group 3 (n = 28) had the lowest attention/executive functioning, slightly low memory and language, elevated amyloid and the worst AD biomarkers, and the fastest rate of everyday functioning and cognitive decline. CONCLUSIONS Psychiatric and health factors likely contributed to Group 2's low memory and language performance. Group 3 was most consistent with biological AD, yet attention/executive function was the lowest score. The complexity of older Veterans' co-morbid conditions may interact with AD pathology to show attention/executive dysfunction (rather than memory) as a prominent early symptom. These results could have important implications for the implementation of AD-modifying drugs in older Veterans.
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Affiliation(s)
- Kelsey R Thomas
- VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Alexandra L Clark
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - Alexandra J Weigand
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Lauren Edwards
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Alin Alshaheri Durazo
- VA San Diego Healthcare System, San Diego, CA, USA
- San Diego State University, San Diego, CA, USA
| | - Rachel Membreno
- VA San Diego Healthcare System, San Diego, CA, USA
- San Diego State University, San Diego, CA, USA
| | - Britney Luu
- VA San Diego Healthcare System, San Diego, CA, USA
- San Diego State University, San Diego, CA, USA
| | - Peter Rantins
- VA San Diego Healthcare System, San Diego, CA, USA
- San Diego State University, San Diego, CA, USA
| | - Monica T Ly
- VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Lindsay J Rotblatt
- VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Katherine J Bangen
- VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Amy J Jak
- VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
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Fu Z, Zhao M, Li Y, He Y, Wang X, Zhou Z, Han Y, Li S. Heterogeneity in subjective cognitive decline in the Sino Longitudinal Study on Cognitive Decline(SILCODE): Empirically derived subtypes, structural and functional verification. CNS Neurosci Ther 2023; 29:4032-4042. [PMID: 37475187 PMCID: PMC10651943 DOI: 10.1111/cns.14327] [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: 01/15/2023] [Revised: 03/01/2023] [Accepted: 06/17/2023] [Indexed: 07/22/2023] Open
Abstract
AIMS We evaluated whether Subjective Cognitive Decline (SCD) subtypes could be empirically derived within the Sino Longitudinal Study on Cognitive Decline (SILCODE) SCD cohort and examined associated neuroimaging markers, biomarkers, and clinical outcomes. METHODS A cluster analysis was performed on eight neuropsychological test scores from 124 SCD SILCODE participants and 57 normal control (NC) subjects. Structural and functional neuroimaging indices were used to evaluate the SCD subgroups. RESULTS Four subtypes emerged: (1) dysexecutive/mixed SCD (n = 23), (2) neuropsychiatric SCD (n = 24), (3) amnestic SCD (n = 22), and (4) cluster-derived normal (n = 55) who exhibited normal performance in neuropsychological tests. Compared with the NC group, each subgroup showed distinct patterns in gray matter (GM) volume and the amplitude of low-frequency fluctuations (ALFF). Lower fractional anisotropy (FA) values were only found in the neuropsychiatric SCD group relative to NC. CONCLUSION The identification of empirically derived SCD subtypes demonstrates the presence of heterogeneity in SCD neuropsychological profiles. The cluster-derived normal group may represent the majority of SCD individuals who do not show progressive cognitive decline; the dysexecutive/mixed SCD and amnestic SCD might represent high-risk groups with progressing cognitive decline; and finally, the neuropsychiatric SCD may represent a new topic in SCD research.
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Affiliation(s)
- Zhenrong Fu
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU)Ministry of EducationWuhanChina
- School of Psychology, Key Laboratory of Human Development and Mental Health of Hubei ProvinceCentral China Normal UniversityWuhanChina
| | - Mingyan Zhao
- Department of PsychologyTangshan Gongren HospitalTangshanChina
| | - Yuxia Li
- Department of NeurologyTangshan Central HospitalTangshanChina
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Yirong He
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Xuetong Wang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Zongkui Zhou
- Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU)Ministry of EducationWuhanChina
- School of Psychology, Key Laboratory of Human Development and Mental Health of Hubei ProvinceCentral China Normal UniversityWuhanChina
| | - Ying Han
- Department of NeurologyXuanwu Hospital of Capital Medical UniversityBeijingChina
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical EngineeringHainan UniversityHaikouChina
- Center of Alzheimer's DiseaseBeijing Institute for Brain DisordersBeijingChina
- National Clinical Research Center for Geriatric DisordersBeijingChina
- Institute of Biomedical EngineeringShenzhen Bay LaboratoryShenzhenChina
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
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Lefort-Besnard J, Naveau M, Delcroix N, Decker LM, Cignetti F. Grey matter volume and CSF biomarkers predict neuropsychological subtypes of MCI. Neurobiol Aging 2023; 131:196-208. [PMID: 37689017 DOI: 10.1016/j.neurobiolaging.2023.07.006] [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] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 09/11/2023]
Abstract
There is increasing evidence of different subtypes of individuals with mild cognitive impairment (MCI). An important line of research is whether neuropsychologically-defined subtypes have distinct patterns of neurodegeneration and cerebrospinal fluid (CSF) biomarker composition. In our study, we demonstrated that MCI participants of the ADNI database (N = 640) can be discriminated into 3 coherent neuropsychological subgroups. Our clustering approach revealed amnestic MCI, mixed MCI, and cluster-derived normal subgroups. Furthermore, classification modeling revealed that specific predictive features can be used to differentiate amnestic and mixed MCI from cognitively normal (CN) controls: CSF Aβ142 concentration for the former and CSF Aβ1-42 concentration, tau concentration as well as grey matter atrophy (especially in the temporal and occipital lobes) for the latter. In contrast, participants from the cluster-derived normal subgroup exhibited an identical profile to CN controls in terms of cognitive performance, brain structure, and CSF biomarker levels. Our comprehensive data analytics strategy provides further evidence that multimodal neuropsychological subtyping is both clinically and neurobiologically meaningful.
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Affiliation(s)
| | - Mikael Naveau
- Normandie Univ, UNICAEN, CNRS, CEA, INSERM, GIP Cyceron, Caen, France
| | - Nicolas Delcroix
- Normandie Univ, UNICAEN, CNRS, CEA, INSERM, GIP Cyceron, Caen, France
| | - Leslie Marion Decker
- Normandie Univ, UNICAEN, INSERM, COMETE, Caen, France; Normandie Univ, UNICAEN, CIREVE, Caen, France.
| | - Fabien Cignetti
- Univ. Grenoble Alpes, CNRS, VetAgro Sup, Grenoble INP, TIMC, Grenoble, France.
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Reyes A, Schneider ALC, Kucharska-Newton AM, Gottesman RF, Johnson EL, McDonald CR. Cognitive phenotypes in late-onset epilepsy: results from the atherosclerosis risk in communities study. Front Neurol 2023; 14:1230368. [PMID: 37745655 PMCID: PMC10513940 DOI: 10.3389/fneur.2023.1230368] [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: 05/28/2023] [Accepted: 08/02/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction Cognitive phenotyping is a widely used approach to characterize the heterogeneity of deficits in patients with a range of neurological disorders but has only recently been applied to patients with epilepsy. In this study, we identify cognitive phenotypes in older adults with late-onset epilepsy (LOE) and examine their demographic, clinical, and vascular profiles. Further, we examine whether specific phenotypes pose an increased risk for progressive cognitive decline. Methods Participants were part of the Atherosclerosis Risk in Communities Study (ARIC), a prospective longitudinal community-based cohort study of 15,792 individuals initially enrolled in 1987-1989. LOE was identified from linked Centers for Medicare and Medicaid Services claims data. Ninety-one participants with LOE completed comprehensive testing either prior to or after seizure onset as part of a larger cohort in the ARIC Neurocognitive Study in either 2011-2013 or 2016-2017 (follow-up mean = 4.9 years). Cognitive phenotypes in individuals with LOE were derived by calculating test-level impairments for each participant (i.e., ≤1 SD below cognitively normal participants on measures of language, memory, and executive function/processing speed); and then assigning participants to phenotypes if they were impaired on at least two tests within a domain. The total number of impaired domains was used to determine the cognitive phenotypes (i.e., Minimal/No Impairment, Single Domain, or Multidomain). Results At our baseline (Visit 5), 36.3% met criteria for Minimal/No Impairment, 35% for Single Domain Impairment (with executive functioning/ processing speed impaired in 53.6%), and 28.7% for Multidomain Impairment. The Minimal/No Impairment group had higher education and occupational complexity. There were no differences in clinical or vascular risk factors across phenotypes. Of those participants with longitudinal data (Visit 6; n = 24), 62.5% declined (i.e., progressed to a more impaired phenotype) and 37.5% remained stable. Those who remained stable were more highly educated compared to those that declined. Discussion Our results demonstrate the presence of identifiable cognitive phenotypes in older adults with LOE. These results also highlight the high prevalence of cognitive impairments across domains, with deficits in executive function/processing speed the most common isolated impairment. We also demonstrate that higher education was associated with a Minimal/No Impairment phenotype and lower risk for cognitive decline over time.
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Affiliation(s)
- Anny Reyes
- Department of Radiation Medicine & Applied Sciences, University of California, San Diego, La Jolla, CA, United States
| | - Andrea L. C. Schneider
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Anna M. Kucharska-Newton
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Rebecca F. Gottesman
- National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD, United States
| | - Emily L. Johnson
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Carrie R. McDonald
- Department of Radiation Medicine & Applied Sciences, University of California, San Diego, La Jolla, CA, United States
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States
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McDonald CR, Busch RM, Reyes A, Arrotta K, Barr W, Block C, Hessen E, Loring DW, Drane DL, Hamberger MJ, Wilson SJ, Baxendale S, Hermann BP. Development and application of the International Classification of Cognitive Disorders in Epilepsy (IC-CoDE): Initial results from a multi-center study of adults with temporal lobe epilepsy. Neuropsychology 2023; 37:301-314. [PMID: 35084879 PMCID: PMC9325925 DOI: 10.1037/neu0000792] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
[Correction Notice: An Erratum for this article was reported online in Neuropsychology on Sep 15 2022 (see record 2023-01997-001). In the original article, there was an error in Figure 2. In the box at the top left of the figure, the fourth explanation incorrectly stated, "Generalized impairment = At least one test < -1.0 or -1.5SD in three or more domains." The correct wording is "Generalized impairment = At least two tests < -1.0 or -1.5SD in each of three or more domains." All versions of this article have been corrected.] Objective: To describe the development and application of a consensus-based, empirically driven approach to cognitive diagnostics in epilepsy research-The International Classification of Cognitive Disorders in Epilepsy (IC-CoDE) and to assess the ability of the IC-CoDE to produce definable and stable cognitive phenotypes in a large, multi-center temporal lobe epilepsy (TLE) patient sample. METHOD Neuropsychological data were available for a diverse cohort of 2,485 patients with TLE across seven epilepsy centers. Patterns of impairment were determined based on commonly used tests within five cognitive domains (language, memory, executive functioning, attention/processing speed, and visuospatial ability) using two impairment thresholds (≤1.0 and ≤1.5 standard deviations below the normative mean). Cognitive phenotypes were derived across samples using the IC-CoDE and compared to distributions of phenotypes reported in existing studies. RESULTS Impairment rates were highest on tests of language, followed by memory, executive functioning, attention/processing speed, and visuospatial ability. Application of the IC-CoDE using varying operational definitions of impairment (≤ 1.0 and ≤ 1.5 SD) produced cognitive phenotypes with the following distribution: cognitively intact (30%-50%), single-domain (26%-29%), bi-domain (14%-19%), and generalized (10%-22%) impairment. Application of the ≤ 1.5 cutoff produced a distribution of phenotypes that was consistent across cohorts and approximated the distribution produced using data-driven approaches in prior studies. CONCLUSIONS The IC-CoDE is the first iteration of a classification system for harmonizing cognitive diagnostics in epilepsy research that can be applied across neuropsychological tests and TLE cohorts. This proof-of-principle study in TLE offers a promising path for enhancing research collaborations globally and accelerating scientific discoveries in epilepsy. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Xue W, He W, Yan M, Zhao H, Pi J. Exploring Shared Biomarkers of Myocardial Infarction and Alzheimer's Disease via Single-Cell/Nucleus Sequencing and Bioinformatics Analysis. J Alzheimers Dis 2023; 96:705-723. [PMID: 37840493 PMCID: PMC10657707 DOI: 10.3233/jad-230559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND Patients are at increased risk of dementia, including Alzheimer's disease (AD), after myocardial infarction (MI), but the biological link between MI and AD is unclear. OBJECTIVE To understand the association between the pathogenesis of MI and AD and identify common biomarkers of both diseases. METHODS Using public databases, we identified common biomarkers of MI and AD. Least absolute shrinkage and selection operator (LASSO) regression and protein-protein interaction (PPI) network were performed to further screen hub biomarkers. Functional enrichment analyses were performed on the hub biomarkers. Single-cell/nucleus analysis was utilized to further analyze the hub biomarkers at the cellular level in carotid atherosclerosis and AD datasets. Motif enrichment analysis was used to screen key transcription factors. RESULTS 26 common differentially expressed genes were screened between MI and AD. Function enrichment analyses showed that these differentially expressed genes were mainly associated with inflammatory pathways. A key gene, Regulator of G-protein Signaling 1 (RGS1), was obtained by LASSO regression and PPI network. RGS1 was confirmed to mainly express in macrophages and microglia according to single-cell/nucleus analysis. The difference in expression of RGS1 in macrophages and microglia between disease groups and controls was statistically significant (p < 0.0001). The expression of RGS1 in the disease groups was upregulated with the differentiation of macrophages and microglia. RelA was a key transcription factor regulating RGS1. CONCLUSION Macrophages and microglia are involved in the inflammatory response of MI and AD. RGS1 may be a key biomarker in this process.
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Affiliation(s)
- Weiqi Xue
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Weifeng He
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Mengyuan Yan
- Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Huanyi Zhao
- First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Jianbin Pi
- Department of Cardiovascular Disease, The Eighth Clinical Medical College of Guangzhou University of Chinese Medicine, Foshan, Guangdong, China
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Zhao K, Zheng Q, Dyrba M, Rittman T, Li A, Che T, Chen P, Sun Y, Kang X, Li Q, Liu B, Liu Y, Li S. Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2104538. [PMID: 35098696 PMCID: PMC9036024 DOI: 10.1002/advs.202104538] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/30/2021] [Indexed: 05/28/2023]
Abstract
Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality patterns associated with the clinical manifestations of each subtype. An individual-level R2SN is constructed for N = 605 normal controls (NCs), N = 766 MCI patients, and N = 283 Alzheimer's disease (AD) patients. MCI patients' R2SN profiles are clustered into two subtypes using nonnegative matrix factorization. The patterns of brain alterations, gene expression, and the risk of cognitive decline in each subtype are evaluated. MCI patients are clustered into "similar to the pattern of NCs" (N-CI, N = 252) and "similar to the pattern of AD" (A-CI, N = 514) subgroups. Significant differences are observed between the subtypes with respect to the following: 1) clinical measures; 2) multimodal neuroimaging; 3) the proportion of progression to dementia (61.54% for A-CI and 21.77% for N-CI) within three years; 4) enriched genes for potassium-ion transport and synaptic transmission. Stratification into the two subtypes provides new insight for risk assessment and precise early intervention for MCI patients.
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Affiliation(s)
- Kun Zhao
- Beijing Advanced Innovation Centre for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100191China
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing100876China
| | - Qiang Zheng
- School of Computer and Control EngineeringYantai UniversityYantai264005China
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE)Rostock18147Germany
| | - Timothy Rittman
- Department of Clinical NeurosciencesUniversity of CambridgeCambridge Biomedical CampusCambridgeCB2 0SZUK
| | - Ang Li
- State Key Laboratory of Brain and Cognitive Science, Institute of BiophysicsChinese Academy of SciencesBeijing100101China
| | - Tongtong Che
- Beijing Advanced Innovation Centre for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100191China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China
| | - Yuqing Sun
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China
| | - Qiongling Li
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijing100875China
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijing100875China
| | - Yong Liu
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing100876China
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Shuyu Li
- Beijing Advanced Innovation Centre for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100191China
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijing100875China
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Arce Rentería M, Manly JJ, Vonk JM, Mejia Arango S, Michaels Obregon A, Samper-Ternent R, Wong R, Barral S, Tosto G. Midlife Vascular Factors and Prevalence of Mild Cognitive Impairment in Late-Life in Mexico. J Int Neuropsychol Soc 2022; 28:351-361. [PMID: 34376262 PMCID: PMC8831650 DOI: 10.1017/s1355617721000539] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE To estimate the prevalence of mild cognitive impairment (MCI) and its subtypes and investigate the impact of midlife cardiovascular risk factors on late-life MCI among the aging Mexican population. METHOD Analyses included a sample of non-demented adults over the age of 55 living in both urban and rural areas of Mexico (N = 1807). MCI diagnosis was assigned based on a comprehensive cognitive assessment assessing the domains of memory, executive functioning, language, and visuospatial ability. The normative sample was selected by means of the robust norms approach. Cognitive impairment was defined by a 1.5-SD cut-off per cognitive domain using normative corrections for age, years of education, and sex. Risk factors included age, education, sex, rurality, depression, insurance status, workforce status, hypertension, diabetes, stroke, and heart disease. RESULTS The prevalence of amnestic MCI was 5.9%. Other MCI subtypes ranged from 4.2% to 7.7%. MCI with and without memory impairment was associated with older age (OR = 1.01 [1.01, 1.05]; OR = 1.03 [1.01, 1.04], respectively) and residing in rural areas (OR = 1.49 [1.08, 2.06]; OR = 1.35 [1.03, 1.77], respectively). Depression (OR = 1.07 [1.02, 1.12]), diabetes (OR = 1.37 [1.03, 1.82]), and years of education (OR = 0.94 [0.91, 0.97]) were associated with MCI without memory impairment. Midlife CVD increased the odds of MCI in late-life (OR = 1.76 [1.19, 2.59], which was driven by both midlife hypertension and diabetes (OR = 1.70 [1.18, 2.44]; OR = 1.88 [1.19, 2.97], respectively). CONCLUSIONS Older age, depression, low education, rurality, and midlife hypertension and diabetes were associated with higher risk of late-life MCI among older adults in Mexico. Our findings suggest that the causes of cognitive impairment are multifactorial and vary by MCI subtype.
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Affiliation(s)
- Miguel Arce Rentería
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University College of Physicians and Surgeons, New York City, NY, USA
| | - Jennifer J. Manly
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University College of Physicians and Surgeons, New York City, NY, USA
| | - Jet M.J. Vonk
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University College of Physicians and Surgeons, New York City, NY, USA
- Julius Center for Health Sciences and Primary Care, Department of Epidemiology, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Silvia Mejia Arango
- Department of Population Studies, El Colegio de la Frontera Norte, Tijuana, Baja California, Mexico
| | | | - Rafael Samper-Ternent
- Sealy Center on Aging, University of Texas Medical Branch at Galveston, Galveston, Texas, USA
- Department of Internal Medicine, University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Rebeca Wong
- Sealy Center on Aging, University of Texas Medical Branch at Galveston, Galveston, Texas, USA
| | - Sandra Barral
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University College of Physicians and Surgeons, New York City, NY, USA
| | - Giuseppe Tosto
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University College of Physicians and Surgeons, New York City, NY, USA
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Abstract
OBJECTIVE The ability to recognize others' emotions is a central aspect of socioemotional functioning. Emotion recognition impairments are well documented in Alzheimer's disease and other dementias, but it is less understood whether they are also present in mild cognitive impairment (MCI). Results on facial emotion recognition are mixed, and crucially, it remains unclear whether the potential impairments are specific to faces or extend across sensory modalities. METHOD In the current study, 32 MCI patients and 33 cognitively intact controls completed a comprehensive neuropsychological assessment and two forced-choice emotion recognition tasks, including visual and auditory stimuli. The emotion recognition tasks required participants to categorize emotions in facial expressions and in nonverbal vocalizations (e.g., laughter, crying) expressing neutrality, anger, disgust, fear, happiness, pleasure, surprise, or sadness. RESULTS MCI patients performed worse than controls for both facial expressions and vocalizations. The effect was large, similar across tasks and individual emotions, and it was not explained by sensory losses or affective symptomatology. Emotion recognition impairments were more pronounced among patients with lower global cognitive performance, but they did not correlate with the ability to perform activities of daily living. CONCLUSIONS These findings indicate that MCI is associated with emotion recognition difficulties and that such difficulties extend beyond vision, plausibly reflecting a failure at supramodal levels of emotional processing. This highlights the importance of considering emotion recognition abilities as part of standard neuropsychological testing in MCI, and as a target of interventions aimed at improving social cognition in these patients.
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Need for an Update for the Guideline for the Management of Mild Cognitive Impairment. Dement Neurocogn Disord 2022; 21:107-116. [DOI: 10.12779/dnd.2022.21.4.107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 11/09/2022] Open
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Fountain-Zaragoza S, Braun SE, Horner MD, Benitez A. Comparison of conventional and actuarial neuropsychological criteria for mild cognitive impairment in a clinical setting. J Clin Exp Neuropsychol 2021; 43:753-765. [PMID: 34962226 DOI: 10.1080/13803395.2021.2007857] [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: 10/19/2022]
Abstract
INTRODUCTION Evidence-based practice in neuropsychology involves the use of validated tests, cutoff scores, and interpretive algorithms to identify clinically significant cognitive deficits. Recently, actuarial neuropsychological criteria (ANP) for identifying mild cognitive impairment were developed, demonstrating improved criterion validity and temporal stability compared to conventional criteria (CNP). However, benefits of the ANP criteria have not been investigated in non-research, clinical settings with varied etiologies, severities, and comorbidities. This study compared the utility of CNP and ANP criteria using data from a memory disorders clinic. METHOD Data from 500 non-demented older adults evaluated in a Veterans Affairs Medical Center memory disorders clinic were retrospectively analyzed. We applied CNP and ANP criteria to the Repeatable Battery for the Assessment of Neuropsychological Status, compared outcomes to consensus clinical diagnoses, and conducted cluster analyses of scores from each group. RESULTS The majority (72%) of patients met both the CNP and ANP criteria and both approaches were susceptible to confounding factors such as invalid test data and mood disturbance. However, the CNP approach mislabeled impairment in more patients with non-cognitive disorders and intact cognition. Comparatively, the ANP approach misdiagnosed patients with depression at a third of the rate and those with no diagnosis at nearly half the rate of CNP. Cluster analyses revealed groups with: 1) minimal impairment, 2) amnestic impairment, and 3) multi-domain impairment. The ANP approach yielded subgroups with more distinct neuropsychological profiles. CONCLUSIONS We replicated previous findings that the CNP approach is over-inclusive, particularly for those determined to have no cognitive disorder by a consensus team. The ANP approach yielded fewer false positives and better diagnostic specificity than the CNP. Despite clear benefits of the ANP vs. CNP, there was substantial overlap in their performance in this heterogeneous sample. These findings highlight the critical role of clinical interpretation when wielding these empirically-derived tools.
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Affiliation(s)
- Stephanie Fountain-Zaragoza
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA.,Ralph H. Johnson Department of Veterans Affairs Medical Center, Mental Health Service, Charleston, SC, USA
| | - Sarah Ellen Braun
- Department of Neurology, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Michael David Horner
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA.,Ralph H. Johnson Department of Veterans Affairs Medical Center, Mental Health Service, Charleston, SC, USA
| | - Andreana Benitez
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
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Devlin KN, Brennan L, Saad L, Giovannetti T, Hamilton RH, Wolk DA, Xie SX, Mechanic-Hamilton D. Diagnosing Mild Cognitive Impairment Among Racially Diverse Older Adults: Comparison of Consensus, Actuarial, and Statistical Methods. J Alzheimers Dis 2021; 85:627-644. [PMID: 34864658 DOI: 10.3233/jad-210455] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Actuarial and statistical methods have been proposed as alternatives to conventional methods of diagnosing mild cognitive impairment (MCI), with the aim of enhancing diagnostic and prognostic validity, but have not been compared in racially diverse samples. OBJECTIVE We compared the agreement of consensus, actuarial, and statistical MCI diagnostic methods, and their relationship to race and prognostic indicators among diverse older adults. METHODS Participants (N = 354; M age = 71; 68% White, 29% Black) were diagnosed with MCI or normal cognition (NC) according to clinical consensus, actuarial neuropsychological criteria (Jak/Bondi), and latent class analysis (LCA). We examined associations with race/ethnicity, longitudinal cognitive and functional change, and incident dementia. RESULTS MCI rates by consensus, actuarial criteria, and LCA were 44%, 53%, and 41%, respectively. LCA identified three MCI subtypes (memory; memory/language; memory/executive) and two NC classes (low normal; high normal). Diagnostic agreement was substantial, but agreement of the actuarial method with consensus and LCA was weaker than the agreement between consensus and LCA. Among cases classified as MCI by actuarial criteria only, Black participants were over-represented, and outcomes were generally similar to those of NC participants. Consensus diagnoses best predicted longitudinal outcomes overall, whereas actuarial diagnoses best predicted longitudinal functional change among Black participants. CONCLUSION Consensus diagnoses optimize specificity in predicting dementia, but among Black older adults, actuarial diagnoses may be more sensitive to early signs of decline. Results highlight the need for cross-cultural validity in MCI diagnosis and should be explored in community- and population-based samples.
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Affiliation(s)
- Kathryn N Devlin
- Department of Psychology, Drexel University, Philadelphia, PA, USA
| | - Laura Brennan
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Laura Saad
- Department of Psychology, Rutgers University, New Brunswick, NJ, USA
| | | | - Roy H Hamilton
- Alzheimer's Disease Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Alzheimer's Disease Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sharon X Xie
- Alzheimer's Disease Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dawn Mechanic-Hamilton
- Alzheimer's Disease Research Center, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
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Grey-matter brain healthcare quotient and cognitive function: A large cohort study of an MRI brain screening system in Japan. Cortex 2021; 145:97-104. [PMID: 34695701 DOI: 10.1016/j.cortex.2021.09.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 07/24/2021] [Accepted: 09/14/2021] [Indexed: 11/22/2022]
Abstract
There is sometimes a divergence between brain atrophy and impairments in cognitive function. The present study aimed to assess the relationship between cognitive function and the grey-matter brain healthcare quotient (GM-BHQ), which represents brain volume as a deviation value. In addition, we aimed to investigate lifestyle factors that can help maintain cognitive function despite brain atrophy. A total of 1,757 adults included in a Japanese MRI brain screening cohort underwent MRI. We classified the participants into two age groups: under 65 years old (young adult/middle age group) and over 64 years old (elder group). The GM-BHQ was more strongly correlated with cognitive function in the young adult/middle age group than in the elder group (p < .01). Regression analysis revealed that years of education was associated with the maintenance of cognitive function despite brain atrophy (p < .05). In conclusion, our findings suggest that the relationship between brain volume and cognitive function becomes more obscure with age.
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Edmonds EC, Smirnov DS, Thomas KR, Graves LV, Bangen KJ, Delano-Wood L, Galasko DR, Salmon DP, Bondi MW. Data-Driven vs Consensus Diagnosis of MCI: Enhanced Sensitivity for Detection of Clinical, Biomarker, and Neuropathologic Outcomes. Neurology 2021; 97:e1288-e1299. [PMID: 34376506 PMCID: PMC8480404 DOI: 10.1212/wnl.0000000000012600] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 07/01/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Given prior work demonstrating that mild cognitive impairment (MCI) can be empirically differentiated into meaningful cognitive subtypes, we applied actuarial methods to comprehensive neuropsychological data from the University of California San Diego Alzheimer's Disease Research Center (ADRC) in order to identify cognitive subgroups within ADRC participants without dementia and to examine cognitive, biomarker, and neuropathologic trajectories. METHODS Cluster analysis was performed on baseline neuropsychological data (n = 738; mean age 71.8). Survival analysis examined progression to dementia (mean follow-up 5.9 years). CSF Alzheimer disease (AD) biomarker status and neuropathologic findings at follow-up were examined in a subset with available data. RESULTS Five clusters were identified: optimal cognitively normal (CN; n = 130) with above-average cognition, typical CN (n = 204) with average cognition, nonamnestic MCI (naMCI; n = 104), amnestic MCI (aMCI; n = 216), and mixed MCI (mMCI; n = 84). Progression to dementia differed across MCI subtypes (mMCI > aMCI > naMCI), with the mMCI group demonstrating the highest rate of CSF biomarker positivity and AD pathology at autopsy. Actuarial methods classified 29.5% more of the sample with MCI and outperformed consensus diagnoses in capturing those who had abnormal biomarkers, progressed to dementia, or had AD pathology at autopsy. DISCUSSION We identified subtypes of MCI and CN with differing cognitive profiles, clinical outcomes, CSF AD biomarkers, and neuropathologic findings over more than 10 years of follow-up. Results demonstrate that actuarial methods produce reliable cognitive phenotypes, with data from a subset suggesting unique biological and neuropathologic signatures. Findings indicate that data-driven algorithms enhance diagnostic sensitivity relative to consensus diagnosis for identifying older adults at risk for cognitive decline.
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Affiliation(s)
- Emily C Edmonds
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla.
| | - Denis S Smirnov
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla
| | - Kelsey R Thomas
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla
| | - Lisa V Graves
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla
| | - Katherine J Bangen
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla
| | - Lisa Delano-Wood
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla
| | - Douglas R Galasko
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla
| | - David P Salmon
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla
| | - Mark W Bondi
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., D.R.G., M.W.B.); and Departments of Psychiatry (E.C.E., K.R.T., L.V.G., K.J.B., L.D.-W., M.W.B.) and Neurosciences (D.S.S., D.R.G., D.P.S.), University of California San Diego, La Jolla
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Zammit AR, Yang J, Buchman AS, Leurgans SE, Muniz-Terrera G, Lipton RB, Hall CB, Boyle P, Bennett DA. Latent Cognitive Class at Enrollment Predicts Future Cognitive Trajectories of Decline in a Community Sample of Older Adults. J Alzheimers Dis 2021; 83:641-652. [PMID: 34334404 DOI: 10.3233/jad-210484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND Methods that can identify subgroups with different trajectories of cognitive decline are crucial for isolating the biologic mechanisms which underlie these groupings. OBJECTIVE This study grouped older adults based on their baseline cognitive profiles using a latent variable approach and tested the hypothesis that these groups would differ in their subsequent trajectories of cognitive change. METHODS In this study we applied time-varying effects models (TVEMs) to examine the longitudinal trajectories of cognitive decline across different subgroups of older adults in the Rush Memory and Aging Project. RESULTS A total of 1,662 individuals (mean age = 79.6 years, SD = 7.4, 75.4%female) participated in the study; these were categorized into five previously identified classes of older adults differing in their baseline cognitive profiles: Superior Cognition (n = 328, 19.7%), Average Cognition (n = 767, 46.1%), Mixed-Domains Impairment (n = 71, 4.3%), Memory-Specific Impairment (n = 274, 16.5%), and Frontal Impairment (n = 222, 13.4%). Differences in the trajectories of cognition for these five classes persisted during 8 years of follow-up. Compared with the Average Cognition class, The Mixed-Domains and Memory-Specific Impairment classes showed steeper rates of decline, while other classes showed moderate declines. CONCLUSION Baseline cognitive classes of older adults derived through the use of latent variable methods were associated with distinct longitudinal trajectories of cognitive decline that did not converge during an average of 8 years of follow-up.
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Affiliation(s)
- Andrea R Zammit
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Jingyun Yang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Aron S Buchman
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Sue E Leurgans
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | | | - Richard B Lipton
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Charles B Hall
- Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Patricia Boyle
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.,Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
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21
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Plini ERG, O’Hanlon E, Boyle R, Sibilia F, Rikhye G, Kenney J, Whelan R, Melnychuk MC, Robertson IH, Dockree PM. Examining the Role of the Noradrenergic Locus Coeruleus for Predicting Attention and Brain Maintenance in Healthy Old Age and Disease: An MRI Structural Study for the Alzheimer's Disease Neuroimaging Initiative. Cells 2021; 10:1829. [PMID: 34359997 PMCID: PMC8306442 DOI: 10.3390/cells10071829] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/08/2021] [Accepted: 07/08/2021] [Indexed: 12/18/2022] Open
Abstract
The noradrenergic theory of Cognitive Reserve (Robertson, 2013-2014) postulates that the upregulation of the locus coeruleus-noradrenergic system (LC-NA) originating in the brainstem might facilitate cortical networks involved in attention, and protracted activation of this system throughout the lifespan may enhance cognitive stimulation contributing to reserve. To test the above-mentioned theory, a study was conducted on a sample of 686 participants (395 controls, 156 mild cognitive impairment, 135 Alzheimer's disease) investigating the relationship between LC volume, attentional performance and a biological index of brain maintenance (BrainPAD-an objective measure, which compares an individual's structural brain health, reflected by their voxel-wise grey matter density, to the state typically expected at that individual's age). Further analyses were carried out on reserve indices including education and occupational attainment. Volumetric variation across groups was also explored along with gender differences. Control analyses on the serotoninergic (5-HT), dopaminergic (DA) and cholinergic (Ach) systems were contrasted with the noradrenergic (NA) hypothesis. The antithetic relationships were also tested across the neuromodulatory subcortical systems. Results supported by Bayesian modelling showed that LC volume disproportionately predicted higher attentional performance as well as biological brain maintenance across the three groups. These findings lend support to the role of the noradrenergic system as a key mediator underpinning the neuropsychology of reserve, and they suggest that early prevention strategies focused on the noradrenergic system (e.g., cognitive-attentive training, physical exercise, pharmacological and dietary interventions) may yield important clinical benefits to mitigate cognitive impairment with age and disease.
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Affiliation(s)
- Emanuele R. G. Plini
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
| | - Erik O’Hanlon
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
- Department of Psychiatry, Royal College of Surgeons in Ireland, Hospital Rd, Beaumont, 9QRH+4F Dublin, Ireland
- Department of Psychiatry, School of Medicine Dublin, Trinity College Dublin, 152-160 Pearse St, 8QV3+99 Dublin, Ireland;
| | - Rory Boyle
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
| | - Francesca Sibilia
- Department of Psychiatry, School of Medicine Dublin, Trinity College Dublin, 152-160 Pearse St, 8QV3+99 Dublin, Ireland;
| | - Gaia Rikhye
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
| | - Joanne Kenney
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
| | - Robert Whelan
- Department of Psychology, Global Brain Health Institute, Trinity College Dublin, Lloyd Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland;
| | - Michael C. Melnychuk
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
| | - Ian H. Robertson
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
- Department of Psychology, Global Brain Health Institute, Trinity College Dublin, Lloyd Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland;
| | - Paul M. Dockree
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
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22
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Ge X, Zhang D, Qiao Y, Zhang J, Xu J, Zheng Y. Association of Tau Pathology With Clinical Symptoms in the Subfields of Hippocampal Formation. Front Aging Neurosci 2021; 13:672077. [PMID: 34335226 PMCID: PMC8317580 DOI: 10.3389/fnagi.2021.672077] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 05/20/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: To delineate the relationship between clinical symptoms and tauopathy of the hippocampal subfields under different amyloid statuses. Methods: One hundred and forty-three subjects were obtained from the ADNI project, including 87 individuals with normal cognition, 46 with mild cognitive impairment, and 10 with Alzheimer's disease (AD). All subjects underwent the tau PET, amyloid PET, T1W, and high-resolution T2W scans. Clinical symptoms were assessed by the Neuropsychiatric Inventory (NPI) total score and Alzheimer's Disease Assessment Scale cognition 13 (ADAS-cog-13) total score, comprising memory and executive function scores. The hippocampal subfields including Cornu Ammonis (CA1-3), subiculum (Sub), and dentate gyrus (DG), as well as the adjacent para-hippocampus (PHC) and entorhinal cortex (ERC), were segmented automatically using the Automatic Segmentation of Hippocampal Subfields (ASHS) software. The relationship between tauopathy/volume of the hippocampal subfields and assessment scores was calculated using partial correlation analysis under different amyloid status, by controlling age, gender, education, apolipoprotein E (APOE) allele ɛ4 carrier status, and, time interval between the acquisition time of tau PET and amyloid PET scans. Results: Compared with amyloid negative (A-) group, individuals from amyloid positive (A+) group are more impaired based on the Mini-mental State Examination (MMSE; p = 3.82e-05), memory (p = 6.30e-04), executive function (p = 0.0016), and ADAS-cog-13 scores (p = 5.11e-04). Significant decrease of volume (CA1, DG, and Sub) and increase of tau deposition (CA1, Sub, ERC, and PHC) of the hippocampal subfields of both hemispheres were observed for the A+ group compared to the A- group. Tauopathy of ERC is significantly associated with memory score for the A- group, and the associated regions spread into Sub and PHC for the A+ group. The relationship between the impairment of behavior or executive function and tauopathy of the hippocampal subfield was discovered within the A+ group. Leftward asymmetry was observed with the association between assessment scores and tauopathy of the hippocampal subfield, which is more prominent for the NPI score for the A+ group. Conclusion: The associations of tauopathy/volume of the hippocampal subfields with clinical symptoms provide additional insight into the understanding of local changes of the human HF during the AD continuum and can be used as a reference for future studies.
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Affiliation(s)
- Xinting Ge
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
| | - Dan Zhang
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Yuchuan Qiao
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Jiong Zhang
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Junhai Xu
- College of Intelligence and Computing, Tianjin Key Lab of Cognitive Computing and Application, Tianjin University, Tianjin, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
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23
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Wang T, Hong Y, Wang Q, Su R, Ng ML, Xu J, Wang L, Yan N. Identification of Mild Cognitive Impairment Among Chinese Based on Multiple Spoken Tasks. J Alzheimers Dis 2021; 82:185-204. [PMID: 33998535 DOI: 10.3233/jad-201387] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Previous studies explored the use of noninvasive biomarkers of speech and language for the detection of mild cognitive impairment (MCI). Yet, most of them employed single task which might not have adequately captured all aspects of their cognitive functions. OBJECTIVE The present study aimed to achieve the state-of-the-art accuracy in detecting individuals with MCI using multiple spoken tasks and uncover task-specific contributions with a tentative interpretation of features. METHODS Fifty patients clinically diagnosed with MCI and 60 healthy controls completed three spoken tasks (picture description, semantic fluency, and sentence repetition), from which multidimensional features were extracted to train machine learning classifiers. With a late-fusion configuration, predictions from multiple tasks were combined and correlated with the participants' cognitive ability assessed using the Montreal Cognitive Assessment (MoCA). Statistical analyses on pre-defined features were carried out to explore their association with the diagnosis. RESULTS The late-fusion configuration could effectively boost the final classification result (SVM: F1 = 0.95; RF: F1 = 0.96; LR: F1 = 0.93), outperforming each individual task classifier. Besides, the probability estimates of MCI were strongly correlated with the MoCA scores (SVM: -0.74; RF: -0.71; LR: -0.72). CONCLUSION Each single task tapped more dominantly to distinct cognitive processes and have specific contributions to the prediction of MCI. Specifically, picture description task characterized communications at the discourse level, while semantic fluency task was more specific to the controlled lexical retrieval processes. With greater demands on working memory load, sentence repetition task uncovered memory deficits through modified speech patterns in the reproduced sentences.
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Affiliation(s)
- Tianqi Wang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China.,Speech Science Laboratory, The University of Hong Kong, Hong Kong, China
| | - Yin Hong
- Health Management Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Quanyi Wang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China
| | - Rongfeng Su
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China
| | - Manwa Lawrence Ng
- Speech Science Laboratory, The University of Hong Kong, Hong Kong, China
| | - Jun Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lan Wang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China
| | - Nan Yan
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China
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24
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Emrani S, Lamar M, Price CC, Wasserman V, Matusz E, Au R, Swenson R, Nagele R, Heilman KM, Libon DJ. Alzheimer's/Vascular Spectrum Dementia: Classification in Addition to Diagnosis. J Alzheimers Dis 2021; 73:63-71. [PMID: 31815693 DOI: 10.3233/jad-190654] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Alzheimer's disease (AD) and vascular dementia (VaD) are the two most common types of dementia. Although the combination of these disorders, called 'mixed' dementia, is recognized, the prevailing clinical and research perspective continues to consider AD and VaD as independent disorders. A review of recent neuropathological and neuropsychological literature reveals that these two disorders frequently co-occur and so-called 'pure' AD or VaD is comparatively rare. In addition, recent research shows that vascular dysfunction not only potentiates AD pathology, but that pathological changes in AD may subsequently induce vascular disorders. On the basis of these data, we propose that the neurobiological underpinnings underlying AD/VaD dementia and their neuropsychological phenotypes are best understood as existing along a clinical/pathological continuum or spectrum. We further propose that in conjunction with current diagnostic criteria, statistical modeling techniques using neuropsychological test performance should be leveraged to construct a system to classify AD/VaD spectrum dementia in order to test hypotheses regarding how mechanisms related to AD and VaD pathology interact and influence each other.
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Affiliation(s)
- Sheina Emrani
- Department of Psychology, Rowan University, Glassboro, NJ, USA
| | - Melissa Lamar
- Department of Behavioral Sciences and the Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Catherine C Price
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | | | - Emily Matusz
- New Jersey Institute for Successful Aging, School of Osteopathic Medicine, Rowan University, Glassboro, NJ, USA
| | - Rhoda Au
- Department of Anatomy and Neurobiology, Neurology, Epidemiology, Boston University Schools of Medicine & Public Health, Boston, MA, USA
| | - Rodney Swenson
- Clinical Professor in the Department of Psychiatry and Behavioral Science at the University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA
| | - Robert Nagele
- New Jersey Institute for Successful Aging, School of Osteopathic Medicine, Rowan University, Glassboro, NJ, USA
| | - Kenneth M Heilman
- Department of Neurology, Brain Rehabilitation Research Center, Malcom Randall VA Medical Center, Center for Cognitive Aging and Memory - Clinical Translational Research Program, and Center for Neuropsychological Studies, University of Florida, Gainseville, FL, USA
| | - David J Libon
- Department of Psychology, Rowan University, Glassboro, NJ, USA.,New Jersey Institute for Successful Aging, School of Osteopathic Medicine, Rowan University, Glassboro, NJ, USA
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25
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Norman M, Wilson SJ, Baxendale S, Barr W, Block C, Busch RM, Fernandez A, Hessen E, Loring DW, McDonald CR, Hermann BP. Addressing neuropsychological diagnostics in adults with epilepsy: Introducing the International Classification of Cognitive Disorders in Epilepsy: The IC CODE Initiative. Epilepsia Open 2021; 6:266-275. [PMID: 34033259 PMCID: PMC8166800 DOI: 10.1002/epi4.12478] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 01/25/2021] [Accepted: 01/30/2021] [Indexed: 01/03/2023] Open
Abstract
This paper addresses the absence of an international diagnostic taxonomy for cognitive disorders in patients with epilepsy. Initiated through the 2020 Memorandum of Understanding between the International League Against Epilepsy and the International Neuropsychological Society, neuropsychological representatives from both organizations met to address the problem and consequences of the absence of an international diagnostic taxonomy for cognitive disorders in epilepsy, overview potential solutions, and propose specific solutions going forward. The group concluded that a classification of cognitive disorders in epilepsy, including an overall taxonomy and associated operational criteria, was clearly lacking and sorely needed. This paper reviews the advantages and shortcomings of four existing cognitive diagnostic approaches, including taxonomies derived from the US National Neuropsychology Network, DSM-V Neurocognitive Disorders, the Mild Cognitive Impairment classification from the aging/preclinical dementia literature, and the Research Domain Criteria Initiative. We propose a framework to develop a consensus-based classification system for cognitive disorders in epilepsy that will be international in scope and be applicable for clinical practice and research globally and introduce the International Classification of Cognitive Disorders in Epilepsy (IC-CODE) project.
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Affiliation(s)
- Marc Norman
- Department of Psychiatry, University of California, San Diego, San Diego, CA, USA.,Executive Director of the International Neuropsychological Society
| | - Sarah J Wilson
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Vic., Australia.,Chair, Diagnostic Methods Commission, International League Against Epilepsy
| | - Sallie Baxendale
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - William Barr
- Departments of Neurology and Psychiatry, NYU-Langone Medical Center and NYU Grossman School of Medicine, New York, NY, USA
| | - Cady Block
- Department of Neurology and Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
| | - Robyn M Busch
- Epilepsy Center and Department of Neurology, Cleveland Clinic, Cleveland, OH, USA
| | - Alberto Fernandez
- Neuropsychology Department, Universidad Nacional de Córdoba & Universidad Católica de Córdoba, Córdoba, Argentina
| | - Erik Hessen
- Departments of Psychology and Neurology, University of Oslo and Akershus University Hospital, Oslo, Norway.,Chair of the European Federation of Psychological Association's Standing Committee on Clinical Neuropsychology
| | - David W Loring
- Department of Neurology and Pediatrics, Emory University School of Medicine, Atlanta, GA, USA.,Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, USA
| | - Carrie R McDonald
- Department of Psychiatry, University of California, San Diego, San Diego, CA, USA.,Center for Multimodal Imaging and Genetics, University of California, San Diego, San Diego, CA, USA
| | - Bruce P Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
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26
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Jiang L, Cui H, Zhang C, Cao X, Gu N, Zhu Y, Wang J, Yang Z, Li C. Repetitive Transcranial Magnetic Stimulation for Improving Cognitive Function in Patients With Mild Cognitive Impairment: A Systematic Review. Front Aging Neurosci 2021; 12:593000. [PMID: 33519418 PMCID: PMC7842279 DOI: 10.3389/fnagi.2020.593000] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Accepted: 12/02/2020] [Indexed: 12/14/2022] Open
Abstract
Background: Mild cognitive impairment (MCI) is an early stage of Alzheimer's disease. Repetitive transcranial magnetic stimulation (rTMS) has been widely employed in MCI research. However, there is no reliable systematic evidence regarding the effects of rTMS on MCI. The aim of this review was to evaluate the efficacy and safety of rTMS in the treatment of MCI. Methods: A comprehensive literature search of nine electronic databases was performed to identify articles published in English or Chinese before June 20, 2019. The identified articles were screened, data were extracted, and the methodological quality of the included trials was assessed. The meta-analysis was performed using the RevMan 5.3 software. We used the GRADE approach to rate the quality of the evidence. Results: Nine studies comprising 369 patients were included. The meta-analysis showed that rTMS may significantly improve global cognitive function (standardized mean difference [SMD] 2.09, 95% confidence interval [CI] 0.94 to 3.24, p = 0.0004, seven studies, n = 296; low-quality evidence) and memory (SMD 0.44, 95% CI 0.16 to 0.72, p = 0.002, six studies, n = 204; moderate-quality evidence). However, there was no significant improvement in executive function and attention (p > 0.05). Subgroup analyses revealed the following: (1) rTMS targeting the left hemisphere significantly enhanced global cognitive function, while rTMS targeting the bilateral hemispheres significantly enhanced global cognitive function and memory; (2) high-frequency rTMS significantly enhanced global cognitive function and memory; and (3) a high number of treatments ≥20 times could improve global cognitive function and memory. There was no significant difference in dropout rate (p > 0.05) between the rTMS and control groups. However, patients who received rTMS had a higher rate of mild adverse effects (risk ratio 2.03, 95% CI 1.16 to 3.52, p = 0.01, seven studies, n = 317; moderate-quality evidence). Conclusions: rTMS appears to improve global cognitive function and memory in patients with MCI and may have good acceptability and mild adverse effects. Nevertheless, these results should be interpreted cautiously due to the relatively small number of trials, particularly for low-frequency rTMS.
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Affiliation(s)
- Lijuan Jiang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huiru Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Caidi Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinyi Cao
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Nannan Gu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yikang Zhu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China.,Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Beijing, China
| | - Zhi Yang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China.,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.,Laboratory of Psychological Heath and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China.,Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Beijing, China.,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
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27
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Petkus AJ, Younan D, Wang X, Beavers DP, Espeland MA, Gatz M, Gruenewald T, Kaufman JD, Chui HC, Millstein J, Rapp SR, Manson JE, Resnick SM, Wellenius GA, Whitsel EA, Widaman K, Chen JC. Associations Between Air Pollution Exposure and Empirically Derived Profiles of Cognitive Performance in Older Women. J Alzheimers Dis 2021; 84:1691-1707. [PMID: 34744078 PMCID: PMC9057084 DOI: 10.3233/jad-210518] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Elucidating associations between exposures to ambient air pollutants and profiles of cognitive performance may provide insight into neurotoxic effects on the aging brain. OBJECTIVE We examined associations between empirically derived profiles of cognitive performance and residential concentrations of particulate matter of aerodynamic diameter < 2.5 (PM2.5) and nitrogen dioxide (NO2) in older women. METHOD Women (N = 2,142) from the Women's Health Initiative Study of Cognitive Aging completed a neuropsychological assessment measuring attention, visuospatial, language, and episodic memory abilities. Average yearly concentrations of PM2.5 and NO2 were estimated at the participant's addresses for the 3 years prior to the assessment. Latent profile structural equation models identified subgroups of women exhibiting similar profiles across tests. Multinomial regressions examined associations between exposures and latent profile classification, controlling for covariates. RESULT Five latent profiles were identified: low performance across multiple domains (poor multi-domain; n = 282;13%), relatively poor verbal episodic memory (poor memory; n = 216; 10%), average performance across all domains (average multi-domain; n = 974; 45%), superior memory (n = 381; 18%), and superior attention (n = 332; 15%). Using women with average cognitive ability as the referent, higher PM2.5 (per interquartile range [IQR] = 3.64μg/m3) was associated with greater odds of being classified in the poor memory (OR = 1.29; 95% Confidence Interval [CI] = 1.10-1.52) or superior attention (OR = 1.30; 95% CI = 1.10-1.53) profiles. NO2 (per IQR = 9.86 ppb) was associated with higher odds of being classified in the poor memory (OR = 1.38; 95% CI = 1.17-1.63) and lower odds of being classified with superior memory (OR = 0.81; 95% CI = 0.67-0.97). CONCLUSION Exposure to PM2.5 and NO2 are associated with patterns of cognitive performance characterized by worse verbal episodic memory relative to performance in other domains.
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Affiliation(s)
- Andrew J. Petkus
- University of Southern California, Department of Neurology, Los Angeles, CA, USA
| | - Diana Younan
- University of Southern California, Department of Population and Public Health Sciences, Los Angeles, CA, USA
| | - Xinhui Wang
- University of Southern California, Department of Neurology, Los Angeles, CA, USA
| | - Daniel P. Beavers
- Wake Forest School of Medicine, Department of Biostatistics, Winston-Salem, NC, USA
| | - Mark A. Espeland
- Wake Forest School of Medicine, Department of Biostatistics, Winston-Salem, NC, USA
| | - Margaret Gatz
- University of Southern California, Center for Economic and Social Research, Los Angeles, CA, USA
| | - Tara Gruenewald
- Chapman University, Department of Psychology, Orange, CA, USA
| | - Joel D. Kaufman
- University of Washington, Department of Environmental and Occupational Health Sciences, Seattle, WA, USA
| | - Helena C. Chui
- University of Southern California, Department of Neurology, Los Angeles, CA, USA
| | - Joshua Millstein
- University of Southern California, Department of Population and Public Health Sciences, Los Angeles, CA, USA
| | - Stephen R. Rapp
- Wake Forest School of Medicine, Department of Psychiatry and Behavioral Medicine, Winston-Salem, NC, USA
| | - JoAnn E. Manson
- Harvard Medical School, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Susan M. Resnick
- National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, MD, USA
| | | | - Eric A. Whitsel
- University of North Carolina, Departments of Epidemiology and Medicine, Chapel Hill, NC, USA
| | - Keith Widaman
- University of California, Riverside, Graduate School of Education, Riverside, CA, USA
| | - Jiu-Chiuan Chen
- University of Southern California, Department of Neurology, Los Angeles, CA, USA
- University of Southern California, Department of Population and Public Health Sciences, Los Angeles, CA, USA
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Reyes A, Kaestner E, Edmonds EC, Christina Macari A, Wang ZI, Drane DL, Punia V, Busch RM, Hermann BP, McDonald CR. Diagnosing cognitive disorders in older adults with epilepsy. Epilepsia 2020; 62:460-471. [PMID: 33258159 DOI: 10.1111/epi.16780] [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: 09/26/2020] [Revised: 11/09/2020] [Accepted: 11/09/2020] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To characterize the nature and prevalence of cognitive disorders in older adults with temporal lobe epilepsy (TLE) and compare their cognitive profiles to patients with amnestic mild cognitive impairment (ie, aMCI). METHODS Seventy-one older patients with TLE, 77 aMCI, and 69 normal aging controls (NACs), all 55-80 years of age, completed neuropsychological measures of memory, language, executive function, and processing speed. An actuarial neuropsychological method designed to diagnose MCI was applied to individual patients to identify older adults with TLE who met diagnostic criteria for MCI (TLE-MCI). A linear classifier was performed to evaluate how well the diagnostic criteria differentiated patients with TLE-MCI from aMCI. In TLE, the contribution of epilepsy-related and vascular risk factors to cognitive impairment was evaluated using multiple regression. RESULTS Forty-three TLE patients (60%) met criteria for TLE-MCI, demonstrating marked deficits in both memory and language. When patients were analyzed according to age at seizure onset, 63% of those with an early onset (<50 years) versus 56% of those with late onset (≥ 50 years) met criteria for TLE-MCI. A classification model between TLE-MCI and aMCI correctly classified 81.1% (90.6% specificity, 61.3% sensitivity) of the cohort based on neuropsychological scores. Whereas TLE-MCI showed greater deficits in language relative to aMCI, patients with aMCI showed greater rapid forgetting on memory measures. Both epilepsy-related risk factors and the presence of leukoaraiosis on MRI contributed to impairment profiles in TLE-MCI. SIGNIFICANCE Cognitive impairment is a common comorbidity in epilepsy and it presents in a substantial number of older adults with TLE. Although the underlying etiologies are unknown in many patients, the TLE-MCI phenotype may be secondary to an accumulation of epilepsy and vascular risk factors, signal the onset of a neurodegenerative disease, or represent a combination of factors.
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Affiliation(s)
- Anny Reyes
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA.,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA.,Department of Psychiatry, University of California, San Diego, CA, USA
| | - Erik Kaestner
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA.,Department of Psychiatry, University of California, San Diego, CA, USA
| | - Emily C Edmonds
- Department of Psychiatry, University of California, San Diego, CA, USA.,Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Anna Christina Macari
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Zhong Irene Wang
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Daniel L Drane
- Departments of Neurology and Pediatrics, Emory University School of Medicine, Atlanta, GA, USA.,Department of Neurology, University of Washington, Seattle, WA, USA
| | - Vineet Punia
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Robyn M Busch
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.,Department of Neurology, Cleveland Clinic, Cleveland, OH, USA
| | - Bruce P Hermann
- Department of Neurology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Carrie R McDonald
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA.,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA.,Department of Psychiatry, University of California, San Diego, CA, USA
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Pini L, Wennberg A, Mitolo M, Meneghello F, Burgio F, Semenza C, Venneri A, Mantini D, Vallesi A. Quality of sleep predicts increased frontoparietal network connectivity in patients with mild cognitive impairment. Neurobiol Aging 2020; 95:205-213. [DOI: 10.1016/j.neurobiolaging.2020.07.026] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 07/13/2020] [Accepted: 07/25/2020] [Indexed: 11/27/2022]
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Thomas KR, Cook SE, Bondi MW, Unverzagt FW, Gross AL, Willis SL, Marsiske M. Application of neuropsychological criteria to classify mild cognitive impairment in the active study. Neuropsychology 2020; 34:862-873. [PMID: 33197199 PMCID: PMC8376229 DOI: 10.1037/neu0000694] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Objective: Comprehensive neuropsychological criteria (NP criteria) for mild cognitive impairment (MCI) has reduced diagnostic errors and better predicted progression to dementia than conventional MCI criteria that rely on a single impaired score and/or subjective report. This study aimed to implement an actuarial approach to classifying MCI in the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study. Method: ACTIVE study participants (N = 2,755) were classified as cognitively normal (CN) or as having MCI using NP criteria. Estimated proportion of MCI participants and reversion rates were examined as well as baseline characteristics by MCI subtype. Mixed effect models examined associations of MCI subtype with 10-year trajectories of self-reported independence and difficulty performing instrumental activities of daily living (IADLs). Results: The proportion of MCI participants was estimated to be 18.8%. Of those with MCI at baseline, 19.2% reverted to CN status for all subsequent visits. At baseline, the multidomain-amnestic MCI group generally had the greatest breadth and depth of cognitive impairment and reported the most IADL difficulty. Longitudinally, MCI participants showed faster IADL decline than CN participants (multidomain-amnestic MCI > single domain-amnestic MCI > nonamnestic MCI). Conclusion: NP criteria identified a proportion of MCI and reversion rate within ACTIVE that is consistent with prior studies involving community-dwelling samples. The pattern of everyday functioning change suggests that being classified as MCI, particularly amnestic MCI, is predictive of future loss of independence. Future work will apply these classifications in ACTIVE to better understand the relationships between MCI and health, social, and cognitive intervention-related factors. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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Affiliation(s)
- Kelsey R. Thomas
- Veterans Affairs San Diego Healthcare System, San Diego, CA,Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA
| | - Sarah E. Cook
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC
| | - Mark W. Bondi
- Veterans Affairs San Diego Healthcare System, San Diego, CA,Department of Psychiatry, University of California, San Diego School of Medicine, La Jolla, CA
| | | | - Alden L. Gross
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | - Sherry L. Willis
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
| | - Michael Marsiske
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL
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Blanken AE, Dutt S, Li Y, Nation DA. Disentangling Heterogeneity in Alzheimer's Disease: Two Empirically-Derived Subtypes. J Alzheimers Dis 2020; 70:227-239. [PMID: 31177226 DOI: 10.3233/jad-190230] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Clinical-pathological Alzheimer's disease (AD) subtypes may help distill heterogeneity in patient presentation. To date, no studies have utilized neuropsychological and biological markers to identify preclinical subtypes with longitudinal stability. OBJECTIVE The objective of this study was to empirically derive AD endophenotypes using a combination of cognitive and biological markers. METHODS Hierarchical cluster analysis grouped dementia-free older adults using memory, executive and language abilities, and cerebrospinal fluid amyloid-β and phosphorylated tau. Brain volume differences, neuropsychological trajectory, and progression to dementia were compared, controlling for age, gender, education, and apolipoprotein E4 (ApoE4). RESULTS Subgroups included asymptomatic-normal (n = 653) with unimpaired cognition and subthreshold biomarkers, typical AD (TAD; n = 191) showing marked memory decline, high ApoE4 rates and abnormal biomarkers, and atypical AD (AAD; n = 132) with widespread cognitive decline, intermediate biomarker levels, older age, less education and more white matter lesions. Cognitive profiles showed longitudinal stability with corresponding patterns of cortical atrophy, despite nearly identical rates of progression to AD dementia. CONCLUSION Two clinical-pathological AD subtypes are identified with potential implications for preventative efforts.
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Affiliation(s)
- Anna E Blanken
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Shubir Dutt
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Yanrong Li
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Daniel A Nation
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
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Balachandra AR, Kaestner E, Bahrami N, Reyes A, Lalani S, Macari AC, Paul BM, Bonilha L, McDonald CR. Clinical utility of structural connectomics in predicting memory in temporal lobe epilepsy. Neurology 2020; 94:e2424-e2435. [PMID: 32358221 PMCID: PMC7455364 DOI: 10.1212/wnl.0000000000009457] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 12/02/2019] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To determine the predictive power of white matter neuronal networks (i.e., structural connectomes [SCs]) in discriminating memory-impaired patients with temporal lobe epilepsy (TLE) from those with normal memory. METHODS T1- and diffusion MRI (dMRI), clinical variables, and neuropsychological measures of verbal memory were available for 81 patients with TLE. Prediction of memory impairment was performed with a tree-based classifier (XGBoost) for 4 models: (1) a clinical model including demographic and clinical features, (2) a hippocampal volume (HCV) model, (3) a tract model including 5 temporal lobe white matter association tracts derived from a dMRI atlas, and (4) an SC model based on dMRI. SCs were derived by extracting cortical-cortical connections from a temporal lobe subnetwork with probabilistic tractography. Principal component (PC) analysis was then applied to reduce the dimensionality of the SC, yielding 10 PCs. Multimodal models were also tested combining SCs and tracts with HCV. Each model was trained on 48 patients from 1 epilepsy center and tested on 33 patients from a different center. RESULTS Multimodal models that included the SC + HCV model yielded the highest classification accuracy (81%; 0.90 sensitivity; 0.67 specificity), outperforming the clinical model (61%; p < 0.001) and HCV model (66%; p < 0.001). In addition, the unimodal SC model (76% accuracy) and tract model (73% accuracy) outperformed the clinical model (p < 0.001) and HCV model (p < 0.001) for classifying patients with TLE with and without memory impairment. Furthermore, the SC identified that short-range temporal-temporal connections were important contributors to memory performance. CONCLUSION SCs and tract-based models are stronger predictors of memory impairment in TLE than HCVs and clinical variables. However, SCs may provide additional information about local cortical-cortical connectivity contributing to memory that is not captured in large association tracts.
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Affiliation(s)
- Akshara R Balachandra
- From the Center for Multimodal Imaging and Genetics (A.R.B., E.K., N.B., A.R., A.C.M., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R., C.R.M.); Department of Neurology (S.L., B.M.P.), University of California, San Francisco; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; and Boston University School of Medicine (A.R.B.), MA
| | - Erik Kaestner
- From the Center for Multimodal Imaging and Genetics (A.R.B., E.K., N.B., A.R., A.C.M., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R., C.R.M.); Department of Neurology (S.L., B.M.P.), University of California, San Francisco; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; and Boston University School of Medicine (A.R.B.), MA
| | - Naeim Bahrami
- From the Center for Multimodal Imaging and Genetics (A.R.B., E.K., N.B., A.R., A.C.M., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R., C.R.M.); Department of Neurology (S.L., B.M.P.), University of California, San Francisco; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; and Boston University School of Medicine (A.R.B.), MA
| | - Anny Reyes
- From the Center for Multimodal Imaging and Genetics (A.R.B., E.K., N.B., A.R., A.C.M., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R., C.R.M.); Department of Neurology (S.L., B.M.P.), University of California, San Francisco; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; and Boston University School of Medicine (A.R.B.), MA
| | - Sanam Lalani
- From the Center for Multimodal Imaging and Genetics (A.R.B., E.K., N.B., A.R., A.C.M., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R., C.R.M.); Department of Neurology (S.L., B.M.P.), University of California, San Francisco; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; and Boston University School of Medicine (A.R.B.), MA
| | - Anna Christina Macari
- From the Center for Multimodal Imaging and Genetics (A.R.B., E.K., N.B., A.R., A.C.M., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R., C.R.M.); Department of Neurology (S.L., B.M.P.), University of California, San Francisco; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; and Boston University School of Medicine (A.R.B.), MA
| | - Brianna M Paul
- From the Center for Multimodal Imaging and Genetics (A.R.B., E.K., N.B., A.R., A.C.M., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R., C.R.M.); Department of Neurology (S.L., B.M.P.), University of California, San Francisco; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; and Boston University School of Medicine (A.R.B.), MA
| | - Leonardo Bonilha
- From the Center for Multimodal Imaging and Genetics (A.R.B., E.K., N.B., A.R., A.C.M., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R., C.R.M.); Department of Neurology (S.L., B.M.P.), University of California, San Francisco; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; and Boston University School of Medicine (A.R.B.), MA
| | - Carrie R McDonald
- From the Center for Multimodal Imaging and Genetics (A.R.B., E.K., N.B., A.R., A.C.M., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R., C.R.M.); Department of Neurology (S.L., B.M.P.), University of California, San Francisco; Department of Neurology (L.B.), Medical University of South Carolina, Charleston; and Boston University School of Medicine (A.R.B.), MA.
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Machulda MM, Lundt ES, Albertson SM, Spychalla AJ, Schwarz CG, Mielke MM, Jack CR, Kremers WK, Vemuri P, Knopman DS, Jones DT, Bondi MW, Petersen RC. Cortical atrophy patterns of incident MCI subtypes in the Mayo Clinic Study of Aging. Alzheimers Dement 2020; 16:1013-1022. [PMID: 32418367 PMCID: PMC7383989 DOI: 10.1002/alz.12108] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 03/13/2020] [Accepted: 03/23/2020] [Indexed: 11/11/2022]
Abstract
INTRODUCTION We examined differences in cortical thickness in empirically derived mild cognitive impairment (MCI) subtypes in the Mayo Clinic Study of Aging. METHODS We compared cortical thickness of four incident MCI subtypes (n = 192) to 1257 cognitive unimpaired individuals. RESULTS The subtle cognitive impairment cluster had atrophy in the entorhinal and parahippocampal cortex. The amnestic, dysnomic, and dysexecutive clusters also demonstrated entorhinal cortex atrophy as well as thinning in temporal, parietal, and frontal isocortex in somewhat different patterns. DISCUSSION We found patterns of atrophy in each of the incident MCI clusters that corresponded to their patterns of cognitive impairment. The identification of MCI subtypes based on cognitive and structural features may allow for more efficient trial and study designs. Given individuals in the subtle cognitive impairment cluster have less structural changes and cognitive decline and may represent the earliest group, this could be a unique group to target with early interventions.
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Affiliation(s)
- Mary M Machulda
- Division of Neurocognitive Disorders, Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Emily S Lundt
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Sabrina M Albertson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Michelle M Mielke
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.,Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Walter K Kremers
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | | | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark W Bondi
- Department of Psychiatry, University of California San Diego School of Medicine, La Jolla, California, USA.,Veterans Affairs San Diego Healthcare System, San Diego, California, USA
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Improving Anti-Neurodegenerative Benefits of Acetylcholinesterase Inhibitors in Alzheimer's Disease: Are Irreversible Inhibitors the Future? Int J Mol Sci 2020; 21:ijms21103438. [PMID: 32414155 PMCID: PMC7279429 DOI: 10.3390/ijms21103438] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 05/01/2020] [Accepted: 05/11/2020] [Indexed: 02/06/2023] Open
Abstract
Decades of research have produced no effective method to prevent, delay the onset, or slow the progression of Alzheimer's disease (AD). In contrast to these failures, acetylcholinesterase (AChE, EC 3.1.1.7) inhibitors slow the clinical progression of the disease and randomized, placebo-controlled trials in prodromal and mild to moderate AD patients have shown AChE inhibitor anti-neurodegenerative benefits in the cortex, hippocampus, and basal forebrain. CNS neurodegeneration and atrophy are now recognized as biomarkers of AD according to the National Institute on Aging-Alzheimer's Association (NIA-AA) criteria and recent evidence shows that these markers are among the earliest signs of prodromal AD, before the appearance of amyloid. The current AChE inhibitors (donepezil, rivastigmine, and galantamine) have short-acting mechanisms of action that result in dose-limiting toxicity and inadequate efficacy. Irreversible AChE inhibitors, with a long-acting mechanism of action, are inherently CNS selective and can more than double CNS AChE inhibition possible with short-acting inhibitors. Irreversible AChE inhibitors open the door to high-level CNS AChE inhibition and improved anti-neurodegenerative benefits that may be an important part of future treatments to more effectively prevent, delay the onset, or slow the progression of AD.
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35
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Edmonds EC, Weigand AJ, Hatton SN, Marshall AJ, Thomas KR, Ayala DA, Bondi MW, McDonald CR. Patterns of longitudinal cortical atrophy over 3 years in empirically derived MCI subtypes. Neurology 2020; 94:e2532-e2544. [PMID: 32393648 DOI: 10.1212/wnl.0000000000009462] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 12/04/2019] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE We previously identified 4 empirically derived mild cognitive impairment (MCI) subtypes via cluster analysis within the Alzheimer's Disease Neuroimaging Initiative (ADNI) and demonstrated high correspondence between patterns of cortical thinning at baseline and each cognitive subtype. We aimed to determine whether our MCI subtypes demonstrate unique longitudinal atrophy patterns. METHODS ADNI participants (295 with MCI and 134 cognitively normal [CN]) underwent annual structural MRI and neuropsychological assessments. General linear modeling compared vertex-wise differences in cortical atrophy rates between each MCI subtype and the CN group. Linear mixed models examined trajectories of cortical atrophy over 3 years within lobar regions of interest. RESULTS Compared to the CN group, those with amnestic MCI (memory deficit) initially demonstrated greater atrophy rates within medial temporal lobe regions that became more widespread over time. Those with dysnomic/amnestic MCI (naming/memory deficits) showed greater atrophy rates largely localized to temporal lobe regions. The mixed MCI (impairment in all cognitive domains) group showed greater atrophy rates in widespread regions at all time points. The cluster-derived normal group, who had intact neuropsychological performance and normal cortical thickness at baseline despite their MCI diagnosis via conventional diagnostic criteria, continued to show normal cognition and minimal cortical atrophy over 3 years. CONCLUSIONS ADNI's purported amnestic MCI sample produced more refined cognitive subtypes with unique longitudinal cortical atrophy rates. These novel MCI subtypes reliably reflect underlying atrophy, reduce false-positive diagnostic errors, and improve prediction of clinical course. Such improvements have implications for the selection of participants for clinical trials and for providing more precise risk assessment for individuals diagnosed with MCI.
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Affiliation(s)
- Emily C Edmonds
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., M.W.B.); Department of Psychiatry (E.C.E., S.N.H., K.R.T., M.W.B., C.R.M.), Center for Multimodal Imaging and Genetics (S.N.H., A.M., C.R.M.), Department of Neurosciences (S.N.H.), and Center for Behavior Genetics of Aging (S.N.H.), University of California San Diego, La Jolla; Joint Doctoral Program in Clinical Psychology (A.J.W., J.E.), San Diego State University/University of California San Diego; Department of Psychology (A.J.M.), University of Southern California, Los Angeles; and Department of Biology (D.A.A.), San Diego State University, CA.
| | - Alexandra J Weigand
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., M.W.B.); Department of Psychiatry (E.C.E., S.N.H., K.R.T., M.W.B., C.R.M.), Center for Multimodal Imaging and Genetics (S.N.H., A.M., C.R.M.), Department of Neurosciences (S.N.H.), and Center for Behavior Genetics of Aging (S.N.H.), University of California San Diego, La Jolla; Joint Doctoral Program in Clinical Psychology (A.J.W., J.E.), San Diego State University/University of California San Diego; Department of Psychology (A.J.M.), University of Southern California, Los Angeles; and Department of Biology (D.A.A.), San Diego State University, CA
| | - Sean N Hatton
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., M.W.B.); Department of Psychiatry (E.C.E., S.N.H., K.R.T., M.W.B., C.R.M.), Center for Multimodal Imaging and Genetics (S.N.H., A.M., C.R.M.), Department of Neurosciences (S.N.H.), and Center for Behavior Genetics of Aging (S.N.H.), University of California San Diego, La Jolla; Joint Doctoral Program in Clinical Psychology (A.J.W., J.E.), San Diego State University/University of California San Diego; Department of Psychology (A.J.M.), University of Southern California, Los Angeles; and Department of Biology (D.A.A.), San Diego State University, CA
| | - Anisa J Marshall
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., M.W.B.); Department of Psychiatry (E.C.E., S.N.H., K.R.T., M.W.B., C.R.M.), Center for Multimodal Imaging and Genetics (S.N.H., A.M., C.R.M.), Department of Neurosciences (S.N.H.), and Center for Behavior Genetics of Aging (S.N.H.), University of California San Diego, La Jolla; Joint Doctoral Program in Clinical Psychology (A.J.W., J.E.), San Diego State University/University of California San Diego; Department of Psychology (A.J.M.), University of Southern California, Los Angeles; and Department of Biology (D.A.A.), San Diego State University, CA
| | - Kelsey R Thomas
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., M.W.B.); Department of Psychiatry (E.C.E., S.N.H., K.R.T., M.W.B., C.R.M.), Center for Multimodal Imaging and Genetics (S.N.H., A.M., C.R.M.), Department of Neurosciences (S.N.H.), and Center for Behavior Genetics of Aging (S.N.H.), University of California San Diego, La Jolla; Joint Doctoral Program in Clinical Psychology (A.J.W., J.E.), San Diego State University/University of California San Diego; Department of Psychology (A.J.M.), University of Southern California, Los Angeles; and Department of Biology (D.A.A.), San Diego State University, CA
| | - Daniela A Ayala
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., M.W.B.); Department of Psychiatry (E.C.E., S.N.H., K.R.T., M.W.B., C.R.M.), Center for Multimodal Imaging and Genetics (S.N.H., A.M., C.R.M.), Department of Neurosciences (S.N.H.), and Center for Behavior Genetics of Aging (S.N.H.), University of California San Diego, La Jolla; Joint Doctoral Program in Clinical Psychology (A.J.W., J.E.), San Diego State University/University of California San Diego; Department of Psychology (A.J.M.), University of Southern California, Los Angeles; and Department of Biology (D.A.A.), San Diego State University, CA
| | - Mark W Bondi
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., M.W.B.); Department of Psychiatry (E.C.E., S.N.H., K.R.T., M.W.B., C.R.M.), Center for Multimodal Imaging and Genetics (S.N.H., A.M., C.R.M.), Department of Neurosciences (S.N.H.), and Center for Behavior Genetics of Aging (S.N.H.), University of California San Diego, La Jolla; Joint Doctoral Program in Clinical Psychology (A.J.W., J.E.), San Diego State University/University of California San Diego; Department of Psychology (A.J.M.), University of Southern California, Los Angeles; and Department of Biology (D.A.A.), San Diego State University, CA
| | - Carrie R McDonald
- From the Veterans Affairs San Diego Healthcare System (E.C.E., K.R.T., M.W.B.); Department of Psychiatry (E.C.E., S.N.H., K.R.T., M.W.B., C.R.M.), Center for Multimodal Imaging and Genetics (S.N.H., A.M., C.R.M.), Department of Neurosciences (S.N.H.), and Center for Behavior Genetics of Aging (S.N.H.), University of California San Diego, La Jolla; Joint Doctoral Program in Clinical Psychology (A.J.W., J.E.), San Diego State University/University of California San Diego; Department of Psychology (A.J.M.), University of Southern California, Los Angeles; and Department of Biology (D.A.A.), San Diego State University, CA
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Teng L, Li Y, Zhao Y, Hu T, Zhang Z, Yao Z, Hu B. Predicting MCI progression with FDG-PET and cognitive scores: a longitudinal study. BMC Neurol 2020; 20:148. [PMID: 32316912 PMCID: PMC7171825 DOI: 10.1186/s12883-020-01728-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 04/14/2020] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Studies on MCI progression are important for Alzheimer's disease (AD) prevention. 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) has been proven to be a powerful tool for measuring cerebral glucose metabolism. In this study, we proposed a classification framework for MCI prediction with both baseline and multiple follow-up FDG-PET scans as well as cognitive scores of 33 progressive MCI (pMCI) patients and 46 stable MCI (sMCI) patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). METHOD First, PET images were normalized using the Yakushev normalization procedure and registered to the Brainnetome Atlas (BNA). The average metabolic intensities of brain regions were defined as static features. Dynamic features were the intensity variation between baseline and the other three time points and change ratios with the intensity obtained at baseline considered as reference. Mini-mental State Examination (MMSE) scores and Alzheimer's disease Assessment Scale-Cognitive section (ADAS-cog) scores of each time point were collected as cognitive features. And F-score was applied for feature selection. Finally, support vector machine (SVM) with radial basis function (RBF) kernel was used for the three above features. RESULTS Dynamic features showed the best classification performance in accuracy of 88.61% than static features (accuracy of 78.48%). And the combination of cognitive features and dynamic features improved the classification performance in specificity of 95.65% and Area Under Curve (AUC) of 0.9308. CONCLUSION Our results reported that dynamic features are more representative in longitudinal research for MCI prediction work. And dynamic features and cognitive scores complementarily enhance the classification performance in specificity and AUC. These findings may predict the disease course and clinical changes in individuals with mild cognitive impairment.
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Affiliation(s)
- Lirong Teng
- Department of Obstetrics and Gynecology, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100032 P.R. China
| | - Yongchao Li
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, 730000 P.R. China
| | - Yu Zhao
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, 730000 P.R. China
| | - Tao Hu
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, 730000 P.R. China
| | - Zhe Zhang
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, 730000 P.R. China
| | - Zhijun Yao
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, 730000 P.R. China
| | - Bin Hu
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, 730000 P.R. China
| | - Alzheimer’ s Disease Neuroimaging Initiative (ADNI)
- Department of Obstetrics and Gynecology, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100032 P.R. China
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, 730000 P.R. China
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Hessen E, Kirsebom BE, Eriksson CM, Eliassen CF, Nakling AE, Bråthen G, Waterloo KK, Aarsland D, Fladby T. In Brief Neuropsychological Assessment, Amnestic Mild Cognitive Impairment (MCI) Is associated with Cerebrospinal Fluid Biomarkers for Cognitive Decline in Contrast to the Prevailing NIA-AA MCI Criterion. J Alzheimers Dis 2020; 67:715-723. [PMID: 30614807 PMCID: PMC6398834 DOI: 10.3233/jad-180964] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background: In the care of persons with cognitive problems, it is important to use a valid mild cognitive impairment (MCI) criterion that discriminates well between normal and pathological aging. Objective: To find the brief neuropsychological screening criterion that best correlates with cerebrospinal fluid (CSF) biomarkers for cognitive decline and dementia in persons seeking help for cognitive problems. Methods: 452 consecutively recruited patients (age 40–80 years) from memory-clinics in the Norwegian national multicentre longitudinal study Dementia Disease Initiation were included. CSF data as well as full data from brief neuropsychological screening were available for all patients. Results: Amnestic MCI, including at least one memory test below T-score 40, outperformed the conventional US National Institute on Aging-Alzheimer’s Association (NIA-AA) MCI criterion. Only amnestic MCI was significantly associated with biomarker pattern of NIA-AA stage 2 (low CSF Aβ42 concentrations and elevated tau) in multivariate regression analysis. Conclusions: The finding that amnestic MCI based on brief neuropsychological assessment is significantly associated with CSF biomarkers for cognitive decline and Alzheimer’s disease is in accordance with longitudinal studies that find memory impairment; both in itself and especially in combination with other cognitive deficit to constitute a risk factor for subsequent cognitive decline and dementia. The prevalence of pathological biomarkers for Alzheimer’s disease is common in the elderly and the clinical significance of present findings depend on longitudinal validation.
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Affiliation(s)
- Erik Hessen
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway.,Institute of Psychology, University of Oslo, Oslo, Norway
| | - Bjørn-Eivind Kirsebom
- Department of Neurology, University Hospital of North Norway, Tromsø, Norway.,Department of Psychology, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø
| | - Cecilia Magdalena Eriksson
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway.,Institute of Psychology, University of Oslo, Oslo, Norway.,Department of Geriatric Psychiatry, Akershus University Hospital, Lørenskog, Norway
| | - Carl Fredrik Eliassen
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway.,Institute of Psychology, University of Oslo, Oslo, Norway
| | | | - Geir Bråthen
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Heath Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Neurology and Clinical Neurophysiology, University Hospital of Trondheim, Trondheim, Norway
| | - Knut K Waterloo
- Department of Neurology, University Hospital of North Norway, Tromsø, Norway.,Department of Psychology, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø
| | - Dag Aarsland
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway.,Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Center for Age-Related Diseases, Stavanger University Hospital, Stavanger, Norway
| | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway.,Institute of Clinical Medicine, Campus Ahus, University of Oslo, Oslo, Norway
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Kaestner E, Balachandra AR, Bahrami N, Reyes A, Lalani SJ, Macari AC, Voets NL, Drane DL, Paul BM, Bonilha L, McDonald CR. The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy. Neuroimage Clin 2019; 25:102125. [PMID: 31927128 PMCID: PMC6953962 DOI: 10.1016/j.nicl.2019.102125] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 12/10/2019] [Accepted: 12/13/2019] [Indexed: 11/05/2022]
Abstract
OBJECTIVE The distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment. METHODS T1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results. RESULTS The SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance. CONCLUSION The SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance.
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Affiliation(s)
- Erik Kaestner
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Akshara R Balachandra
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Naeim Bahrami
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Anny Reyes
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Sanam J Lalani
- Department of Neurology, University of California - San Francisco, San Francisco, CA, USA
| | - Anna Christina Macari
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
| | - Natalie L Voets
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Daniel L Drane
- Departments of Neurology and Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, University of Washington, Seattle, WA, USA
| | - Brianna M Paul
- Department of Neurology, University of California - San Francisco, San Francisco, CA, USA
| | - Leonardo Bonilha
- Medical University of South Carolina, Department of Neurology, USA
| | - Carrie R McDonald
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, CA, USA.
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Garcia-Alvarez L, Gomar JJ, Sousa A, Garcia-Portilla MP, Goldberg TE. Breadth and depth of working memory and executive function compromises in mild cognitive impairment and their relationships to frontal lobe morphometry and functional competence. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2019; 11:170-179. [PMID: 30911598 PMCID: PMC6416209 DOI: 10.1016/j.dadm.2018.12.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
INTRODUCTION The extent of working memory (WM) and executive function (EF) impairment in mild cognitive impairment (MCI) is not well-characterized. METHODS We compared 48 patients with MCI, 124 noncognitively impaired elderly healthy controls, and 57 patients with Alzheimer's disease (AD) on multiple WM/EF measures, frontal lobe integrity indexes, and functioning. RESULTS Patients with MCI demonstrated worse performance on nearly all WM/EF tests. This profile of impairment was refined in a factor analysis that identified three primary WM/EF constructs: WM storage; speed and controlled visual search; and manipulation of information and problem solving. EF impairments were associated with reductions in prefrontal cortical thickness. WM/EF accounted for over 50% of the variance in functional competence. DISCUSSION In MCI, WM/EF impairments are far from rare, based on specific compromises to frontal cortex circuitry, and are associated with loss of everyday functioning. WM/EF impairments, even at this potentially prodromal stage of AD, have clinically deleterious consequences.
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Affiliation(s)
- Leticia Garcia-Alvarez
- Department of Psychiatry, University of Oviedo, Oviedo, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Spain
- Fundación para la Investigación e Innovación Biosanitaria del Principado de Asturias (Finba), Oviedo, Spain
| | - Jesus J. Gomar
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Spain
- The Litwin-Zucker Alzheimer's Research Center, The Feinstein Institute for Medical Research, Manhasset, NY, USA
- FIDMAG Hermanas Hospitalarias Research Foundation, SantBoi de Llobregat, Spain
| | - Amber Sousa
- The Litwin-Zucker Alzheimer's Research Center, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Maria P. Garcia-Portilla
- Department of Psychiatry, University of Oviedo, Oviedo, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Spain
- Fundación para la Investigación e Innovación Biosanitaria del Principado de Asturias (Finba), Oviedo, Spain
| | - Terry E. Goldberg
- Division of Geriatric Psychiatry, Psychiatry, Columbia University Medical Center, NY, USA
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Sun P, Lou W, Liu J, Shi L, Li K, Wang D, Mok VC, Liang P. Mapping the patterns of cortical thickness in single- and multiple-domain amnestic mild cognitive impairment patients: a pilot study. Aging (Albany NY) 2019; 11:10000-10015. [PMID: 31756169 PMCID: PMC6914405 DOI: 10.18632/aging.102362] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 10/05/2019] [Indexed: 01/26/2023]
Abstract
Amnestic mild cognitive impairment (aMCI) is considered as a transitional stage between the expected cognitive decline of normal aging and Alzheimer’s disease (AD). Structural brain difference has shown the potential in cognitive related diagnosis, however cortical thickness patterns transferred from aMCI to AD, especially in the subtypes of aMCI, is still unclear. In this study, we investigated the cortical thickness discrepancies among AD, aMCI and normal control (NC) entities, especially for two subtypes of aMCI - multiple-domain aMCI (aMCI-m) and single-domain aMCI (aMCI-s). Both region of interest (ROI)-based and vertex-based statistical strategies were performed for group-level cortical thickness comparison. Spearman correlation was utilized to identify the correlation between cortical thickness and clinical neuropsychological scores. The result demonstrated that there was a significant cortical thickness decreasing tendency in fusiform gyrus from NC to aMCI-s to aMCI-m to finally AD in both left and right hemispheres. Meanwhile, the two subtypes of aMCI showed cortical thickness difference in middle temporal gyrus in left hemisphere. Spearman correlation indicated that neuropsychological scores had significant correlations with entorhinal, inferior temporal and middle temporal gyrus. The findings suggested that cortical thickness might serve as a potential imaging biomarker for the differential diagnosis of cognitive impairment.
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Affiliation(s)
- Pan Sun
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Wutao Lou
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Jianghong Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.,BrainNow Research Institute, Shenzhen, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Lab of MRI and Brain Informatics, Beijing, China
| | - Defeng Wang
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China
| | - Vincent Ct Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.,Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong, China
| | - Peipeng Liang
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
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Bangen KJ, Thomas KR, Weigand AJ, Sanchez DL, Delano-Wood L, Edmonds EC, Carmichael OT, Schwarz CG, Brickman AM, Bondi MW. Pattern of regional white matter hyperintensity volume in mild cognitive impairment subtypes and associations with decline in daily functioning. Neurobiol Aging 2019; 86:134-142. [PMID: 31791658 DOI: 10.1016/j.neurobiolaging.2019.10.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Revised: 09/04/2019] [Accepted: 10/26/2019] [Indexed: 01/18/2023]
Abstract
White matter hyperintensities (WMHs), a marker of small-vessel cerebrovascular disease, increase risk for mild cognitive impairment (MCI). Less is known about whether regional WMHs distinguish MCI subtypes and predict decline in everyday functioning. About 618 Alzheimer's Disease Neuroimaging Initiative participants (301 cognitively normal [CN]; 232 amnestic MCI [aMCI]; 85 nonamnestic MCI [naMCI]) underwent neuropsychological testing, MRI, and assessment of everyday functioning. aMCI participants showed greater temporal (p = 0.002) and occipital WMHs (p = 0.030) relative to CN whereas naMCI participants had greater frontal (p = 0.045), temporal (p = 0.003), parietal (p = 0.018), and occipital (p < 0.001) WMH compared with CN. Relative to those with aMCI, individuals with naMCI showed greater occipital WMH (p = 0.013). Greater WMH in temporal (p = 0.001) and occipital regions (p = 0.006) was associated with faster decline in everyday functioning across the sample. Temporal lobe WMHs were disproportionately associated with accelerated functional decline among naMCI (p = 0.045). Regional WMH volumes vary across cognitive groups and predict functional decline. Cerebrovascular markers may help identify individuals at risk for decline and distinguish subtypes of cognitive impairment.
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Affiliation(s)
- Katherine J Bangen
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, CA, USA.
| | - Kelsey R Thomas
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, CA, USA
| | - Alexandra J Weigand
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Danielle L Sanchez
- Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Lisa Delano-Wood
- Department of Psychiatry, University of California, San Diego, CA, USA; Psychology Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - Emily C Edmonds
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, CA, USA
| | | | | | - Adam M Brickman
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA; Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA; Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Mark W Bondi
- Department of Psychiatry, University of California, San Diego, CA, USA; Psychology Service, VA San Diego Healthcare System, San Diego, CA, USA
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Thomas KR, Edmonds EC, Eppig JS, Wong CG, Weigand AJ, Bangen KJ, Jak AJ, Delano-Wood L, Galasko DR, Salmon DP, Edland SD, Bondi MW. MCI-to-normal reversion using neuropsychological criteria in the Alzheimer's Disease Neuroimaging Initiative. Alzheimers Dement 2019; 15:1322-1332. [PMID: 31495605 DOI: 10.1016/j.jalz.2019.06.4948] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 05/06/2019] [Accepted: 06/12/2019] [Indexed: 10/26/2022]
Abstract
INTRODUCTION The low mild cognitive impairment (MCI) to cognitively normal (CN) reversion rate in the Alzheimer's Disease Neuroimaging Initiative (2-3%) suggests the need to examine reversion by other means. We applied comprehensive neuropsychological criteria (NP criteria) to determine the resulting MCI to CN reversion rate. METHODS Participants with CN (n = 641) or MCI (n = 569) were classified at baseline and year 1 using NP criteria. Demographic, neuropsychological, and Alzheimer's disease biomarker variables as well as progression to dementia were examined across stable CN, reversion, and stable MCI groups. RESULTS NP criteria produced a one-year reversion rate of 15.8%. Reverters had demographics, Alzheimer's disease biomarkers, and risk-of-progression most similar to the stable CN group and showed the most improvement on neuropsychological measures from baseline to year 1. DISCUSSION NP criteria produced a reversion rate that is consistent with, albeit modestly improved from, reversion rates in meta-analyses. Reverters' biomarker profiles and progression rates suggest that NP criteria accurately tracked with underlying pathophysiologic status.
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Affiliation(s)
- Kelsey R Thomas
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Veteran Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Emily C Edmonds
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Veteran Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Joel S Eppig
- San Diego State University/University of California, San Diego (SDSU/UCSD) Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Christina G Wong
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Veteran Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Alexandra J Weigand
- San Diego State University/University of California, San Diego (SDSU/UCSD) Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Katherine J Bangen
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Veteran Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Amy J Jak
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Veteran Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Lisa Delano-Wood
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Veteran Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Douglas R Galasko
- Veteran Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - David P Salmon
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Steven D Edland
- Department of Biostatistics, University of California, San Diego, La Jolla, CA, USA; Department of Family and Preventative Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Mark W Bondi
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA; Veteran Affairs San Diego Healthcare System, San Diego, CA, USA.
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Neuropsychological latent classes at enrollment and postmortem neuropathology. Alzheimers Dement 2019; 15:1195-1207. [PMID: 31420203 DOI: 10.1016/j.jalz.2019.05.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 04/30/2019] [Accepted: 05/21/2019] [Indexed: 12/13/2022]
Abstract
INTRODUCTION We classified individuals based on their baseline performance on cognitive measures and investigated the association between cognitive classifications and neuropathological findings ∼7 years later, as an external validator. METHODS Brain autopsies of 779 decedents were examined. Baseline latent class analysis on 10 neuropsychological measures was previously assigned: mixed-domains impairment (n = 39, 5%), memory-specific impairment (n = 210, 27%), frontal impairment (n = 113, 14.5%), average cognition (n = 360, 46.2%), and superior cognition (n = 57, 7.3%). Linear regressions and risks ratios were used to examine the relation of latent class assignment at enrollment with neuropathological indices. RESULTS Amyloid β, tau, and transactive response DNA-binding protein 43 were associated with mixed-domains impairment and memory-specific impairment classes ∼7 years before death. Moderate arteriolosclerosis was associated with membership in the frontal impairment class. DISCUSSION Our findings support the use of latent class models that incorporate more comprehensive neuropsychological measures to classify cognitive impairment.
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44
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Kaestner E, Reyes A, Macari AC, Chang YH, Paul B, Hermann B, McDonald CR. Identifying the neural basis of a language-impaired phenotype of temporal lobe epilepsy. Epilepsia 2019; 60:1627-1638. [PMID: 31297795 PMCID: PMC6687533 DOI: 10.1111/epi.16283] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 06/17/2019] [Accepted: 06/17/2019] [Indexed: 12/29/2022]
Abstract
OBJECTIVE To identify neuroimaging and clinical biomarkers associated with a language-impaired phenotype in refractory temporal lobe epilepsy (TLE). METHODS Eighty-five patients with TLE were characterized as language-impaired (TLE-LI) or non-language-impaired (TLE-NLI) based on comprehensive neuropsychological testing. Structural magnetic resonance imaging (MRI), diffusion tensor imaging, and functional MRI (fMRI) were obtained in patients and 47 healthy controls (HC). fMRI activations and cortical thickness were calculated within language regions of interest, and fractional anisotropy (FA) was calculated within deep white matter tracts associated with language. Analyses of variance were performed to test for differences among the groups in imaging measures. Receiver operator characteristic curves were used to determine how well different clinical versus imaging measures discriminated TLE-LI from TLE-NLI. RESULTS TLE-LI patients showed significantly less activation within left superior temporal cortex compared to HC and TLE-NLI, regardless of side of seizure onset. TLE-LI also showed decreased FA in the inferior longitudinal fasciculus and arcuate fasciculus compared to HC. Cortical thickness did not differ between groups in any region. A model that included language-related fMRI activations within the superior temporal gyrus, age at onset, and demographic variables was the most predictive of language impairment (area under the curve = 0.80). SIGNIFICANCE These findings demonstrate a unique imaging signature associated with a language-impaired phenotype in TLE, characterized by functional and microstructural alterations within the language network. Reduced left superior temporal activation combined with compromise to language association tracts underlies this phenotype, extending our previous work on cognitive phenotypes that could have implications for treatment-planning or cognitive progression in TLE.
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Affiliation(s)
- Erik Kaestner
- Center for Multimodal Imaging and Genetics, University of California, San Diego
| | - Anny Reyes
- Center for Multimodal Imaging and Genetics, University of California, San Diego
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego
| | | | - Yu-Hsuan Chang
- Center for Multimodal Imaging and Genetics, University of California, San Diego
| | - Brianna Paul
- Department of Neurology, University of California – San Francisco, San Francisco
- UCSF Comprehensive Epilepsy Center, San Francisco
| | - Bruce Hermann
- Matthews Neuropsychology Section, University of Wisconsin
| | - Carrie R. McDonald
- Center for Multimodal Imaging and Genetics, University of California, San Diego
- UCSD Comprehensive Epilepsy Center, San Diego
- Department of Psychiatry, University of California, San Diego
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Machulda MM, Lundt ES, Albertson SM, Kremers WK, Mielke MM, Knopman DS, Bondi MW, Petersen RC. Neuropsychological subtypes of incident mild cognitive impairment in the Mayo Clinic Study of Aging. Alzheimers Dement 2019; 15:878-887. [PMID: 31128864 PMCID: PMC6646057 DOI: 10.1016/j.jalz.2019.03.014] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 03/12/2019] [Accepted: 03/25/2019] [Indexed: 12/14/2022]
Abstract
INTRODUCTION We evaluated whether incident mild cognitive impairment (MCI) subtypes could be empirically derived in the Mayo Clinic Study of Aging. METHODS We performed cluster analysis on neuropsychological data from 506 participants with incident MCI. RESULTS The 3-cluster solution resulted in (1) amnestic, (2) dysexecutive, (3) dysnomic subtypes. The 4-cluster solution produced these same three groups and a fourth group with subtle cognitive impairment (SCI). The SCI cluster was a subset of the amnestic cluster and distinct from well-matched cognitively unimpaired participants based on memory and global z-score area under the receiver operating characteristic curve analyses and probability of progression to MCI/dementia. DISCUSSION We empirically identified three neuropsychological subtypes of MCI that share some features with MCI subtypes identified in the Alzheimer's Disease Neuroimaging Initiative. The fourth subtype with SCI in the Mayo Clinic Study of Aging differed from the fourth cluster-derived normal group in Alzheimer's Disease Neuroimaging Initiative and could represent a group to target with early interventions.
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Affiliation(s)
- Mary M Machulda
- Division of Neurocognitive Disorders, Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA.
| | - Emily S Lundt
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Sabrina M Albertson
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Walter K Kremers
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Michelle M Mielke
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Department of Neurology, College of Medicine and Science, Mayo Clinic, Rochester, MN, USA
| | - David S Knopman
- Department of Neurology, College of Medicine and Science, Mayo Clinic, Rochester, MN, USA
| | - Mark W Bondi
- Department of Psychiatry, University of California San Diego, School of Medicine, La Jolla, CA, USA; Veterans Affairs San Diego Healthcare System, San Diego, CA, USA
| | - Ronald C Petersen
- Department of Neurology, College of Medicine and Science, Mayo Clinic, Rochester, MN, USA
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Yang H, Xu H, Li Q, Jin Y, Jiang W, Wang J, Wu Y, Li W, Yang C, Li X, Xiao S, Shi F, Wang T. Study of brain morphology change in Alzheimer's disease and amnestic mild cognitive impairment compared with normal controls. Gen Psychiatr 2019; 32:e100005. [PMID: 31179429 PMCID: PMC6551438 DOI: 10.1136/gpsych-2018-100005] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 03/13/2019] [Accepted: 03/15/2019] [Indexed: 11/25/2022] Open
Abstract
Background With an aggravated social ageing level, the number of patients with Alzheimer’s disease (AD) is gradually increasing, and mild cognitive impairment (MCI) is considered to be an early form of Alzheimer’s disease. How to distinguish diseases in the early stage for the purposes of early diagnosis and treatment is an important topic. Aims The purpose of our study was to investigate the differences in brain cortical thickness and surface area among elderly patients with AD, elderly patients with amnestic MCI (aMCI) and normal controls (NC). Methods 20 AD patients, 21 aMCIs and 25 NC were recruited in the study. FreeSurfer software was used to calculate cortical thickness and surface area among groups. Results The patients with AD had less cortical thickness both in the left and right hemisphere in 17 of the 36 brain regions examined than the patients with aMCI or NC. The patients with AD also had smaller cerebral surface area both in the left and right hemisphere in 3 of the 36 brain regions examined than the patients with aMCI or NC. Compared with the NC, the patients with aMCI only had slight atrophy in the inferior parietal lobe of the left hemisphere, and no significant difference was found. Conclusion AD, as well as aMCI (to a lesser extent), is associated with reduced cortical thickness and surface area in a few brain regions associated with cognitive impairment. These results suggest that cortical thickness and surface area could be used for early detection of AD.
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Affiliation(s)
- Huanqing Yang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Hua Xu
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Qingfeng Li
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Yan Jin
- IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Weixiong Jiang
- Informational Science and Engineering Department, Hunan First Normal University, Changsha, China
| | - Jinghua Wang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Yina Wu
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Li
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Cece Yang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Xia Li
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Shifu Xiao
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Feng Shi
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Tao Wang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai, China.,Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
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Reyes A, Kaestner E, Bahrami N, Balachandra A, Hegde M, Paul BM, Hermann B, McDonald CR. Cognitive phenotypes in temporal lobe epilepsy are associated with distinct patterns of white matter network abnormalities. Neurology 2019; 92:e1957-e1968. [PMID: 30918094 DOI: 10.1212/wnl.0000000000007370] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 12/31/2018] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE To identify distinct cognitive phenotypes in temporal lobe epilepsy (TLE) and evaluate patterns of white matter (WM) network alterations associated with each phenotype. METHODS Seventy patients with TLE were characterized into 4 distinct cognitive phenotypes based on patterns of impairment in language and verbal memory measures (language and memory impaired, memory impaired only, language impaired only, no impairment). Diffusion tensor imaging was obtained in all patients and in 46 healthy controls (HC). Fractional anisotropy (FA) and mean diffusivity (MD) of the WM directly beneath neocortex (i.e., superficial WM [SWM]) and of deep WM tracts associated with memory and language were calculated for each phenotype. Regional and network-based SWM analyses were performed across phenotypes. RESULTS The language and memory impaired group and the memory impaired group showed distinct patterns of microstructural abnormalities in SWM relative to HC. In addition, the language and memory impaired group showed widespread alterations in WM tracts and altered global SWM network topology. Patients with isolated language impairment exhibited poor network structure within perisylvian cortex, despite relatively intact global SWM network structure, whereas patients with no impairment appeared similar to HC across all measures. CONCLUSIONS These findings demonstrate a differential pattern of WM microstructural abnormalities across distinct cognitive phenotypes in TLE that can be appreciated at both the regional and network levels. These findings not only help to unravel the underlying neurobiology associated with cognitive impairment in TLE, but they could also aid in establishing cognitive taxonomies or in the prediction of cognitive course in TLE.
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Affiliation(s)
- Anny Reyes
- From the San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R.); Center for Multimodal Imaging and Genetics (A.R., E.K., N.B., A.B., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; Department of Neurology (M.H., B.M.P.), University of California, San Francisco; UCSF Comprehensive Epilepsy Center (M.H., B.M.P.), San Francisco; Matthews Neuropsychology Section (B.H.), University of Wisconsin-Madison; and UCSD Comprehensive Epilepsy Center (C.R.M.), San Diego, CA
| | - Erik Kaestner
- From the San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R.); Center for Multimodal Imaging and Genetics (A.R., E.K., N.B., A.B., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; Department of Neurology (M.H., B.M.P.), University of California, San Francisco; UCSF Comprehensive Epilepsy Center (M.H., B.M.P.), San Francisco; Matthews Neuropsychology Section (B.H.), University of Wisconsin-Madison; and UCSD Comprehensive Epilepsy Center (C.R.M.), San Diego, CA
| | - Naeim Bahrami
- From the San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R.); Center for Multimodal Imaging and Genetics (A.R., E.K., N.B., A.B., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; Department of Neurology (M.H., B.M.P.), University of California, San Francisco; UCSF Comprehensive Epilepsy Center (M.H., B.M.P.), San Francisco; Matthews Neuropsychology Section (B.H.), University of Wisconsin-Madison; and UCSD Comprehensive Epilepsy Center (C.R.M.), San Diego, CA
| | - Akshara Balachandra
- From the San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R.); Center for Multimodal Imaging and Genetics (A.R., E.K., N.B., A.B., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; Department of Neurology (M.H., B.M.P.), University of California, San Francisco; UCSF Comprehensive Epilepsy Center (M.H., B.M.P.), San Francisco; Matthews Neuropsychology Section (B.H.), University of Wisconsin-Madison; and UCSD Comprehensive Epilepsy Center (C.R.M.), San Diego, CA
| | - Manu Hegde
- From the San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R.); Center for Multimodal Imaging and Genetics (A.R., E.K., N.B., A.B., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; Department of Neurology (M.H., B.M.P.), University of California, San Francisco; UCSF Comprehensive Epilepsy Center (M.H., B.M.P.), San Francisco; Matthews Neuropsychology Section (B.H.), University of Wisconsin-Madison; and UCSD Comprehensive Epilepsy Center (C.R.M.), San Diego, CA
| | - Brianna M Paul
- From the San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R.); Center for Multimodal Imaging and Genetics (A.R., E.K., N.B., A.B., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; Department of Neurology (M.H., B.M.P.), University of California, San Francisco; UCSF Comprehensive Epilepsy Center (M.H., B.M.P.), San Francisco; Matthews Neuropsychology Section (B.H.), University of Wisconsin-Madison; and UCSD Comprehensive Epilepsy Center (C.R.M.), San Diego, CA
| | - Bruce Hermann
- From the San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R.); Center for Multimodal Imaging and Genetics (A.R., E.K., N.B., A.B., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; Department of Neurology (M.H., B.M.P.), University of California, San Francisco; UCSF Comprehensive Epilepsy Center (M.H., B.M.P.), San Francisco; Matthews Neuropsychology Section (B.H.), University of Wisconsin-Madison; and UCSD Comprehensive Epilepsy Center (C.R.M.), San Diego, CA
| | - Carrie R McDonald
- From the San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology (A.R.); Center for Multimodal Imaging and Genetics (A.R., E.K., N.B., A.B., C.R.M.) and Department of Psychiatry (C.R.M.), University of California, San Diego; Department of Neurology (M.H., B.M.P.), University of California, San Francisco; UCSF Comprehensive Epilepsy Center (M.H., B.M.P.), San Francisco; Matthews Neuropsychology Section (B.H.), University of Wisconsin-Madison; and UCSD Comprehensive Epilepsy Center (C.R.M.), San Diego, CA.
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Anthony M, Lin F. A Systematic Review for Functional Neuroimaging Studies of Cognitive Reserve Across the Cognitive Aging Spectrum. Arch Clin Neuropsychol 2019; 33:937-948. [PMID: 29244054 DOI: 10.1093/arclin/acx125] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 11/27/2017] [Indexed: 12/29/2022] Open
Abstract
Objective Cognitive reserve has been proposed to explain the discrepancy between clinical symptoms and the effects of aging or Alzheimer's pathology. Functional magnetic resonance imaging (fMRI) may help elucidate how neural reserve and compensation delay cognitive decline and identify brain regions associated with cognitive reserve. This systematic review evaluated neural correlates of cognitive reserve via fMRI (resting-state and task-related) studies across the cognitive aging spectrum (i.e., normal cognition, mild cognitive impairment, and Alzheimer's disease). Method This review examined published articles up to March 2017. There were 13 cross-sectional observational studies that met the inclusion criteria, including relevance to cognitive reserve, subjects 60 years or older with normal cognition, mild cognitive impairment, and/or Alzheimer's disease, at least one quantitative measure of cognitive reserve, and fMRI as the imaging modality. Quality assessment of included studies was conducted using the Newcastle-Ottawa Scale adapted for cross-sectional studies. Results Across the cognitive aging spectrum, medial temporal regions and an anterior or posterior cingulate cortex-seeded default mode network were associated with neural reserve. Frontal regions and the dorsal attentional network were related to neural compensation. Compared to neural reserve, neural compensation was more common in mild cognitive impairment and Alzheimer's disease. Conclusions Neural reserve and compensation both support cognitive reserve, with compensation more common in later stages of the cognitive aging spectrum. Longitudinal and intervention studies are needed to investigate changes between neural reserve and compensation during the transition between clinical stages, and to explore the causal relationship between cognitive reserve and potential neural substrates.
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Affiliation(s)
- Mia Anthony
- School of Nursing, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - Feng Lin
- School of Nursing, University of Rochester Medical Center, Rochester, NY 14642, USA.,Department of Psychiatry, University of Rochester Medical Center, Rochester, NY 14642, USA.,Department of Brain and Cognitive Science, University of Rochester, Rochester, NY 14642, USA.,Department of Neuroscience, University of Rochester Medical Center, Rochester, NY 14642, USA.,Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA
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Early versus late MCI: Improved MCI staging using a neuropsychological approach. Alzheimers Dement 2019; 15:699-708. [PMID: 30737119 DOI: 10.1016/j.jalz.2018.12.009] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 11/26/2018] [Accepted: 12/16/2018] [Indexed: 01/14/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) separates "early" and "late" mild cognitive impairment (MCI) based on a single memory test. We compared ADNI's MCI classifications to our neuropsychological approach, which more broadly assesses cognitive abilities. METHODS Three hundred thirty-six ADNI-2 participants were classified as "early" or "late" MCI. Cluster analysis was performed on neuropsychological test data, and participants were reclassified based on cluster results. These two staging approaches were compared on progression rates, cerebrospinal fluid biomarkers, and cortical thickness profiles. RESULTS There was little correspondence between the two staging methods. ADNI's early MCI group included a large proportion of false-positive diagnostic errors. The reclassified neuropsychological MCI groups showed steeper survival curves and more abnormal biomarkers. CONCLUSIONS Our novel neuropsychological approach improved the staging of MCI by (1) capturing individuals at an early symptomatic stage, (2) minimizing false-positive cases, and (3) identifying a late MCI group further along the disease trajectory.
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50
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Thomas KR, Eppig JS, Weigand AJ, Edmonds EC, Wong CG, Jak AJ, Delano-Wood L, Galasko DR, Salmon DP, Edland SD, Bondi MW. Artificially low mild cognitive impairment to normal reversion rate in the Alzheimer's Disease Neuroimaging Initiative. Alzheimers Dement 2019; 15:561-569. [PMID: 30610833 DOI: 10.1016/j.jalz.2018.10.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 10/09/2018] [Accepted: 10/30/2018] [Indexed: 12/01/2022]
Abstract
INTRODUCTION We examined reasons for low mild cognitive impairment (MCI)-to-cognitively normal (CN) reversion rates in the Alzheimer's Disease Neuroimaging Initiative (ADNI). METHODS CN and MCI participants were identified as remaining stable, progressing, or reverting at 1-year of follow-up (Year 1). Application of ADNI's MCI criteria at Year 1 in addition to Alzheimer's disease biomarkers by group were examined. RESULTS The MCI-to-CN reversion rate was 3.0%. When specific components were examined, 22.5% of stable MCI participants had normal memory performance at Year 1 and their Alzheimer's disease biomarkers were consistent with the stable CN group. At Year 1, when all MCI criteria were not met, the more subjective Clinical Dementia Rating rather than objective memory measure appeared to drive continuation of the MCI diagnosis. DISCUSSION Results demonstrate an artificially low 1-year MCI-to-CN reversion rate in ADNI-diagnosed participants. If the Logical Memory cutoffs had been consistently applied, the reversion rate would have been at least 21.8%.
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Affiliation(s)
- Kelsey R Thomas
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, School of Medicine, La Jolla, CA, USA
| | - Joel S Eppig
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Alexandra J Weigand
- San Diego State University/University of California, San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Emily C Edmonds
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, School of Medicine, La Jolla, CA, USA
| | - Christina G Wong
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, School of Medicine, La Jolla, CA, USA
| | - Amy J Jak
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, School of Medicine, La Jolla, CA, USA
| | - Lisa Delano-Wood
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, School of Medicine, La Jolla, CA, USA
| | - Douglas R Galasko
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, School of Medicine, La Jolla, CA, USA; Department of Neurosciences, University of California, San Diego, School of Medicine, La Jolla, CA, USA
| | - David P Salmon
- Department of Neurosciences, University of California, San Diego, School of Medicine, La Jolla, CA, USA
| | - Steven D Edland
- Department of Biostatistics, University of California, San Diego, La Jolla, CA, USA; Department of Family and Preventative Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Mark W Bondi
- Veterans Affairs San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, School of Medicine, La Jolla, CA, USA.
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