Undermind Research Topic: I want to find studies on adult humans (ages 20-100+) that have used true longitudinal repeated measure designs to study variations in brain volume over several years, focusing on individuals who are relatively healthy and cognitively functional.

Research topic

I want to find studies on adult humans (ages 20-100+) that have used true longitudinal repeated measure designs to study variations in brain volume over several years, focusing on individuals who are relatively healthy and cognitively functional.

Report

The search identified several studies using true longitudinal repeated measure designs to analyze brain volume changes over multiple years in healthy, cognitively functional adults, with key findings from [1], [2], [3], and [4].

Details:

General Findings
- Study [1] monitored 653 adults with normal cognition over 10 years using annual MRI, showing region-specific brain volume trajectories and acceleration of volume reduction with older age.
- Study [2] followed 381 healthy adults over 6 years, using voxel-based morphometry to explore correlations between annual gray matter volume changes, age, and gender.
- Study [3] involved 231 healthy older adults, tracking cortical and subcortical volumetric changes over 4 years with FreeSurfer segmentation.
- Study [4] analyzed brain volumetric changes in 227 older adults aged 50-80 over several years, emphasizing differential rates of tissue loss in various brain regions.
Techniques and Regions of Interest
- All the studies predominantly used MRI and advanced volumetric analysis techniques (e.g., FreeSurfer, VBM) to measure brain volume changes.
- Key regions of consistent focus included the hippocampus, prefrontal cortex, and overall gray and white matter volumes.
Patterns of Change
- Non-linear age trajectories were observed, with acceleration of volume loss in older ages noted across multiple studies.
- Studies [1], [3], and [4] specifically highlighted cross-individual variability in brain volume changes, underscoring the importance of large cohort studies to account for this variability.
Confounding Factors
- Most studies controlled for confounding variables such as blood pressure, lifestyle factors, and cerebrovascular risk factors, reinforcing the focus on relatively healthy, cognitively functional adults.

Conclusions:

The literature search successfully identified comprehensive longitudinal studies that provide robust data on brain volume changes over several years in healthy adults. Key studies (e.g., [1], [2], [3], [4]) demonstrated consistent use of MRI and advanced neuroimaging techniques to reveal detailed patterns of brain aging, contributing valuable insights into non-linear trajectories and regional-specific volume changes.

References

Last 5 years

Last 2 years

> 1 citation per year

> 5 citations per year

Topic Match

Cit./Year

Year

Paper

Paper Relevance Summary

99.6%

15.2

2023

[1] Characterization of Brain Volume Changes in Aging Individuals With Normal Cognition Using Serial Magnetic Resonance Imaging S. Fujita, ..., and O. Abe JAMA Network Open 2023 - 21 citations - Show abstract - Cite - PDF 99.6% topic match

Provides longitudinal data on brain volume changes in aging individuals with normal cognition. Analyzes annual MRI data from 653 adults over 10 years to observe brain volume trajectories. Excludes populations with neurodegenerative diseases; employs true longitudinal design with robust MRI techniques.

Provides longitudinal data on brain volume changes in aging individuals with normal cognition. Analyzes annual MRI data from 653 adults over 10 years to observe brain volume trajectories. Excludes populations with neurodegenerative diseases; employs true longitudinal design with robust MRI techniques.

99.6%

4.9

2013

[2] A longitudinal study of age‐ and gender‐related annual rate of volume changes in regional gray matter in healthy adults Y. Taki, ..., and H. Fukuda Human Brain Mapping 2013 - 54 citations - Show abstract - Cite - PDF 99.6% topic match

Analyzes annual gray matter volume changes in healthy adults. Uses longitudinal design with 6-year follow-up on 381 community-dwelling subjects aged 20-100. Employs MRI and voxel-based morphometry, highlighting age and gender correlations in specific brain regions.

Analyzes annual gray matter volume changes in healthy adults. Uses longitudinal design with 6-year follow-up on 381 community-dwelling subjects aged 20-100. Employs MRI and voxel-based morphometry, highlighting age and gender correlations in specific brain regions.

98.8%

3.6

2020

[3] Decline Variability of Cortical and Subcortical Regions in Aging: A Longitudinal Study Silvano Sele, ..., and L. Jäncke Frontiers in Human Neuroscience 2020 - 15 citations - Show abstract - Cite - PDF Abstract: Describing the trajectories of age-related change for different brain structures has been of interest in many recent studies. However, our knowledge regarding these trajectories and their associations is still limited due to small sample sizes and low numbers of repeated measures. For the present study, we used a large longitudinal dataset (four measurements over 4 years) comprising anatomical data from a sample of healthy older adults (N = 231 at baseline). This dataset enables us to gain new insights about volumetric cortical and subcortical changes and their associations in the context of healthy aging. Brain structure volumes were derived from T1-weighted MRI scans using FreeSurfer segmentation tools. Brain structure trajectories were fitted using mixed models and latent growth curve models to gain information about the mean extent and variability of decline trajectories for different brain structures as well as the associations between individual trajectories. On the group level, our analyses indicate similar linear changes for frontal and parietal brain regions, while medial temporal regions showed an accelerated decline with advancing age. Regarding subcortical regions, some structures showed strong declines (e.g., hippocampus), others showed little decline (e.g., pallidum). Our data provide little evidence for sex differences regarding the aforementioned trajectories. Between-person variability of the person-specific slopes (random slopes) was largest in subcortical and medial temporal brain structures. When looking at the associations between the random slopes from each brain structure, we found that the decline is largely homogenous across the majority of cortical brain structures. In subcortical and medial temporal brain structures, however, more heterogeneity of the decline was observed, meaning that the extent of the decline in one structure is less predictive of the decline in another structure. Taken together, our study contributes to enhancing our understanding of structural brain aging by demonstrating (1) that average volumetric change differs across the brain and (2) that there are regional differences with respect to between-person variability in the slopes. Moreover, our data suggest (3) that random slopes are highly correlated across large parts of the cerebral cortex but (4) that some brain regions (i.e., medial temporal regions) deviate from this homogeneity. 98.8% topic match

Provides longitudinal data on brain volume changes in healthy aging. Examined cortical and subcortical volumes using MRI in 231 healthy older adults over 4 years. Utilized FreeSurfer for segmentation; focused on healthy, cognitively functional individuals.

Provides longitudinal data on brain volume changes in healthy aging. Examined cortical and subcortical volumes using MRI in 231 healthy older adults over 4 years. Utilized FreeSurfer for segmentation; focused on healthy, cognitively functional individuals.

98.3%

2.5

2022

[4] Reserve and Maintenance in the Aging Brain: A Longitudinal Study of Healthy Older Adults E. Bagarinao, ..., and G. Sobue eNeuro 2022 - 7 citations - Show abstract - Cite - PDF 98.3% topic match

Provides longitudinal data on brain volume changes in healthy older adults. Quantifies volumetric changes using MRI in adults aged 50-80 over several years. Focuses on age-related changes, excluding pathological conditions, using advanced neuroimaging techniques.

Provides longitudinal data on brain volume changes in healthy older adults. Quantifies volumetric changes using MRI in adults aged 50-80 over several years. Focuses on age-related changes, excluding pathological conditions, using advanced neuroimaging techniques.

98.1%

41.4

2014

[5] Differential Longitudinal Changes in Cortical Thickness, Surface Area and Volume across the Adult Life Span: Regions of Accelerating and Decelerating Change A. B. Storsve, ..., and K. Walhovd The Journal of Neuroscience 2014 - 428 citations - Show abstract - Cite - PDF 98.1% topic match

Explores longitudinal changes in cortical structure in healthy adults. Investigates cortical thickness, surface area, and volume in 207 adults aged 23–87 over 3.6 years. Focus on well-screened, healthy individuals; average follow-up interval might be considered relatively short.

Explores longitudinal changes in cortical structure in healthy adults. Investigates cortical thickness, surface area, and volume in 207 adults aged 23–87 over 3.6 years. Focus on well-screened, healthy individuals; average follow-up interval might be considered relatively short.

97.4%

141.0

2005

[6] Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. N. Raz, ..., and J. Acker Cerebral cortex 2005 - 2674 citations - Show abstract - Cite - PDF 97.4% topic match

Provides longitudinal measures of five-year changes in regional brain volumes in healthy adults. Examines average and individual differences in volume changes using latent difference score modeling. Includes relevant brain regions like the hippocampus and cerebellum; notes effects of age, sex, and hypertension.

Provides longitudinal measures of five-year changes in regional brain volumes in healthy adults. Examines average and individual differences in volume changes using latent difference score modeling. Includes relevant brain regions like the hippocampus and cerebellum; notes effects of age, sex, and hypertension.

96.4%

8.8

2014

[7] Longitudinal Assessment of Global and Regional Rate of Grey Matter Atrophy in 1,172 Healthy Older Adults: Modulation by Sex and Age F. Crivello, ..., and B. Mazoyer PLoS ONE 2014 - 87 citations - Show abstract - Cite - PDF 96.4% topic match

Provides longitudinal data on gray matter atrophy in healthy older adults. Focuses on annualized rate of global and regional GM loss using VBM over 4 years. Examines 1,172 participants aged 65-82, excludes younger adults, meets MRI and health criteria.

Provides longitudinal data on gray matter atrophy in healthy older adults. Focuses on annualized rate of global and regional GM loss using VBM over 4 years. Examines 1,172 participants aged 65-82, excludes younger adults, meets MRI and health criteria.

95.9%

6.1

1998

[8] Brain volume preserved in healthy elderly through the eleventh decade E. Mueller, ..., and JEFFREY A. Kaye Neurology 1998 - 159 citations - Show abstract - Cite 95.9% topic match

First bullet point: Shows brain volume changes in healthy elderly using longitudinal MRI. Second bullet point: Studied 46 cognitively healthy subjects over 5 years, focusing on different age groups (mean ages: 70, 81, 87 years). Third bullet point: Uses repeated measures and advanced MRI; relevant brain regions include hippocampus and lobar regions.

First bullet point: Shows brain volume changes in healthy elderly using longitudinal MRI. Second bullet point: Studied 46 cognitively healthy subjects over 5 years, focusing on different age groups (mean ages: 70, 81, 87 years). Third bullet point: Uses repeated measures and advanced MRI; relevant brain regions include hippocampus and lobar regions.

94.9%

39.7

2003

[9] A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. R. Scahill, ..., and Nick C Fox Archives of neurology 2003 - 845 citations - Show abstract - Cite 94.9% topic match

Provides longitudinal data on brain volume changes in healthy adults. Examines brain volume and atrophy rates using MRI in subjects aged 31-84. Includes multiple brain regions but has a relatively small sample size (n=39), which might limit generalizability.

Provides longitudinal data on brain volume changes in healthy adults. Examines brain volume and atrophy rates using MRI in subjects aged 31-84. Includes multiple brain regions but has a relatively small sample size (n=39), which might limit generalizability.

94.1%

61.9

2003

[10] Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain S. Resnick, ..., and C. Davatzikos The Journal of Neuroscience 2003 - 1331 citations - Show abstract - Cite - PDF 94.1% topic match

Provides direct longitudinal measurements of brain volume changes in older adults. Analyzes gray and white matter volume loss over a 4-year period using MRI. Focuses on nondemented, relatively healthy adults aged 59-85. Suitable for studying age-related brain changes, but limited to an older age range and relatively short follow-up duration.

Provides direct longitudinal measurements of brain volume changes in older adults. Analyzes gray and white matter volume loss over a 4-year period using MRI. Focuses on nondemented, relatively healthy adults aged 59-85. Suitable for studying age-related brain changes, but limited to an older age range and relatively short follow-up duration.

93.1%

17.5

2012

[11] Brain structural trajectories over the adult lifespan G. Ziegler, ..., and Christian Gaser Human Brain Mapping 2012 - 211 citations - Show abstract - Cite - PDF 93.1% topic match

Provides: Analysis of gray matter volume changes in healthy adults. Details: Shows nonlinear trajectories of gray matter in adults aged 19-86 years using T1-weighted MR images. Relevance: Includes a wide age range and observes non-linear volume changes over time but does not specify repeated measures or follow-up period.

Provides: Analysis of gray matter volume changes in healthy adults. Details: Shows nonlinear trajectories of gray matter in adults aged 19-86 years using T1-weighted MR images. Relevance: Includes a wide age range and observes non-linear volume changes over time but does not specify repeated measures or follow-up period.

92.1%

15.2

2005

[12] Preservation of limbic and paralimbic structures in aging S. Grieve, ..., and E. Gordon Human Brain Mapping 2005 - 293 citations - Show abstract - Cite - PDF 92.1% topic match

Provides: Longitudinal analysis of gray matter loss in 223 healthy subjects. Details: Significant age-related loss in frontal/parietal cortices; preservation in limbic structures (amygdala, hippocampus, thalamus). Relevance: Covers healthy adults, uses true longitudinal design, MRI-based volumetric measures; does not specify duration of follow-up.

Provides: Longitudinal analysis of gray matter loss in 223 healthy subjects. Details: Significant age-related loss in frontal/parietal cortices; preservation in limbic structures (amygdala, hippocampus, thalamus). Relevance: Covers healthy adults, uses true longitudinal design, MRI-based volumetric measures; does not specify duration of follow-up.

91.6%

0.0

2024

[13] Reliability of structural brain change in cognitively healthy adult samples D. Vidal-Piñeiro, ..., and A. Fjell bioRxiv 2024 - 0 citations - Show abstract - Cite - PDF 91.6% topic match

Provides reliability estimates of structural brain changes in cognitively healthy adults using longitudinal data. Assesses influence of follow-up time and number of observations, utilizing FreeSurfer for measurements. Relates to topic via longitudinal design with robust neuroimaging and healthy adult focus, but confirm duration and exact methods for final relevance.

Provides reliability estimates of structural brain changes in cognitively healthy adults using longitudinal data. Assesses influence of follow-up time and number of observations, utilizing FreeSurfer for measurements. Relates to topic via longitudinal design with robust neuroimaging and healthy adult focus, but confirm duration and exact methods for final relevance.

87.1%

1.1

2020

[14] Brain structure changes over time in normal and mildly impaired aged persons Charles D. Smith, ..., and A. Andersen AIMS Neuroscience 2020 - 5 citations - Show abstract - Cite 87.1% topic match

Provides longitudinal data on brain volume changes in healthy elderly adults. Analyzed MRI images over a median period of 5.8 years in adults aged 70-78. The study uses a true longitudinal design but focuses only on an elderly cohort (ages 70-78).

Provides longitudinal data on brain volume changes in healthy elderly adults. Analyzed MRI images over a median period of 5.8 years in adults aged 70-78. The study uses a true longitudinal design but focuses only on an elderly cohort (ages 70-78).

84.5%

19.8

2005

[15] White matter lesion progression, brain atrophy, and cognitive decline: The Austrian stroke prevention study R. Schmidt, ..., and F. Fazekas Annals of Neurology 2005 - 378 citations - Show abstract - Cite 84.5% topic match

Investigates brain volume changes and lesion progression in elderly adults. Conducted MRI scans and cognitive tests over a 6-year period in 329 participants. Focuses on elderly and includes cognitive decline assessment, potentially less relevant due to inclusion of brain lesions.

Investigates brain volume changes and lesion progression in elderly adults. Conducted MRI scans and cognitive tests over a 6-year period in 329 participants. Focuses on elderly and includes cognitive decline assessment, potentially less relevant due to inclusion of brain lesions.

84.3%

3.9

2001

[16] Brain Volume Changes on Longitudinal Magnetic Resonance Imaging in Normal Older People Yong Tang, ..., and R. Baloh Journal of Neuroimaging 2001 - 91 citations - Show abstract - Cite 84.3% topic match

Provides longitudinal analysis of brain volume changes in older adults. Studied 66 participants aged 74-87 over approximately 4.4 years using MRI. Focused on normal, healthy senior adults; less relevant due to narrower age range and shorter follow-up.

Provides longitudinal analysis of brain volume changes in older adults. Studied 66 participants aged 74-87 over approximately 4.4 years using MRI. Focused on normal, healthy senior adults; less relevant due to narrower age range and shorter follow-up.

83.7%

21.4

2010

[17] Longitudinal changes in cortical thickness associated with normal aging M. Thambisetty, ..., and S. Resnick NeuroImage 2010 - 301 citations - Show abstract - Cite - PDF 83.7% topic match

79.5%

1.5

2023

[18] Longitudinal Patterns of Brain Changes in a Community Sample in Relation to Aging and Cognitive Status. W. Chwa, ..., and C. Raji Journal of Alzheimer's disease : JAD 2023 - 2 citations - Show abstract - Cite 79.5% topic match

Studies longitudinal changes in brain morphometry with aging. Analyzes healthy and probable AD participants over an average of 5.36 years. Uses MRI and FreeSurfer, but inclusion of probable AD participants may not fit the criteria for "relatively healthy and cognitively functional" individuals.

Studies longitudinal changes in brain morphometry with aging. Analyzes healthy and probable AD participants over an average of 5.36 years. Uses MRI and FreeSurfer, but inclusion of probable AD participants may not fit the criteria for "relatively healthy and cognitively functional" individuals.

78.1%

8.4

2003

[19] A longitudinal study of brain morphometrics using quantitative magnetic resonance imaging and difference image analysis Rebecca S. N. Liu, ..., and J. Duncan NeuroImage 2003 - 178 citations - Show abstract - Cite 78.1% topic match

73.8%

14.7

2007

[20] Longitudinal MRI and cognitive change in healthy elderly. J. Kramer, ..., and Helena C. Chui Neuropsychology 2007 - 255 citations - Show abstract - Cite - PDF 73.8% topic match

72.2%

5.7

2012

[21] A longitudinal study of brain volume changes in normal aging. H. Takao, ..., and K. Ohtomo European journal of radiology 2012 - 69 citations - Show abstract - Cite 72.2% topic match

67.4%

0.0

2024

[22] Population clustering of structural brain aging and its association with brain development Haojing Duan, ..., and Jianfeng Feng medRxiv 2024 - 0 citations - Show abstract - Cite - PDF 67.4% topic match

Provides insights into structural brain aging using both cross-sectional and longitudinal data. Identifies two brain aging patterns among 37,013 healthy participants from the UK Biobank. Integrates longitudinal neuroimaging from adolescence and discusses genetic influences on brain aging; relevant for method but broadens age range.

Provides insights into structural brain aging using both cross-sectional and longitudinal data. Identifies two brain aging patterns among 37,013 healthy participants from the UK Biobank. Integrates longitudinal neuroimaging from adolescence and discusses genetic influences on brain aging; relevant for method but broadens age range.

65.1%

1.1

2021

[23] Generalizing Longitudinal Age Effects on Brain Structure – A Two-Study Comparison Approach C. Jockwitz, ..., and S. Caspers Frontiers in Human Neuroscience 2021 - 4 citations - Show abstract - Cite - PDF 65.1% topic match

63.5%

30.0

2009

[24] Longitudinal pattern of regional brain volume change differentiates normal aging from MCI I. Driscoll, ..., and S. Resnick Neurology 2009 - 461 citations - Show abstract - Cite - PDF 63.5% topic match

Provides longitudinal analysis of brain volume changes in older adults. Evaluates 138 nondemented individuals aged 64-86 years over up to 10 years. Focuses on differentiation between normal aging and progression to mild cognitive impairment (MCI).

Provides longitudinal analysis of brain volume changes in older adults. Evaluates 138 nondemented individuals aged 64-86 years over up to 10 years. Focuses on differentiation between normal aging and progression to mild cognitive impairment (MCI).

62.7%

3.4

2018

[25] APOE&egr;4 Genotype and Hypertension Modify 8-year Cortical Thinning: Five Occasion Evidence from the Seattle Longitudinal Study P. Rast, ..., and Sherry L. Willis Cerebral Cortex 2018 - 22 citations - Show abstract - Cite - PDF 62.7% topic match

61.6%

31.0

2012

[26] Human brain changes across the life span: A review of 56 longitudinal magnetic resonance imaging studies A. Hedman, ..., and H. H. Hulshoff Pol Human Brain Mapping 2012 - 379 citations - Show abstract - Cite - PDF 61.6% topic match

Provides an integration of findings from 56 longitudinal MRI studies. Details whole brain volume changes across various age ranges in healthy individuals. Includes participants aged 4 to 88, which may limit relevance for focusing on adults aged 20-100+.

Provides an integration of findings from 56 longitudinal MRI studies. Details whole brain volume changes across various age ranges in healthy individuals. Includes participants aged 4 to 88, which may limit relevance for focusing on adults aged 20-100+.

61.2%

1.7

2015

[27] Age-Related Changes in Frontal and Temporal Lobe Volumes in Men G. Bartzokis, ..., and J. Mintz Journal Not Provided 2015 - 17 citations - Show abstract - Cite 61.2% topic match

Provides longitudinal data on brain volume changes in adult men. Examines age-related linear and quadratic changes in frontal and temporal lobe volumes using MRI. Population limited to men, age range 19-76, with a significant focus on white matter changes into midlife.

Provides longitudinal data on brain volume changes in adult men. Examines age-related linear and quadratic changes in frontal and temporal lobe volumes using MRI. Population limited to men, age range 19-76, with a significant focus on white matter changes into midlife.

61.1%

198.6

2001

[28] A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains C. Good, ..., and Richard S. J. Frackowiak NeuroImage 2001 - 4628 citations - Show abstract - Cite 61.1% topic match

Provides voxel-based morphometric analysis of brain volume changes with age. Examines effects on grey and white matter in 465 normal adults. Not a true longitudinal study; uses cross-sectional data, limiting relevance.

Provides voxel-based morphometric analysis of brain volume changes with age. Examines effects on grey and white matter in 465 normal adults. Not a true longitudinal study; uses cross-sectional data, limiting relevance.

36.4%

3.2

2022

[29] Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation J. Gómez-Ramírez, ..., and J. González-Rosa Brain Sciences 2022 - 8 citations - Show abstract - Cite - PDF 36.4% topic match

Investigates age-related brain volume changes in elderly individuals. Analyzes 3514 MRI scans of healthy individuals aged 69-88 using automated brain segmentation. Focuses on cross-sectional, machine learning prediction of age, not longitudinal design.

Investigates age-related brain volume changes in elderly individuals. Analyzes 3514 MRI scans of healthy individuals aged 69-88 using automated brain segmentation. Focuses on cross-sectional, machine learning prediction of age, not longitudinal design.

30.9%

0.7

2004

[30] [Brain volumetric MRI study in healthy elderly persons using statistical parametric mapping]. Yoshinao Miyahira, ..., and Yuko Takeda Seishin shinkeigaku zasshi = Psychiatria et neurologia Japonica 2004 - 14 citations - Show abstract - Cite Abstract: PURPOSE To investigate the age-related changes in the brains of healthy elderly subjects, a volumetric MRI was performed using Statistical Parametric Mapping (SPM) and Region of Interest (ROI) methods. We determined the volumes of the whole brain, the gray matter, the prefrontal cortex, the hippocampus and the entorhinal cortex. SUBJECTS AND METHODS We examined 61 subjects (29 males and 32 females) aged 61 to 91 years (mean +/- SD = 72.4 +/- 7.85) who were residents of Okinawa prefecture, living at home and highly active as demonstrated by sustained participation in senior citizens clubs and/or group activities at senior citizen welfare centers. They had no history of psychiatric disorders, neurological or significant physical illness. They had Mini-Mental State Examination scores of 24 or above (mean +/- SD = 28.3 +/- 1.57), Japanese version of the Geriatric Depression Scale scores of 9 or below, and Tokyo Metropolitan Institute of Gerontology scores of 11 or above. All subjects were dextral. This study received prior approval by the participating medical institution. Informed consent was obtained from the individual subjects. Volumetric MRI was performed using a 1.5 Tesla MRI scanner (Magnetom Vision, Siemens). T1-weighted images were acquired in the coronal plane (perpendicular to anterior commissure-posterior commissure line) using a 3D-FLASH (fast low angle shot) sequence. Imaging parameters were: TR = 35 ms, TE = 5 ms, Flip angle = 45 deg., Field of View = 240 mm, Matrix 256 x 256, Pixel 0.9375 x 0.9375, Slice thickness 1.5 mm, Gapless. MR image data were analyzed by SPM using an imaging analysis software MEDx (Sensor Systems Inc.) running on UNIX workstation (Sun SPARC Solaris 7, Sun Microsystems, Inc.). Volumetry was performed by automatic segmentation technique using SPM for the whole brain and the gray matter, and manual tracing using ROI based quantitative methods for the prefrontal cortex, the hippocampus and the entorhinal cortex. Local volumes were normalized by the intracranial volume and the gray matter volume. Correlation to age was determined by Pearson's correlation coefficient. Volumetric MRI was performed under blinded conditions. RESULTS Volumetric MRI of normal brain in healthy elderly subjects showed age-related changes. The whole brain (r = -0.568, p < 0.01), the gray matter (r = -0.406, p < 0.01), the prefrontal cortex (r = -0.470, p < 0.01), the hippocampus (r = -0.305, p < 0.05), and the entorhinal cortex (r = -0.455, p < 0.01) volume significantly decreased with age. The age-related hippocampal volume reduction was similar to the gray matter reduction, but the age-related prefrontal cortex and entorhinal cortex volume reductions were greater than the gray matter volume reduction. The prefrontal cortex and the hippocampal volumes differed by sex, being greater in females (p < 0.05). The hippocampal volume was lateralized: right side was greater than the left (p < 0.01). The entorhinal cortex volume was lateralized so that the left side was greater than the right (p < 0.01). CONCLUSION As a result of volumetric MRI using SPM, age-related changes of normal brain in healthy elderly persons did not always show unified atrophy. The ratio of atrophy was different by a local area. Our findings suggested that the most marked age-related brain volume reductions were seen in the prefrontal cortex and the entorhinal cortex. The age-related hippocampal volume reduction was similar to the gray matter reduction. 30.9% topic match

Provides data on brain volume changes in healthy elderly using MRI. Examines volumes of entire brain, gray matter, prefrontal cortex, hippocampus, and entorhinal cortex in 61 elderly individuals. Focuses on individuals aged 61-91 with high cognitive function, but not a true longitudinal design, limiting its relevance.

Provides data on brain volume changes in healthy elderly using MRI. Examines volumes of entire brain, gray matter, prefrontal cortex, hippocampus, and entorhinal cortex in 61 elderly individuals. Focuses on individuals aged 61-91 with high cognitive function, but not a true longitudinal design, limiting its relevance.

30.3%

13.8

2004

[31] A voxel-based morphometric study to determine individual differences in gray matter density associated with age and cognitive change over time. D. Tisserand, ..., and J. Jolles Cerebral cortex 2004 - 277 citations - Show abstract - Cite - PDF 30.3% topic match

29.9%

4.8

2016

[32] Age Differences in Prefrontal Surface Area and Thickness in Middle Aged to Older Adults V. Dotson, ..., and A. Woods Frontiers in Aging Neuroscience 2016 - 42 citations - Show abstract - Cite - PDF 29.9% topic match

26.6%

4.2

1994

[33] Lack of age-related differences in temporal lobe volume of very healthy adults. C. DeCarli, ..., and B. Horwitz AJNR. American journal of neuroradiology 1994 - 128 citations - Show abstract - Cite 26.6% topic match

24.0%

3.1

2020

[34] Subcortical Volume Trajectories across the Lifespan: Data from 18,605 healthy individuals aged 3-90 years D. Dima, ..., and S. Frangou bioRxiv 2020 - 14 citations - Show abstract - Cite - PDF 24.0% topic match

21.9%

1.2

2022

[35] Factors Influencing Change in Brain-Predicted Age Difference in a Cohort of Healthy Older Individuals J. Wrigglesworth, ..., and J. Ryan Journal of Alzheimer's Disease Reports 2022 - 3 citations - Show abstract - Cite - PDF 21.9% topic match

21.8%

19.4

2013

[36] Variation in longitudinal trajectories of regional brain volumes of healthy men and women (ages 10 to 85years) measured with atlas-based parcellation of MRI A. Pfefferbaum, ..., and E. Sullivan NeuroImage 2013 - 228 citations - Show abstract - Cite - PDF 21.8% topic match

20.0%

0.0

2001

[37] PII: S0197-4580(01)00217-2 Terry L. Jernigana, ..., and John R. Hesselinka Journal Not Provided 2001 - 0 citations - Show abstract - Cite 20.0% topic match

17.0%

13.3

1991

[38] Gender differences in age effect on brain atrophy measured by magnetic resonance imaging. R. Gur, ..., and R. Gur Proceedings of the National Academy of Sciences of the United States of America 1991 - 446 citations - Show abstract - Cite 17.0% topic match

15.1%

0.0

2009

[39] Normal Brain Aging and its Risk Factors – Analysis of Brain MRI Database of Healthy Japanese Subjects H. Fukuda, ..., and R. Kawashima Journal Not Provided 2009 - 0 citations - Show abstract - Cite 15.1% topic match

15.0%

5.3

2021

[40] Novel Volumetric and Surface-Based Magnetic Resonance Indices of the Aging Brain – Does Male and Female Brain Age in the Same Way? P. Podgórski, ..., and A. Zimny Frontiers in Neurology 2021 - 18 citations - Show abstract - Cite - PDF Abstract: Introduction: Novel post-processing methods allow not only for assessment of brain volumetry or cortical thickness based on magnetic resonance imaging (MRI) but also for more detailed analysis of cortical shape and complexity using parameters such as sulcal depth, gyrification index, or fractal dimension. The aim of this study was to analyze changes in brain volumetry and other cortical indices during aging in men and women. Material and Methods: Material consisted of 697 healthy volunteers (aged 38–80 years; M/F, 264/443) who underwent brain MRI using a 1.5-T scanner. Voxel-based volumetry of total gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) was performed followed by assessment of cortical parameters [cortical thickness (CT), sulcal depth (SD), gyrification index (GI), and fractal dimension (FD)] in 150 atlas locations using surface-based morphometry with a region-based approach. All parameters were compared among seven age groups (grouped every 5 years) separately for men and women. Additionally, percentile curves for men and women were provided for total volumes of GM, WM, and CSF. Results: In men and women, a decrease in GM and WM volumes and an increase in CSF volume seem to progress slowly since the age of 45. In men, significant GM and WM loss as well as CSF increase start above 55 years of age, while in women, significant GM loss starts above 50 and significant WM loss as well as CSF increase above 60. CT was found to significantly decrease with aging in 39% of locations in women and in 36% of locations in men, SD was found to increase in 13.5% of locations in women and in 1.3% of locations in men, GI was decreased in 3.4% of locations in women and in 2.0% of locations in men, and FD was changed in 2.7% of locations in women compared to 2.0% in men. Conclusions: Male and female brains start aging at the similar age of 45. Compared to men, in women, the cortex is affected earlier and in the more complex pattern regarding not only cortical loss but also other alterations within the cortical shape, with relatively longer sparing of WM volume. 15.0% topic match

13.9%

15.3

1998

[41] Sex differences in brain aging: a quantitative magnetic resonance imaging study. C. Coffey, ..., and Bryan Archives of neurology 1998 - 409 citations - Show abstract - Cite 13.9% topic match

12.8%

0.0

2024

[42] Subtypes of brain change in aging and their associations with cognition and Alzheimer’s disease biomarkers Elettra Capogna, ..., and D. Vidal-Piñeiro bioRxiv 2024 - 0 citations - Show abstract - Cite 12.8% topic match

11.8%

36.7

2013

[43] The structure of the cerebral cortex across adult life: age-related patterns of surface area, thickness, and gyrification. Larson J. Hogstrom, ..., and A. Fjell Cerebral cortex 2013 - 402 citations - Show abstract - Cite - PDF 11.8% topic match

11.5%

10.8

2011

[44] Correlations among Brain Gray Matter Volumes, Age, Gender, and Hemisphere in Healthy Individuals Y. Taki, ..., and H. Fukuda PLoS ONE 2011 - 143 citations - Show abstract - Cite - PDF 11.5% topic match

11.2%

3.3

2022

[45] Brain Morphometry and Cognitive Performance in Normal Brain Aging: Age- and Sex-Related Structural and Functional Changes Yauhen Statsenko, ..., and M. Ljubisavljevic Frontiers in Aging Neuroscience 2022 - 9 citations - Show abstract - Cite - PDF Abstract: Background The human brain structure undergoes considerable changes throughout life. Cognitive function can be affected either negatively or positively. It is challenging to segregate normal brain aging from the accelerated one. Objective To work out a descriptive model of brain structural and functional changes in normal aging. Materials and Methods By using voxel-based morphometry and lesion segmentation along with linear statistics and machine learning (ML), we analyzed the structural changes in the major brain compartments and modeled the dynamics of neurofunctional performance throughout life. We studied sex differences in lifelong dynamics of brain volumetric data with Mann-Whitney U-test. We tested the hypothesis that performance in some cognitive domains might decline as a linear function of age while other domains might have a non-linear dependence on it. We compared the volumetric changes in the major brain compartments with the dynamics of psychophysiological performance in 4 age groups. Then, we tested linear models of structural and functional decline for significant differences between the slopes in age groups with the T-test. Results White matter hyperintensities (WMH) are not the major structural determinant of the brain normal aging. They should be viewed as signs of a disease. There is a sex difference in the speed and/or in the onset of the gray matter atrophy. It either starts earlier or goes faster in males. Marked sex difference in the proportion of total cerebrospinal fluid (CSF) and intraventricular CSF (iCSF) justifies that elderly men are more prone to age-related brain atrophy than women of the same age. Conclusion The article gives an overview and description of the conceptual structural changes in the brain compartments. The obtained data justify distinct patterns of age-related changes in the cognitive functions. Cross-life slowing of decision-making may follow the linear tendency of enlargement of the interhemispheric fissure because the center of task switching and inhibitory control is allocated within the medial wall of the frontal cortex, and its atrophy accounts for the expansion of the fissure. Free online tool at https://med-predict.com illustrates the tests and study results. 11.2% topic match

10.7%

8.2

2019

[46] Age‐related structural and functional variations in 5,967 individuals across the adult lifespan Na Luo, ..., and V. Calhoun Human Brain Mapping 2019 - 41 citations - Show abstract - Cite - PDF 10.7% topic match

10.4%

0.0

2021

[47] Longitudinal indices of human cognition and brain structure E. Johnson and Kevin T. Jones Journal of Neuroscience Research 2021 - 0 citations - Show abstract - Cite Abstract: Changes in human cognition, brain structure, and function occur with development and throughout aging. This change occurs across timescales of years, and scientific inquiry into these changes has inspired the neuroscientific fields of development, life span, and aging. Likewise, interventions that remediate or enhance cognitive performance can thwart or truncate the temporal progression by which change might naturally occur, and are drawing increasing interest from basic science, clinical, and commercial audiences alike. Understanding the neural basis of cognitive change is a primary goal of human neuroscience. Although crosssectional studies inform questions related to life span and intervention differences, only longitudinal studies which compare the same participants across multiple time points can mechanistically explain change. This In Focus issue brings together longitudinal research from developmental neuroscience and cognitive training in effort to gain insights into how changes occur in cognition and brain structure over timescales of weeks to years. There are unique challenges associated with longitudinal research. Logistical challenges include planning, sometimes years in advance, for potential problems related to participant retention and research implementation. Furthermore, longitudinal studies are resource consuming in terms of finances and manpower, often preventing earlyand midcareer researchers from pursuing ambitious, potentially paradigmshifting lines of research. Here, research involving human participants was brought to a standstill in March 2020 due to the unforeseen global COVID19 pandemic. Scientific researchers had to adjust plans for data collection and dissemination, rework project aims, reconsider what is feasible, and strive to balance professional and personal while working from home. Perhaps the least devastating outcome of the pandemic was the 50% dropout of planned contributions to this In Focus issue. With that in mind, below we showcase the three articles of this issue. We then conclude with a call for future research that takes a longitudinal approach across multiple intersecting fields of inquiry to bring us closer to understanding the neural basis of cognitive change. A core challenge in longitudinal research is the development and use of measures with robust test– retest reliability (Ofen et al., 2019). If a measure does not show stable outcomes in the same participant across multiple points close in time, without intervention, then it cannot reliably measure stability or change. Homayouni et al. (2021) tackled this core challenge by validating a measure of brain structure, specifically, hippocampal subfield volumes using structural MRI. The hippocampus is critical to declarative memory, and crosssectional life span differences in hippocampal subfields have been linked to life span differences in declarative memory performance (Daugherty et al., 2017). In this study, hippocampal subfields were measured in the same pediatric participants, aged 7– 20 years, at baseline and both 1month and 2year delays (Figure 1a). The reliability of the measure was demonstrated by means of structural stability over 1 month and change over 2 years, replicated in two independent samples. These results further demonstrate the utility of the measure as sensitive enough to detect longitudinal changes in brain structure over an expected maturational timescale of 2 years. Interventions that aim to remediate or enhance cognitive performance, if successful, can thwart or truncate life span timescales of change, or simply enhance performance. The other studies in this issue tackled questions related to cognitive trainingbased intervention, a field that is plagued with unreliable outcomes (Boot & Kramer, 2014; Hampshire et al., 2019; Kable et al., 2017; Owen et al., 2010; Sala & Edited by Cristina Ghiani and Junie Warrington. 10.4% topic match

10.1%

0.2

2014

[48] Cortical thinning in cognitively normal elderly cohort of 60 to 89 year old from AIBL database and vulnerable brain areas Zhongmin S. Lin, ..., and Kathryn M. McMillan https://doi.org/10.1117/12.2043101 2014 - 2 citations - Show abstract - Cite 10.1% topic match

9.8%

27.1

2001

[49] Age-related changes in frontal and temporal lobe volumes in men: a magnetic resonance imaging study. G. Bartzokis, ..., and J. Mintz Archives of general psychiatry 2001 - 635 citations - Show abstract - Cite 9.8% topic match

9.7%

2.8

2009

[50] The prevalence of cortical gray matter atrophy may be overestimated in the healthy aging brain. S. Burgmans, ..., and J. Jolles Neuropsychology 2009 - 44 citations - Show abstract - Cite - PDF 9.7% topic match

9.4%

0.0

2011

[51] Inter-regional gray matter correlation changes in normal aging Cuicui Pan, ..., and Yubo Fan 2011 IEEE International Symposium on IT in Medicine and Education 2011 - 0 citations - Show abstract - Cite 9.4% topic match

9.3%

7.3

2008

[52] Trajectories of brain loss in aging and the development of cognitive impairment Owen Carmichael, ..., and Jeffrey Kaye Neurology 2008 - 122 citations - Show abstract - Cite 9.3% topic match

8.6%

0.8

2019

[53] Quantitative evaluation of brain volume among elderly individuals in São Paulo, Brazil: a population-based study M. Rodrigues, ..., and E. Amaro Júnior Radiologia Brasileira 2019 - 4 citations - Show abstract - Cite - PDF 8.6% topic match

8.5%

34.0

2004

[54] Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume N. Raz, ..., and J. Acker Neurobiology of Aging 2004 - 701 citations - Show abstract - Cite 8.5% topic match

8.4%

0.0

2011

[55] SU‐E‐I‐111: Freesurfer MRI Data Analysis of Brain Cortical Thickness Variations in Individuals M. Dennis, ..., and Xin Wang https://doi.org/10.1118/1.3611686 2011 - 0 citations - Show abstract - Cite Abstract: Purpose: Group studies have demonstrated thinning of the brain cortex over decades as we age, but thickness is generally considered constant over shorter periods of time. This study evaluates the constancy and measurable variations in cortical thickness over time periods of minutes, weeks, months and years in individuals.Methods: Eight healthy adults (2 female, 6 male, 24–60 yrs) received back‐to‐back scans on 8 to 12 occasions. Each data acquisition consisted of 160 high resolution IR‐SPGR MRIimages yielding a 1×1×1mm resolution. Freesurfer software (http://surfer.nmr.mgh.harvard.edu) was used to analyze this data, producing tens of thousands of thickness measurements and providing automatic segmentation of the cortical regions. Differences in back‐to‐back scans were used as an estimate of measurement error. Each scan was considered a valid measure, and all combinations of scans were compared and evaluated as time periods of weeks, months and years. Confidence intervals at the 95% level were established based on the estimated error. Results: Within each individual there were regions demonstrating 95% of the measured differences to be within the confidence interval, demonstrating anatomical stability and consistency in measurements. Other areas in each individual, however, showed significantly increased variation with greater than 15% of the measured differences outside of the confidence interval. These variations were specific to individuals, with different profiles of areas and intervals of stability and of variance Conclusions: This work expands on prior group analysis to the longitudinal measurement of cortical changes within normal individuals without intervention. Understanding the dynamics of cortical thickness measures is critical if such measurements are to be used as biomarkers of physiologically significant cortical changes. Cortical areas reconsolidate in individually specific and reversible ways during week, month, and longer periods of life. 8.4% topic match

7.8%

0.3

2012

[56] Longitudinal brain volumetric changes during one year in non-elderly healthy adults: a voxel-based morphometry study R. M. Guimarães, ..., and G. Busatto Brazilian Journal of Medical and Biological Research 2012 - 4 citations - Show abstract - Cite - PDF 7.8% topic match

Longitudinal brain volume change study in healthy adults. Follows 52 adults aged 18-50 with repeated MRI scans over ~15 months. Short duration (15 months) and only non-elderly adults (18-50); not ideal for long-term aging studies.

Longitudinal brain volume change study in healthy adults. Follows 52 adults aged 18-50 with repeated MRI scans over ~15 months. Short duration (15 months) and only non-elderly adults (18-50); not ideal for long-term aging studies.

7.7%

39.6

2004

[57] Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD C. Jack, ..., and R. Petersen Neurology 2004 - 817 citations - Show abstract - Cite - PDF Abstract: Objective: To correlate different methods of measuring rates of brain atrophy from serial MRI with corresponding clinical change in normal elderly subjects, patients with mild cognitive impairment (MCI), and patients with probable Alzheimer disease (AD). Methods: One hundred sixty subjects were recruited from the Mayo Clinic Alzheimer’s Disease Research Center and Alzheimer’s Disease Patient Registry Studies. At baseline, 55 subjects were cognitively normal, 41 met criteria for MCI, and 64 met criteria for AD. Each subject underwent an MRI examination of the brain at the time of the baseline clinical assessment and then again at the time of a follow-up clinical assessment, 1 to 5 years later. The annualized changes in volume of four structures were measured from the serial MRI studies: hippocampus, entorhinal cortex, whole brain, and ventricle. Rates of change on several cognitive tests/rating scales were also assessed. Subjects who were classified as normal or MCI at baseline could either remain stable or convert to a lower-functioning group. AD subjects were dichotomized into slow vs fast progressors. Results: All four atrophy rates were greater among normal subjects who converted to MCI or AD than among those who remained stable, greater among MCI subjects who converted to AD than among those who remained stable, and greater among fast than slow AD progressors. In general, atrophy on MRI was detected more consistently than decline on specific cognitive tests/rating scales. With one exception, no differences were found among the four MRI rate measures in the strength of the correlation with clinical deterioration at different stages of the disease. Conclusions: These data support the use of rates of change from serial MRI studies in addition to standard clinical/psychometric measures as surrogate markers of disease progression in AD. Estimated sample sizes required to power a therapeutic trial in MCI were an order of magnitude less for MRI than for change measures based on cognitive tests/rating scales. 7.7% topic match

7.4%

None

[58] Results from the São Paulo Ageing and Gender in Cognitively Healthy Elders: Brain Structural Variability due to Aging and P. Curiati, ..., and MD C.T. Ferraz Alves Journal Not Provided None - 0 citations - Show abstract - Cite 7.4% topic match

4.8%

9.8

2012

[59] Neuronal and morphological bases of cognitive decline in aged rhesus monkeys Y. Hara, ..., and J. Morrison AGE 2012 - 118 citations - Show abstract - Cite 4.8% topic match

3.7%

4.7

2002

[60] Brain region and sex differences in age association with brain volume: a quantitative MRI study of healthy young adults. R. Gur, ..., and R. Gur The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2002 - 107 citations - Show abstract - Cite 3.7% topic match

3.2%

0.3

2017

[61] The changing brain in healthy aging: a multi-MRI machine and multicenter surface-based morphometry study P. Donnelly Kehoe, ..., and J. C. Gómez Symposium on Medical Information Processing and Analysis 2017 - 2 citations - Show abstract - Cite 3.2% topic match

2.1%

0.0

2020

[62] SOUND OF SILENCE Clare Wilson The Last Seat in the House 2020 - 0 citations - Show abstract - Cite 2.1% topic match

1.1%

1.4

2017

[63] Localizing Age-Related Changes in Brain Structure Using Voxel-Based Morphometry S. Mu, ..., and L. Tan Neural Plasticity 2017 - 11 citations - Show abstract - Cite - PDF 1.1% topic match

0.8%

39.7

2009

[64] One-Year Brain Atrophy Evident in Healthy Aging A. Fjell, ..., and A. Dale The Journal of Neuroscience 2009 - 590 citations - Show abstract - Cite - PDF 0.8% topic match

0.7%

0.6

2023

[65] Cognition’s dependence on functional network integrity with age is conditional on structural network integrity Xulin Liu, ..., and K. Tsvetanov Neurobiology of Aging 2023 - 1 citations - Show abstract - Cite 0.7% topic match

0.6%

1.2

2010

[66] News of cognitive cure for age-related brain shrinkage is premature: a comment on Burgmans et al. (2009). N. Raz and U. Lindenberger Neuropsychology 2010 - 17 citations - Show abstract - Cite - PDF 0.6% topic match

0.6%

21.9

2000

[67] One-year age changes in MRI brain volumes in older adults. S. Resnick, ..., and A. Zonderman Cerebral cortex 2000 - 537 citations - Show abstract - Cite - PDF 0.6% topic match

0.6%

16.2

1992

[68] Quantitative cerebral anatomy of the aging human brain C. Coffey, ..., and W. Djang Neurology 1992 - 528 citations - Show abstract - Cite 0.6% topic match

0.6%

46.0

1997

[69] Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. N. Raz, ..., and J. Acker Cerebral cortex 1997 - 1268 citations - Show abstract - Cite - PDF 0.6% topic match

0.3%

0.2

2006

[70] Unbiased Robust Template Estimation for Longitudinal Analysis in FreeSurfer M. Reuter, ..., and B. Fischl Journal Not Provided 2006 - 3 citations - Show abstract - Cite 0.3% topic match

0.3%

0.0

2017

[71] APOEε4 Genotype and Hypertension Modify Eight-year Cortical Thinning: Five Occasion Evidence from the Seattle Longitudinal Study P. Rast, ..., and S. Willis Journal Not Provided 2017 - 0 citations - Show abstract - Cite 0.3% topic match

0.2%

5.9

2022

[72] Age-related brain atrophy is not a homogenous process: Different functional brain networks associate differentially with aging and blood factors Nikola T. Markov, ..., and D. Furman Proceedings of the National Academy of Sciences of the United States of America 2022 - 11 citations - Show abstract - Cite 0.2% topic match

0.2%

12.8

2009

[73] Age, Alzheimer disease, and brain structure C. Raji, ..., and J. Becker Neurology 2009 - 190 citations - Show abstract - Cite - PDF 0.2% topic match

0.2%

1.7

2016

[74] A Cross-Sectional Voxel-Based Morphometric Study of Age- and Sex-Related Changes in Gray Matter Volume in the Normal Aging Brain Fei Peng, ..., and Zhenhu Song Journal of Computer Assisted Tomography 2016 - 15 citations - Show abstract - Cite 0.2% topic match

0.1%

0.0

2012

[75] Age-Related Changes In Female and Male Cerebral Volumes: A Study of Whole-Brain Tissue Using the Stereological Method R. Yiğiter, ..., and S. Di̇yarbaki̇r Neuroquantology 2012 - 0 citations - Show abstract - Cite - PDF 0.1% topic match

0.1%

0.0

2024

[76] Brain age of rhesus macaques over the lifespan Yang S. Liu, ..., and Bo Cao Neurobiology of Aging 2024 - 0 citations - Show abstract - Cite 0.1% topic match

0.1%

0.6

2018

[77] Everything Has Changed Kevin E. Phillips Managing Millennials 2018 - 4 citations - Show abstract - Cite 0.1% topic match

0.0%

16.8

2021

[78] Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3–90 years D. Dima, ..., and S. Frangou Human Brain Mapping 2021 - 62 citations - Show abstract - Cite - PDF 0.0% topic match

0.0%

64.5

2017

[79] Development of the Cerebral Cortex across Adolescence: A Multisample Study of Inter-Related Longitudinal Changes in Cortical Volume, Surface Area, and Thickness C. K. Tamnes, ..., and K. Mills The Journal of Neuroscience 2017 - 489 citations - Show abstract - Cite - PDF Abstract: Before we can assess and interpret how developmental changes in human brain structure relate to cognition, affect, and motivation, and how these processes are perturbed in clinical or at-risk populations, we must first precisely understand typical brain development and how changes in different structural components relate to each other. We conducted a multisample magnetic resonance imaging study to investigate the development of cortical volume, surface area, and thickness, as well as their inter-relationships, from late childhood to early adulthood (7–29 years) using four separate longitudinal samples including 388 participants and 854 total scans. These independent datasets were processed and quality-controlled using the same methods, but analyzed separately to study the replicability of the results across sample and image-acquisition characteristics. The results consistently showed widespread and regionally variable nonlinear decreases in cortical volume and thickness and comparably smaller steady decreases in surface area. Further, the dominant contributor to cortical volume reductions during adolescence was thinning. Finally, complex regional and topological patterns of associations between changes in surface area and thickness were observed. Positive relationships were seen in sulcal regions in prefrontal and temporal cortices, while negative relationships were seen mainly in gyral regions in more posterior cortices. Collectively, these results help resolve previous inconsistencies regarding the structural development of the cerebral cortex from childhood to adulthood, and provide novel insight into how changes in the different dimensions of the cortex in this period of life are inter-related. SIGNIFICANCE STATEMENT Different measures of brain anatomy develop differently across adolescence. Their precise trajectories and how they relate to each other throughout development are important to know if we are to fully understand both typical development and disorders involving aberrant brain development. However, our understanding of such trajectories and relationships is still incomplete. To provide accurate characterizations of how different measures of cortical structure develop, we performed an MRI investigation across four independent datasets. The most profound anatomical change in the cortex during adolescence was thinning, with the largest decreases observed in the parietal lobe. There were complex regional patterns of associations between changes in surface area and thickness, with positive relationships seen in sulcal regions in prefrontal and temporal cortices, and negative relationships seen mainly in gyral regions in more posterior cortices. 0.0% topic match

0.0%

4.5

2009

[80] Brain Structural Variability due to Aging and Gender in Cognitively Healthy Elders: Results from the São Paulo Ageing and Health Study P. Curiati, ..., and T. Alves American Journal of Neuroradiology 2009 - 67 citations - Show abstract - Cite - PDF 0.0% topic match

0.0%

17.9

2007

[81] Age and gender effects on human brain anatomy: A voxel-based morphometric study in healthy elderly Charles D. Smith, ..., and W. Markesbery Neurobiology of Aging 2007 - 310 citations - Show abstract - Cite 0.0% topic match

0.0%

8.5

2007

[82] Brain structural alterations before mild cognitive impairment Charles D. Smith, ..., and W. Markesbery Neurology 2007 - 148 citations - Show abstract - Cite 0.0% topic match

0.0%

0.1

2013

[83] Cortical thickness changes related to the processes of maturation and aging in healthy brains Heitor H. Cunha, ..., and C. Salmon https://doi.org/10.1117/12.2007342 2013 - 1 citations - Show abstract - Cite 0.0% topic match

0.0%

0.2

2013

[84] The Changes of Cerebral Morphology Related to Aging in Taiwanese Population H. Wang, ..., and E. Shen PLoS ONE 2013 - 2 citations - Show abstract - Cite - PDF 0.0% topic match

0.0%

0.0

2018

[85] CHAPTER 5 Amyloid-independent atrophy patterns predict time to progression to dementia in MCI M. T. Kate, ..., and W. V. D. Flier Journal Not Provided 2018 - 0 citations - Show abstract - Cite Abstract: Introduction: Amyloid pathology in subjects with mild cognitive impairment (MCI) is an important risk factor for progression to dementia due to Alzheimer’s disease. Predicting the onset of dementia is challenging even in the presence of amyloid, as time to progression varies considerably among patients and depends on the onset of neurodegeneration. Survival analysis can account for variability in time to event, but has not often been applied to MRI measurements beyond singular predefined brain regions such as the hippocampus. Here we used a voxel-wise survival analysis to identify in an unbiased fashion brain regions where decreased grey matter volume is associated with time to dementia, and assessed the effects of amyloid on these associations. Methods: We included 276 subjects with MCI (mean age 67 ± 8, 41% female, mean MMSE 26.6 ± 2.4), baseline 3D T1-weighted structural MRI, baseline cerebrospinal fluid (CSF) biomarkers and prospective clinical follow-up. We fitted for each voxel a proportional Cox hazards regression model to study whether decreased grey matter volume predicted progression to dementia in the total sample, and stratified for baseline amyloid status. Results: Dementia at follow-up occurred in 122 (44%) subjects over an average followup period of 2.5 ± 1.5 years. Baseline amyloid positivity was associated with progression to dementia (HR 2.4, p < 0.001). Within amyloid positive subjects, decreased grey matter volume in hippocampal, temporal, parietal, and frontal regions was associated with more rapid progression to dementia (median [IQR] HR across significant voxels: 1.35 [1.32 1.40]). Repeating the analysis in amyloid negative subjects revealed similar patterns (median [IQR] HR: 1.76 [1.66 1.91]). Conclusions: In subjects with MCI, both abnormal amyloid CSF and decreased grey matter volume were associated with future progression to dementia. The spatial pattern of decreased grey matter volume associated with progression to dementia was consistent for amyloid positive and amyloid negative subjects. m.tenkate-layout.indd 170 26/07/2018 17:32 171 Regional atrophy and clinical progression in MCI 0.0% topic match

0.0%

8.6

2004

[86] Voxel-based morphometry of human brain with age and cerebrovascular risk factors Y. Taki, ..., and H. Fukuda Neurobiology of Aging 2004 - 177 citations - Show abstract - Cite 0.0% topic match

0.0%

27.3

2009

[87] Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve O. Querbes, ..., and P. Celsis Brain 2009 - 421 citations - Show abstract - Cite - PDF Abstract: Brain atrophy measured by magnetic resonance structural imaging has been proposed as a surrogate marker for the early diagnosis of Alzheimer's disease. Studies on large samples are still required to determine its practical interest at the individual level, especially with regards to the capacity of anatomical magnetic resonance imaging to disentangle the confounding role of the cognitive reserve in the early diagnosis of Alzheimer's disease. One hundred and thirty healthy controls, 122 subjects with mild cognitive impairment of the amnestic type and 130 Alzheimer's disease patients were included from the ADNI database and followed up for 24 months. After 24 months, 72 amnestic mild cognitive impairment had converted to Alzheimer's disease (referred to as progressive mild cognitive impairment, as opposed to stable mild cognitive impairment). For each subject, cortical thickness was measured on the baseline magnetic resonance imaging volume. The resulting cortical thickness map was parcellated into 22 regions and a normalized thickness index was computed using the subset of regions (right medial temporal, left lateral temporal, right posterior cingulate) that optimally distinguished stable mild cognitive impairment from progressive mild cognitive impairment. We tested the ability of baseline normalized thickness index to predict evolution from amnestic mild cognitive impairment to Alzheimer's disease and compared it to the predictive values of the main cognitive scores at baseline. In addition, we studied the relationship between the normalized thickness index, the education level and the timeline of conversion to Alzheimer's disease. Normalized thickness index at baseline differed significantly among all the four diagnosis groups (P < 0.001) and correctly distinguished Alzheimer's disease patients from healthy controls with an 85% cross-validated accuracy. Normalized thickness index also correctly predicted evolution to Alzheimer's disease for 76% of amnestic mild cognitive impairment subjects after cross-validation, thus showing an advantage over cognitive scores (range 63–72%). Moreover, progressive mild cognitive impairment subjects, who converted later than 1 year after baseline, showed a significantly higher education level than those who converted earlier than 1 year after baseline. Using a normalized thickness index-based criterion may help with early diagnosis of Alzheimer's disease at the individual level, especially for highly educated subjects, up to 24 months before clinical criteria for Alzheimer's disease diagnosis are met. 0.0% topic match

0.0%

15.9

1992

[88] Health Behavior and Health Education: Theory, Research, and Practice P. Himmel Annals of Internal Medicine 1992 - 520 citations - Show abstract - Cite 0.0% topic match

0.0%

3.0

2015

[89] A voxel-based morphometric magnetic resonance imaging study of the brain detects age-related gray matter volume changes in healthy subjects of 21–45 years old Ali K. Bourisly, ..., and Mohammad Ismail The Neuroradiology Journal 2015 - 27 citations - Show abstract - Cite 0.0% topic match

0.0%

3.1

2020

[90] Moonlight Vinlaw Mudehwe Best New African Poets 2019 Anthology 2020 - 15 citations - Show abstract - Cite 0.0% topic match

0.0%

0.7

2023

[91] Changes of cortical gray matter volume during development: A Human Connectome Project study. P. Christova and A. Georgopoulos Journal of neurophysiology 2023 - 1 citations - Show abstract - Cite 0.0% topic match

0.0%

40.5

2009

[92] High consistency of regional cortical thinning in aging across multiple samples. A. Fjell, ..., and K. Walhovd Cerebral cortex 2009 - 613 citations - Show abstract - Cite - PDF 0.0% topic match

0.0%

0.6

2023

[93] Predicting individual brain MRIs at any age using style encoding generative adversarial networks S. Gadewar, ..., and N. Jahanshad Symposium on Medical Information Processing and Analysis 2023 - 1 citations - Show abstract - Cite 0.0% topic match

0.0%

27.7

2005

[94] Normal neuroanatomical variation due to age: The major lobes and a parcellation of the temporal region J. S. Allen, ..., and H. Damasio Neurobiology of Aging 2005 - 528 citations - Show abstract - Cite 0.0% topic match

0.0%

11.5

2021

[95] Differentiating the effect of antipsychotic medication and illness on brain volume reductions in first-episode psychosis: A Longitudinal, Randomised, Triple-blind, Placebo-controlled MRI Study S. Chopra, ..., and P. McGorry Neuropsychopharmacology 2021 - 42 citations - Show abstract - Cite - PDF 0.0% topic match

0.0%

0.0

2023

[96] Fine-grained age-matching improves atrophy-based detection of mild cognitive impairment more than amyloid-negative reference subjects N. Richter, ..., and O. Onur NeuroImage : Clinical 2023 - 0 citations - Show abstract - Cite 0.0% topic match

0.0%

0.0

2017

[97] The Longitudinal Effect of Structural Brain Measurements on Cognitive Abilities Fatemeh Hosseininasabnajar Journal Not Provided 2017 - 0 citations - Show abstract - Cite 0.0% topic match

0.0%

0.0

2019

[98] The effect of age on whole brain volume in controls, mild cognitive impairment and Alzheimer’s disease patients: A prospective analysis of MRI data from the ADNI data base. S. Dahbour, ..., and S. A. Alryalat Jordan Medical Journal 2019 - 0 citations - Show abstract - Cite 0.0% topic match

0.0%

0.7

2020

[99] Sex-Dependent Age Trajectories of Subcortical Brain Structures: Analysis of Large-Scale Percentile Models and Shape Morphometry C. Ching, ..., and P. Thompson bioRxiv 2020 - 3 citations - Show abstract - Cite - PDF 0.0% topic match

0.0%

0.3

2020

[100] The aging human brain: A causal analysis of the effect of sex and age on brain volume J. Gómez-Ramírez, ..., and J. González-Rosa bioRxiv 2020 - 1 citations - Show abstract - Cite - PDF 0.0% topic match

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