Voxel-based correlation of 18F-THK5351 accumulation with gray matter structural networks in cognitively normal older adults

Abstract Background No previous studies have examined the correlations between tau and gray matter network alterations in cognitively normal (CN) older adults. Here, we investigated the correlations between 18F-THK5351 and local network measures at the voxel level. Material and methods We recruited 47 amyloid-negative CN older adults (65.0 ± 7.9 years, 55% women). All participants underwent structural magnetic resonance imaging (MRI) and 11C-Pittsburgh compound-B and 18F-THK5351 positron emission tomography (PET) scans. Single-subject gray matter networks extracted from T1-weighted MRI data based on cortical similarities were analyzed using the graph theoretical approach. The 18F-THK5351 PET and four local network measures (betweenness centrality, clustering coefficient, characteristic path length, and degree) were evaluated to calculate voxel-wise correlations among the imaging modalities. Result Significant positive correlations between 18F-THK5351 and local network measures were detected in the bilateral caudate. Conclusion Our findings suggest that tau and neuroinflammation in CN older adults may influence local gray matter network in the caudate.

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Voxel-based correlation of 18F-THK5351 accumulation and gray matter volume in the brain of cognitively normal older adults

EJNMMI Research ◽

10.1186/s13550-019-0552-3 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Yoko Shigemoto ◽

Daichi Sone ◽

Miho Ota ◽

Norihide Maikusa ◽

Masayo Ogawa ◽

...

Keyword(s):

Older Adults ◽

Gray Matter ◽

Gray Matter Volume ◽

Cognitively Normal ◽

Matter Volume ◽

The Brain

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Gray Matter Volume Covariance Networks, Social Support, and Cognition in Older Adults

The Journals of Gerontology Series B ◽

10.1093/geronb/gbz023 ◽

2019 ◽

Vol 75 (6) ◽

pp. 1219-1229 ◽

Cited By ~ 3

Author(s):

Kelly Cotton ◽

Joe Verghese ◽

Helena M Blumen

Keyword(s):

Social Support ◽

Older Adults ◽

Executive Function ◽

Gray Matter ◽

Gray Matter Volume ◽

Neural Substrates ◽

Functional Health ◽

Matter Volume ◽

With Memory ◽

Gray Matter Network

Abstract Objective We examined the neural substrates of social support in older adults. Social support is associated with better outcomes in many facets of aging—including cognitive and functional health—but the underlying neural substrates remain largely unexplored. Methods Voxel-based morphometry and multivariate statistics were used to identify gray matter volume covariance networks associated with social support in 112 older adults without dementia (M age = 74.6 years, 50% female), using the Medical Outcomes Study Social Support Survey. Results A gray matter network associated with overall social support was identified and included prefrontal, hippocampal, amygdala, cingulate, and thalamic regions. A gray matter network specifically associated with tangible social support (e.g., someone to help you if you were confined to bed) was also identified, included prefrontal, hippocampal, cingulate, insular, and thalamic regions, and correlated with memory and executive function. Discussion Gray matter networks associated with overall and tangible social support in this study were composed of regions previously associated with memory, executive function, aging, and dementia. Longitudinal research of the interrelationships between social support, brain structure, and cognition is needed, but strengthening social support may represent a new path toward improving cognition in aging that should be explored.

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Voxel-based correlation of 18F-THK5351 accumulation with gray matter structural networks in cognitively normal older adults

eNeurologicalSci ◽

10.1016/j.ensci.2021.100343 ◽

2021 ◽

Vol 23 ◽

pp. 100343

Author(s):

Yoko Shigemoto ◽

Daichi Sone ◽

Norihide Maikusa ◽

Yukio Kimura ◽

Fumio Suzuki ◽

...

Keyword(s):

Older Adults ◽

Gray Matter ◽

Cognitively Normal ◽

Structural Networks

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What factors explain racial differences in memory‐related gray matter volume regions of interest among cognitively normal older adults?

Alzheimer s & Dementia ◽

10.1002/alz.047637 ◽

2020 ◽

Vol 16 (S10) ◽

Author(s):

Sara L Godina ◽

Caterina Rosano ◽

Peter Gianaros ◽

Howard J Aizenstein ◽

Michelle C Carlson ◽

...

Keyword(s):

Older Adults ◽

Racial Differences ◽

Gray Matter ◽

Gray Matter Volume ◽

Regions Of Interest ◽

Cognitively Normal ◽

Matter Volume

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Longitudinal cortical network reorganization in early relapsing–remitting multiple sclerosis

Therapeutic Advances in Neurological Disorders ◽

10.1177/1756286419838673 ◽

2019 ◽

Vol 12 ◽

pp. 175628641983867 ◽

Cited By ~ 7

Author(s):

Vinzenz Fleischer ◽

Nabin Koirala ◽

Amgad Droby ◽

René-Maxime Gracien ◽

Ralf Deichmann ◽

...

Keyword(s):

Disease Activity ◽

Gray Matter ◽

Network Dynamics ◽

Network Connectivity ◽

Clustering Coefficient ◽

Local Network ◽

Cortical Atrophy ◽

Disease Manifestation ◽

Local Efficiency ◽

Relapsing Remitting

Background: Network science provides powerful access to essential organizational principles of the brain. The aim of this study was to investigate longitudinal evolution of gray matter networks in early relapsing–remitting MS (RRMS) compared with healthy controls (HCs) and contrast network dynamics with conventional atrophy measurements. Methods: For our longitudinal study, we investigated structural cortical networks over 1 year derived from 3T MRI in 203 individuals (92 early RRMS patients with mean disease duration of 12.1 ± 14.5 months and 101 HCs). Brain networks were computed based on cortical thickness inter-regional correlations and fed into graph theoretical analysis. Network connectivity measures (modularity, clustering coefficient, local efficiency, and transitivity) were compared between patients and HCs, and between patients with and without disease activity. Moreover, we calculated longitudinal brain volume changes and cortical atrophy patterns. Results: Our analyses revealed strengthening of local network properties shown by increased modularity, clustering coefficient, local efficiency, and transitivity over time. These network dynamics were not detectable in the cortex of HCs over the same period and occurred independently of patients’ disease activity. Most notably, the described network reorganization was evident beyond detectable atrophy as characterized by conventional morphometric methods. Conclusion: In conclusion, our findings provide evidence for gray matter network reorganization subsequent to clinical disease manifestation in patients with early RRMS. An adaptive cortical response with increased local network characteristics favoring network segregation could play a primordial role for maintaining brain function in response to neuroinflammation.

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Comprehension of an Elevated Amyloid Positron Emission Tomography Biomarker Result by Cognitively Normal Older Adults

JAMA Neurology ◽

10.1001/jamaneurol.2017.2954 ◽

2018 ◽

Vol 75 (1) ◽

pp. 44 ◽

Cited By ~ 24

Author(s):

Jessica Mozersky ◽

Pamela Sankar ◽

Kristin Harkins ◽

Sara Hachey ◽

Jason Karlawish

Keyword(s):

Older Adults ◽

Positron Emission Tomography ◽

Emission Tomography ◽

Amyloid Positron Emission Tomography ◽

Cognitively Normal ◽

Positron Emission

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Gray matter networks and cognitive impairment in multiple sclerosis

Multiple Sclerosis Journal ◽

10.1177/1352458517751650 ◽

2018 ◽

Vol 25 (3) ◽

pp. 382-391 ◽

Cited By ~ 7

Author(s):

Carolina M Rimkus ◽

Menno M Schoonheim ◽

Martijn D Steenwijk ◽

Hugo Vrenken ◽

Anand JC Eijlers ◽

...

Keyword(s):

Multiple Sclerosis ◽

Cognitive Impairment ◽

Gray Matter ◽

Random Network ◽

Neurological Dysfunction ◽

Single Subject ◽

Information Processing Speed ◽

Memory And Attention ◽

Network Disruptions ◽

Gray Matter Network

Background: Coordinated patterns of gray matter morphology can be represented as networks, and network disruptions may explain cognitive dysfunction related to multiple sclerosis (MS). Objective: To investigate whether single-subject gray matter network properties are related to impaired cognition in MS. Methods: We studied 148 MS patients (99 female) and 33 healthy controls (HC, 21 female). Seven network parameters were computed and compared within MS between cognitively normal and impaired subjects, and associated with performance on neuropsychological tests in six cognitive domains with regression models. Analyses were controlled for age, gender, whole-brain gray matter volumes, and education level. Results: Compared to MS subjects with normal cognition, MS subjects with cognitive impairment showed a more random network organization as indicated by lower lambda values (all p < 0.05). Worse average cognition and executive function were associated with lower lambda values. Impaired information processing speed, working memory, and attention were associated with lower clustering values. Conclusion: Our findings indicate that MS subjects with a more randomly organized gray matter network show worse cognitive functioning, suggesting that single-subject gray matter graphs may capture neurological dysfunction due to MS.

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