The Effect of Aging on Resting State Connectivity of Predefined Networks in the Brain

Resting state brain network segregation is associated with walking speed and working memory in older adults

Older adults exhibit larger individual differences in walking ability and cognitive function than young adults. Characterizing intrinsic brain connectivity differences in older adults across a wide walking performance spectrum may provide insight into the mechanisms of functional decline in some older adults and resilience in others. Thus, the objectives of this study were to: (1) determine whether young adults and high- and low-functioning older adults show group differences in brain network segregation, and (2) determine whether network segregation is associated with working memory and walking function in these groups. The analysis included 21 young adults and 81 older adults. Older adults were further categorized according to their physical function using a standardized assessment; 54 older adults had low physical function while 27 were considered high functioning. Structural and functional resting state magnetic resonance images were collected using a Siemens Prisma 3T scanner. Working memory was assessed with the NIH Toolbox list sorting test. Walking speed was assessed with a 400 m walk test at participants' self-selected speed. We found that network segregation in mobility-related networks (sensorimotor, vestibular) was higher in older adults with higher physical function compared to older adults with lower physical function. There were no group differences in laterality effects on network segregation. We found multivariate associations between working memory and walking speed with network segregation scores. The interaction of left sensorimotor network segregation and age groups was associated with higher working memory function. Higher left sensorimotor, left vestibular, right anterior cingulate cortex, and interaction of left anterior cingulate cortex network segregation and age groups were associated with faster walking speed. These results are unique and significant because they demonstrate higher network segregation is largely related to higher physical function and not age alone.

Dogs' olfactory resting-state functional connectivity is modulated by age and brain shape

Humans have long applied canine olfaction in various contexts. Dog olfactory brain networks have recently been mapped by anatomical measures, but functional connections remain unexplored. Also, whereas individual characteristics, including age, sex, and brain shape, are known to affect olfactory performance, their covariation with olfactory functional networks is unknown. To address these, we investigated dogs' resting-state functional connectivities between anatomically defined olfactory regions and assessed whether and how their olfactory functional network is affected by age, sex, and brain shape. Olfactory functional connectivity strength exhibited negative correlations with both age and brain shape: older dogs and those with rounder-shaped brains demonstrated lower functional connectivity, respectively, but no effect of sex was found. The results suggest that both aging and brain morphology can negatively impact a dog's sense of smell, and older dogs and dogs with rounder-shaped brains may have diminished olfactory performance.

Abnormal Brain Functional Connectivity in Patients with Chronic Obstructive Pulmonary Disease and Correlations with Clinical and Cognitive Parameters

Background

Cognitive impairment is a major comorbidity of chronic obstructive pulmonary disease (COPD), but the underlying mechanisms are not fully understood. In this study, we used resting-state functional magnetic resonance imaging to investigate brain functional connectivity (FC) abnormalities in patients with COPD and explored the correlation between abnormal FC and COPD-related clinical parameters.

Methods

Forty-one patients with COPD, without a definite diagnosis of cognitive impairment or depression, and 30 age- and sex-matched controls were recruited. A total of 184 resting-state functional connectivity (RSFC) maps were generated for all seed points. Welch's t-test was used to assess differences in RSFC between the COPD and control groups, and the correlation coefficients between RSFC and clinical parameters were calculated.

Results

Patients with COPD had lower scores on the Mini-Mental State Exam (MMSE) and Korean version of the Montreal Cognitive Assessment and higher scores on the Beck Depression Inventory than the control group. Additionally, patients with COPD showed decreased RSFC in the left middle-posterior cingulate cortex, left posterior-dorsal cingulate cortex, and right superior occipital gyrus and increased RSFC in the left superior temporal sulcus, left posterior transverse collateral sulcus, right occipital pole, and right precentral gyrus. The regions showing differences in FC correlated with MMSE score, COPD symptom assessment scales, such as the COPD Assessment Test and modified Medical Research Council Dyspnea Scale, and pulmonary function parameters, including forced expiratory volume in one second and forced vital capacity.

Conclusion

Patients with COPD showed significant differences in FC within specific brain regions that correlated with symptoms, cognition, and lung function.

Network segregation during episodic memory shows age-invariant relations with memory performance from 7 to 82 years

Lower episodic memory capability, as seen in development and aging compared with younger adulthood, may partly depend on lower brain network segregation. Here, our objective was twofold: (1) test this hypothesis using within- and between-network functional connectivity (FC) during episodic memory encoding and retrieval, in two independent samples (n = 734, age 7-82 years). (2) Assess associations with age and the ability to predict memory comparing task-general FC and memory-modulated FC. In a multiverse-inspired approach, we performed tests across multiple analytic choices. Results showed that relationships differed based on these analytic choices and were mainly present in the largest dataset,. Significant relationships indicated that (i) memory-modulated FC predicted memory performance and associated with memory in an age-invariant manner. (ii) In line with the so-called neural dedifferentiation view, task-general FC showed lower segregation with higher age in adults which was associated with worse memory performance. In development, although there were only weak signs of a neural differentiation, that is, gradually higher segregation with higher age, we observed similar lower segregation-worse memory relationships. This age-invariant relationships between FC and episodic memory suggest that network segregation is pivotal for memory across the healthy lifespan.

Depthwise cortical iron relates to functional connectivity and fluid cognition in healthy aging

Age-related differences in fluid cognition have been associated with both the merging of functional brain networks, defined from resting-state functional magnetic resonance imaging (rsfMRI), and with elevated cortical iron, assessed by quantitative susceptibility mapping (QSM). Limited information is available, however, regarding the depthwise profile of cortical iron and its potential relation to functional connectivity. Here, using an adult lifespan sample (n = 138; 18-80 years), we assessed relations among graph theoretical measures of functional connectivity, column-based depthwise measures of cortical iron, and fluid cognition (i.e., tests of memory, perceptual-motor speed, executive function). Increased age was related both to less segregated functional networks and to increased cortical iron, especially for superficial depths. Functional network segregation mediated age-related differences in memory, whereas depthwise iron mediated age-related differences in general fluid cognition. Lastly, higher mean parietal iron predicted lower network segregation for adults younger than 45 years of age. These findings suggest that functional connectivity and depthwise cortical iron have distinct, complementary roles in the relation between age and fluid cognition in healthy adults.

Influence of individual's age on the characteristics of brain effective connectivity

Given the increasing number of older adults in society, there is a growing need for studies on changes in the aging brain. The aim of this research is to investigate the effective connectivity of different age groups using resting-state functional magnetic resonance imaging (fMRI) and graph theory. By examining connectivity in different age groups, a better understanding of age-related changes can be achieved. Lifespan pilot data from the Human Connectome Project (HCP) were used to examine dynamic effective connectivity (dEC) changes across different age groups. The Granger causality method with time windowing was employed to calculate dEC. After extracting graph measures, statistical analyses were performed to compare the age groups. Support vector machine and decision tree classifiers were used to classify the different age groups based on the extracted graph measures. Based on the obtained results, it can be concluded that there are significant differences in the effective connectivity among the three age groups. Statistical analyses revealed disassortativity. The global efficiency exhibited a decreasing trend, and the transitivity measure showed an increasing trend with the advancing age. The decision tree classifier showed an accuracy of 86.67 % with Kruskal-Wallis selected features. This study demonstrates that changes in effective connectivity across different age brackets can serve as a tool for better understanding brain function during the aging process.

Attention-dependent coupling with forebrain and brainstem neuromodulatory nuclei differs across the lifespan

Attentional states reflect the changing behavioral relevance of stimuli in one's environment, having important consequences for learning and memory. Supporting well-established cortical contributions, attentional states are hypothesized to originate from subcortical neuromodulatory nuclei, such as the basal forebrain (BF) and locus coeruleus (LC), which are among the first to change with aging. Here, we characterized the interplay between BF and LC neuromodulatory nuclei and their relation to two common afferent cortical targets important for attention and memory, the posterior cingulate cortex and hippocampus, across the adult lifespan. Using an auditory target discrimination task during functional MRI, we examined the influence of attentional and behavioral salience on task-dependent functional connectivity in younger (19-45 years) and older adults (66-86 years). In younger adults, BF functional connectivity was largely driven by target processing, while LC connectivity was associated with distractor processing. These patterns are reversed in older adults. This age-dependent connectivity pattern generalized to the nucleus basalis of Meynert and medial septal subnuclei. Preliminary data from middle-aged adults indicates a transitional stage in BF and LC functional connectivity. Overall, these results reveal distinct roles of subcortical neuromodulatory systems in attentional salience related to behavioral relevance and their potential reversed roles with aging, consistent with managing increased salience of behaviorally irrelevant distraction in older adults. Such prominent differences in functional coupling across the lifespan from these subcortical neuromodulatory nuclei suggests they may be drivers of widespread cortical changes in neurocognitive aging, and middle age as an opportune time for intervention.

Structural damage-driven brain compensation among near-centenarians and centenarians without dementia

Compensation has been proposed as a mechanism to explain how individuals in very old age remain able to maintain normal cognitive functioning. Previous studies have provided evidence on the role of increasing functional connectivity as a compensatory mechanism for age-related white matter damage. However, we lack direct investigation into how these mechanisms contribute to the preservation of cognition in the very old population. We examined a cohort of near-centenarians and centenarians without dementia (aged 95-103 years, n=44). We constructed a structural disconnection matrix based on the disruption of white matter pathways caused by white matter hyperintensities (WMHs), aiming to explore the relationship between functional connections, cognitive preservation and white matter damage. Our results revealed that structural damage can reliably explain the variations of functional connections or cognitive maintenance. Notably, we found significant correlations between the weights in the functional connectivity model and the weights in the cognition model. We observed positive correlations between models for brain disconnections and cognitive function in near-centenarians and centenarians. The strongest effects were found between attention and somatomotor network (SMN) (r=0.397, p<0.001), memory and SMN (r=0.333 p<0.001), fluency and visual network (VIS) - control network (CN) (r=0.406, p<0.001), language and VIS (r=0.309, p<0.001), visuospatial ability and VIS-default mode network (DMN) (r=0.464, p<0.001), as well as global cognition and VIS-DMN (r=0.335, p<0.001). These findings suggest that enhancement of functional connectivity may serve as a compensatory mechanism, such that it mitigates the effects of white matter damage and contributes to preserved cognitive performance in very old age.

Age- and Sex-Specific Patterns in Adult Brain Network Segregation

The human brain is organized into several segregated associative and sensory functional networks, each responsible for various aspects of cognitive and sensory processing. These functional networks become less segregated over the adult lifespan, possibly contributing to cognitive decline that is observed during advanced age. To date, a comprehensive understanding of decreasing network segregation with age has been hampered by (1) small sample sizes, (2) lack of investigation at different spatial scales, (3) the limited age range of participants, and more importantly (4) an inadequate consideration of sex (biological females and males) differences. This study aimed to address these shortcomings. Resting-state functional magnetic resonance imaging data were collected from 357 cognitively intact participants (18.2-91.8 years; 49.9 ± 17.1 years; 27.70 ± 1.72 MoCA score, 203 [56.8%] females), and the segregation index (defined as one minus the ratio of between-network connectivity to within-network connectivity) was calculated at three spatial scales of brain networks: whole-brain network, intermediate sensory and associative networks, as well as core visual (VIS), sensorimotor (SMN), frontoparietal (FPN), ventral attention (VAN), dorsal attention (DAN), and default mode networks (DMN). Where applicable, secondary within-, between-, and pairwise connectivity analyses were also conducted to investigate the origin of any observed age and sex effects on network segregation. For any given functional metric, linear and quadratic age effects, sex effects, and respective age by sex interaction effects were assessed using backwards iterative linear regression modeling. Replicating previous work, brain networks were found to become less segregated across adulthood. Specifically, negative quadratic decreases in whole-brain network, intermediate associative network, VAN, and DMN segregation index were observed. Intermediate sensory networks, VIS, and SMN exhibited negative linear decreases in segregation index. Secondary analysis revealed that this process of age-related functional reorganization was preferential as functional connectivity was observed to increase either between anatomically adjacent associative networks (DMN-DAN, FPN-DAN) or between anterior associative and posterior sensory networks (VIS-DAN, VIS-DMN, VIS-FPN, SMN-DMN, and SMN-FPN). Inherent sex differences in network segregation index were also observed. Specifically, whole-brain, associative, DMN, VAN, and FPN segregation index was greater in females compared to males, irrespective of age. Secondary analysis found that females have reduced functional connectivity between associative networks (DAN-VAN, VAN-FPN) compared to males and independent of age. A notable linear age-related decrease in FPN SI was also only observed for females and not males. The observed findings support the notion that functional networks reorganize across the adult lifespan, becoming less segregated. This decline may reflect underlying neurocognitive aging mechanisms like neural dedifferentiation, inefficiency, and compensation. The aging trajectories and rates of decreasing network segregation, however, vary across associative and sensory networks. This study also provides preliminary evidence of inherent sex differences in network organization, where associative networks are more segregated in females than males. These inherent sex differences suggest that female functional networks may be more efficient and functionally specialized compared to males across adulthood. Given these findings, future studies should take a more focused approach to examining sex differences across the lifespan, incorporating multimodal methodologies.

Investigating dynamic brain functional redundancy as a mechanism of cognitive reserve

Introduction

Individuals with higher cognitive reserve (CR) are thought to be more resilient to the effects of age-related brain changes on cognitive performance. A potential mechanism of CR is redundancy in brain network functional connectivity (BFR), which refers to the amount of time the brain spends in a redundant state, indicating the presence of multiple independent pathways between brain regions. These can serve as back-up information processing routes, providing resiliency in the presence of stress or disease. In this study we aimed to investigate whether BFR modulates the association between age-related brain changes and cognitive performance across a broad range of cognitive domains.

Methods

An open-access neuroimaging and behavioral dataset (n = 301 healthy participants, 18-89 years) was analyzed. Cortical gray matter (GM) volume, cortical thickness and brain age, extracted from structural T1 images, served as our measures of life-course related brain changes (BC). Cognitive scores were extracted from principal component analysis performed on 13 cognitive tests across multiple cognitive domains. Multivariate linear regression tested the modulating effect of BFR on the relationship between age-related brain changes and cognitive performance.

Results

PCA revealed three cognitive test components related to episodic, semantic and executive functioning. Increased BFR predicted reduced performance in episodic functioning when considering cortical thickness and GM volume as measures of BC. BFR significantly modulated the relationship between cortical thickness and episodic functioning. We found neither a predictive nor modulating effect of BFR on semantic or executive performance, nor a significant effect when defining BC via brain age.

Discussion

Our results suggest that BFR could serve as a metric of CR when considering certain cognitive domains, specifically episodic functioning, and defined dimensions of BC. These findings potentially indicate the presence of multiple underlying mechanisms of CR.

Cortical Functional Connectivity Changes in the Body-First and Brain-First Subtypes of Parkinson's Disease

BACKGROUND

Rapid eye movement (REM) sleep behavior disorder (RBD) may precede motor symptoms in Parkinson's disease (PD) by years. According to a recent hypothesis, premotor RBD (pRBD) is a marker of the PD body-first subtype, where synucleinopathy originates from the peripheral autonomic nervous system. Conversely, in the brain-first subtype, pathology would arise in the brain. Functional connectivity (FC) could provide additional insight into the neurodegenerative process of these putative PD subtypes.

OBJECTIVES

We aim to analyze the possible FC differences between early-stage PD patients with (PDpRBD+) and without (PDpRBD-) pRBD using high-density electroencephalography (EEG).

METHODS

We enrolled 28 PDpRBD+, 35 PDpRBD-, and 35 healthy controls (HC). Data were recorded with a 64-channel EEG system, and a source-reconstruction method was used to identify brain-region activity. FC was calculated using the weighted phase-lag index in θ, α, β, and low-γ bands. Statistical analysis was conducted using network-based statistic.

RESULTS

We found a significant trend of decreased α-FC across PDpRBD+, PDpRBD-, and HC, mainly in prefrontal and temporal areas. The altered α-FC correlated with Montreal Cognitive Assessment scores in PDpRBD+ and, to a lesser extent, PDpRBD- and with gait/postural disturbances in PDpRBD+ patients only. PDpRBD+ and PDpRBD- had similarly increased FC than HC in a β band network, predominantly involving sensorimotor and limbic areas. The increased β network FC was related to bradykinesia severity in both PD subgroups.

CONCLUSIONS

Compared to PDpRBD- (brain-first subtype), PDpRBD+ group (body-first subtype) demonstrates specific EEG-FC dysfunctions in the α band, which may reflect early involvement of the cholinergic ascending system. © 2024 International Parkinson and Movement Disorder Society.

Atypical functional connectome is associated with low reflective functioning in incarcerated adolescents

Introduction

While functional neuroimaging studies have reported on the neural correlates of severe antisocial behaviors, such as delinquency, little is known about whole brain resting state functional connectivity (FC) of incarcerated adolescents (IA). The aim of the present study is to identify potential differences in resting state connectivity between a group of male IA, compared to community adolescents (CA). The second objective is to investigate the relations among FC and psychological factors associated with delinquent behaviors, namely psychopathic traits (callous unemotional traits, interpersonal problems, and impulsivity), socio-cognitive (empathy and reflective functioning RF) impairments and psychological problems (externalizing, internalizing, attention and thought problems).

Methods

31 male IA and 30 male CA participated in 8 minutes resting state functional MRI. Network Based Statistics (NBS) was used to compare FC among 142 brain regions between the two groups. Correlation and regressions analysis were performed to explore the associations between FC and the self-reported psychopathic traits, empathy, RF, and psychological problems.

Results

Compared to the CA, the IA group presented significantly increased resting state FC in a distributed subnetwork including medial prefrontal, posterior and dorsal cingulate, temporal, and occipital regions. Both within the IA group and across the whole sample, increased mean connectivity of the subnetwork correlated with lower RF (RF uncertainty). Across the whole sample, the mean connectivity was associated with higher scores of externalizing problems and impulsivity dimension of psychopathy.

Discussion

While extending the characterization of whole brain resting state FC in IA, our results also provide insights into the neurofunctional mechanisms linking low reflective functioning abilities to externalizing behavior during adolescence.

The Brain's Aging Resting State Functional Connectivity

Resting state networks (RSNs) of the brain are characterized as correlated spontaneous time-varying fluctuations in the absence of goal-directed tasks. These networks can be local or large-scale spanning the brain. The study of the spatiotemporal properties of such networks has helped understand the brain's fundamental functional organization under healthy and diseased states. As we age, these spatiotemporal properties change. Moreover, RSNs exhibit neural plasticity to compensate for the loss of cognitive functions. This narrative review aims to summarize current knowledge from functional magnetic resonance imaging (fMRI) studies on age-related alterations in RSNs. Underlying mechanisms influencing such changes are discussed. Methodological challenges and future directions are also addressed. By providing an overview of the current state of knowledge in this field, this review aims to guide future research endeavors aimed at promoting healthy brain aging and developing effective interventions for age-related cognitive impairment and neurodegenerative diseases.

Hippocampal connectivity dynamics and volumetric alterations predict cognitive status in migraine: A resting-state fMRI study

The etiology of cognitive decline linked to migraine remains unclear, with a growing recurrence rate and potential increased dementia risk among sufferers. Cognitive dysfunction has recently gained attention as a significant problem among migraine sufferers that can be related to alterations in hippocampal function and structure. This study explores hippocampal subfield connectivity and volume changes in migraine patients. We recruited 90 individuals from Alanya University's Neurology Department, including 49 migraine patients and 41 controls, for functional and anatomical imaging. Using the CONN toolbox and FreeSurfer, we assessed functional connectivity and subfield volumes, respectively. Montreal Cognitive Assessment (MOCA) was used to assess cognition in the entire sample. As a result, migraine patients exhibited significantly lower MOCA scores compared to controls (p<.001). Also, we found significant differences in hippocampal subfields between migraine patients and control groups in terms of functional connectivity after adjusting for years of education; here we showed that the left CA3 showed higher connectivity with right MFG and right occipitolateral cortex. Furthermore, the connectivity of left fimbria with the left temporal lobe and hippocampus and the connectivity of the right hippocampal-tail with right insula, heschl's gyrus, and frontorbital cortex were lower in the migraineurs. Additionally, volumes of specific hippocampal subfields were significantly lower in the migraineurs (whole hippocampus p = 0.004, whole hippocampus head p = 0.003, right CA1 head p = 0.006, and right HATA p = 0.005) compared to controls. In conclusion, these findings indicate that migraine-associated cognitive impairment involves significant functional and structural brain changes, particularly in the hippocampus, which may heighten dementia risk. This pioneering study unveils critical hippocampal alterations linked to cognitive function in migraine sufferers, underscoring the potential for these changes to impact dementia development.

Sex hormones shape EEG-based functional connectivity in early-stage Parkinson's disease patients

Parkinson's disease (PD) epidemiology and clinical features are sexually dimorphic. However, there are no data based on EEG functional connectivity (FC). Likewise, the contribution of sex hormones on brain FC has never been evaluated. Here, we aimed to investigate the association between biological sex and sex hormones on cortical FC changes in PD using high-density EEG. This study involved 69 early-stage PD patients (F/M 27/42) and 69 age-matched healthy controls (HC) (F/M 30/39). Sex hormone levels (total-testosterone (TT), estradiol (E2), follicle-stimulating hormone (FSH), and luteinizing hormone (LH)) were assessed in PD patients. Data were recorded with a 64-channel EEG system. Source reconstruction method was used to identify brain activity. Cortico-cortical FC was analysed based on the weighted phase-lag index (wPLI) in θ-α-β-low γ bands. Network-based statistic (NBS) was used to compare FC between genders in HC and PD and to study the relationship between FC and sex hormones in PD. PD exhibited a hypoconnected network at θ and α bands and a hyperconnected network at β band compared to HC. Male HC showed a hyperconnected network at α-band compared to female HC. Conversely, males with PD showed a hypoconnected network at α-band compared to females with PD. In females and males with PD, E2 positively correlated with α-FC, while gonadotropins positively correlated with β-FC. TT positively correlated with the θ-FC only in males. Sex hormones shape EEG-FC in both males and females with PD, supporting their major influence on PD pathophysiology.

Mediterranean diet and brain functional connectivity in a population without dementia

Introduction

Adjustable lifestyle factors, such as diet, are associated with cognitive functions, structural and functional brain measures, but the association between the functional connectivity (FC) and the Mediterranean Diet (Medicine) in population without dementia is yet to be explored.

Methods

The association between MeDi and brain FC in 105 individuals without dementia aged 63 (SD ± 8.72) years old who underwent brain MRI including resting-state (rs) functional MRI (fMRI) was examined. Dietary intake was evaluated through four 24-h recalls using the multiple-pass method and adherence to the MeDi was estimated using the MedDietScore, with higher values indicating greater adherence to MeDi. Multivariable linear regression models were used to investigate the associations between FC (both positive and negative associations) and MedDietScore.

Results

Rs-fMRI analysis revealed significant associations between FC and MedDietScore. The FC between the medial prefrontal cortex and a cluster located in left postcentral gyrus and in the left supramarginal gyrus was positively associated with MedDietScore. On the other hand, the FC between medial visual and right posterior division of both middle and superior temporal gyrus was negatively associated with MedDietScore. Of note, a temporal negative correlation was detected between above-mentioned FC networks. The FC between superior temporal gyrus and occipital regions was associated with participants' attention, executive functions, and memory scores. Furthermore, the associations for attention and executive functions were pronounced in participants with high adherence to MeDi compared to those with low adherence to MeDi.

Discussion

In conclusion, our study documented an association between higher adherence to MeDi and rs-FC in fronto-parietal and temporo-occipital regions, particularly in areas that are involved in cognitive processes altered across normal and pathological aging. From a clinical point of view, our findings support a favorable role of MeDi on FC which may have significant clinical implications in the rapidly aging population. Rs-fMRI is also proposed as a useful tool in the emerging field of nutritional neuroscience and a candidate non-invasive biomarker of brain aging.

Enhancing prediction of human traits and behaviors through ensemble learning of traditional and novel resting-state fMRI connectivity analyses

Recent advances in cognitive neuroscience have focused on using resting-state functional connectivity (RSFC) data from fMRI scans to more accurately predict human traits and behaviors. Traditional approaches generally analyze RSFC by correlating averaged time-series data across regions of interest (ROIs) or networks, which may overlook important spatial signal patterns. To address this limitation, we introduced a novel linear regression technique that estimates RSFC by predicting spatial brain activity patterns in a target ROI from those in a seed ROI. We applied both traditional and our novel RSFC estimation methods to a large-scale dataset from the Human Connectome Project and the Brain Genomics Superstruct Project, analyzing resting-state fMRI data to predict sex, age, personality traits, and psychological task performance. To enhance prediction accuracy, we developed an ensemble learner that combines these qualitatively different methods using a weighted average approach. Our findings revealed that hierarchical clustering of RSFC patterns using our novel method displays distinct whole-brain grouping patterns compared to the traditional approach. Importantly, the ensemble model, integrating these diverse weak learners, outperformed the traditional RSFC method in predicting human traits and behaviors. Notably, the predictions from the traditional and novel methods showed relatively low similarity, indicating that our novel approach captures unique and previously undetected information about human traits and behaviors through fine-grained local spatial patterns of neural activation. These results highlight the potential of combining traditional and innovative RSFC analysis techniques to enrich our understanding of the neural basis of human traits and behaviors.

The Impact of Ageing on Episodic Memory Retrieval: How Valence Influences Neural Functional Connectivity

Age-related decline in episodic memory is often linked to structural and functional changes in the brain. Here, we investigated how these alterations might affect functional connectivity during memory retrieval following exposure to emotional stimuli. Using functional magnetic resonance imaging (fMRI), participants viewed images with varying emotional valences (positive, negative, and neutral) followed by unrelated non-arousing videos and were then asked to retrieve an episodic detail from the previously shown video. We conducted Multivariate Pattern Analysis (MVPA) to identify regions with divergent responses between age groups, which then served as seeds in Seed-Based Connectivity (SBC) analyses. The results revealed an age-related decline in behavioural performance following exposure to negative stimuli but preserved performance following positive stimuli. Young adults exhibited increased functional connectivity following negative valence. Conversely, old adults displayed increased connectivity more scarcely, and only following positive valence. These findings point to an adaptive response of the impact of emotions on task performance that depends on neural adaptations related to ageing. This suggests that age-related changes in functional connectivity might underlie how emotions influence memory, highlighting the need to tailor memory support strategies in older adulthood.

The language network ages well: Preserved selectivity, lateralization, and within-network functional synchronization in older brains

Healthy aging is associated with structural and functional brain changes. However, cognitive abilities differ from one another in how they change with age: whereas executive functions, like working memory, show age-related decline, aspects of linguistic processing remain relatively preserved (Hartshorne et al., 2015). This heterogeneity of the cognitive-behavioral landscape in aging predicts differences among brain networks in whether and how they should change with age. To evaluate this prediction, we used individual-subject fMRI analyses ('precision fMRI') to examine the language-selective network (Fedorenko et al., 2024) and the Multiple Demand (MD) network, which supports executive functions (Duncan et al., 2020), in older adults (n=77) relative to young controls (n=470). In line with past claims, relative to young adults, the MD network of older adults shows weaker and less spatially extensive activations during an executive function task and reduced within-network functional synchronization. However, in stark contrast to the MD network, we find remarkable preservation of the language network in older adults. Their language network responds to language as strongly and selectively as in younger adults, and is similarly lateralized and internally synchronized. In other words, the language network of older adults looks indistinguishable from that of younger adults. Our findings align with behavioral preservation of language skills in aging and suggest that some networks remain young-like, at least on standard measures of function and connectivity.

The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis

The large number of different analytical choices used by researchers is partly responsible for the challenge of replication in neuroimaging studies. For an exhaustive robustness analysis, knowledge of the full space of analytical options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable parameter choices. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the parameter settings within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) parameter tuning within steps, and 3) distinct sequencing of steps. We have developed a decision support application with high educational value called METEOR to facilitate access to the data in order to design well-informed robustness (multiverse) analysis.

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.

Increased between-network connectivity: A risk factor for tau elevation and disease progression

One of the pathologic hallmarks of Alzheimer's disease (AD) is neurofibrillary tau tangles. Despite our knowledge that tau typically initiates in the medial temporal lobe (MTL), the mechanisms driving tau to spread beyond MTL remain unclear. Emerging evidence reveals distinct patterns of functional connectivity change during aging and preclinical AD: while connectivity within-network decreases, connectivity between-network increases. Building upon increased between-network connectivity, our study hypothesizes that this increase may play a critical role in facilitating tau spread in early stages. We conducted a longitudinal study over two to three years intervals on a cohort of 46 healthy elderly participants (mean age 64.23 ± 3.15 years, 26 females). Subjects were examined clinically and utilizing advanced imaging techniques that included resting-state functional MRI (rs-fMRI), structural magnetic resonance imaging (MRI), and a second-generation positron emission tomography (PET) tau tracer, 18F-MK6240. Through unsupervised agglomerative clustering and increase in between-network connectivity, we successfully identified individuals at increased risk of future tau elevation and AD progression. Our analysis revealed that individuals with increased between-network connectivity are more likely to experience more future tau deposition, entorhinal cortex thinning, and lower selective reminding test (SRT) delayed scores. Additionally, in the limbic network, we found a strong association between tau progression and increased between-network connectivity, which was mainly driven by beta-amyloid (Aβ) positive participants. These findings provide evidence for the hypothesis that an increase in between-network connectivity predicts future tau deposition and AD progression, also enhancing our understanding of AD pathogenesis in the preclinical stages.

Review of weighted exponential random graph models frameworks applied to neuroimaging

Neuro-imaging data can often be represented as statistical networks, especially for functional magnetic resonance imaging (fMRI) data, where brain regions are defined as nodes and the functional interactions between those regions are taken as edges. Such networks are commonly divided into classes depending on the type of edges, namely binary or weighted. A binary network means edges can either be present or absent. Whereas the edges of a weighted network are associated with weight values, and fMRI networks belong to weighted networks. Statistical methods are often adopted to analyse such networks, among which, the exponential random graph model (ERGM) is an important network analysis approach. Typically ERGMs are applied to binary networks, and weighted networks often need to be binarised by arbitrarily selecting a threshold value to define the presence of the edges, which can lead to non-robustness and loss of valuable edge weight information representing the strength of fMRI interaction in fMRI networks. While it is therefore important to gain deeper insight in adopting ERGM on weighted networks, there only exists a few different ERGM frameworks for weighted networks; some of these are not directly implementable on fMRI networks based on their original proposal. We systematically review, implement, analyse and compare five such frameworks via a simulation study and provide guidelines on each modelling framework as well as conclude the suitability of them on fMRI networks based on a range of criteria. We concluded that Multi-Layered ERGM is currently the most suitable framework.

Brain topology underlying executive functions across the lifespan: focus on the default mode network

Introduction

While traditional neuroimaging approaches to the study of executive functions (EFs) have typically employed task-evoked paradigms, resting state studies are gaining popularity as a tool for investigating inter-individual variability in the functional connectome and its relationship to cognitive performance outside of the scanner.

Method

Using resting state functional magnetic resonance imaging data from the Human Connectome Project Lifespan database, the present study capitalized on graph theory to chart cross-sectional variations in the intrinsic functional organization of the frontoparietal (FPN) and the default mode (DMN) networks in 500 healthy individuals (from 10 to 100 years of age), to investigate the neural underpinnings of EFs across the lifespan.

Results

Topological properties of both the FPN and DMN were associated with EF performance but not with a control task of picture naming, providing specificity in support for a tight link between neuro-functional and cognitive-behavioral efficiency within the EF domain. The topological organization of the DMN, however, appeared more sensitive to age-related changes relative to that of the FPN.

Discussion

The DMN matures earlier in life than the FPN and it ıs more susceptible to neurodegenerative changes. Because its activity is stronger in conditions of resting state, the DMN might be easier to measure in noncompliant populations and in those at the extremes of the life-span curve, namely very young or elder participants. Here, we argue that the study of its functional architecture in relation to higher order cognition across the lifespan might, thus, be of greater interest compared with what has been traditionally thought.

Eight brain structures mediate the age-related alterations of the working memory: forward and backward digit span tasks

Introduction

Working memory (WM) as one of the executive functions is an essential neurocognitive ability for daily life. Findings have suggested that aging is often associated with working memory and neural decline, but the brain structures and resting-state brain networks that mediate age-related differences in WM remain unclear.

Methods

A sample consisting of 252 healthy participants in the age range of 20 to 70years was used. Several cognitive tasks, including the n-back task and the forward and backward digit span tests were used. Also, resting-state functional imaging, as well as structural imaging using a 3T MRI scanner, were performed, resulting in 85 gray matter volumes and five resting-state networks, namely the anterior and posterior default mode, the right and left executive control, and the salience networks. Also, mediation analyses were used to investigate the role of gray matter volumes and resting-state networks in the relationship between age and WM.

Results

Behaviorally, aging was associated with decreased performance in the digit span task. Also, aging was associated with a decreased gray matter volume in 80 brain regions, and with a decreased activity in the anterior default mode network, executive control, and salience networks. Importantly, the path analysis showed that the GMV of the medial orbitofrontal, precentral, parieto-occipital, amygdala, middle occipital, posterior cingulate, and thalamus areas mediated the age-related differences in the forward digit span task, and the GMV of superior temporal gyrus mediated the age-related differences in the backward digit span task.

Discussion

This study identified the brain structures mediating the relationship between age and working memory, and we hope that our research provides an opportunity for early detection of individuals at risk of age-related memory decline.

Age differences in functional connectivity track dedifferentiation of category representations

With advancing age, the distinctiveness of neural representations of information declines. While the finding of this so-called 'age-related neural dedifferentiation' in category-selective neural regions is well-described, the contribution of age-related changes in network organization to dedifferentiation is unknown. Here, we asked whether age differences in a) whole-brain network segregation (i.e., network dedifferentiation) and b) functional connectivity to category-selective neural regions are related to regional dedifferentiation of categorical representations. Younger and older adults viewed blocks of face and house stimuli in the fMRI scanner. We found an age-related decline in neural distinctiveness for faces in the fusiform gyrus (FG) and for houses in the parahippocampal gyrus (PHG). Functional connectivity analyses revealed age-related dedifferentiation of global network structure as well as age differences in connectivity between the FG and early visual cortices. Interindividual correlations demonstrated that regional distinctiveness was related to network segregation. Together, our findings suggest that dedifferentiation of categorical representations may be linked to age-related reorganization of functional networks.

Age dictates brain functional connectivity and axonal integrity following repetitive mild traumatic brain injuries in mice

Traumatic brain injuries (TBI) present a major public health challenge, demanding an in-depth understanding of age-specific symptoms and risk factors. Aging not only significantly influences brain function and plasticity but also elevates the risk of hospitalizations and death following TBIs. Repetitive mild TBIs (rmTBI) compound these issues, resulting in cumulative and long-term brain damage in the brain. In this study, we investigate the impact of age on brain network changes and white matter properties following rmTBI by employing a multi-modal approach that integrates resting-state functional magnetic resonance imaging (rsfMRI), graph theory analysis, diffusion tensor imaging (DTI), and neurite orientation dispersion and density imaging (NODDI). Our hypothesis is that the effects of rmTBI are worsened in aged animals, with this group showing more pronounced alterations in brain connectivity and white matter structure. Utilizing the closed-head impact model of engineered rotational acceleration (CHIMERA) model, we conducted rmTBIs or sham (control) procedures on young (2.5-3-months-old) and aged (22-months-old) male and female mice to model high-risk groups. Functional and structural imaging unveiled age-related reductions in communication efficiency between brain regions, while injuries induced opposhigh-risking effects on the small-world index across age groups, influencing network segregation. Functional connectivity analysis also identified alterations in 79 out of 148 brain regions by age, treatment (sham vs. rmTBI), or their interaction. Injuries exerted pronounced effects on sensory integration areas, including insular and motor cortices. Age-related disruptions in white matter integrity were observed, indicating alterations in various diffusion directions (mean diffusivity, radial diffusivity, axial diffusivity, and fractional anisotropy) and density neurite properties (dispersion index, intracellular and isotropic volume fraction). Neuroinflammation, assessed through Iba-1 and GFAP markers, correlated with higher dispersion in the optic tract, suggesting a neuroinflammatory response in injured aged animals compared to sham aged. These findings offer insight into the interplay between age, injuries, and brain connectivity, shedding light on the long-term consequences of rmTBI.

Global brain connectivity: Test-retest stability and association with biological and neurocognitive variables

BACKGROUND

Global brain connectivity (GBC) enables measuring brain regions' functional connectivity strength at rest by computing the average correlation between each brain voxel's time-series and that of all other voxels.

NEW METHOD

We used resting-state fMRI (rs-fMRI) data of young adult participants from the Human Connectome Project (HCP) dataset to explore the test-retest stability of GBC, the brain regions with higher or lower GBC, as well as the associations of this measure with age, sex, and fluid intelligence. GBC was computed by considering separately the positive and negative correlation coefficients (positive GBC and negative GBC).

RESULTS

Test-retest stability was higher for positive compared to negative GBC. Areas with higher GBC were located in the default mode network, insula, and visual areas, while regions with lower GBC were in subcortical regions, temporal cortex, and cerebellum. Higher age was related to global reduction of positive GBC. Males displayed higher positive GBC in the whole brain. Fluid intelligence was associated to increased positive GBC in fronto-parietal, occipital and temporal regions.

COMPARISON WITH EXISTING METHOD

Compared to previous works, this study adopted a larger sample size and tested GBC stability using data from different rs-fMRI sessions. Moreover, these associations were examined by testing positive and negative GBC separately.

CONCLUSIONS

Lower stability for negative compared to positive GBC suggests that negative correlations may reflect less stable couplings between brain regions. Our findings indicate a greater importance of positive compared to negative GBC for the associations of functional connectivity strength with biological and neurocognitive variables.

Acute Exercise Improves Large-Scale Brain Network Segregation in Healthy Older Adults

Introduction: Age-related cognitive decline and mental health problems are accompanied by changes in resting-state functional connectivity (rsFC) indices, such as reduced brain network segregation. Meanwhile, exercise can improve cognition, mood, and neural network function in older adults. Studies on effects of exercise on rsFC outcomes in older adults have chiefly focused on changes after exercise training and suggest improved network segregation through enhanced within-network connectivity. However, effects of acute exercise on rsFC measures of neural network integrity in older adults, which presumably underlie changes observed after exercise training, have received less attention. In this study, we hypothesized that acute exercise in older adults would improve functional segregation of major cognition and affect-related brain networks. Methods: To test this, we analyzed rsFC data from 37 healthy and physically active older adults after they completed 30 min of moderate-to-vigorous intensity cycling and after they completed a seated rest control condition. Conditions were performed in a counterbalanced order across separate days in a within-subject crossover design. We considered large-scale brain networks associated with cognition and affect, including the frontoparietal network (FPN), salience network (SAL), default mode network (DMN), and affect-reward network (ARN). Results: We observed that after acute exercise, there was greater segregation between SAL and DMN, as well as greater segregation between SAL and ARN. Conclusion: These findings indicate that acute exercise in active older adults alters rsFC measures in key cognition and affect-related networks in a manner that opposes age-related dedifferentiation of neural networks that may be detrimental to cognition and mental health.

Role of the central nervous system in cell non-autonomous signaling mechanisms of aging and longevity in mammals

Multiple organs orchestrate the maintenance of proper physiological function in organisms throughout their lifetimes. Recent studies have uncovered that aging and longevity are regulated by cell non-autonomous signaling mechanisms in several organisms. In the brain, particularly in the hypothalamus, aging and longevity are regulated by such cell non-autonomous signaling mechanisms. Several hypothalamic neurons have been identified as regulators of mammalian longevity, and manipulating them promotes lifespan extension or shortens the lifespan in rodent models. The hypothalamic structure and function are evolutionally highly conserved across species. Thus, elucidation of hypothalamic function during the aging process will shed some light on the mechanisms of aging and longevity and, thereby benefiting to human health.

Higher-Order Intrinsic Brain Network Trajectories After Antipsychotic Treatment in Medication-Naïve Patients With First-Episode Psychosis

BACKGROUND

Intrinsic brain network connectivity is already altered in first-episode psychosis (FEP), but the longitudinal trajectories of network connectivity, especially in response to antipsychotic treatment, remain poorly understood. The goal of this study was to investigate how antipsychotic medications affect higher-order intrinsic brain network connectivity in FEP.

METHODS

Data from 87 antipsychotic medication-naïve patients with FEP and 87 healthy control participants were used. Medication-naïve patients received antipsychotic treatment for 16 weeks. Resting-state functional connectivity (FC) of the default mode, salience, dorsal attention, and executive control networks were assessed prior to treatment and at 6 and 16 weeks after treatment. We evaluated baseline and FC changes using linear mixed models to test group × time interactions within each network. Associations between FC changes after 16 weeks and response to treatment were also evaluated.

RESULTS

Prior to treatment, significant group differences in all networks were found. However, significant trajectory changes in FC were found only in the default mode and executive control networks. Changes in FC in these networks were associated with treatment response. Several sensitivity analyses showed a consistent normalization of executive control network FC in response to antipsychotic treatment.

CONCLUSIONS

Here, we found that alterations in intrinsic brain network FC were not only alleviated with antipsychotic treatment, but the extent of this normalization was also associated with the degree of reduction in symptom severity. Taken together, our data suggest modulation of intrinsic brain network connectivity (mainly frontoparietal connectivity) as a mechanism underlying antipsychotic treatment response in FEP.

SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks

Functional networks (FNs) hold significant promise in understanding brain function. Independent component analysis (ICA) has been applied in estimating FNs from functional magnetic resonance imaging (fMRI). However, determining an optimal model order for ICA remains challenging, leading to criticism about the reliability of FN estimation. Here, we propose a SMART (splitting-merging assisted reliable) ICA method that automatically extracts reliable FNs by clustering independent components (ICs) obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders. We extend SMART ICA to multi-subject fMRI analysis, validating its effectiveness using simulated and real fMRI data. Based on simulated data, the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters. Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects, the resulting reliable group-level FNs are greatly similar between the two cohorts, and interestingly the subject-specific FNs show progressive changes while age increases. Furthermore, both small-scale and large-scale brain FN templates are provided as benchmarks for future studies. Taken together, SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data, while also providing linkages between different FNs.

Early effect of onabotulinumtoxinA on EEG-based functional connectivity in patients with chronic migraine: A pilot study

OBJECTIVE

In this pilot prospective cohort study, we aimed to evaluate, using high-density electroencephalography (HD-EEG), the longitudinal changes in functional connectivity (FC) in patients with chronic migraine (CM) treated with onabotulinumtoxinA (OBTA).

BACKGROUND

OBTA is a treatment for CM. Several studies have shown the modulatory action of OBTA on the central nervous system; however, research on migraine is limited.

METHODS

This study was conducted at the Neurology Unit of "Policlinico Tor Vergata," Rome, Italy, and included 12 adult patients with CM treated with OBTA and 15 healthy controls (HC). Patients underwent clinical scales at enrollment (T0) and 3 months (T1) from the start of treatment. HD-EEG was recorded using a 64-channel system in patients with CM at T0 and T1. A source reconstruction method was used to identify brain activity. FC in δ-θ-α-β-low-γ bands was analyzed using the weighted phase-lag index. FC changes between HCs and CM at T0 and T1 were assessed using cross-validation methods to estimate the results' reliability.

RESULTS

Compared to HCs at T0, patients with CM showed hyperconnected networks in δ (p = 0.046, area under the receiver operating characteristic curve [AUC: 0.76-0.98], Cohen's κ [0.65-0.93]) and β (p = 0.031, AUC [0.68-0.95], Cohen's κ [0.51-0.84]), mainly involving orbitofrontal, occipital, temporal pole and orbitofrontal, superior temporal, occipital, cingulate areas, and hypoconnected networks in α band (p = 0.029, AUC [0.80-0.99], Cohen's κ [0.42-0.77]), predominantly involving cingulate, temporal pole, and precuneus. Patients with CM at T1, compared to T0, showed hypoconnected networks in δ band (p = 0.032, AUC [0.73-0.99], Cohen's κ [0.53-0.90]) and hyperconnected networks in α band (p = 0.048, AUC [0.58-0.93], Cohen's κ [0.37-0.78]), involving the sensorimotor, orbitofrontal, cingulate, and temporal cortex.

CONCLUSION

These preliminary results showed that patients with CM presented disrupted EEG-FC compared to controls restored by a single session of OBTA treatment, suggesting a primary central modulatory action of OBTA.

Resting-State Functional Networks Correlate with Motor Performance in a Complex Visuomotor Task: An EEG Microstate Pilot Study on Healthy Individuals

Developing motor and cognitive skills is needed to achieve expert (motor) performance or functional recovery from a neurological condition, e.g., after stroke. While extensive practice plays an essential role in the acquisition of good motor performance, it is still unknown whether certain person-specific traits may predetermine the rate of motor learning. In particular, learners' functional brain organisation might play an important role in appropriately performing motor tasks. In this paper, we aimed to study how two critical cognitive brain networks-the Attention Network (AN) and the Default Mode Network (DMN)-affect the posterior motor performance in a complex visuomotor task: virtual surfing. We hypothesised that the preactivation of the AN would affect how participants divert their attention towards external stimuli, resulting in robust motor performance. Conversely, the excessive involvement of the DMN-linked to internally diverted attention and mind-wandering-would be detrimental for posterior motor performance. We extracted seven widely accepted microstates-representing participants mind states at rest-out of the Electroencephalography (EEG) resting-state recordings of 36 healthy volunteers, prior to execution of the virtual surfing task. By correlating neural biomarkers (microstates) and motor behavioural metrics, we confirmed that the preactivation of the posterior DMN was correlated with poor posterior performance in the motor task. However, we only found a non-significant association between AN preactivation and the posterior motor performance. In this EEG study, we propose the preactivation of the posterior DMN-imaged using EEG microstates-as a neural trait related to poor posterior motor performance. Our findings suggest that the role of the executive control system is to preserve an homeostasis between the AN and the DMN. Therefore, neurofeedback-based downregulation of DMN preactivation could help optimise motor training.

Edge-Community Entropy Is a Novel Neural Correlate of Aging and Moderator of Fluid Cognition

Decreased neuronal specificity of the brain in response to cognitive demands (i.e., neural dedifferentiation) has been implicated in age-related cognitive decline. Investigations into functional connectivity analogs of these processes have focused primarily on measuring segregation of nonoverlapping networks at rest. Here, we used an edge-centric network approach to derive entropy, a measure of specialization, from spatially overlapping communities during cognitive task fMRI. Using Human Connectome Project Lifespan data (713 participants, 36-100 years old, 55.7% female), we characterized a pattern of nodal despecialization differentially affecting the medial temporal lobe and limbic, visual, and subcortical systems. At the whole-brain level, global entropy moderated declines in fluid cognition across the lifespan and uniquely covaried with age when controlling for the network segregation metric modularity. Importantly, relationships between both metrics (entropy and modularity) and fluid cognition were age dependent, although entropy's relationship with cognition was specific to older adults. These results suggest entropy is a potentially important metric for examining how neurological processes in aging affect functional specialization at the nodal, network, and whole-brain level.

Effects of Low-Level Light Therapy on Resting-State Connectivity Following Moderate Traumatic Brain Injury: Secondary Analyses of a Double-blinded Placebo-controlled Study

Background Low-level light therapy (LLLT) has been shown to modulate recovery in patients with traumatic brain injury (TBI). However, the impact of LLLT on the functional connectivity of the brain when at rest has not been well studied. Purpose To use functional MRI to assess the effect of LLLT on whole-brain resting-state functional connectivity (RSFC) in patients with moderate TBI at acute (within 1 week), subacute (2-3 weeks), and late-subacute (3 months) recovery phases. Materials and Methods This is a secondary analysis of a prospective single-site double-blinded sham-controlled study conducted in patients presenting to the emergency department with moderate TBI from November 2015 to July 2019. Participants were randomized for LLLT and sham treatment. The primary outcome of the study was to assess structural connectivity, and RSFC was collected as the secondary outcome. MRI was used to measure RSFC in 82 brain regions in participants during the three recovery phases. Healthy individuals who did not receive treatment were imaged at a single time point to provide control values. The Pearson correlation coefficient was estimated to assess the connectivity strength for each brain region pair, and estimates of the differences in Fisher z-transformed correlation coefficients (hereafter, z differences) were compared between recovery phases and treatment groups using a linear mixed-effects regression model. These analyses were repeated for all brain region pairs. False discovery rate (FDR)-adjusted P values were computed to account for multiple comparisons. Quantile mixed-effects models were constructed to quantify the association between the Rivermead Postconcussion Symptoms Questionnaire (RPQ) score, recovery phase, and treatment group. Results RSFC was evaluated in 17 LLLT-treated participants (median age, 50 years [IQR, 25-67 years]; nine female), 21 sham-treated participants (median age, 50 years [IQR, 43-59 years]; 11 female), and 23 healthy control participants (median age, 42 years [IQR, 32-54 years]; 13 male). Seven brain region pairs exhibited a greater change in connectivity in LLLT-treated participants than in sham-treated participants between the acute and subacute phases (range of z differences, 0.37 [95% CI: 0.20, 0.53] to 0.45 [95% CI: 0.24, 0.67]; FDR-adjusted P value range, .010-.047). Thirteen different brain region pairs showed an increase in connectivity in sham-treated participants between the subacute and late-subacute phases (range of z differences, 0.17 [95% CI: 0.09, 0.25] to 0.26 [95% CI: 0.14, 0.39]; FDR-adjusted P value range, .020-.047). There was no evidence of a difference in clinical outcomes between LLLT-treated and sham-treated participants (range of differences in medians, -3.54 [95% CI: -12.65, 5.57] to -0.59 [95% CI: -7.31, 8.49]; P value range, .44-.99), as measured according to RPQ scores. Conclusion Despite the small sample size, the change in RSFC from the acute to subacute phases of recovery was greater in LLLT-treated than sham-treated participants, suggesting that acute-phase LLLT may have an impact on resting-state neuronal circuits in the early recovery phase of moderate TBI. ClinicalTrials.gov Identifier: NCT02233413 © RSNA, 2024 Supplemental material is available for this article.

Associations of conservatism and jumping to conclusions biases with aberrant salience and default mode network

AIM

While conservatism bias refers to the human need for more evidence for decision-making than rational thinking expects, the jumping to conclusions (JTC) bias refers to the need for less evidence among individuals with schizophrenia/delusion compared to healthy people. Although the hippocampus-midbrain-striatal aberrant salience system and the salience, default mode (DMN), and frontoparietal networks ("triple networks") are implicated in delusion/schizophrenia pathophysiology, the associations between conservatism/JTC and these systems/networks are unclear.

METHODS

Thirty-seven patients with schizophrenia and 33 healthy controls performed the beads task, with large and small numbers of bead draws to decision (DTD) indicating conservatism and JTC, respectively. We performed independent component analysis (ICA) of resting functional magnetic resonance imaging (fMRI) data. For systems/networks above, we investigated interactions between diagnosis and DTD, and main effects of DTD. We similarly applied ICA to structural and diffusion MRI to explore the associations between DTD and gray/white matter.

RESULTS

We identified a significant main effect of DTD with functional connectivity between the striatum and DMN, which was negatively correlated with delusion severity in patients, indicating that the greater the anti-correlation between these networks, the stronger the JTC and delusion. We further observed the main effects of DTD on a gray matter network resembling the DMN, and a white matter network connecting the functional and gray matter networks (all P < 0.05, family-wise error [FWE] correction). Function and gray/white matter showed no significant interactions.

CONCLUSION

Our results support the novel association of conservatism and JTC biases with aberrant salience and default brain mode.

Modeling the neurocognitive dynamics of language across the lifespan

Healthy aging is associated with a heterogeneous decline across cognitive functions, typically observed between language comprehension and language production (LP). Examining resting-state fMRI and neuropsychological data from 628 healthy adults (age 18-88) from the CamCAN cohort, we performed state-of-the-art graph theoretical analysis to uncover the neural mechanisms underlying this variability. At the cognitive level, our findings suggest that LP is not an isolated function but is modulated throughout the lifespan by the extent of inter-cognitive synergy between semantic and domain-general processes. At the cerebral level, we show that default mode network (DMN) suppression coupled with fronto-parietal network (FPN) integration is the way for the brain to compensate for the effects of dedifferentiation at a minimal cost, efficiently mitigating the age-related decline in LP. Relatedly, reduced DMN suppression in midlife could compromise the ability to manage the cost of FPN integration. This may prompt older adults to adopt a more cost-efficient compensatory strategy that maintains global homeostasis at the expense of LP performances. Taken together, we propose that midlife represents a critical neurocognitive juncture that signifies the onset of LP decline, as older adults gradually lose control over semantic representations. We summarize our findings in a novel synergistic, economical, nonlinear, emergent, cognitive aging model, integrating connectomic and cognitive dimensions within a complex system perspective.

Cytokine expression patterns predict suppression of vulnerable neural circuits in a mouse model of Alzheimer's disease

Alzheimer's disease is a neurodegenerative disorder characterized by progressive amyloid plaque accumulation, tau tangle formation, neuroimmune dysregulation, synapse an neuron loss, and changes in neural circuit activation that lead to cognitive decline and dementia. Early molecular and cellular disease-instigating events occur 20 or more years prior to presentation of symptoms, making them difficult to study, and for many years amyloid-β, the aggregating peptide seeding amyloid plaques, was thought to be the toxic factor responsible for cognitive deficit. However, strategies targeting amyloid-β aggregation and deposition have largely failed to produce safe and effective therapies, and amyloid plaque levels poorly correlate with cognitive outcomes. However, a role still exists for amyloid-β in the variation in an individual's immune response to early, soluble forms of aggregates, and the downstream consequences of this immune response for aberrant cellular behaviors and creation of a detrimental tissue environment that harms neuron health and causes changes in neural circuit activation. Here, we perform functional magnetic resonance imaging of awake, unanesthetized Alzheimer's disease mice to map changes in functional connectivity over the course of disease progression, in comparison to wild-type littermates. In these same individual animals, we spatiotemporally profile the immune milieu by measuring cytokines, chemokines, and growth factors across various brain regions and over the course of disease progression from pre-pathology through established cognitive deficit. We identify specific signatures of immune activation predicting hyperactivity followed by suppression of intra- and then inter-regional functional connectivity in multiple disease-relevant brain regions, following the pattern of spread of amyloid pathology.

Directed functional connectivity of the default-mode-network of young and older healthy subjects

Alterations in the default mode network (DMN) are associated with aging. We assessed age-dependent changes of DMN interactions and correlations with a battery of neuropsychological tests, to understand the differences of DMN directed connectivity between young and older subjects. Using a novel multivariate analysis method on resting-state functional MRI data from fifty young and thirty-one healthy older subjects, we calculated intra- and inter-DMN 4-nodes directed pathways. For the old subject group, we calculated the partial correlations of inter-DMN pathways with: psychomotor speed and working memory, executive function, language, long-term memory and visuospatial function. Pathways connecting the DMN with visual and limbic regions in older subjects engaged at BOLD low frequency and involved the dorsal posterior cingulate cortex (PCC), whereas in young subjects, they were at high frequency and involved the ventral PCC. Pathways combining the sensorimotor (SM) cortex and the DMN, were SM efferent in the young subjects and SM afferent in the older subjects. Most DMN efferent pathways correlated with reduced speed and working memory. We suggest that the reduced sensorimotor efferent and the increased need to control such activities, cause a higher dependency on external versus internal cues thus suggesting how physical activity might slow aging.

Longitudinal monitoring of the mouse brain reveals heterogenous network trajectories during aging

The human aging brain is characterized by changes in network efficiency that are currently best captured through longitudinal resting-state functional MRI (rs-fMRI). These studies however are challenging due to the long human lifespan. Here we show that the mouse animal model with a much shorter lifespan allows us to follow the functional network organization over most of the animal's adult lifetime. We used a longitudinal study of the functional connectivity of different brain regions with rs-fMRI under anesthesia. Our analysis uncovers network modules similar to those reported in younger mice and in humans (i.e., prefrontal/default mode network (DMN), somatomotor and somatosensory networks). Statistical analysis reveals different patterns of network reorganization during aging. Female mice showed a pattern akin to human aging, with de-differentiation of the connectome, mainly due to increases in connectivity of the prefrontal/DMN cortical networks to other modules. Our male cohorts revealed heterogenous aging patterns with only one group confirming the de- differentiation, while the majority showed an increase in connectivity of the somatomotor cortex to the Nucleus accumbens. In summary, in line with human work, our analysis in mice supports the concept of de-differentiation in the aging mammalian brain and reveals additional trajectories in aging mice networks.

Distinct functional subnetworks of cognitive domains in older adults with minor cognitive deficits

Although past research has established a relationship between functional connectivity and cognitive function, less is known about which cognitive domains are associated with which specific functional networks. This study investigated associations between functional connectivity and global cognitive function and performance in the domains of memory, executive function and psychomotor speed in 166 older adults aged 75-91 years (mean = 80.3 ± 3.8) with minor cognitive deficits (Mini-Mental State Examination scores between 21 and 27). Functional connectivity was assessed within 10 standard large-scale resting-state networks and on a finer spatial resolution between 300 nodes in a functional connectivity matrix. No domain-specific associations with mean functional connectivity within large-scale resting-state networks were found. Node-level analysis revealed that associations between functional connectivity and cognitive performance differed across cognitive functions in strength, location and direction. Specific subnetworks of functional connections were found for each cognitive domain in which higher connectivity between some nodes but lower connectivity between other nodes were related to better cognitive performance. Our findings add to a growing body of literature showing differential sensitivity of functional connections to specific cognitive functions and may be a valuable resource for hypothesis generation of future studies aiming to investigate specific cognitive dysfunction with resting-state functional connectivity in people with beginning cognitive deficits.

Exploring the dynamic interplay between learning and working memory within various cognitive contexts

Introduction

The intertwined relationship between reinforcement learning and working memory in the brain is a complex subject, widely studied across various domains in neuroscience. Research efforts have focused on identifying the specific brain areas responsible for these functions, understanding their contributions in accomplishing the related tasks, and exploring their adaptability under conditions such as cognitive impairment or aging.

Methods

Numerous models have been introduced to formulate either these two subsystems of reinforcement learning and working memory separately or their combination and relationship in executing cognitive tasks. This study adopts the RLWM model as a computational framework to analyze the behavioral parameters of subjects with varying cognitive abilities due to age or cognitive status. A related RLWM task is employed to assess a group of subjects across different age groups and cognitive abilities, as measured by the Montreal Cognitive Assessment tool (MoCA).

Results

Analysis reveals a decline in overall performance accuracy and speed with differing age groups (young vs. middle-aged). Significant differences are observed in model parameters such as learning rate, WM decay, and decision noise. Furthermore, among the middle-aged group, distinctions emerge between subjects categorized as normal vs. MCI based on MoCA scores, notably in speed, performance accuracy, and decision noise.

Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging

Introduction

Physiological nuisance contributions by cardiac and respiratory signals have a significant impact on resting-state fMRI data quality. As these physiological signals are often not recorded, data-driven denoising methods are commonly used to estimate and remove physiological noise from fMRI data. To investigate the efficacy of these denoising methods, one of the first steps is to accurately capture the cardiac and respiratory signals, which requires acquiring fMRI data with high temporal resolution.

Methods

In this study, we used such high-temporal resolution fMRI data to evaluate the effectiveness of several data-driven denoising methods, including global-signal regression (GSR), white matter and cerebrospinal fluid regression (WM-CSF), anatomical (aCompCor) and temporal CompCor (tCompCor), ICA-AROMA. Our analysis focused on the consequence of changes in low-frequency, cardiac and respiratory signal power, as well as age-related differences in terms of functional connectivity (fcMRI).

Results

Our results confirm that the ICA-AROMA and GSR removed the most physiological noise but also more low-frequency signals. These methods are also associated with substantially lower age-related fcMRI differences. On the other hand, aCompCor and tCompCor appear to be better at removing high-frequency physiological signals but not low-frequency signal power. These methods are also associated with relatively higher age-related fcMRI differences, whether driven by neuronal signal or residual artifact. These results were reproduced in data downsampled to represent conventional fMRI sampling frequency. Lastly, methods differ in performance depending on the age group.

Discussion

While this study cautions direct comparisons of fcMRI results based on different denoising methods in the study of aging, it also enhances the understanding of different denoising methods in broader fcMRI applications.

Intrinsic functional brain connectivity changes following aerobic exercise, computerized cognitive training, and their combination in physically inactive healthy late-middle-aged adults: the Projecte Moviment

Lifestyle interventions have positive neuroprotective effects in aging. However, there are still open questions about how changes in resting-state functional connectivity (rsFC) contribute to cognitive improvements. The Projecte Moviment is a 12-week randomized controlled trial of a multimodal data acquisition protocol that investigated the effects of aerobic exercise (AE), computerized cognitive training (CCT), and their combination (COMB). An initial list of 109 participants was recruited from which a total of 82 participants (62% female; age = 58.38 ± 5.47) finished the intervention with a level of adherence > 80%. Only in the COMB group, we revealed an extended network of 33 connections that involved an increased and decreased rsFC within and between the aDMN/pDMN and a reduced rsFC between the bilateral supplementary motor areas and the right thalamus. No global and especially local rsFC changes due to any intervention mediated the cognitive benefits detected in the AE and COMB groups. Projecte Moviment provides evidence of the clinical relevance of lifestyle interventions and the potential benefits when combining them.

Measures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization

Measures of functional brain network segregation and integration vary with an individual's age, cognitive ability, and health status. Based on these relationships, these measures are frequently examined to study and quantify large-scale patterns of network organization in both basic and applied research settings. However, there is limited information on the stability and reliability of the network measures as applied to functional time-series; these measurement properties are critical to understand if the measures are to be used for individualized characterization of brain networks. We examine measurement reliability using several human datasets (Midnight Scan Club and Human Connectome Project [both Young Adult and Aging]). These datasets include participants with multiple scanning sessions, and collectively include individuals spanning a broad age range of the adult lifespan. The measurement and reliability of measures of resting-state network segregation and integration vary in relation to data quantity for a given participant's scan session; notably, both properties asymptote when estimated using adequate amounts of clean data. We demonstrate how this source of variability can systematically bias interpretation of differences and changes in brain network organization if appropriate safeguards are not included. These observations have important implications for cross-sectional, longitudinal, and interventional comparisons of functional brain network organization.

Age dictates brain functional connectivity and axonal integrity following repetitive mild traumatic brain injuries

Traumatic brain injuries (TBI) present a major public health challenge, demanding an in-depth understanding of age-specific signs and vulnerabilities. Aging not only significantly influences brain function and plasticity but also elevates the risk of hospitalizations and death following repetitive mild traumatic brain injuries (rmTBIs). In this study, we investigate the impact of age on brain network changes and white matter properties following rmTBI employing a multi-modal approach that integrates resting-state functional magnetic resonance imaging (rsfMRI), graph theory analysis, diffusion tensor imaging (DTI), and Neurite Orientation Dispersion and Density Imaging (NODDI). Utilizing the CHIMERA model, we conducted rmTBIs or sham (control) procedures on young (2.5-3 months old) and aged (22-month-old) male and female mice to model high risk groups. Functional and structural imaging unveiled age-related reductions in communication efficiency between brain regions, while injuries induced opposing effects on the small-world index across age groups, influencing network segregation. Functional connectivity analysis also identified alterations in 79 out of 148 brain regions by age, treatment (sham vs. rmTBI), or their interaction. Injuries exerted pronounced effects on sensory integration areas, including insular and motor cortices. Age-related disruptions in white matter integrity were observed, indicating alterations in various diffusion directions (mean, radial, axial diffusivity, fractional anisotropy) and density neurite properties (dispersion index, intracellular and isotropic volume fraction). Inflammation, assessed through Iba-1 and GFAP markers, correlated with higher dispersion in the optic tract, suggesting a neuroinflammatory response in aged animals. These findings provide a comprehensive understanding of the intricate interplay between age, injuries, and brain connectivity, shedding light on the long-term consequences of rmTBIs.

Longitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controls

OBJECTIVE

Electroconvulsive therapy (ECT) is effective for major depressive episodes. Understanding of underlying mechanisms has been increased by examining changes of brain connectivity but studies often do not correct for test-retest variability in healthy controls (HC). In this study, we investigated changes in resting-state networks after ECT in a multicenter study.

METHODS

Functional resting-state magnetic resonance imaging data, acquired before start and within one week after ECT, from 90 depressed patients were analyzed, as well as longitudinal data of 24 HC. Group-information guided independent component analysis (GIG-ICA) was used to spatially restrict decomposition to twelve canonical resting-state networks. Selected networks of interest were the default mode network (DMN), salience network (SN), and left and right frontoparietal network (LFPN, and RFPN). Whole-brain voxel-wise analyses were used to assess group differences at baseline, group by time interactions, and correlations with treatment effectiveness. In addition, between-network connectivity and within-network strengths were computed.

RESULTS

Within-network strength of the DMN was lower at baseline in ECT patients which increased after ECT compared to HC, after which no differences were detected. At baseline, ECT patients showed lower whole-brain voxel-wise DMN connectivity in the precuneus. Increase of within-network strength of the LFPN was correlated with treatment effectiveness. We did not find whole-brain voxel-wise or between-network changes.

CONCLUSION

DMN within-network connectivity normalized after ECT. Within-network increase of the LFPN in ECT patients was correlated with higher treatment effectiveness. In contrast to earlier studies, we found no whole-brain voxel-wise changes, which highlights the necessity to account for test-retest effects.

Band-Specific Altered Cortical Connectivity in Early Parkinson's Disease and its Clinical Correlates

BACKGROUND

Functional connectivity (FC) has shown promising results in assessing the pathophysiology and identifying early biomarkers of neurodegenerative disorders, such as Parkinson's disease (PD).

OBJECTIVES

In this study, we aimed to assess possible resting-state FC abnormalities in early-stage PD patients using high-density electroencephalography (EEG) and to detect their clinical relationship with motor and non-motor PD symptoms.

METHODS

We enrolled 26 early-stage levodopa naïve PD patients and a group of 20 healthy controls (HC). Data were recorded with 64-channels EEG system and a source-reconstruction method was used to identify brain-region activity. FC was calculated using the weighted phase-lag index in θ, α, and β bands. Additionally, we quantified the unbalancing between β and lower frequencies through a novel index (β-functional ratio [FR]). Statistical analysis was conducted using a network-based statistical approach.

RESULTS

PD patients showed hypoconnected networks in θ and α band, involving prefrontal-limbic-temporal and frontoparietal areas, respectively, and a hyperconnected network in the β frequency band, involving sensorimotor-frontal areas. The θ FC network was negatively related to Non-Motor Symptoms Scale scores and α FC to the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III gait subscore, whereas β FC and β-FR network were positively linked to the bradykinesia subscore. Changes in θ FC and β-FR showed substantial reliability and high accuracy, precision, sensitivity, and specificity in discriminating PD and HC.

CONCLUSIONS

Frequency-specific FC changes in PD likely reflect the dysfunction of distinct cortical networks, which occur from the early stage of the disease. These abnormalities are involved in the pathophysiology of specific motor and non-motor PD symptoms, including gait, bradykinesia, mood, and cognition. © 2023 International Parkinson and Movement Disorder Society.

Age-related vulnerability of the human brain connectome

Multifactorial models integrating brain variables at multiple scales are warranted to investigate aging and its relationship with neurodegeneration. Our aim was to evaluate how aging affects functional connectivity of pivotal regions of the human brain connectome (i.e., hubs), which represent potential vulnerability 'stations' to aging, and whether such effects influence the functional and structural changes of the whole brain. We combined the information of the functional connectome vulnerability, studied through an innovative graph-analysis approach (stepwise functional connectivity), with brain cortical thinning in aging. Using data from 128 cognitively normal participants (aged 20-85 years), we firstly investigated the topological functional network organization in the optimal healthy condition (i.e., young adults) and observed that fronto-temporo-parietal hubs showed a highly direct functional connectivity with themselves and among each other, while occipital hubs showed a direct functional connectivity within occipital regions and sensorimotor areas. Subsequently, we modeled cortical thickness changes over lifespan, revealing that fronto-temporo-parietal hubs were among the brain regions that changed the most, whereas occipital hubs showed a quite spared cortical thickness across ages. Finally, we found that cortical regions highly functionally linked to the fronto-temporo-parietal hubs in healthy adults were characterized by the greatest cortical thinning along the lifespan, demonstrating that the topology and geometry of hub functional connectome govern the region-specific structural alterations of the brain regions.