Neural processing of speech comprehension in noise predicts individual age using fNIRS-based brain-behavior models.

Speech comprehension in noise depends on complex interactions between peripheral sensory and central cognitive systems. Despite having normal peripheral hearing, older adults show difficulties in speech comprehension. It remains unclear whether the brain's neural responses could indicate aging. The current study examined whether individual brain activation during speech perception in different listening environments could predict age. We applied functional near-infrared spectroscopy to 93 normal-hearing human adults (20 to 70 years old) during a sentence listening task, which contained a quiet condition and 4 different signal-to-noise ratios (SNR = 10, 5, 0, -5 dB) noisy conditions. A data-driven approach, the region-based brain-age predictive modeling was adopted. We observed a significant behavioral decrease with age under the 4 noisy conditions, but not under the quiet condition. Brain activations in SNR = 10 dB listening condition could successfully predict individual's age. Moreover, we found that the bilateral visual sensory cortex, left dorsal speech pathway, left cerebellum, right temporal-parietal junction area, right homolog Wernicke's area, and right middle temporal gyrus contributed most to prediction performance. These results demonstrate that the activations of regions about sensory-motor mapping of sound, especially in noisy conditions, could be sensitive measures for age prediction than external behavior measures.

The effects of pre-cue alpha and cueing strategy on age-related deficits in post-cue alpha activity and target processing during visual spatial attention.

Electroencephalography alpha-band (8-13 Hz) activity during visual spatial attention declines in normal aging. We recently reported the impacts of pre-cue baseline alpha and cueing strategy on post-cue anticipatory alpha activity and target processing in visual spatial attention (Wang et al., Cerebral Cortex, 2023). However, whether these factors affected aging effects remains unaddressed. We investigated this issue in two independent experiments (n = 114) with different cueing strategies (instructional vs. probabilistic). When median-splitting young adults (YA) by their pre-cue alpha power, we found that older adults exhibited similar pre-cue and post-cue alpha activity as YA with lower pre-cue alpha, and only YA with higher pre-cue alpha showed significant post-cue alpha activity, suggesting that diminished anticipatory alpha activity was not specific to aging but likely due to a general decrease with baseline alpha. Moreover, we found that the aging effects on cue-related event-related potentials were dependent on cueing strategy but were relatively independent of pre-cue alpha. However, age-related deficits in target-related N1 attentional modulation might depend on both pre-cue alpha and cueing strategy. By considering the impacts of pre-cue alpha and cueing strategy, our findings offer new insights into age-related deficits in anticipatory alpha activity and target processing during visual spatial attention.

Functional alterations in bipartite network of white and grey matters during aging.

The effects of normal aging on functional connectivity (FC) within various brain networks of gray matter (GM) have been well-documented. However, the age effects on the networks of FC between white matter (WM) and GM, namely WM-GM FC, remains unclear. Evaluating crucial properties, such as global efficiency (GE), for a WM-GM FC network poses a challenge due to the absence of closed triangle paths which are essential for assessing network properties in traditional graph models. In this study, we propose a bipartite graph model to characterize the WM-GM FC network and quantify these challenging network properties. Leveraging this model, we assessed the WM-GM FC network properties at multiple scales across 1,462 cognitively normal subjects aged 22-96 years from three repositories (ADNI, BLSA and OASIS-3) and investigated the age effects on these properties throughout adulthood and during late adulthood (age ≥70 years). Our findings reveal that (1) heterogeneous alterations occurred in region-specific WM-GM FC over the adulthood and decline predominated during late adulthood; (2) the FC density of WM bundles engaged in memory, executive function and processing speed declined with age over adulthood, particularly in later years; and (3) the GE of attention, default, somatomotor, frontoparietal and limbic networks reduced with age over adulthood, and GE of visual network declined during late adulthood. These findings provide unpresented insights into multi-scale alterations in networks of WM-GM functional synchronizations during normal aging. Furthermore, our bipartite graph model offers an extendable framework for quantifying WM-engaged networks, which may contribute to a wide range of neuroscience research.

Characteristics of perivascular space dilatation in normal aging.

The increased incidence of dilated perivascular spaces (dPVSs) visible on MRI has been observed with advancing age, but the relevance of PVS dilatation to normal aging across the lifespan has yet to be fully clarified. In the current study, we sought to find out the age dependence of dPVSs by exploring changes in different characteristics of PVS dilatation across a wide range of age. For 1220 healthy subjects aged between 18 and 100 years, PVSs were automatically segmented and characteristics of PVS dilatation were assessed in terms of the burden, location, and morphology of PVSs in the white matter (WM) and basal ganglia (BG). A machine learning model using the random forests method was constructed to estimate the subjects' age by employing the PVS features. The constructed machine learning model was able to estimate the age of the subjects with an error of 9.53 years on average (correlation = 0.875). The importance of the PVS features indicated the primary contribution of the burden of PVSs in the BG and the additional contribution of locational and morphological changes of PVSs, specifically peripheral extension and reduced linearity, in the WM to age estimation. Indeed, adding the PVS location or morphology features to the PVS burden features provided an improvement to the performance of age estimation. The age dependence of dPVSs in terms of such various characteristics of PVS dilatation in healthy subjects could provide a more comprehensive reference for detecting brain disease-related PVS dilatation.

Astrogliosis, neuritic microstructure, and sex effects: GFAP is an indicator of neuritic orientation in women.

BACKGROUND: Data from human studies suggest that immune dysregulation is associated with Alzheimer's disease (AD) pathology and cognitive decline and that neurites may be affected early in the disease trajectory. Data from animal studies further indicate that dysfunction in astrocytes and inflammation may have a pivotal role in facilitating dendritic damage, which has been linked with negative cognitive outcomes. To elucidate these relationships further, we have examined the relationship between astrocyte and immune dysregulation, AD-related pathology, and neuritic microstructure in AD-vulnerable regions in late life. METHODS: We evaluated panels of immune, vascular, and AD-related protein markers in blood and conducted in vivo multi-shell neuroimaging using Neurite Orientation Dispersion and Density Imaging (NODDI) to assess indices of neuritic density (NDI) and dispersion (ODI) in brain regions vulnerable to AD in a cohort of older adults (n = 109). RESULTS: When examining all markers in tandem, higher plasma GFAP levels were strongly related to lower neurite dispersion (ODI) in grey matter. No biomarker associations were found with higher neuritic density. Associations between GFAP and neuritic microstructure were not significantly impacted by symptom status, APOE status, or plasma Aß42/40 ratio; however, there was a large sex effect observed for neurite dispersion, wherein negative associations between GFAP and ODI were only observed in females. DISCUSSION: This study provides a comprehensive, concurrent appraisal of immune, vascular, and AD-related biomarkers in the context of advanced grey matter neurite orientation and dispersion methodology. Sex may be an important modifier of the complex associations between astrogliosis, immune dysregulation, and brain microstructure in older adults.

NIH Toolbox® Episodic Memory Measure Differentiates Older Adults with Exceptional Memory Capacity from those with Average-for-Age Cognition.

OBJECTIVE: Older adults with exceptional memory function, designated "SuperAgers," include individuals over age 80, with episodic memory at least as good as individuals ages 50s-60s. The Northwestern University SuperAging cohort is defined by performance on an established test of verbal memory. The purpose of this study was to determine if superior verbal memory extends to nonverbal memory in SuperAgers by examining differences in the National Institutes of Health Toolbox® (NIHTB) between older adults with exceptional memory and those with average-for-age cognition. METHOD: SuperAgers (n = 46) and cognitively average-for-age older adults (n = 31) completed a comprehensive neuropsychological battery and the NIHTB Cognition module. Multiple linear regressions were used to examine differences on subtests between groups. RESULTS: There was a significant effect of group on the Picture Sequence Memory score, (p = .007), such that SuperAgers had higher scores than cognitively average-for-age older adults. There were no other group effects across other non-episodic memory NIHTB Cognition measures. CONCLUSIONS: Findings from this study demonstrated stronger performance on the memory measure of the NIHTB in SuperAgers compared to cognitively average-for-age older adults demonstrating superior memory in not only verbal but also nonverbal episodic memory in this group. Additionally, this study adds to the literature validating the NIHTB in older adults, particularly in a novel population of adults over age 80 with exceptional memory.

Learning and vulnerability to phonological and semantic interference in normal aging: an experimental study.

ABSTRACTThis study compares semantic and phonological interference vulnerability across the full range of learning processes. Method: 43 controls aged 61-88 underwent a neuropsychological examination, French adaptation of the LASSI-L, and an experimental phonological test, the TIP-A. Paired sample t-tests, factorial ANOVA and hierarchical regressions were conducted, psychometric properties were calculated. Results: TIP-A efficiently generated phonological interference between concurrent word lists and was associated with short-term memory, unlike LASSI-L. On LASSI-L, proactive interference was higher than retroactive interference; the opposite pattern was found on TIP-A. Memory performance was better explained by age in the semantic than in the phonological task. Age was not associated with interference vulnerability. Intrusions and false recognition were associated with cognitive functioning regardless of age, particularly in the semantic context. Conclusion: To our knowledge, this is the first study to assess phonological and semantic interference using homologous concurrent word list tasks, and not a working memory build-up or DRM paradigm. The pattern obtained illustrates the weak initial memory trace in a phonological context and results are discussed according to depth-of-processing and dual-process theories. Similar paradigms could be studied among various pathologies for a better understanding of generalised interference vulnerability vs. specific semantic or phonological impairment.

Vascular mapping of the human hippocampus using Ferumoxytol-enhanced MRI.

The hippocampus is a small but complex grey matter structure that plays an important role in spatial and episodic memory and can be affected by a wide range of pathologies including vascular abnormalities. In this work, we introduce the use of Ferumoxytol, an ultra-small superparamagnetic iron oxide (USPIO) agent, to induce susceptibility in the arteries (as well as increase the susceptibility in the veins) to map the hippocampal micro-vasculature and to evaluate the quantitative change in tissue fractional vascular density (FVD), in each of its subfields. A total of 39 healthy subjects (aged 35.4 ± 14.2 years, from 18 to 81 years old) were scanned with a high-resolution (0.22×0.44×1 mm3) dual-echo SWI sequence acquired at four time points during a gradual increase in Ferumoxytol dose (final dose = 4 mg/kg). The volumes of each subfield were obtained automatically from the pre-contrast T1-weighted data. The dynamically acquired SWI data were co-registered and adaptively combined to reduce the blooming artifacts from large vessels, preserving the contrast from smaller vessels. The resultant SWI data were used to segment the hippocampal vasculature and to measure the FVD ((volume occupied by vessels)/(total volume)) for each subfield. The hippocampal fissure, along with the fimbria, granular cell layer of the dentate gyrus and cornu ammonis layers (except for CA1), showed higher micro-vascular FVD than the other parts of hippocampus. The CA1 region exhibited a significant correlation with age (R = -0.37, p < 0.05). demonstrating an overall loss of hippocampal vascularity in the normal aging process. Moreover, the vascular density reduction was more prominent than the age correlation with the volume reduction (R = -0.1, p > 0.05) of the CA1 subfield, which would suggest that vascular degeneration may precede tissue atrophy.

SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI.

Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain.

Phosphoproteome profiling of mouse liver during normal aging.

BACKGROUND: Aging is a complex biological process accompanied by a time-dependent functional decline that affects most living organisms. Omics studies help to comprehensively understand the mechanism of aging and discover potential intervention methods. Old mice are frequently obese with a fatty liver. METHODS: We applied mass spectrometry-based phosphoproteomics to obtain a global phosphorylation profile of the liver in mice aged 2 or 18 months. MaxQuant was used for quantitative analysis and PCA was used for unsupervised clustering. RESULTS: Through phosphoproteome analysis, a total of 5,685 phosphosites in 2,335 proteins were filtered for quantitative analysis. PCA analysis of both the phosphoproteome and transcriptome data could distinguish young and old mice. However, from kinase prediction, kinase-substrate interaction analysis, and KEGG functional enrichment analysis done with phosphoproteome data, we observed high phosphorylation of fatty acid biosynthesis, ß-oxidation, and potential secretory processes, together with low phosphorylation of the Egfr-Sos1-Araf/Braf-Map2k1-Mapk1 pathway and Ctnnb1 during aging. Proteins with differentially expressed phosphosites seemed more directly related to the aging-associated fatty liver phenotype than the differentially expressed transcripts. The phosphoproteome may reveal distinctive biological functions that are lost in the transcriptome. CONCLUSIONS: In summary, we constructed a phosphorylation-associated network in the mouse liver during normal aging, which may help to discover novel antiaging strategies.

The 32-Item Multilingual Naming Test: Cultural and Linguistic Biases in Monolingual Chinese-Speaking Older Adults.

OBJECTIVES: This study describes the performance of the Multilingual Naming Test (MINT) by Chinese American older adults who are monolingual Chinese speakers. An attempt was also made to identify items that could introduce bias and warrant attention in future investigation. METHODS: The MINT was administered to 67 monolingual Chinese older adults as part of the standard dementia evaluation at the Alzheimer's Disease Research Center (ADRC) at the Icahn School of Medicine at Mount Sinai (ISMMS), New York, USA. A diagnosis of normal cognition (n = 38), mild cognitive impairment (n = 12), and dementia (n = 17) was assigned to all participants at clinical consensus conferences using criterion sheets developed at the ADRC at ISMMS. RESULTS: MINT scores were negatively correlated with age and positively correlated with education, showing sensitivity to demographic factors. One item, butterfly, showed no variations in responses across diagnostic groups. Inclusion of responses from different regions of China changed the answers from "incorrect" to "correct" on 20 items. The last five items, porthole, anvil, mortar, pestle, and axle, yielded a high nonresponse rate, with more than 70% of participants responding with "I don't know." Four items, funnel, witch, seesaw, and wig, were not ordered with respect to item difficulty in the Chinese language. Two items, gauge and witch, were identified as culturally biased for the monolingual group. CONCLUSIONS: Our study highlights the cultural and linguistic differences that might influence the test performance. Future studies are needed to revise the MINT using more universally recognized items of similar word frequency across different cultural and linguistic groups.

Structural connectivity mapping in human hippocampal-subfields using super-resolution hybrid diffusion imaging: a feasibility study.

PURPOSE: The goal of the current study was to introduce a new methodology that holds a promise to be used in hippocampus-aging studies using sub-millimeter super-resolution hybrid diffusion imaging (HYDI) MRI. METHODS: HYDI diffusion data were acquired in two groups of older and younger healthy participants recruited from the Indiana Alzheimer's Disease Research Center and community. These data were then transformed into super-resolution diffusion images before the hippocampal subfield analyses. We studied the correlation between the subjects' age and the structural connectivity involving the hippocampal subfields and the connectivity between the whole hippocampus and the cerebral cortex. RESULTS: Structural integrity derived from the tractography streamlines between the hippocampal subfields was reduced in older than younger adults. CONCLUSION: The findings offered a new promising framework, and they opened avenues for future studies to explore the relationship between the structural connectivity in the hippocampal area and different types of dementia.

Cortical Networks Underpinning Compensation of Verbal Fluency in Normal Aging.

Elucidating compensatory mechanisms underpinning phonemic fluency (PF) may help to minimize its decline due to normal aging or neurodegenerative diseases. We investigated cortical brain networks potentially underpinning compensation of age-related differences in PF. Using graph theory, we constructed networks from measures of thickness for PF, semantic, and executive-visuospatial cortical networks. A total of 267 cognitively healthy individuals were divided into younger age (YA, 38-58 years) and older age (OA, 59-79 years) groups with low performance (LP) and high performance (HP) in PF: YA-LP, YA-HP, OA-LP, OA-HP. We found that the same pattern of reduced efficiency and increased transitivity was associated with both HP (compensation) and OA (aberrant network organization) in the PF and semantic cortical networks. When compared with the OA-LP group, the higher PF performance in the OA-HP group was associated with more segregated PF and semantic cortical networks, greater participation of frontal nodes, and stronger correlations within the PF cortical network. We conclude that more segregated cortical networks with strong involvement of frontal nodes seemed to allow older adults to maintain their high PF performance. Nodal analyses and measures of strength were helpful to disentangle compensation from the aberrant network organization associated with OA.

Longitudinal resting-state functional connectivity and regional brain atrophy-based biomarkers of preclinical cognitive impairment in healthy old adults.

BACKGROUND: Intervention against age-related neurodegenerative diseases may be difficult once extensive structural and functional deteriorations have already occurred in the brain. AIM: Investigating 6-year longitudinal changes and implications of regional brain atrophy and functional connectivity in the triple-network model as biomarkers of preclinical cognitive impairment in healthy aging. METHODS: We acquired longitudinal cognitive scores and magnetic resonance imaging (MRI) data from 74 healthy old adults. Resting-state functional MRI (rs-fMRI) analysis was conducted using FSL6.0.1 to examine functional connectivity changes and regional brain morphometries were quantified using FreeSurfer5.3. Finally, we cross-validated and compared two support vector machine (SVM) regression models to predict future 6-year cognition score from the baseline regional brain atrophy and resting-state functional connectivity (rs-FC) measures. RESULTS: After a 6-year follow-up, our results (P < 0.05-corrected) indicated significant connectivity reduction within all the three brain networks, significant differences in regional brain volumes and cortical thickness. We also observed significant improvement in episodic memory and significant decline in executive functions. Finally, comparing the two models, we observed that regional brain atrophy predictors were more efficient in approximating future 6-year cognitive scores (R = 0.756, P < 0.0001) than rs-FC predictors (R = 0.6, P < 0.0001). CONCLUSION: This study used longitudinal data to keep subject variability low and to increase the validity of the results. We demonstrated significant changes in structural and functional MRI over 6 years. Our findings present a potential neuroimaging-based biomarker to detect cognitive impairment and prevent risks of neurodegenerative diseases in healthy old adults.

Olfactory impairment in men and mice related to aging and amyloid-induced pathology.

Olfaction, or the sense of smell, is one of the most ancient senses in men and mice, important for a large variety of innate and acquired behaviors. Clinical data reveal an early impairment of olfaction during normal aging and in the course of neurodegenerative diseases, but the underlying cellular/molecular mechanisms remain obscure. In the current review, we compare different aspects of the aging- and Alzheimer's disease related impairment of olfaction in men and mice, aiming at the identification of common morbidities and biomarkers, which can be analyzed in detail in the appropriate mouse models. We also identify common, often interdependent (patho)physiological pathways, including but not limited to extracellular amyloid depositions, neuroinflammation, ɛ4 allele of the apolipoprotein E, CNS insulin resistance, and the impairment of adult neurogenesis, to be targeted by basic and clinical research.

Relationship between the disrupted topological efficiency of the structural brain connectome and glucose hypometabolism in normal aging.

Normal aging is accompanied by structural degeneration and glucose hypometabolism in the human brain. However, the relationship between structural network disconnections and hypometabolism in normal aging remains largely unknown. In the present study, by combining MRI and PET techniques, we investigated the metabolic mechanism of the structural brain connectome and its relationship with normal aging in a cross-sectional, community-based cohort of 42 cognitively normal elderly individuals aged 57-84 years. The structural connectome was constructed based on diffusion MRI tractography, and the network efficiency metrics were quantified using graph theory analyses. FDG-PET scanning was performed to evaluate the glucose metabolic level in the cortical regions of the individuals. The results of this study demonstrated that both network efficiency and cortical metabolism decrease with age (both p < 0.05). In the subregions of the bilateral thalamus, significant correlations between nodal efficiency and cortical metabolism could be observed across subjects. Individual-level analyses indicated that brain regions with higher nodal efficiency tend to exhibit higher metabolic levels, implying a tight coupling between nodal efficiency and glucose metabolism (r = 0.56, p = 1.15 × 10-21). Moreover, efficiency-metabolism coupling coefficient significantly increased with age (r = 0.44, p = 0.0046). Finally, the main findings were also reproducible in the ADNI dataset. Together, our results demonstrate a close coupling between structural brain connectivity and cortical metabolism in normal elderly individuals and provide new insight that improve the present understanding of the metabolic mechanisms of structural brain disconnections in normal aging.

Brain aging and speech perception: Effects of background noise and talker variability.

Speech perception can be challenging, especially for older adults. Despite the importance of speech perception in social interactions, the mechanisms underlying these difficulties remain unclear and treatment options are scarce. While several studies have suggested that decline within cortical auditory regions may be a hallmark of these difficulties, a growing number of studies have reported decline in regions beyond the auditory processing network, including regions involved in speech processing and executive control, suggesting a potentially diffuse underlying neural disruption, though no consensus exists regarding underlying dysfunctions. To address this issue, we conducted two experiments in which we investigated age differences in speech perception when background noise and talker variability are manipulated, two factors known to be detrimental to speech perception. In Experiment 1, we examined the relationship between speech perception, hearing and auditory attention in 88 healthy participants aged 19 to 87 years. In Experiment 2, we examined cortical thickness and BOLD signal using magnetic resonance imaging (MRI) and related these measures to speech perception performance using a simple mediation approach in 32 participants from Experiment 1. Our results show that, even after accounting for hearing thresholds and two measures of auditory attention, speech perception significantly declined with age. Age-related decline in speech perception in noise was associated with thinner cortex in auditory and speech processing regions (including the superior temporal cortex, ventral premotor cortex and inferior frontal gyrus) as well as in regions involved in executive control (including the dorsal anterior insula, the anterior cingulate cortex and medial frontal cortex). Further, our results show that speech perception performance was associated with reduced brain response in the right superior temporal cortex in older compared to younger adults, and to an increase in response to noise in older adults in the left anterior temporal cortex. Talker variability was not associated with different activation patterns in older compared to younger adults. Together, these results support the notion of a diffuse rather than a focal dysfunction underlying speech perception in noise difficulties in older adults.

Age-related estimates of aggregate g-ratio of white matter structures assessed using quantitative magnetic resonance neuroimaging.

The g-ratio, defined as the inner-to-outer diameter of a myelinated axon, is associated with the speed of nerve impulse conduction, and represents an index of axonal myelination and integrity. It has been shown to be a sensitive and specific biomarker of neurodevelopment and neurodegeneration. However, there have been very few magnetic resonance imaging studies of the g-ratio in the context of normative aging; characterizing regional and time-dependent cerebral changes in g-ratio in cognitively normal subjects will be a crucial step in differentiating normal from abnormal microstructural alterations. In the current study, we investigated age-related differences in aggregate g-ratio, that is, g-ratio averaged over all fibers within regions of interest, in several white matter regions in a cohort of 52 cognitively unimpaired participants ranging in age from 21 to 84 years. We found a quadratic, U-shaped, relationship between aggregate g-ratio and age in most cerebral regions investigated, suggesting myelin maturation until middle age followed by a decrease at older ages. As expected, we observed that these age-related differences vary across different brain regions, with the frontal lobes and parietal lobes exhibiting slightly earlier ages of minimum aggregate g-ratio as compared to more posterior structures such as the occipital lobes and temporal lobes; this agrees with the retrogenesis paradigm. Our results provide evidence for a nonlinear association between age and aggregate g-ratio in a sample of adults from a highly controlled population. Finally, sex differences in aggregate g-ratio were observed in several cerebral regions, with women exhibiting overall lower values as compared to men; this likely reflects the greater myelin content in women's brain, in agreement with recent investigations.

Functional brain connectivity changes across the human life span: From fetal development to old age.

The dynamic of the temporal correlations between brain areas, called functional connectivity (FC), undergoes complex transformations through the life span. In this review, we aim to provide an overview of these changes in the nonpathological brain from fetal life to advanced age. After a brief description of the main methods, we propose that FC development can be divided into four main phases: first, before birth, a strong change in FC leads to the emergence of functional proto-networks, involving mainly within network short-range connections. Then, during the first years of life, there is a strong widespread organization of networks which starts with segregation processes followed by a continuous increase in integration. Thereafter, from adolescence to early adulthood, a refinement of existing networks in the brain occurs, characterized by an increase in integrative processes until about 40 years. Middle age constitutes a pivotal period associated with an inversion of the functional brain trajectories with a decrease in segregation process in conjunction to a large-scale reorganization of between network connections. Studies suggest that these processes are in line with the development of cognitive and sensory functions throughout life as well as their deterioration. During aging, results support the notion of dedifferentiation processes, which refer to the decrease in functional selectivity of the brain regions, resulting in more diffuse and less specialized FC, associated with the disruption of cognitive functions with age. The inversion of developmental processes during aging is in accordance with the developmental models of neuroanatomy for which the latest matured regions are the first to deteriorate.

Structural aspects of the aging invertebrate brain.

Aging is characterized by a decline in neuronal function in all animal species investigated so far. Functional changes are accompanied by and may be in part caused by, structurally visible degenerative changes in neurons. In the mammalian brain, normal aging shows abnormalities in dendrites and axons, as well as ultrastructural changes in synapses, rather than global neuron loss. The analysis of the structural features of aging neurons, as well as their causal link to molecular mechanisms on the one hand, and the functional decline on the other hand is crucial in order to understand the aging process in the brain. Invertebrate model organisms like Drosophila and C. elegans offer the opportunity to apply a forward genetic approach to the analysis of aging. In the present review, we aim to summarize findings concerning abnormalities in morphology and ultrastructure in invertebrate brains during normal aging and compare them to what is known for the mammalian brain. It becomes clear that despite of their considerably shorter life span, invertebrates display several age-related changes very similar to the mammalian condition, including the retraction of dendritic and axonal branches at specific locations, changes in synaptic density and increased accumulation of presynaptic protein complexes. We anticipate that continued research efforts in invertebrate systems will significantly contribute to reveal (and possibly manipulate) the molecular/cellular pathways leading to neuronal aging in the mammalian brain.