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Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging
Open accessAbstract
With age, the brain undergoes both structural and functional alterations, probably resulting in reported cognitive declines. Relatively few investigations have sought to identify those areas that remain intact with aging, or undergo the least deterioration, which might underlie cognitive preservations. Our aim here was to establish a comprehensive profile of both structural and functional changes in the aging brain, using up-to-date voxel-based methodology (i.e. optimized voxel-based morphometry (VBM) procedure; resting-state (18)FDG-PET with correction for partial volume effects (PVE)) in 45 optimally healthy subjects aged 20-83 years. Negative and positive correlations between age and both gray matter (GM) volume and (18)FDG uptake were assessed. The frontal cortex manifested the greatest deterioration, both structurally and functionally, whereas the anterior hippocampus, the thalamus and (functionally) the posterior cingulate cortex were the least affected. Our results support the developmental theory which postulates that the first regions to emerge phylogenetically and ontogenetically are the most resistant to age effects, and the last ones the most vulnerable. Furthermore, the lesser affected anterior hippocampal region, together with the lesser functional alteration of the posterior cingulate cortex, appear to mark the parting of the ways between normal aging and Alzheimer's disease, which is characterized by early and prominent deterioration of both structures.
Overview
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Written from the paper’s full text.
TL;DR
In 45 optimally healthy adults aged 20–83, normal aging showed the greatest structural and metabolic deterioration in frontal cortex, while anterior hippocampus, thalamus, and posterior cingulate cortex were among the least affected. The authors interpret this pattern as supporting developmental theory and as distinguishing normal aging from Alzheimer’s disease.
Key findings
- Absolute gray matter (GM) loss was mainly bilateral frontal (dorsolateral prefrontal, sensorimotor, orbitofrontal), with additional loss in superior occipital/parietal cortices, insula, posterior hippocampus, cerebellum, and periventricular/caudate/anterior thalamic regions.
- Highest 18FDG uptake decreases were bilateral superior medial frontal, motor, anterior/middle cingulate, parietal regions (left-predominant), and superior temporal pole encroaching on insular/orbitofrontal cortex.
- Least GM decline was mainly right anterior mesial temporal structures (anterior hippocampus, entorhinal cortex, amygdala), lateral thalamic nuclei, right calcarine region, and brainstem; least 18FDG decrease was bilateral mesial temporal (hippocampus, amygdala, parahippocampal gyrus), putamen/pallidum/lateral thalamic nuclei, right posterior cingulate/precuneus, occipito-temporal cortex, and cerebellum.
- Complementary analyses: right hippocampal head volume vs age was r = .07 (p = .65); HipMask relative metabolic hippocampal ratios vs age was r = .51 (p < .001), and HipMask measures correlated with SPM hippocampal peaks at r = .73 left and r = .70 right (p < .001 for both).
Methods
- Cross-sectional study of 45 right-handed healthy subjects aged 20–83 years (mean 49.4 ± 18; 24 females, 21 males), screened with stringent clinical, MRI, and neuropsychological criteria; MRI and resting 18FDG-PET were completed within two months.
- MRI analyzed with optimized voxel-based morphometry (VBM); PET corrected for partial volume effects using a modified Müller-Gärtner method, coregistered/normalized to a customized template, and smoothed with a 14-mm isotropic Gaussian filter.
- SPM2 voxelwise correlations with age used p < .01 FDR corrected, minimum cluster size k = 20; proportional scaling assessed relative GM and relative 18FDG changes, with complementary ROI and HipMask analyses for hippocampus.
Limitations
- The paper states that PET and MR spatial resolution differed, preventing direct comparison of atrophy and hypometabolism; possible age effects on intrinsic MR signal/contrast; PVE correction accuracy depends on MRI-PET realignment and MRI segmentation; and the cross-sectional design cannot definitively resolve hippocampal impairment because of possible cohort effects.
- The text also makes evident that PET data were relative rather than CMRglc because raw FDG uptake has no physiological meaning and CMRglc requires invasive arterial blood sampling.
- A formal psychiatric interview was not performed, as noted in the inclusion criteria.
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Claims the paper makes
42 Not checked yet · 40 Own result · 11 Own interpretation · 5 Own method · 1 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The frontal cortex manifested the greatest deterioration, both structurally and functionally, whereas the anterior hippocampus, the thalamus and (functionally) the posterior cingulate cortex were the least affected.”
- Also stated in: § 3.1. Absolute GM volume loss (Table 1, Figure 1A) · p. 10 · plain
“Absolute age-related GM volume loss was mainly located bilaterally in the frontal lobes (dorsolateral prefrontal cortex, sensorimotor areas and orbitofrontal regions).”
- Also stated in: § 3.3. Highest 18 FDG uptake decreases (Table 3, Figure 1B) · p. 10 · plain
“A relative age-related decrease in 18 FDG uptake was found bilaterally in the superior medial frontal, motor, anterior and middle cingulate cortices, as well as in the bilateral parietal regions (with left-sided predominance), including the supramarginal, superior and inferior parietal cortices.”
plaincorrelationaln = 45- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Cross-sectional age-related differences in brain structure and metabolism reflect true aging effects rather than cohort effects.
- Also stated in: § 3.1. Absolute GM volume loss (Table 1, Figure 1A) · p. 10 · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The positive correlation between age and GM volume showed that the least GM decline was mainly located in the anterior right mesial temporal lobe structures (and the left counterpart at p<0.05 FDR corrected), including the anterior part of the hippocampus, entorhinal cortex and amygdala, lateral thalamic nuclei, right calcarine region and brainstem.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Cross-sectional age-related differences in brain structure and metabolism reflect true aging effects rather than cohort effects.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Absolute age-related GM volume loss was mainly located bilaterally in the frontal lobes (dorsolateral prefrontal cortex, sensorimotor areas and orbitofrontal regions).”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Cross-sectional age-related differences in brain structure and metabolism reflect true aging effects rather than cohort effects.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A relative age-related decrease in 18 FDG uptake was found bilaterally in the superior medial frontal, motor, anterior and middle cingulate cortices, as well as in the bilateral parietal regions (with left-sided predominance), including the supramarginal, superior and inferior parietal cortices.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Cross-sectional age-related differences in brain structure and metabolism reflect true aging effects rather than cohort effects.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The least 18 FDG uptake decreases with aging was found in the bilateral mesial temporal lobes (hippocampus, amygdala and parahippocampal gyrus).”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Cross-sectional age-related differences in brain structure and metabolism reflect true aging effects rather than cohort effects.
None of the sources cited for this claim have been checked yet.
In the paper
“Consistent with the literature, less metabolic decrease was found within the inferior temporal (fusiform cortex) and occipital areas (calcarine area and lingual gyrus) [32,49,58,86].”
What the sources say
- Not checked yetcites [32] Herholz, 2002, Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET, Neuroimage source
- Not checked yetcites [49] Moeller, 1996, The metabolic topography of normal aging, J. Cereb. Blood Flow Metab. source
- Not checked yetcites [58] Petit-Taboué, 1998, Effects of healthy aging on the regional cerebral metabolic rate of glucose assessed with statistical parametric mapping, Neuroimage source
- Not checked yetcites [86] Willis, 2002, Age, sex and laterality effects on cerebral glucose metabolism in healthy adults, Psychiatry Res. (Neuroimaging) source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“It is worth noting that the posterior cingulate cortex is one of the least metabolically impaired areas with healthy aging.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“By controlling for errors liable to generate inaccurate findings, our data provide further evidence that the subcortical nuclei (putamen, pallidum) and thalamus are quite spared by increasing age.”
boosted- Asserted: passes — The authors put it forward as true.
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In the paper
“Regarding the metabolic data, we found that the anterior brain areas, notably a large part of the superior medial prefrontal cortex and the anterior and middle parts of the cingulate cortex, were the most metabolically affected in normal aging.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Moreover, we found substantial correlations between the measures obtained in each subject using the HipMask method, and the intensity values extracted from the voxel-based analysis in the left [-36;-14;-18] and right [38;-22;-12] hippocampal peaks (r = .73 and r = .70, respectively, p < .001 for both), suggesting that the SPM findings are robust and not due to spatial normalization errors.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 27
- The quote only partly states this claim. 16
- Probably not a substantial claim. 21
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The frontal cortex manifested the greatest deterioration, both structurally and functionally, whereas the anterior hippocampus, the thalamus and (functionally) the posterior cingulate cortex were the least affected.”
- Also stated in: § 3.2. Least GM volume decreases (Table 2, Figure 1A) · p. 10 · plain
“The positive correlation between age and GM volume showed that the least GM decline was mainly located in the anterior right mesial temporal lobe structures (and the left counterpart at p<0.05 FDR corrected), including the anterior part of the hippocampus, entorhinal cortex and amygdala, lateral thalamic nuclei, right calcarine region and brainstem.”
- Also stated in: § 3.4. Least 18 FDG uptake decreases (Table 4, Figure 1B) · p. 11 · plain
“The least 18 FDG uptake decreases with aging was found in the bilateral mesial temporal lobes (hippocampus, amygdala and parahippocampal gyrus).”
plainn = 45- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
- Rests on an assumption: Cross-sectional age-related differences in brain structure and metabolism reflect true aging effects rather than cohort effects.
- Also stated in: § 3.2. Least GM volume decreases (Table 2, Figure 1A) · p. 10 · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Atrophy was also found in the bilateral superior occipital and superior parietal cortices, the insular region surrounding the superior temporal areas, the posterior part of the hippocampus, the cerebellum and the periventricular areas spanning the caudate nuclei and anterior thalamic nucleus.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
- Rests on an assumption: Cross-sectional age-related differences in brain structure and metabolism reflect true aging effects rather than cohort effects.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The metabolic rate of the superior temporal pole encroaching on the insular and orbitofrontal cortices was particularly affected.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
- Rests on an assumption: Cross-sectional age-related differences in brain structure and metabolism reflect true aging effects rather than cohort effects.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The putamen, pallidum and lateral thalamic nuclei were metabolically less impaired, as well as the right posterior cingulate cortex and precuneus, both sides of the occipito-temporal cortex (calcarine, fusiform, lingual and inferior occipital cortices) and the cerebellum.”
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: Cross-sectional age-related differences in brain structure and metabolism reflect true aging effects rather than cohort effects.
None of the sources cited for this claim have been checked yet.
In the paper
“One of the limbic structures - the hippocampus - showed a lesser degree of metabolic impairment, thus confirming the findings of Willis et al. [86], who found 'preserved' hippocampal relative CMRglc (cerebral metabolic rate of glucose), and those of a very recent longitudinal study [5] over 9 years in older healthy subjects which highlighted an increased relative rCBF in the left hippocampus.”
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Finally, it is worth noting that the gray matter losses in the frontal cortex and those in the hippocampus are very different, the decline in frontal cortex proceeding monotonically downward from an early age.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“0 | -27 | -12 | 5.83 | 828 | Brainstem”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“19 | -21 | 5 | 5.63 | 768 | Thalamus (lateral nuclei) R”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“58 | -26 | 50 | 5.01 | 740 | Supramarginal/parietal inf R”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“21 | -9 | -29 | 5.68 | 1902 | Entorhinal R | 28/36”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“46 | 22 | -6 | 4.73 | 206 | Orbitofrontal inf R”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“18 | -14 | 10 | 5.40 | 95 | Thalamus (lateral nuclei) R”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“-40 | 14 | -10 | 5.46 | 481 | Orbitofrontal inf L”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“6 | 20 | 66 | 6.05 | 1520 | Frontal mid/sup/sup med R”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“-36 | -52 | 56 | 7.54 | 1372 | Parietal inf/sup/supramarginal L”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“-10 | -60 | -36 | 5.34 | 412 | Cerebellum L/R”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“-4 | 20 | 66 | 6.46 | 2681 | Frontal mid/sup/sup med L”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“26 | -64 | 18 | 7.60 | 6071 | Amygdala R”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“-16 | -19 | 5 | 5.70 | 626 | Thalamus (lateral nuclei) L”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“-36 | -16 | 22 | 6.68 | 3747 | Amygdala L”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“-16 | -16 | 8 | 5.83 | 145 | Thalamus (lateral nuclei) L”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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The quote only partly states this claim.
In the paper
“Our results support the developmental theory which postulates that the first regions to emerge phylogenetically and ontogenetically are the most resistant to age effects, and the last ones the most vulnerable.”
- Also stated in: § 4. Discussion · p. 13 · plain
“From an evolutionary perspective, our structural findings support the 'developmental hypothesis' postulated by Grieve et al. [31] to account for structural changes in normal aging.”
What the sources say
- Not checked yetcites Grieve et al. [31] Grieve, 2005, Preservation of limbic and paralimbic structures in aging, Hum. Brain Mapp. source
plaincorrelationaln = 45- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Also stated in: § 4. Discussion · p. 13 · plain
None of the sources cited for this claim have been checked yet.
In the paper
“Overall, the literature consistently reports global brain atrophy in normal adults, generally more pronounced in frontal areas (for a review, [63]), often followed by parietal, temporal and insular gray matter (GM) loss [1,2,29,31,70,81,87].”
What the sources say
- Not checked yetcites [63] Raz, 2001, Ageing and the brain
- Not checked yetcites [1] Abe, 2006, Aging in the CNS: comparison of gray/white matter volume and diffusion tensor data, Neurobiol. Aging
- Not checked yetcites [2] Allen, 2005, Normal neuroanatomical variation due to age: the major lobes and a parcellation of the temporal region, Neurobiol. Aging source
- Not checked yetcites [29] Good, 2001, A voxel-based morphometric study of aging in 465 normal adult human brains, Neuroimage source
- Not checked yetcites [31] Grieve, 2005, Preservation of limbic and paralimbic structures in aging, Hum. Brain Mapp. source
- Not checked yetcites [70] Resnick, 2000, One-year age changes in MRI brain volumes in older adults, Cereb. Cortex source
- Not checked yetcites [81] Tisserand, 2004, A voxel-based morphometric study to determine individual differences in gray matter density associated with age and cognitive change over time, Cereb. Cortex source
- Not checked yetcites [87] Yanase, 2005, Brain FDG PET study of normal aging in Japanese: effect of atrophy correction, Eur. J. Nucl. Med. Mol. Imaging source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“With age, the brain undergoes structural and functional deterioration, which is thought to be responsible for the reported cognitive decline [3,62,47].”
What the sources say
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“These authors, using ROI analyses (including frontal lobe, hippocampal and parahippocampal areas) by decades, found that the frontal cortex volume declined linearly and had the strongest age association, whereas the hippocampal volume remained steady until about 60 years of age, while thereafter its volume began to rapidly decline.”
What the sources say
- Not checked yetcites Allen et al. [2] Allen, 2005, Normal neuroanatomical variation due to age: the major lobes and a parcellation of the temporal region, Neurobiol. Aging source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Authors have systematically reported GM loss in the frontal lobe with aging [1,29,31,37,38,81,84,87], also affecting the insula, Heschl gyrus, anterior cingulate cortex, sensorimotor areas and cerebellum.”
What the sources say
- Not checked yetcites [1] Abe, 2006, Aging in the CNS: comparison of gray/white matter volume and diffusion tensor data, Neurobiol. Aging
- Not checked yetcites [29] Good, 2001, A voxel-based morphometric study of aging in 465 normal adult human brains, Neuroimage source
- Not checked yetcites [31] Grieve, 2005, Preservation of limbic and paralimbic structures in aging, Hum. Brain Mapp. source
- Not checked yetcites [37] Lehmbeck, 2006, Combining voxel-based morphometry and diffusion tensor imaging to detect age-related brain changes, Neuroreport source
- Not checked yetcites [38] Lemaître, 2005, Age- and sex-related effects on the neuroanatomy of healthy elderly, Neuroimage source
- Not checked yetcites [81] Tisserand, 2004, A voxel-based morphometric study to determine individual differences in gray matter density associated with age and cognitive change over time, Cereb. Cortex source
- Not checked yetcites [84] Van Laere, 2001, Brain perfusion SPECT: age- and sex-related effects correlated with voxel-based morphometric findings in healthy adults, Radiology source
- Not checked yetcites [87] Yanase, 2005, Brain FDG PET study of normal aging in Japanese: effect of atrophy correction, Eur. J. Nucl. Med. Mol. Imaging source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“All these studies being considered together, the occipital and posterior cingulate cortices, cerebellum, thalamus, basal ganglia and limbic structures have been reported to be the least altered. However, many contradictory results preclude any firm conclusion, given that decreased metabolism has been found in the limbic areas [42], basal ganglia [32], or posterior cingulate cortex [89].”
What the sources say
- Not checked yetcites [32] Herholz, 2002, Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET, Neuroimage source
- Not checked yetcites [49] Moeller, 1996, The metabolic topography of normal aging, J. Cereb. Blood Flow Metab. source
- Not checked yetcites [58] Petit-Taboué, 1998, Effects of healthy aging on the regional cerebral metabolic rate of glucose assessed with statistical parametric mapping, Neuroimage source
- Not checked yetcites [86] Willis, 2002, Age, sex and laterality effects on cerebral glucose metabolism in healthy adults, Psychiatry Res. (Neuroimaging) source
- Not checked yetcites [42] Martin, 1991, Decreases in regional cerebral blood flow with normal aging, J. Cereb. Blood Flow Metab. source
- Not checked yetcites [89] Zuendorf, 2003, Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging, Hum. Brain Mapp. source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All these analyses were performed using a statistical threshold of p<.01 FDR, corrected for multiple comparisons, with a minimum cluster size k=20.”
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Also, using the former method, the hippocampal head was found to be more resilient to the normal aging process than the hippocampal tail [24,64], whereas in contrast a substantial decrease in anterior hippocampal volume has been observed in Alzheimer's disease [4,43,64].”
What the sources say
- Not checked yetcites [24] Driscoll, 2003, The aging hippocampus: cognitive, biochemical and structural findings, Cereb. Cortex source
- Not checked yetcites [64] Raz, 2000, Aging of the brain and its impact on cognitive performance: integration of structural and functional findings
- Not checked yetcites [4] Baron, 2001, In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease, Neuroimage source
- Not checked yetcites [43] Matsuda, 2002, Longitudinal evaluation of both morphologic and functional changes in the same individuals with Alzheimer's disease, J. Nucl. Med.
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“PVE correction (PVEc) was performed very recently in two PET studies, providing the most reliable results in this field so far [33,87].”
What the sources say
- Not checked yetcites [33] Ibanez, 2004, Resting state glucose metabolism is not reduced in normotensive healthy men during aging, after correction for brain atrophy, Brain Res. Bull. source
- Not checked yetcites [87] Yanase, 2005, Brain FDG PET study of normal aging in Japanese: effect of atrophy correction, Eur. J. Nucl. Med. Mol. Imaging source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“PET data were first corrected for PVE . We used an optimized voxel-based technique, originally devised by Müller-Gärtner et al. [52], and modified slightly by Rousset et al. [72].”
What the sources say
- Not checked yetcites Müller-Gärtner et al. [52] Müller-Gärtner, 1992, Measurement of radiotracer concentration in brain gray matter using positron emission tomography: MRI-based correction for partial volume effects, J. Cereb. Blood Flow Metab. source
- Not checked yetcites Rousset et al. [72] Rousset, 1998, Correction for partial volume effects in PET: principle and validation, J. Nucl. Med.
- Not checked yetcites Quarantelli et al. [61] Quarantelli, 2004, Integrated software for the analysis of brain PET/SPECT studies with partial-volume-effect correction, J. Nucl. Med.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
In the paper
“The authors report that only the right precentral gyrus remained relatively intact across age in men, as opposed to the left superior temporal and postcentral areas in women; they did not report data for the group as a whole.”
What the sources say
- Not checked yetcites [87] Yanase, 2005, Brain FDG PET study of normal aging in Japanese: effect of atrophy correction, Eur. J. Nucl. Med. Mol. Imaging source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Since this structure and adjacent areas are deteriorated first and foremost in Alzheimer's disease [4,19,36], differentiating the mesial temporal GM evolution in normal and pathological aging constitutes a key issue.”
What the sources say
- Not checked yetcites [4] Baron, 2001, In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease, Neuroimage source
- Not checked yetcites [19] Delacourte, 1999, The biochemical pathway of neurofibrillary degeneration in aging and Alzheimer's disease, Neurology source
- Not checked yetcites [36] Kantarci, 2003, Neuroimaging in Alzheimer disease: an evidence-based review, Neuroimaging Clin. N. Am. source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“Most have revealed prominent GM loss in prefrontal regions [40,59,65,71,76] with smaller but significant decline in other regions, notably in mesial temporal areas.”
What the sources say
- Not checked yetcites [40] Liu, 2003, A longitudinal study of brain morphometrics using quantitative magnetic resonance imaging and difference image analysis, Neuroimage source
- Not checked yetcites [59] Pfefferbaum, 1998, A controlled study of cortical gray matter and ventricular changes in alcoholic men over a 5-year interval, Arch. Gen. Psychiatry source
- Not checked yetcites [65] Raz, 2005, Regional brain changes in aging healthy adults: general trends, individual differences and modifiers, Cereb. Cortex source
- Not checked yetcites [71] Rettmann, 2006, Cross-sectional and longitudinal analyses of anatomical sulcal changes associated with aging, Cereb. Cortex source
- Not checked yetcites [76] Scahill, 2003, A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging, Arch. Neurol. source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“This discrepancy may result from the samples selected in these studies. Thus, preservation of the hippocampus was found when the sample covered a large life span (17-79 years in Good et al. [29] and 8-79 in Grieve et al. [31]), whereas atrophy was observed when age effects were evaluated within older samples, i.e. 50 or 60 years and over ([38,70,81, but see [84]).”
What the sources say
- Not checked yetcites [29] Good, 2001, A voxel-based morphometric study of aging in 465 normal adult human brains, Neuroimage source
- Not checked yetcites [31] Grieve, 2005, Preservation of limbic and paralimbic structures in aging, Hum. Brain Mapp. source
- Not checked yetcites [38] Lemaître, 2005, Age- and sex-related effects on the neuroanatomy of healthy elderly, Neuroimage source
- Not checked yetcites [70] Resnick, 2000, One-year age changes in MRI brain volumes in older adults, Cereb. Cortex source
- Not checked yetcites [81] Tisserand, 2004, A voxel-based morphometric study to determine individual differences in gray matter density associated with age and cognitive change over time, Cereb. Cortex source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The authors showed prominent atrophy of the prefrontal cortex, and relative sparing of the temporal and parahippocampal cortices, corroborating several previous volumetric findings (see also [68]).”
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“They showed that the variance of hippocampal volume was not different in young and older adults, and they gave an interesting discussion about the large hippocampal volume inter-individual variability they observed in young healthy subjects.”
What the sources say
- Not checked yetcites [41] Lupien, 2007, Hippocampal volume is as variable in young as in older adults: implications for the notion of hippocampal atrophy in humans, Neuroimage source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Ibanez et al. [33] failed to find any remaining hypometabolic areas after PVEc. However, differences in the population and statistics they selected may have induced a loss of statistical power in their study, as they have compared two small groups (young vs. older groups), rather than correlating PET data and age across the whole sample.”
What the sources say
- Not checked yetcites Ibanez et al. [33] Ibanez, 2004, Resting state glucose metabolism is not reduced in normotensive healthy men during aging, after correction for brain atrophy, Brain Res. Bull. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“According to 18 FDG-PET studies of the postnatal maturation of the brain [16], the newborn period is characterized by a high metabolic rate in the mesial temporal region, cerebellar vermis, thalamus, basal ganglia, cingulate cortex and sensorimotor cortex. At three months, glucose metabolism increases in the parietal, occipital and temporal cortices. Frontal glucose consumption begins to increase”
What the sources say
- Not checked yetcites [16] Chugani, 1998, A critical period of brain development: studies of cerebral glucose utilization with PET, Prev. Med. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Thus, our findings support the idea that episodic memory deficits associated with normal aging reflect above all a failure to implement an efficient strategy in the retrieval of episodic information, with encoding and ecphoric processes being relatively unaffected.”
What the sources say
- Not checked yetcites [11] Cabeza, 2000, Imaging cognition II: an empirical review of 275 PET and fMRI studies, J. Cogn. Neurosci. source
- Not checked yetcites [20] Desgranges, 1998, The functional neuroanatomy of episodic memory: the role of the frontal lobes, the hippocampal formation, and other areas, Neuroimage source
- Not checked yetcites [22] Desgranges, 2002, The neural basis of intrusions in free recall and cued recall: a PET study in Alzheimer's disease, Neuroimage source
- Not checked yetcites [50] Morcom, 2003, Age effects on the neural correlates of successful memory encoding, Brain source
- Not checked yetcites [78] Schacter, 1996, The role of hippocampus and frontal cortex in age-related memory changes: a PET study, Neuroreport source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“the SPM segmentation algorithm seems to be more particularly recommended for clinical data [88]”
What the sources say
- Not checked yetcites [88] Zaidi, 2006, Links comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET, Neuroimage source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“According to this model, the rostral portion of the hippocampus is particularly heavily involved in encoding processes, whereas its caudal portion is mainly involved in retrieval processes.”
What the sources say
- Not checked yetcites [39] Lepage, 1998, PET activations of memory encoding and retrieval: the HIPER model, Hippocampus source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may state two separate claims as one.
In the paper
“From an evolutionary perspective, our structural findings support the 'developmental hypothesis' postulated by Grieve et al. [31] to account for structural changes in normal aging. This hypothesis stipulates that the first structures to neurally develop are the most resistant to aging effects, whereas those that emerge during the latter stages of development are the first to deteriorate.”
What the sources say
- Not checked yetcites Grieve et al. [31] Grieve, 2005, Preservation of limbic and paralimbic structures in aging, Hum. Brain Mapp. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“An increasing number of studies have consistently showed that this structure is the most metabolically impaired region in prodromal Alzheimer's disease [15,53]”
What the sources say
- Not checked yetcites [15] Chételat, 2003, Dissociating atrophy and hypometabolism impact on episodic memory in mild cognitive impairment, Brain source
- Not checked yetcites [53] Nestor, 2003, Retrosplenial cortex (BA29/30) hypometabolism in mild cognitive impairment (prodromal Alzheimer's disease), Eur. J. Neurosci. source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“suggesting that the SPM findings are robust and not due to spatial normalization errors.”
hedged- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“The hippocampal volume increases in children [82], until in the young adult, then remains relatively stable until the late 60's when it begins to decline (see introduction), giving rise to a curvilinear function.”
What the sources say
- Not checked yetcites [82] Toga, 2006, Mapping brain maturation, Trends Neurosci. source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Although there is no given segmentation or PVE correction algorithms providing the best results under all conditions”
What the sources say
- Not checked yetcites [61] Quarantelli, 2004, Integrated software for the analysis of brain PET/SPECT studies with partial-volume-effect correction, J. Nucl. Med.
- Not checked yetcites [74] Rousset, 2005, Correction for partial volume effects in emission tomography
- Not checked yetcites [88] Zaidi, 2006, Links comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET, Neuroimage source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Secondly, we note the remote possibility of age effects on intrinsic MR signal and contrast.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Previous studies have yielded discrepant results regarding these structures, reporting either 'preservation' [49,58,86], or alteration (before PVEc [33], see also [32]).”
What the sources say
- Not checked yetcites [49] Moeller, 1996, The metabolic topography of normal aging, J. Cereb. Blood Flow Metab. source
- Not checked yetcites [58] Petit-Taboué, 1998, Effects of healthy aging on the regional cerebral metabolic rate of glucose assessed with statistical parametric mapping, Neuroimage source
- Not checked yetcites [86] Willis, 2002, Age, sex and laterality effects on cerebral glucose metabolism in healthy adults, Psychiatry Res. (Neuroimaging) source
- Not checked yetcites [33] Ibanez, 2004, Resting state glucose metabolism is not reduced in normotensive healthy men during aging, after correction for brain atrophy, Brain Res. Bull. source
- Not checked yetcites [32] Herholz, 2002, Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET, Neuroimage source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we can consider that we have done our best to reduce (although not totally exclude) the risk of realignment and segmentation errors.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“considering the fact that coregistration and segmentation have been individually checked for accuracy”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because the absolute values have been divided by the global mean in these analyses, the decrease of the global mean induced an increase of the relative values with age, indicating that the highlighted regions were those that undergo the least age-related GM loss and decreased 18 FDG uptake.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“It is well-known that in prodromal Alzheimer's disease, the hippocampus is the first to become atrophic [14]”
What the sources say
- Not checked yetcites [14] Chételat, 2002, Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment, Neuroreport source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“the modified Müller-Gärtner PVE correction algorithm seems to be the most accurate voxel-based method, and the least dependent on segmentation errors [61]”
What the sources say
- Not checked yetcites [61] Quarantelli, 2004, Integrated software for the analysis of brain PET/SPECT studies with partial-volume-effect correction, J. Nucl. Med.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper relies on this without support.
No prior study has assessed both structural and functional deterioration and relative preservation in normal aging in the same sample using voxel-based analyses, optimized VBM and PVEc.
“To our knowledge, no study has assessed both structural and functional deterioration and relative preservation in normal aging in the same sample and using up-to-date methodology, i.e. voxel-based analyses, the optimized-VBM procedure and PVEc.”
The paper relies on this without support.
Cross-sectional age-related differences in brain structure and metabolism reflect true aging effects rather than cohort effects.
“Forty-five healthy subjects aged between 20 and 83 years (mean: 49.4 ± 18 years, 24 females and 21 males) were selected according to stringent prospective inclusion/exclusion criteria.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Regional reductions in brain glucose utilization occur with aging and to a greater extent in MCI and AD [185-193].”
From The Alzheimer’s disease mitochondrial cascade hypothesis: progress and perspectives (2014) · cites this paper as [189]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Legends · p. 31
“the absolute GM loss in the frontal cortex (peak coordinates [x; y; z] ↔ [49; 13; 47]; r = .64)”
§ 2.4. MRI and PET data analyses · p. 9
“we assessed negative correlations between age and absolute GM volume to obtain absolute age-related GM volume loss”
Citations
Cites
A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001) · NeuroImage · doi:10.1006/nimg.2001.0786 · Cited by 4,561
Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies
Cabeza, R., & Nyberg, L. (2000) · Journal of Cognitive Neuroscience · doi:10.1162/08989290051137585 · Cited by 3,598
Regional Brain Changes in Aging Healthy Adults: General Trends, Individual Differences and Modifiers
Raz, N., Lindenberger, U., Rodrigue, K. M., Kennedy, K. M., Head, D., Williamson, A., Dahle, C., Gerstorf, D., & Acker, J. D. (2005) · Cerebral Cortex · doi:10.1093/cercor/bhi044 · Cited by 2,875
Hemispheric asymmetry reduction in older adults: the HAROLD model
Cabeza, R. (2002) · Psychology and Aging · doi:10.1037/0882-7974.17.1.85 · Cited by 2,125
The handbook of aging and cognition
Craik, F. I. M., & Salthouse, T. A. (1992) · doi:10.4324/9781410601988 · Cited by 2,064
Hemispheric asymmetry reduction in older adults: The HAROLD model.
Cabeza, R. E. L. (2002) · Psychology and Aging · doi:10.1037//0882-7974.17.1.85 · Cited by 2,054
Thinning of the Cerebral Cortex in Aging
Salat, D. H. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh032 · Cited by 1,851
Hemispheric encoding/retrieval asymmetry in episodic memory: positron emission tomography findings.
Tulving, E., Kapur, S., Craik, F. I. M., Moscovitch, M., & Houle, S. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.6.2016 · Cited by 1,621
Memory and Executive Function in Aging and AD
Buckner, R. L. (2004) · Neuron · doi:10.1016/j.neuron.2004.09.006 · Cited by 1,508
Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain
Resnick, S. M., Pham, D. L., Kraut, M. A., Zonderman, A. B., & Davatzikos, C. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-08-03295.2003 · Cited by 1,425
Research works printed but not linked: 2
- Cardebat, 1990, Evocation lexicale formelle et sémantique chez des sujets normaux: performances et dynamiques de production en fonction du sexe, de l’âge et du niveau d’étude, Acta Neurol. Belg., 90, 207
- Eustache, 1998, L’évaluation clinique de la mémoire. Revue Neurologique (Paris), 154, 18
Cited by
Sorted by citations
Items 201–300 of 393
Evers, E. A. T., Klaassen, E. B., Rombouts, S. A. R. B., Backes, W. H., & Jolles, J. (2012) · Brain Connectivity · doi:10.1089/brain.2011.0060 · Cited by 23
“Previous studies have shown substantial grey matter loss (Kalpouzos et al., 2009; Raz et al., 1997) and white matter degradation (Pfefferbaum et al., 2005; Salat et al., 2005; Yoon et al., 2008) in especially the prefrontal cortex in healthy aging.”
Yakushev, I. Y., Gerhard, A., Müller, M. J., Lorscheider, M., Buchholz, H., Schermuly, I., Weibrich, C., Hammers, A., Stoeter, P., Schreckenberger, M., et al. (2011) · Brain Structure and Function · doi:10.1007/s00429-011-0302-4 · Cited by 21
“For simplification of terminology and by analogy with the previous reports (Neumeister et al. 2005; Kalpouzos et al. 2009; Weiss et al. 2005) measures of the hippocampal head are referred to as ‘‘anterior hippocampal’’, and those of the body tail as ‘‘posterior hippocampal’’ hereafter.”
MethodPET imaging in extratemporal epilepsy requires consideration of electroclinical findings
Kaufmann, E., Rémi, J., Vollmar, C., Goc, J., Loesch, A. M., Rominger, A., & Noachtar, S. (2016) · Epilepsy Research · doi:10.1016/j.eplepsyres.2016.05.010 · Cited by 21
Nugent, S., Castellano, C., Bocti, C., Dionne, I. J., Fülöp, T., & Cunnane, S. C. (2015) · Biogerontology · doi:10.1007/s10522-015-9595-7 · Cited by 20
“A recent detailed investigation of morphological and functional changes in the brain with aging reported having excluded those with an MMSE of \24/30 or diabetes or obesity (Lee et al. 2015).”
BackgroundSchönknecht, O. D. P., Hunt, A. M., Toro, P., Guenther, T., Henze, M., Haberkorn, U., & Schröder, J. (2011) · Clinical EEG and Neuroscience · doi:10.1177/155005941104200207 · Cited by 20
Adult age differences in learning and generalization of feedback-based associations.
Simon, J. R., & Gluck, M. A. (2013) · Psychology and Aging · doi:10.1037/a0033844 · Cited by 20
Gray matter volume covariance networks associated with social networks in older adults
Blumen, H. M., & Verghese, J. (2018) · Social Neuroscience · doi:10.1080/17470919.2018.1535999 · Cited by 20
“In normal aging, for example, the greatest gray matter atrophy is observed in prefrontal cortex regions – including dorsolateral and orbitofrontal regions – yet, considerable atrophy is also observed in sensorimotor, occipital, and insular regions (Kalpouzos et al., 2009; Raz et al., 1997).”
BackgroundInfluentialWei, D., Zhuang, K., Chen, Q., Yang, W., Liu, W., Wang, K., Sun, J., & Qiu, J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/177279 · Cited by 22
“…trajectory of the total gray matter volume (Allen et al., 2005; Giorgio et al., 2010; Good et al., 2001; Grieve et al., 2005; Hasan et al., 2007); as well as, cortical gray matter volume was found to be a decline over adulthood (Kalpouzos et al., 2009; Sullivan et al., 2004; Walhovd et al., 2005a).”
BackgroundEffects of aging on brain volumes in healthy individuals across adulthood
Beheshti, I., Maikusa, N., & Matsuda, H. (2019) · Neurological Sciences · doi:10.1007/s10072-019-03817-3 · Cited by 20
Ourry, V., Gonneaud, J., Landeau, B., Moulinet, I., Touron, E., Dautricourt, S., Du, G. L., Mézenge, F., André, C., Bejanin, A., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117819 · Cited by 21
Self-citationKumar, P., Osahon, O. W., & Sekhar, R. V. (2023) · Antioxidants · doi:10.3390/antiox12051042 · Cited by 22
An Automated Pipeline for the Analysis of PET Data on the Cortical Surface
Marcoux, A., Burgos, N., Bertrand, A., Teichmann, M., Routier, A., Wen, J., Samper‐Gonzàlez, J., Bottani, S., Durrleman, S., Habert, M., et al. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00094 · Cited by 22
“These preprocessing steps are consistent with what is commonly done (Ishii et al., 2001; Chételat et al., 2008; Kalpouzos et al., 2009; Gray et al., 2013; Madhavan et al., 2013; Ewers et al., 2014).”
ResultNordin, K., Nyberg, L. H., Andersson, M., Karalija, N., Riklund, K., Bäckman, L., & Salami, A. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab023 · Cited by 17
“2017), whereas atrophy in the posterior hippocampus has been reported as most pronounced across samples including both younger and older adults (Malykhin et al. 2008; Kalpouzos et al. 2009; Nordin et al. 2018).”
BackgroundRauchs, G., Harand, C., Bertran, F., Desgranges, B., & Eustache, F. (2010) · Revue Neurologique · doi:10.1016/j.neurol.2010.03.021 · Cited by 19
Self-citationWoost, T. B., Dukart, J., Frisch, S., Barthel, H., Sabri, O., Mueller, K., & Schroeter, M. L. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.05.008 · Cited by 18
“Age and sex were included as covariates in all analyses (Kalpouzos et al., 2009).”
MethodFDG-PET Imaging of Dementia and Neurodegenerative Disease
Dave, A., Hansen, N. J., Downey, R. T., & Johnson, C. M. (2020) · Seminars in Ultrasound CT and MRI · doi:10.1053/j.sult.2020.08.010 · Cited by 18
Cheung, M., Lee, T., Sze, S. L., & Chan, A. S.-Y. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1095111 · Cited by 19
“Several magnetic resonance imaging (MRI) studies have reported an age-related reduction in gray and white matter volume in various parts of the frontal lobe, such as the dorsolateral prefrontal cortex and the orbitofrontal cortex (Raz et al., 1997, 2005; Kalpouzos et al., 2009).”
BackgroundBinding in working memory and frontal lobe in normal aging: is there any similarity with autism?
Lecouvey, G., Quinette, P., Kalpouzos, G., Guillery‐Girard, B., Bejanin, A., Gonneaud, J., Abbas, A., Viader, F., Eustache, F., & Desgranges, B. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00090 · Cited by 18
Self-citationMolecular Imaging and the Neuropathologies of Parkinson’s Disease
Cumming, P. D., & Borghammer, P. (2011) · Current topics in behavioral neurosciences · doi:10.1007/7854_2011_165 · Cited by 18
“3e, similar patterns were reported when aged subjects were compared to young controls (Borghammer et al. 2009b; Kalpouzos et al. 2009; Moeller et al. 1996) and also in Alzheimer’s disease patients (Borghammer et al.”
Age‐related changes in the cerebral substrates of cognitive procedural learning
Hubert, V., Beaunieux, H., Chételat, G., Platel, H., Landeau, B., Viader, F., Desgranges, B., & Eustache, F. (2008) · Human Brain Mapping · doi:10.1002/hbm.20605 · Cited by 18
Self-citationCárdenas‐Tueme, M., Trujillo-Villarreal, L. A., Ramírez-Amaya, V., Garza‐Villarreal, E. A., Camacho‐Morales, A., & Reséndez‐Pérez, D. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119039 · Cited by 18
Stretton, J., Schweizer, S., & Dalgleish, T. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1453-21.2022 · Cited by 22
“First, we observed decline in all gray matter sources associated with increasing age, in line with previous studies of age-related GMV loss (Kalpouzos et al., 2009).”
ResultAge-Related Differences in the Human Hippocampus: Behavioral, Structural and Functional Measures
Grady, C. L., & Ryan, J. D. (2017) · Springer eBooks · doi:10.1007/978-3-319-50406-3_7 · Cited by 23
Age related metabolic modifications in the migraine brain
Martinez, M. L., D’Ostilio, K., Coppola, G., Parisi, V., de Noordhout, A. M., Magis, D., Schoenen, J. E., Scholtes, F., & Versijpt, J. J. (2019) · Cephalalgia · doi:10.1177/0333102419828984 · Cited by 17
“Preservation of metabolism in these structures is consistent with the functional importance of thalamo-limbic circuits in sensory integration, arousal, emotion, and memory (25,26), and it is likely that these same underpinnings push it forth in migraine.”
BackgroundLongitudinal changes in grey matter and cognitive performance over four years of healthy aging
Neufeld, N., Parker, A. F., Kwan, H., Mazerolle, E. L., & Gawryluk, J. R. (2022) · Neuroimage Reports · doi:10.1016/j.ynirp.2022.100140 · Cited by 15
Yan, Y., Hulbert, J. C., Zhuang, K., Liu, W., Wei, D., Qiu, J., Anderson, M. C., & Yang, W. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac336 · Cited by 16
“For example, greater cortical thickness/grey matter volume in the lingual gyrus predicts better long-term free recall across the lifespan (Walhovd et al., 2006; Kalpouzos et al., 2009).”
BackgroundLa mémoire prospective au cours du vieillissement : déclin ou préservation ?
Kalpouzos, G., Eustache, F., & Desgranges, B. (2008) · NPG. Neurologie, psychiatrie, gériatrie/NPG · doi:10.1016/j.npg.2008.06.003 · Cited by 16
Self-citationChallenges to determining whether DHA can protect against age-related cognitive decline
Hennebelle, M., Harbeby, E., Tremblay, S., Chouinard‐Watkins, R., Pifferi, F., Plourde, M., Guesnet, P., & Cunnane, S. C. (2015) · Clinical Lipidology · doi:10.2217/clp.14.61 · Cited by 16
Neuroimaging Biomarkers of Caloric Restriction on Brain Metabolic and Vascular Functions
Lin, A., Parikh, I. J., Hoffman, J. D., & Ma, D. (2017) · Current Nutrition Reports · doi:10.1007/s13668-017-0187-9 · Cited by 16
Pareek, V., Rallabandi, V. P. S., & Roy, P. K. (2018) · Magnetic Resonance Insights · doi:10.1177/1178623x18799926 · Cited by 16
“We here consider the adult aging process from age 20 years onwards across the full lifespan divided into three groups, that is, the young age group (20-40), middle age adult group (41-60), and old age group (61-85 years).”
MethodAllocca, M., Linguanti, F., Calcagni, M. L., Cistaro, A., Gaudieri, V., Guerra, U. P., Morbelli, S. D., Nobili, F. M., Pappatà, S., Sestini, S., et al. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10214932 · Cited by 16
“In contrast, other authors observed a continuing trend of hypo-metabolism in the aging brain, suggesting that the reduction of brain metabolism cannot be accounted for merely by only atrophy [20,25,28,30,58].”
BackgroundResultDifferences in topological progression profile among neurodegenerative diseases from imaging data
Garbarino, S., Lorenzi, M., Oxtoby, N. P., Vinke, E. J., Marinescu, R. V., Eshaghi, A., Ikram, M. A., Niessen, W. J., Ciccarelli, O., Barkhof, F., et al. (2019) · eLife · doi:10.7554/elife.49298 · Cited by 16
FDG-PET assessment of the locus coeruleus in Alzheimer’s disease
Liu, K. Y., Acosta‐Cabronero, J., Hong, Y. T., Yi, Y., Hämmerer, D., & Howard, R. J. (2021) · Neuroimage Reports · doi:10.1016/j.ynirp.2020.100002 · Cited by 17
“…et al., 1993), including primary visual cortex, putamen, pre and post-central gyrus (primary sensorimotor cortex), cerebellum, thalamus and pons (Kalpouzos et al., 2009; Minoshima et al., 1995) (although signi fi - cant hypometabolism has been demonstrated in the anterior thalamic nucleus…”
Is there a common underlying mechanism for age-related decline in cortical thickness?
Ecker, C., Stahl, D. R., Daly, E., Johnston, P., Thomson, A., & Murphy, D. (2009) · Neuroreport · doi:10.1097/wnr.0b013e32832ec181 · Cited by 15
Biomarkers for Early Diagnosis of Alzheimer’s Disease in the Oldest Old: Yes or No?
Paolacci, L., Giannandrea, D., Mecocci, P., & Parnetti, L. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-161127 · Cited by 15
Squarzoni, P., Duran, F., Busatto, G. F., & Alves, T. C. T. F. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-161036 · Cited by 15
Timpe, J. C., Rowe, K. C., Matsui, J., Magnotta, V. A., & Denburg, N. L. (2011) · Journal of Cognitive Psychology · doi:10.1080/20445911.2011.578065 · Cited by 14
“The medial frontal cortices and anterior cingulate cortex show grey matter metabolic decline with age in PET imaging studies (Kalpouzos et al., 2009; Pardo et al., 2007).”
BackgroundPatel, A. B., Veeraiah, P., Shameem, M., Kumar, J. M., & Saba, K. (2021) · The FASEB Journal · doi:10.1096/fj.202001704rr · Cited by 14
Correlation between baseline regional gray matter volume and global gray matter volume decline rate
Taki, Y., Kinomura, S., Sato, K., Goto, R., Wu, K., Kawashima, R., & Fukuda, H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.071 · Cited by 14
“…et al., 2004; Taki et al., 2004; Lemaitre et al., 2005), the baseline regional gray matter volumes such as that of the PCC/precuneus and the hippocampus show more preservation in aging compared with other graymatter regions (Sowell et al., 2003; Grieve et al., 2005; Kalpouzos et al., 2009).”
Schönknecht, O. D. P., Hunt, A. M., Toro, P., Henze, M., Haberkorn, U., & Schröder, J. (2009) · Neuroscience Letters · doi:10.1016/j.neulet.2009.10.014 · Cited by 13
Similarity between structural and proxy estimates of brain connectivity
Lizarraga, A., Ripp, I., Sala, A., Shi, K., Duering, M., Koch, K., & Yakushev, I. Y. (2023) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x231204769 · Cited by 12
“53 Finally, Pearson correlations were calculated between each pair of regions across subjects.”
MethodDecoding molecular mechanisms: brain aging and Alzheimer’s disease
Hayat, M., Syed, R. A., Qaiser, H., Uzair, M., Al-Regaiey, K., Khallaf, R. A., Albassam, L. A. M., Kaleem, I., Wang, X., Wang, R., et al. (2024) · Neural Regeneration Research · doi:10.4103/nrr.nrr-d-23-01403 · Cited by 14
Pardo, J. V., Nyabwari, S. M., Lee, J. T., & Initiative, A. D. N. (2020) · Cerebral Cortex Communications · doi:10.1093/texcom/tgaa020 · Cited by 13
“Several studies highlight relative preservation of PCC structure and metabolism in the absence of preclinical or early AD (e.g., Buckner et al. 2005; Yanase et al. 2005; Kalpouzos et al. 2009; Shen et al. 2012; Bonte et al. 2017; Ishibashi et al. 2018).”
Background[18F]FDG PET may differentiate cerebral amyloid angiopathy from Alzheimer’s disease
Bergeret, S., Queneau, M., Rodallec, M. H., Curis, E., Dumurgier, J., Hugon, J., Paquet, C., Farid, K., & Baron, J. (2021) · European Journal of Neurology · doi:10.1111/ene.14743 · Cited by 13
“As the age difference seemed likely to represent a confounding factor given the known agerelated effects on restingstate regional [18F]FDG uptake,[38,39] we performed a two- sample t test which was significant (p = 0.”
MethodSelf-citationMakani, P., Thioux, M., Pyott, S., & van Dijk, P. (2022) · Brain Science · doi:10.3390/brainsci12091192 · Cited by 13
Functional Characterization of Atrophy Patterns Related to Cognitive Impairment
Schnellbächer, G. J., Hoffstaedter, F., Eickhoff, S. B., Caspers, S., Nickl‐Jockschat, T., Fox, P. T., Laird, A. R., Schulz, J. B., Reetz, K., & Dogan, I. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00018 · Cited by 13
“In accordance with this, the PCC is known for its relatively good preservation in age (73), and seems to be subject to relatively fewer alterations in normal aging than pathological processes such as Alzheimer’s disease and MCI (74).”
BackgroundHennebelle, M., Courchesne‐Loyer, A., St‐Pierre, V., et al. (2016) · Nutrition · doi:10.1016/j.nut.2016.03.025 · Cited by 13
Morand, A., Segobin, S. H., Lecouvey, G., Gonneaud, J., Eustache, F., Rauchs, G., & Desgranges, B. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa232 · Cited by 12
“7 studies have shown a link between this gray matter atrophy and a decline in cognition, notably episodic memory (Kalpouzos et al. 2009; Ramanoël et al. 2018).”
BackgroundSelf-citationThe effects of aging and Alzheimer’s disease on associative recognition memory
Hanaki, R., Abe, N., Fujii, T., Ueno, A., Nishio, Y., Hiraoka, K., Shimomura, T., Iizuka, O., Shinohara, M., Hirayama, K., et al. (2011) · Neurological Sciences · doi:10.1007/s10072-011-0748-4 · Cited by 12
“have reported that aging induces structural and functional deterioration in the prefrontal cortex, but not in the medial temporal lobe [34, 35].”
BackgroundHuang, S., Tsai, S., Hsu, J., & Huang, Y.-L. (2010) · International Psychogeriatrics · doi:10.1017/s1041610210000633 · Cited by 12
Anomaly detection for the individual analysis of brain PET images
Burgos, N., Cardoso, M. J., Samper‐Gonzàlez, J., Habert, M., Durrleman, S., Sebastien Ourselin, A., & Colliot, O. (2021) · Journal of medical imaging · doi:10.1117/1.jmi.8.2.024003 · Cited by 12
Tsentidou, G., Μoraitou, D., & Tsolaki, M. (2021) · Brain Sciences · doi:10.3390/brainsci11050627 · Cited by 13
“It is the prefrontal regions that mainly seem to be affected, in addition to the parietal and temporal cortices [45,46].”
BackgroundMcNair, L. F., Andersen, J. V., & Waagepetersen, H. S. (2023) · Neurochemistry International · doi:10.1016/j.neuint.2023.105626 · Cited by 11
“In particular, whereas several areas of the cerebral cortex was reported as exhibiting the highest decrease in glucose uptake in old age, the hippocampus was reported amongst the regions with least reduction in glucose uptake capacity (Kalpouzos et al., 2009).”
BackgroundPiazzi, L., Balata, D., Cecchi, E., Gennaro, P., & Serena, F. (2014) · Scientia Marina · doi:10.3989/scimar.03989.28a · Cited by 11
Chemotherapy effects on brain glucose metabolism at restPreprint
Baudino, B., D’Agata, F., Castellano, G., Caroppo, P., Cauda, S., Parente, A., Manfredi, M., Geda, E., Orsi, L., Cauda, F., et al. (2011) · Nature Precedings · doi:10.1038/npre.2011.5637.1 · Cited by 11
“Maccormick RE (2006) Possible acceleration of aging by adjuvant chemotherapy: a cause of early onset frailty?”
BackgroundInfluentialXu, K., Niu, N., Li, X., Chen, Y., Wang, D., Zhang, J., Chen, Y., Li, H., Wei, D., Chen, K., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac248 · Cited by 9
Stretton, J., Schweizer, S., & Dalgleish, T. (2022) · Apollo (University of Cambridge) · doi:10.17863/cam.86431 · Cited by 10
Maglione, A., do Nascimento, B. P. P., Ribeiro, M. O., de Souza, T. J. L., da Silva, R. E. C., Sato, M. A., Penatti, C. A. A., Britto, L. R., de Souza, J. S., Maciel, R. M. B., et al. (2022) · Metabolic Brain Disease · doi:10.1007/s11011-022-01055-9 · Cited by 11
“This aspartyl protease is responsible for the amyloidogenic cleavage of APP and it is increased 1 3 2744 presubiculum, are the least resistant to the effects of several stressors (Grieve et al. 2005; Kalpouzos et al., 2009; La Joie et al., 2010).”
BackgroundConstruction and comparative evaluation of different activity detection methods in brain FDG-PET
Buchholz, H., Wenzel, F., Gartenschläger, M., et al. (2015) · BioMedical Engineering OnLine · doi:10.1186/s12938-015-0073-x · Cited by 10
“For example, SPM was chosen to detect and to differentiate types of dementia [1–4], Huntington’s chorea [5], temporal lobe epilepsy [6] and age-related hypometabolisms [7–11].”
MethodThe in vivo topography of cortical changes in healthy aging and prodromal Alzheimer's disease
Prestia, A., Baglieri, A., Pievani, M., Bonetti, M., Rasser, P. E., Thompson, P. M., Marino, S., Bramanti, P., & Frisoni, G. B. (2013) · Supplements to Clinical neurophysiology · doi:10.1016/b978-0-7020-5307-8.00004-1 · Cited by 10
Bartos, L. M., Kunte, S. T., Wagner, S., Beumers, P., Schaefer, R., Zatcepin, A., Li, Y., Griessl, M., Hoermann, L., Wind-Mark, K., et al. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120860 · Cited by 14
“In contrast, subcortical structures in rodents are poorly classified and exhibit heterogeneity in terms of neuronal density (Keller et al., 2018).”
BackgroundEffects of Sleep and Age on Prospective Memory Consolidation: A Walk in a Virtual Museum
Rehel, S., Legrand, N., Lecouvey, G., Lanièpce, A., Bertran, F., Fleury, P., Madeleine, S., Eustache, F., Desgranges, B., & Rauchs, G. (2019) · Clocks & Sleep · doi:10.3390/clockssleep1030028 · Cited by 9
“This component also relies on the hippocampus [74], which is also known to undergo structural and functional changes in ageing (for reviews, see [73,75,76]).”
BackgroundSelf-citationAge-related alterations of brain metabolic network based on [18F]FDG-PET of rats
Xue, X., Wu, J., Huo, B., Xing, X., Ma, J., Li, Y., Zheng, M., Hua, X., & Xu, J. (2022) · Aging · doi:10.18632/aging.203851 · Cited by 9
Sex dimorphism of cortical water diffusion in normal aging measured by magnetic resonance imaging
Ng, S., Hsu, W.-C., Wai, Y., Lee, J.-D., Chan, H., Chen, Y.-L., Fung, H., Wu, Y., Tsai, M.-L., & Wang, J. (2013) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2013.00071 · Cited by 9
Pathomechanisms of Atrophy in Insular Cortex in Alzheimer’s Disease
Moon, Y., Moon, W., & Han, S. (2015) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/1533317514566113 · Cited by 9
“Previous studies reported agerelated gray matter volume loss mainly located in the frontal lobe, superior temporal, hippocampus, insula, and superior parietal cortices.(16,17) The present study showed negative correlation with age and insular cortical volume; however, there was no significance.”
ResultHeiss, W. (2014) · Springer eBooks · doi:10.1007/978-3-642-42014-6_3 · Cited by 9
Kalpouzos, G., & Persson, J. (2024) · Cortex · doi:10.1016/j.cortex.2024.12.004 · Cited by 10
Self-citationNormal Aging: Alterations in Scalp EEG Using Broadband and Band-Resolved Topographic Maps
Javed, E., Croce, P., Zappasodi, F., & Gratta, C. D. (2020) · Frontiers in Physics · doi:10.3389/fphy.2020.00082 · Cited by 8
“[45] suggested decline in metabolic activity at ACC and prefrontal cortex using Positron Emission Tomography.”
MethodSun, J., Han, J. J., & Chen, W. (2024) · Scientific Reports · doi:10.1038/s41598-024-78712-9 · Cited by 8
Ferrari, F., Gorini, A., & Villa, R. F. (2014) · Neurochemical Research · doi:10.1007/s11064-014-1482-0 · Cited by 8
Levy‐Gigi, E., & Kéri, S. (2015) · Psychology and Aging · doi:10.1037/a0039181 · Cited by 8
Age-Related Changes in the Neural Processes of Reward-Directed Action and Inhibition of Action
Le, T. M., Chao, H. H., Levy, I., & Li, C.-S. R. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01121 · Cited by 9
“loss (Kalpouzos et al., 2009; Peelle et al., 2012) and metabolic reduction (Petit-Taboue et al.”
BackgroundJauny, G., Petit, M. L., Segobin, S. H., Merck, C., Belliard, S., Eustache, F., Laisney, M., & Hinault, T. (2024) · Cortex · doi:10.1016/j.cortex.2024.11.013 · Cited by 7
Self-citationNetwork Occlusion Sensitivity Analysis Identifies Regional Contributions to Brain Age Prediction
He, L., Wang, S., Chen, C., Wang, Y., Fan, Q., Chu, C., Fan, L., & Xu, J. (2025) · Human Brain Mapping · doi:10.1002/hbm.70239 · Cited by 9
“For example, the gray matter volume in the frontal and parietal lobes and some temporal lobe regions substantially decreased with age (Grieve et al. 2005; Kalpouzos et al. 2009; Sullivan et al. 1995; Zimmerman et al. 2006).”
Bauckneht, M., Chincarini, A., Brendel, M., Rominger, A., Beyer, L., Bruffaerts, R., Vandenberghe, R. R., Kramberger, M. G., Trošt, M., Garibotto, V., et al. (2021) · Alzheimer s & Dementia · doi:10.1002/alz.12294 · Cited by 7
18F-FDG-PET correlates of aging and disease course in ALS as revealed by distinct PVC approaches
Ferraro, P. M., Campi, C., Miceli, A., et al. (2022) · European Journal of Radiology Open · doi:10.1016/j.ejro.2022.100394 · Cited by 7
Zimmermann, G., Joly, L., Schoepfer, P., Doyen, M., Roch, V., Grignon, R., Salvi, P., Marie, P., Bénétos, A., & Verger, A. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.908063 · Cited by 7
Whi, W., Choi, H., Paeng, J. C., Cheon, G. J., Kang, K. W., & Lee, D. S. (2021) · EJNMMI Physics · doi:10.1186/s40658-021-00424-0 · Cited by 7
“This might have resulted from the physiologic decrease in gray matter volume accompanied by a normal aging process [38, 39].”
BackgroundBrain aging: Evaluation of pH using phosphorus magnetic resonance spectroscopy
Cichocka, M., Kozub, J., & Urbanik, A. (2018) · Geriatrics and gerontology international/Geriatrics & gerontology international · doi:10.1111/ggi.13272 · Cited by 6
The Use of 18F-FDG PET in the Diagnostic Workup of Alzheimer’s Dementia
Ortner, M. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7704-8_14 · Cited by 6
Wang, Y., Chen, K., Zhang, J., Li, Y., Li, K., Jin, Z., Ye, Q., & Guo, X. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00105 · Cited by 7
“A number of structural magnetic resonance imaging (MRI) studies have shown that normal aging-related morphological changes involve significant reductions in gray matter volume or cortical thickness (Good et al., 2001; Taki et al., 2004; Smith et al., 2007; Kalpouzos et al., 2009).”
BackgroundAPOE4-related differences in cortical thickness are modulated by sex in middle age
Leclaire, K. N., Blujus, J. K., Korthauer, L. E., & Driscoll, I. F. (2024) · Brain Imaging and Behavior · doi:10.1007/s11682-024-00911-9 · Cited by 6
“Though the ACC is not typically implicated in the pathophysiology of AD, loss of ACC integrity has been related to normal aging (Kalpouzos et al., 2009; Pardo et al., 2007; Vaidya et al., 2007), dementia-related apathy (Le Heron et al., 2018), and is predictive of the behavioral variant of…”
Molecular Imaging with PET in the Assessment of Vascular Dementia and Cerebrovascular Disease
Patil, S., Patel, D., Kata, R., Teichner, E. M., Subtirelu, R. C., Ayubcha, C., Werner, T. J., & Alavi, A. A. (2024) · PET Clinics · doi:10.1016/j.cpet.2024.09.001 · Cited by 6
Tomadesso, C., de Lizarrondo, S. M., Ali, C., Landeau, B., Mézenge, F., Perrotin, A., Sayette, V. D. L., Vivien, D., & Chételat, G. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.871214 · Cited by 7
“These age-related brain changes include decreases in gray matter volume and glucose metabolism, especially in frontal, cingulate and insular cortices, but also sensorimotor, and perisylvian regions and to a lesser extend parietal and temporal areas, including the hippocampus (Kalpouzos et al., 2009; Fjell and Walhovd,…”
BackgroundSelf-citationGuimarães, R. M., Schaufelberger, M. S., Santos, L. C., Duran, F., Menezes, P. R., Scazufca, M., Gouvea, M. T. V., & Busatto, G. F. (2012) · Brazilian Journal of Medical and Biological Research · doi:10.1590/s0100-879x2012007500046 · Cited by 5
“While some studies show that there are only volume losses of this region in elderly life or related to pathological cerebral aging (1,2), more recent investigations have shown that there is volume reduction in this area also in young healthy adults, although less marked than in the elderly (5,14).”
BackgroundResultFalasco, G., Damián, A., Urrutia, L., Niell, N., Lago, G., Bérgamo, Y., Chrem, P., Quagliata, A., Bentancourt, C., Calandri, I. L., et al. (2021) · Current Alzheimer Research · doi:10.2174/1567205018666211116095035 · Cited by 5
Bourgin, J., Silvert, L., Borg, C., Morand, A., Sauvée, M., Moreaud, O., & Hot, P. (2020) · Brain and Cognition · doi:10.1016/j.bandc.2020.105624 · Cited by 6
“, limbic system) are the most resistant to normal aging (Kalpouzos et al., 2009).”
BackgroundHajra, S. G., Meltzer, J. A., Keerthi, P., Pappas, C., Sekuler, A. B., Cam-CAN Group, & Liu, C. C. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1473178 · Cited by 5
Guo, X., Wang, Y., Chen, K., Wu, X., Zhang, J., Li, K., Jin, Z., & Li, Y. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00122 · Cited by 4
“Some studies have indeed depicted accelerated loss of gray matter volume in the local sensorimotor cortex, including the SMA, and the bilateral precentral and postcentral gyri in older adults (Good et al., 2001; Resnick et al., 2003; Kalpouzos et al., 2009).”
BackgroundBastin, C., Simon, J., Kurth, S., Collette, F., & Salmon, É. (2013) · Open Repository and Bibliography (University of Liège) · doi:10.1684/nrp.2013.0278 · Cited by 3
“Ce déclin a été associé à une variété de changements cérébraux qui accompagnent l’avancée en âge (réduction de la densité de matière grise, perturbation de la connectivité structurelle et fonctionnelle) et qui touchent des régions clés dans les réseaux sous-tendant la mémoire, avec une vulnérabilité préférentielle du…”
Le XXIe siècle sera neuropsychologique ou ne sera pas !
Eustache, F., Desgranges, B., Lambert, J., Belleville, S., & Platel, H. (2008) · Revue Neurologique · doi:10.1016/s0035-3787(08)73293-4 · Cited by 3
Self-citationIndividual identification for different age groups using functional connectivity strength
Zhang, Y., Liu, S., & Yu, X. (2019) · Neurological Sciences · doi:10.1007/s10072-019-04109-6 · Cited by 3
Haudry, S., Turpin, A., Landeau, B., Mézenge, F., Delarue, M., Hébert, O., Marchant, N. L., Klimecki, O., Collette, F., Gonneaud, J., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-79687-3 · Cited by 3
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Structural Neuroimaging in Alzheimer’s Disease
Matsuda, H., & Imabayashi, E. (2017) · Springer eBooks · doi:10.1007/978-4-431-55133-1_3 · Cited by 4
Age-Related Glucose Metabolism in FDG-PET Brain Studies
Seifar, F., Hajibonabi, F., Parnianfard, N., Ranjkesh, M., Valikhani, E., & Yazdchi, M. R. (2018) · Translational Neuroscience Research and Reviews · doi:10.36959/817/520 · Cited by 3
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Zimmermann, G., Joly, L., Schoepfer, P., Doyen, M., Roch, V., Grignon, R., Salvi, P., Marie, P., Bénétos, A., & Verger, A. (2024) · Journal of Hypertension · doi:10.1097/hjh.0000000000003886 · Cited by 2
Aktywność fizyczna a kondycja psychiczna osób starszych
Żaroń, A., & Piskunowicz, M. (2018) · Rocznik Andragogiczny · doi:10.12775/ra.2017.009 · Cited by 3
“Zdania są podzielone w kwestii zmian w strukturach płatów skroniowych (Kalpouzos et al., 2009; Fjell et al., 2009; Fjell et al., 2013).”
Targeting Mitochondria and Redox Dyshomeostasis in Brain Ageing: An Update
Cardoso, S., & Moreira, P. I. (2021) · Healthy ageing and longevity · doi:10.1007/978-3-030-84965-8_8 · Cited by 2
Metadata
- Authors
- Kalpouzos, G., Chételat, G., Baron, J.-C., Landeau, B., Mevel, K., Godeau, C., Barré, L., Constans, J.-M., Viader, F., Eustache, F., & Desgranges, B.
- Affiliations
- Inserm (FR)
- Centre National de la Recherche Scientifique (FR)
- Centre Hospitalier Universitaire de Caen Normandie (FR)
- Corresponding author
- Desgranges, Béatrice
- ORCID
- Venue
- Neurobiology of Aging
- Publisher
- Elsevier BV
- Published
- 2009-01
- Volume
- 30
- Issue
- 1
- Pages
- 112-124
- ISSN
- 0197-4580 (ISSN-L)
- OpenAlex
- W2067165730
- Semantic Scholar
- bf607728c1e82244d92309ed602209a9f8de7586
- Cited by
- 392
- References
- 111
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 17 · 2023: 14 · 2022: 25 · 2021: 21 · 2020: 21 · 2019: 26 · 2018: 27 · 2017: 28 · 2016: 22 · 2015: 24 · 2014: 29 · 2013: 33 · 2012: 19
- FWCI
- 9.46
- Type
- journal article
- Language
- en
- Open access
- green
- inserm.hal.science
- Licence
- Locations
- Neurobiology of Aging (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Developmental and Educational Psychology › Aging and Gerontology Research
- Keywords
- normal brain aging, gray matter volume, glucose metabolism, voxel-based morphometry, FDG-PET, partial volume correction, frontal cortex, hippocampus, thalamus, posterior cingulate cortex, cognitive preservation, Alzheimer's disease, brain aging
- MeSH terms
- Adult, Aged, Aged, 80 and over, Aging, Brain, Female, Humans, Male, Metabolic Clearance Rate, Middle Aged, Neurons, Organ Size, Radionuclide Imaging, Tissue Distribution, Radiopharmaceuticals, Fluorodeoxyglucose F18, Young Adult
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