Inter- and intra-individual variation in brain structural-cognition relationships in aging

Mackay, Clare E.; Jansen, Michelle G; Devenyi, Gabriel A.; O'Donoghue, M Clare; Kivimäki, Mika; Singh‐Manoux, Archana; Zsoldos, Enikő; Ebmeier, Klaus P.; Chakravarty, M. Mallar; Suri, Sana

doi:10.1016/j.neuroimage.2022.119254

Cited by 19 publications

(22 citation statements)

References 130 publications

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“…Stronger genetic effects are though reportedly observed on structural (relative to functional) brain indices, while structure-function coupling in connectomic features is also under substantial genetic modulation ( Arnatkeviciute et al, 2021a , Arnatkeviciute et al, 2021b , Baum et al, 2020 , Gu et al, 2021 ). Consequently, investigations of synchronised structure-function development would augment the findings herein reported, particularly given the robust predictive power of both white and grey matter indices for AD and MDD ( Huang et al, 2022 , Lu et al, 2022 ), as well as psychopathology risk ( Aggarwal et al, 2022 , Fenchel et al, 2022 , Lalousis et al, 2022 , Park et al, 2022 , Patel et al, 2022 , Zhao et al, 2021a , Zhao et al, 2021b ).…”

Section: Limitations and Future Directionsmentioning

confidence: 68%

Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing

Petrican

Fornito

2023

Developmental Cognitive Neuroscience

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Section: Limitations and Future Directionsmentioning

confidence: 68%

Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing

Petrican

Fornito

2023

Developmental Cognitive Neuroscience

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“…Interindividual variability has been suggested to be a link between observed human behavior, cognition, and brain structure (Kanai & Rees, 2011; Llera et al., 2019). Thus, investigating interindividual variability within the human brain is a crucial area of focus in neuroscience (Genon et al., 2022), and has been the topic of several projects such as the investigation of variations in structural brain development and age‐related cognitive decline (Mills et al., 2021; Patel et al., 2022). Yet, the analysis of interindividual variability within the human brain remains challenging, especially in small structures where errors in region of interest definition are augmented (Jones & Cercignani, 2010).…”

Section: Discussionmentioning

confidence: 99%

Assessing individual variability of the entorhinal subfields in health and disease

Oltmer,

Greve,

Cerri

et al. 2023

J of Comparative Neurology

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Investigating interindividual variability is a major field of interest in neuroscience. The entorhinal cortex (EC) is essential for memory and affected early in the progression of Alzheimer's disease (AD). We combined histology ground‐truth data with ultrahigh‐resolution 7T ex vivo MRI to analyze EC interindividual variability in 3D. Further, we characterized (1) entorhinal shape as a whole, (2) entorhinal subfield range and midpoints, and (3) subfield architectural location and tau burden derived from 3D probability maps. Our results indicated that EC shape varied but was not related to demographic or disease factors at this preclinical stage. The medial intermediate subfield showed the highest degree of location variability in the probability maps. However, individual subfields did not display the same level of variability across dimensions and outcome measure, each providing a different perspective. For example, the olfactory subfield showed low variability in midpoint location in the superior–inferior dimension but high variability in anterior–posterior, and the subfield entorhinal intermediate showed a large variability in volumetric measures but a low variability in location derived from the 3D probability maps. These findings suggest that interindividual variability within the entorhinal subfields requires a 3D approach incorporating multiple outcome measures. This study provides 3D probability maps of the individual entorhinal subfields and respective tau pathology in the preclinical stage (Braak I and II) of AD. These probability maps illustrate the subfield average and may serve as a checkpoint for future modeling.

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“…The same is likely true for brain aging, as some older individuals display relative maintenance of brain structure and function, whereas others show marked age-related changes ( Nyberg et al. 2012 ; Patel et al. 2022 ).…”

Section: Introductionmentioning

confidence: 94%

Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates

et al. 2022

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It is well documented that some brain regions, such as association cortices, caudate, and hippocampus, are particularly prone to age-related atrophy, but it has been hypothesized that there are individual differences in atrophy profiles. Here, we document heterogeneity in regional-atrophy patterns using latent-profile analysis of 1,482 longitudinal magnetic resonance imaging observations. The results supported a 2-group solution reflecting differences in atrophy rates in cortical regions and hippocampus along with comparable caudate atrophy. The higher-atrophy group had the most marked atrophy in hippocampus and also lower episodic memory, and their normal caudate atrophy rate was accompanied by larger baseline volumes. Our findings support and refine models of heterogeneity in brain aging and suggest distinct mechanisms of atrophy in striatal versus hippocampal-cortical systems.

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Inter- and intra-individual variation in brain structural-cognition relationships in aging

Cited by 19 publications

References 130 publications

Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing

Adolescent neurodevelopment and psychopathology: The interplay between adversity exposure and genetic risk for accelerated brain ageing

Assessing individual variability of the entorhinal subfields in health and disease

Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates

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