Literature DB >> 32592850

Longitudinal analysis of regional cerebellum volumes during normal aging.

Shuo Han1, Yang An2, Aaron Carass3, Jerry L Prince4, Susan M Resnick2.   

Abstract

Some cross-sectional studies suggest reduced cerebellar volumes with aging, but there have been few longitudinal studies of age changes in cerebellar subregions in cognitively healthy older adults. In this work, 2,023 magnetic resonance (MR) images of 822 cognitively normal participants from the Baltimore Longitudinal Study of Aging (BLSA) were analyzed. Participants ranged in age from 50 to 95 years (mean 70.7 years) at the baseline assessment. Follow-up intervals were 1-9 years (mean 3.7 years) for participants with two or more visits. We used a recently developed cerebellum parcellation algorithm based on convolutional neural networks to divide the cerebellum into 28 subregions. Linear mixed effects models were applied to the volume of each cerebellar subregion to investigate cross-sectional and longitudinal age effects, as well as effects of sex and their interactions, after adjusting for intracranial volume. Our findings suggest spatially varying atrophy patterns across the cerebellum with respect to age and sex both cross-sectionally and longitudinally. Published by Elsevier Inc.

Entities:  

Keywords:  Cerebellum; Longitudinal analysis; Normal aging

Year:  2020        PMID: 32592850     DOI: 10.1016/j.neuroimage.2020.117062

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  11 in total

1.  Age-volume associations in cerebellar lobules by sex and reproductive stage.

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Review 2.  Don't forget the little brain: A framework for incorporating the cerebellum into the understanding of cognitive aging.

Authors:  Jessica A Bernard
Journal:  Neurosci Biobehav Rev       Date:  2022-03-26       Impact factor: 9.052

3.  Cerebellar Dentate Connectivity across Adulthood: A Large-Scale Resting State Functional Connectivity Investigation.

Authors:  Jessica A Bernard; Hannah K Ballard; Trevor Bryan Jackson
Journal:  Cereb Cortex Commun       Date:  2021-08-10

4.  Multimodal biological brain age prediction using magnetic resonance imaging and angiography with the identification of predictive regions.

Authors:  Pauline Mouches; Matthias Wilms; Deepthi Rajashekar; Sönke Langner; Nils D Forkert
Journal:  Hum Brain Mapp       Date:  2022-02-09       Impact factor: 5.399

5.  Differential Relationships Between Brain Structure and Dual Task Walking in Young and Older Adults.

Authors:  Kathleen E Hupfeld; Justin M Geraghty; Heather R McGregor; C J Hass; Ofer Pasternak; Rachael D Seidler
Journal:  Front Aging Neurosci       Date:  2022-03-11       Impact factor: 5.750

6.  Prediction in the Aging Brain: Merging Cognitive, Neurological, and Evolutionary Perspectives.

Authors:  Rachel M Brown; Stefan L K Gruijters; Sonja A Kotz
Journal:  J Gerontol B Psychol Sci Soc Sci       Date:  2022-09-01       Impact factor: 4.942

7.  Shaky scaffolding: Age differences in cerebellar activation revealed through activation likelihood estimation meta-analysis.

Authors:  Jessica A Bernard; An D Nguyen; Hanna K Hausman; Ted Maldonado; Hannah K Ballard; T Bryan Jackson; Sydney M Eakin; Yana Lokshina; James R M Goen
Journal:  Hum Brain Mapp       Date:  2020-09-16       Impact factor: 5.038

8.  Cerebellar network organization across the human menstrual cycle.

Authors:  Morgan Fitzgerald; Laura Pritschet; Tyler Santander; Scott T Grafton; Emily G Jacobs
Journal:  Sci Rep       Date:  2020-11-26       Impact factor: 4.379

9.  Toward a unified analysis of cerebellum maturation and aging across the entire lifespan: A MRI analysis.

Authors:  José E Romero; Pierrick Coupe; Enrique Lanuza; Gwenaelle Catheline; José V Manjón
Journal:  Hum Brain Mapp       Date:  2021-01-01       Impact factor: 5.038

10.  Neuroanatomical norms in the UK Biobank: The impact of allometric scaling, sex, and age.

Authors:  Camille Michèle Williams; Hugo Peyre; Roberto Toro; Franck Ramus
Journal:  Hum Brain Mapp       Date:  2021-07-16       Impact factor: 5.038

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