Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI

Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets

Objective classification biomarkers that are developed using resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for psychiatric disorders. Unfortunately, no widely accepted biomarkers are available at present, partially because of the large variety of analysis pipelines for their development. In this study, we comprehensively evaluated analysis pipelines using a large-scale, multi-site fMRI dataset for major depressive disorder (MDD). We explored combinations of options in four sub-processes of the analysis pipelines: six types of brain parcellation, four types of functional connectivity (FC) estimations, three types of site-difference harmonization, and five types of machine-learning methods. A total of 360 different MDD classification biomarkers were constructed using the SRPBS dataset acquired with unified protocols (713 participants from four sites) as the discovery dataset, and datasets from other projects acquired with heterogeneous protocols (449 participants from four sites) were used for independent validation. We repeated the procedure after swapping the roles of the two datasets to identify superior pipelines, regardless of the discovery dataset. The classification results of the top 10 biomarkers showed high similarity, and weight similarity was observed between eight of the biomarkers, except for two that used both data-driven parcellation and FC computation. We applied the top 10 pipelines to the datasets of other psychiatric disorders (autism spectrum disorder and schizophrenia), and eight of the biomarkers exhibited sufficient classification performance for both disorders. Our results will be useful for establishing a standardized pipeline for classification biomarkers.

Neural mechanisms of disease pathology and cognition in young-onset Alzheimer's disease variants

BackgroundLate-onset Alzheimer's disease is consistently associated with alterations in the default-mode network (DMN)-a large-scale brain network associated with self-related processing and memory. However, the functional organization of DMN is far less clear in young-onset Alzheimer's disease (YOAD).ObjectiveThe current study aimed to identify effective connectivity changes in the core DMN nodes between YOAD variants and healthy controls.MethodsWe assessed resting-state DMN effective connectivity in two common YOAD variants (i.e., amnestic variant (n = 26) and posterior cortical atrophy (n = 13) and healthy participants (n = 24) to identify disease- and variant-specific connectivity differences using spectral dynamic causal modelling.ResultsPatients with the amnestic variant showed increased connectivity from prefrontal cortex to posterior DMN nodes relative to healthy controls, whereas patients with posterior cortical atrophy exhibited decreased posterior DMN connectivity. Right hippocampus connectivity differentiated the two patient groups. Furthermore, disease-related connectivity alterations were also predictive of group membership and cognitive performance.ConclusionsThese findings suggest that resting-state DMN effective connectivity provides a new understanding of neural mechanisms underlying the disease pathology and cognition in YOAD.

Disruptive resting state networks characterizing depressive comorbidity in Alzheimer's disease and mild cognitive impairment

BackgroundDepressive comorbidity in neurodegeneration has been shown to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). However, its pathophysiology is not completely understood.ObjectiveHere, we characterize aberrant functional resting state networks (RSNs) characterizing depressive comorbidity in both AD and MCI.MethodsWe conducted a systematic literature review on Scopus, PubMed, and Web of Science to extract experiments that compared resting state scans of depressed and non-depressed MCI or AD patients. We employed Activation Likelihood Estimation (ALE) meta-analysis on eligible studies resulting from the search, to describe regions of significant co-activation across studies.ResultsThe systematic search resulted in 17 experiments, with 303 participants in total. The ALE yielded 10 clusters of significant co-activation distributed in the five major RSNs and across cortico-basal ganglia-thalamic circuits.ConclusionsDepressive comorbidity in neurodegeneration presents signature aberrant resting-state fluctuations. Understanding these within- and between-network alterations may be useful for future diagnostic and therapeutic applications.

Localization of Network-Level Atrophy in Preclinical Alzheimer's Disease

Brain atrophy may precede symptoms in Alzheimer's disease (AD), but it remains unclear whether atrophy in this preclinical stage falls within a distinct brain network or is associated with transitional cognitive changes. We investigated cortical thickness in cognitively unimpaired older adults with varying amyloid-β accumulation and estimated the connectivity of each individual's atrophy pattern using a large normative connectome (n = 1000). A distinct network was connected to atrophy patterns in amyloid-β-positive (n = 1242) versus negative (n = 536) participants. This preclinical AD network was similar to a previously published atrophy network associated with AD dementia (r = 0.8284, p = 0.016). In leave-one-out cross-validation, atrophy patterns connected to the preclinical AD network were associated with lower cognitive performance (p = 0.0018), greater subjective cognitive decline (p < 0.001), and amyloid-β levels (p < 0.001). Atrophy in preclinical AD maps to a network similar to AD dementia that is associated with amyloid-β and cognition, demonstrating an atrophy-related network across the continuum of AD.

Impaired parvalbumin interneurons in the retrosplenial cortex as the cause of sex-dependent vulnerability in Alzheimer's disease

Alzheimer's disease is a debilitating neurodegenerative disorder with no cure and few treatment options. In early stages of Alzheimer's disease, impaired metabolism and functional connectivity of the retrosplenial cortex strongly predict future cognitive impairments. Therefore, understanding Alzheimer's disease-related deficits in the retrosplenial cortex is critical for understanding the origins of cognitive impairment and identifying early treatment targets. Using the 5xFAD mouse model, we discovered early, sex-dependent alterations in parvalbumin-interneuron transcriptomic profiles. This corresponded with impaired parvalbumin-interneuron activity, which was sufficient to induce cognitive impairments and dysregulate retrosplenial functional connectivity. In fMRI scans from patients with mild cognitive impairment and Alzheimer's disease, we observed a similar sex-dependent dysregulation of retrosplenial cortex functional connectivity and, in postmortem tissue from subjects with Alzheimer's disease, a loss of parvalbumin interneurons. Reversal of cognitive deficits by stimulation of parvalbumin interneurons in the retrosplenial cortex suggests that this may serve as a promising therapeutic strategy.

FREQ-NESS Reveals the Dynamic Reconfiguration of Frequency-Resolved Brain Networks During Auditory Stimulation

The brain is a dynamic system whose network organization is often studied by focusing on specific frequency bands or anatomical regions, leading to fragmented insights, or by employing complex and elaborate methods that hinder straightforward interpretations. To address this issue, a new analytical pipeline named FREQuency-resolved Network Estimation via Source Separation (FREQ-NESS) is introduced. This pipeline is designed to estimate the activation and spatial configuration of simultaneous brain networks across frequencies by analyzing the frequency-resolved multivariate covariance between whole-brain voxel time series. In this study, FREQ-NESS is applied to source-reconstructed magnetoencephalography (MEG) data during resting state and isochronous auditory stimulation. Our results reveal simultaneous, frequency-specific brain networks during resting state, such as the default mode, alpha-band, and motor-beta networks. During auditory stimulation, FREQ-NESS detects: 1) emergence of networks attuned to the stimulation frequency, 2) spatial reorganization of existing networks, such as alpha-band networks shifting from occipital to sensorimotor areas, 3) stability of networks unaffected by auditory stimuli. Furthermore, auditory stimulation significantly enhances cross-frequency coupling, with the phase of auditory networks attuned to the stimulation modulating gamma band amplitude in medial temporal lobe networks. In conclusion, FREQ-NESS effectively maps the brain's spatiotemporal dynamics, providing a comprehensive view of brain function by revealing a landscape of simultaneous, frequency-resolved networks and their interaction.

Translating Molecular Psychiatry: From Biomarkers to Personalized Therapies-A Narrative Review

In this review, we explore the biomarkers of different psychiatric disorders, such as major depressive disorder, generalized anxiety disorder, schizophrenia, and bipolar disorder. Moreover, we show the interplay between genetic and environmental factors. Novel techniques such as genome-wide association studies (GWASs) have identified numerous risk loci and single-nucleotide polymorphisms (SNPs) implicated in these conditions, contributing to a better understanding of their mechanisms. Moreover, the impact of genetic variations on drug metabolisms, particularly through cytochrome P450 (CYP450) enzymes, highlights the importance of pharmacogenomics in optimizing psychiatric treatment. This review also explores the role of neurotransmitter regulation, immune system interactions, and metabolic pathways in psychiatric disorders. As the technology advances, integrating genetic markers into clinical practice will be crucial in advancing precision psychiatry, improving diagnostic accuracy and therapeutic interventions for individual patients.

Aging-dependent loss of functional connectivity in a mouse model of Alzheimer's disease and reversal by mGluR5 modulator

Amyloid accumulation in Alzheimer's disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks. Here, we conduct a longitudinal study in a double knock-in mouse model for AD (AppNL-G-F/hMapt), monitoring brain connectivity by means of resting-state fMRI. While the 4-month-old AD mice are indistinguishable from wild-type controls (WT), decreased connectivity in the default-mode network is significant for the AD mice relative to WT mice by 6 months of age and is pronounced by 9 months of age. In a second cohort of 20-month-old mice with persistent functional connectivity deficits for AD relative to WT, we assess the impact of two-months of oral treatment with a silent allosteric modulator of mGluR5 (BMS-984923/ALX001) known to rescue synaptic density. Functional connectivity deficits in the aged AD mice are reversed by the mGluR5-directed treatment. The longitudinal application of fMRI has enabled us to define the preclinical time trajectory of AD-related changes in functional connectivity, and to demonstrate a translatable metric for monitoring disease emergence, progression, and response to synapse-rescuing treatment.

Investigation of electrical conductivity changes during brain functional activity in 3T MRI

Blood oxygenation level-dependent functional magnetic resonance imaging (fMRI) is widely used to visualize brain activation regions by detecting hemodynamic responses associated with increased metabolic demand. Although alternative MRI methods have been employed to monitor functional activities, the investigation of in-vivo electrical property changes during brain function remains limited. In this study, the relationship between fMRI signals and electrical conductivity (measured at the Larmor frequency) changes was explored using phase-based electrical property tomography. Results revealed consistent patterns: conductivity changes showed negative correlations, with conductivity decreasing in functionally active regions whereas B1 phase mapping exhibited positive correlations around the activation regions. These observations were consistent across the motor and visual cortex activations To further substantiate these findings, electromagnetic radio-frequency simulations that modeled activation states with varying conductivities were conducted, demonstrating trends similar to in-vivo results for B1 phase and conductivity. Notably, we observed that false-positive activation signals could occur depending on the level of noise and the reconstruction method applied. These findings suggested that in-vivo electrical conductivity changes can indeed be measured during brain activity. However, further investigation is needed to fully understand the underlying mechanisms driving these measurements.

Cortical Structure in Nodes of the Default Mode Network Estimates General Intelligence

INTRODUCTION

A growing number of studies implicate functional brain networks in intelligence, but it is unclear if network nodal structure relates to intelligence.

METHODS

Using MRI, we studied the relationship of the general intelligence factor (g) with cortical thickness (CT), local gyrification index (LGI), and voxel-based morphometry in the nodes of the default mode network (DMN) and task-positive network (TPN) in a cohort of 44 young, healthy adults. Employing a novel strategy, we performed repeated analyses with multiple sets of g estimates to remove false positives.

RESULTS

CT and LGI in medial and temporal nodes of the DMN were reliably correlated with g (p < 0.05; Pearson's coefficient: ‑0.52 to ‑0.25 and 0.22 to 0.41, respectively). Linear regression models were developed with these parameters to estimate individual g scores, with a median adj. R2 of 0.25.

CONCLUSION

Cortical thickness and gyrification in key nodes of the Default Mode Network correlate with intelligence. Linear regression models with these cortical parameters may provide an estimate of the g factor.

Improving executive functioning and reducing the risk of Alzheimer's disease with music therapy: A narrative review of potential neural mechanisms

The incidence of Alzheimer's disease (AD) and the concurrent cost of healthcare will increase as the population continues to age. Pharmaceutical interventions effectively manage symptoms of AD but carry side effects and ineffectively address underlying causes and disease prevention. Non-pharmaceutical interventions for AD, such as music training and therapy do not carry these side effects and can improve symptoms, and should therefore be explored as stand-alone or co-therapy for AD. In addition, music encapsulates modifiable lifestyle factors, such as cognitive stimulation, that have been shown to delay progression of and prevent AD. However, the neural mechanisms underpinning how music improves AD symptoms are not fully understood and whether music can target compensatory processes, activate neural networks, or even slow or prevent AD needs further research. Research suggests neural mechanism may involve stimulating brain areas to promote neurogenesis, dopaminergic rewards systems, and the default mode network (DMN). Alternatively, this review proposes that music improve symptoms of AD via the fronto-parietal control network (FPCN), the salience network (SN) and DMN, and neural compensation. This review will then present evidence for how music could activate the FPCN, SN, and DMN to improve their efficiency, organization, and cognitive functions they govern, protecting the brain from damage, slowing progression, and possibly preventing AD. Establishing how music improves symptoms of AD can lead to tailored music therapy protocols that target functional neural networks responsible for impaired executive functions common in AD.

Neural correlates of subjective cognitive decline in Alzheimer's disease: a systematic review of structural and functional brain changes for early diagnosis and intervention

Background

Subjective Cognitive Decline (SCD) is increasingly recognized as a preclinical stage of Alzheimer's disease (AD), representing a critical window for early detection and intervention. Understanding the structural and functional neural changes in SCD can improve diagnosis, monitoring, and management of this early stage of disease.

Methods

A systematic review was conducted using PubMed, Web of Science, and Scopus databases to identify studies examining neuroanatomical, neurofunctional, and neuroimaging findings in individuals with SCD. Inclusion criteria emphasized studies exploring SCD's potential as an early biomarker for AD progression.

Results

A total of 2.283 studies were screened, with 17 meeting the inclusion criteria. Evidence indicates that SCD is associated with cortical thinning and reductions in gray matter volume (GMV), particularly in the hippocampus, entorhinal cortex, and medial temporal lobe. Functional imaging studies reveal disruptions in the default mode network (DMN), executive control networks (ECN), and sensorimotor networks (SMN), indicating both compensatory mechanisms and early dysfunction. Dynamic functional connectivity studies report reduced brain activity efficiency, while graph theory analyses show decreased network integration. Advanced neuroimaging techniques and machine learning (ML) approaches demonstrate significant promise in detecting subtle neural changes in SCD, with applications for early diagnosis and monitoring disease progression.

Conclusion

SCD represents a heterogeneous condition characterized by mixed compensatory and degenerative neural changes, marking a critical early stage in the AD continuum. Combining structural and functional brain alterations with advanced neuroimaging and ML methodologies provides valuable biomarkers for early detection. Future longitudinal and multimodal studies are essential to standardize methodologies, account for individual variability, and develop personalized interventions aimed at mitigating progression to dementia.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024616052, CRD42024616052.

Functional connectivity of default mode network in non-hospitalized patients with post-COVID cognitive complaints

Introduction

Neurologic impairment is common in patients with acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. While patients with severe COVID have a higher prevalence of neurologic symptoms, as many as one in five patients with mild COVID may also be affected, exhibiting impaired memory as well as other cognitive dysfunctions.

Methods

To characterize the effect of COVID on the brain, the current study recruited a group of adults with post-COVID cognitive complaints but with mild, non-hospitalized cases. They were then evaluated through formal neuropsychological testing and underwent functional MRI of the brain. The participants in our study performed nearly as expected for cognitively intact individuals. Additionally, we characterized the functional connectivity of the default mode network (DMN), which is known for cognitive functions including memory as well as the attention functions involved in normal aging and degenerative diseases.

Results

Along with the retention of functional connectivity in the DMN, our results found the DMN to be associated with neurocognitive performance through region-of-interest and whole-brain analyses. The connectivity between key nodes of the DMN was positively correlated with cognitive scores (r = 0.51, p = 0.02), with higher performers exhibiting higher DMN connectivity.

Discussion

Our findings provide neuroimaging evidence of the functional connectivity of brain networks among individuals experiencing cognitive deficits beyond the recovery of mild COVID. These imaging outcomes indicate expected functional trends in the brain, furthering understanding and guidance of the DMN and neurocognitive deficits in patients recovering from COVID.

The Journey of the Default Mode Network: Development, Function, and Impact on Mental Health

The Default Mode Network has been extensively studied in recent decades due to its central role in higher cognitive processes and its relevance for understanding mental disorders. This neural network, characterized by synchronized and coherent activity at rest, is intrinsically linked to self-reflection, mental exploration, social interaction, and emotional processing. Our understanding of the DMN extends beyond humans to non-human animals, where it has been observed in various species, highlighting its evolutionary basis and adaptive significance throughout phylogenetic history. Additionally, the DMN plays a crucial role in brain development during childhood and adolescence, influencing fundamental cognitive and emotional processes. This literature review aims to provide a comprehensive overview of the DMN, addressing its structural, functional, and evolutionary aspects, as well as its impact from infancy to adulthood. By gaining a deeper understanding of the organization and function of the DMN, we can advance our knowledge of the neural mechanisms that underlie cognition, behavior, and mental health. This, in turn, can lead to more effective therapeutic strategies for a range of neuropsychiatric conditions.

APOE ε4 influences the dynamic functional connectivity variability and cognitive performance in Alzheimer's disease

BackgroundApolipoprotein E (APOE) ε4 is the most significant genetic risk factor for sporadic Alzheimer's disease (AD). However, its impact on the dynamic changes in resting-state functional connectivity (FC), particularly concerning network formation, interaction, and dissolution over time, remains largely unexplored in AD.ObjectiveThis study aims to explore the effect of APOE ε4 on dynamic FC (dFC) variability and cognitive performance in AD.MethodsWe analyzed the dFC of AD patients, comparing APOE ε4 carriers (n = 33) with non-carriers (n = 41). The whole-brain dFC was assessed by calculating dynamic fractional amplitude of low-frequency fluctuations (dfALFF) and dynamic regional homogeneity (dReHo). To further explore the relationship between cognitive function and dFC in AD patients, we conducted a correlation analysis. Mediation analysis was also performed to determine whether dFC mediates the link between the APOE ε4 and cognitive decline in AD patients.ResultsAD patients carrying the APOE ε4 exhibited more severe cognitive impairment, along with reduced dReHo and dfALFF in both the left and right posterior cerebellar lobes. In these carriers, the dFC analysis showed lower dFC between the left posterior cerebellar lobe and the left middle temporal gyrus, which was positively correlated with executive function and information processing speed. Additionally, mediation analysis indicated that APOE ε4 influences dFC in this brain region, contributing to executive dysfunction in AD.ConclusionsThese findings offer preliminary evidence that APOE ε4 modulates fluctuating communication within the cerebellar lobe and the dFC between the cerebellar lobe and the temporal gyrus in AD.

Functional and vascular neuroimaging in maritime pilots with long-term sleep disruption

The mechanism underlying the possible causal association between long-term sleep disruption and Alzheimer's disease remains unclear Musiek et al. 2015. A hypothesised pathway through increased brain amyloid load was not confirmed in previous work in our cohort of maritime pilots with long-term work-related sleep disruption Thomas et al. Alzheimer's Res Ther 2020;12:101. Here, using functional MRI, T2-FLAIR, and arterial spin labeling MRI scans, we explored alternative neuroimaging biomarkers related to both sleep disruption and AD: resting-state network co-activation and between-network connectivity of the default mode network (DMN), salience network (SAL) and frontoparietal network (FPN), vascular damage and cerebral blood flow (CBF). We acquired data of 16 maritime pilots (56 ± 2.3 years old) with work-related long-term sleep disruption (23 ± 4.8 working years) and 16 healthy controls (59 ± 3.3 years old), with normal sleep patterns (Pittsburgh Sleep Quality Index ≤ 5). Maritime pilots did not show altered co-activation in either the DMN, FPN, or SAL and no differences in between-network connectivity. We did not detect increased markers of vascular damage in maritime pilots, and additionally, maritime pilots did not show altered CBF-patterns compared to healthy controls. In summary, maritime pilots with long-term sleep disruption did not show neuroimaging markers indicative of preclinical AD compared to healthy controls. These findings do not resemble those of short-term sleep deprivation studies. This could be due to resiliency to sleep disruption or selection bias, as participants have already been exposed to and were able to deal with sleep disruption for multiple years, or to compensatory mechanisms Mentink et al. PLoS ONE. 2021;15(12):e0237622. This suggests the relationship between sleep disruption and AD is not as strong as previously implied in studies on short-term sleep deprivation, which would be beneficial for all shift workers suffering from work-related sleep disruptions.

Whole-brain functional connectivity predicts regional tau PET in preclinical Alzheimer's disease

Preclinical Alzheimer's disease (AD), characterized by the abnormal accumulation of amyloid prior to cognitive symptoms, presents a critical opportunity for early intervention. Past work has described functional connectivity changes in preclinical disease, yet the interplay between AD pathology and the functional connectome during this window remains unexplored. We applied connectome-based predictive modeling to investigate the ability of resting-state whole-brain functional connectivity to predict tau (18F-flortaucipir) and amyloid (18F-florbetapir) PET binding in a preclinical AD cohort (A4, n =342, age 65-85). Separate predictive models were developed for each of 14 regions, and model performance was assessed using a Spearman's correlation between predicted and observed PET binding standard uptake value ratios. We assessed the validity of significant models by applying them to an external dataset, and visualized the underlying connectivity that was positively and negatively correlated to posterior cingulate tau binding, the most successful model. We found that whole brain functional connectivity predicts regional tau PET, outperforming amyloid PET models. The best performing tau models were for regions affected in Braak stage IV-V regions (posterior cingulate, precuneus, lateral occipital cortex, middle temporal, inferior temporal, and Bank STS), while models for regions of earlier tau pathology (entorhinal, parahippocampal, fusiform, and amygdala) performed poorly. Importantly, tau models generalized to a symptomatic AD cohort (ADNI; amyloid positive, n = 211, age 55-90), in tau-elevated but not tau-negative individuals. For the posterior cingulate A4 tau model, the most successful model, the predictive edges positively correlated with posterior cingulate tau predominantly came from nodes within temporal, limbic, and cerebellar regions. The most predictive edges negatively associated to tau were from nodes of heteromodal association areas, particularly within the prefrontal and parietal cortices. These findings reveal that whole-brain functional connectivity predicts tau PET in preclinical AD and generalizes to a clinical dataset specifically in individuals with abnormal tau PET, highlighting the relevance of the functional connectome for the early detection and monitoring of AD pathology.

Spliceosome protein alterations differentiate hubs of the default mode connectome during the progression of Alzheimer's disease

Default mode network (DMN) is comprised in part of the frontal (FC), precuneus (PreC), and posterior cingulate (PCC) cortex and displays amyloid and tau pathology in Alzheimer's disease (AD). The PreC hub appears the most resilient to AD pathology, suggesting differential vulnerability within the DMN. However, the mechanisms that underlie this differential pathobiology remain obscure. Here, we investigated changes in RNA polymerase II (RNA pol II) and splicing proteins U1-70K, U1A, SRSF2, and hnRNPA2B1, phosphorylated AT8 tau, 3R and 4Rtau isoforms containing neurons and amyloid plaques in layers III and V-VI in FC, PreC, and PCC obtained from individuals with a preclinical diagnosis of no cognitive impairment (NCI), mild cognitive impairment (MCI), and mild/moderate mAD. We found a significant increase in pS5-RNA pol II levels in FC NCI, U1-70K in PreC MCI and mAD, and hnRNPA2B1 and SRSF2 levels in PCC mAD. 1N3Rtau levels were significantly increased in FC, decreased in PreC in mAD, and unchanged in PCC, whereas 1N4Rtau increased in mAD across the hubs. SRSF2, U1-70K, U1A, and hnRNPA2B1 nuclear optical density (OD), size, and number were unchanged across groups in FC and PCC, while PreC OD hnRNPA2B1 was significantly greater in mAD. Mislocalized U1A and U1-70K tangle-like structures were found in a few PCC cases and colocalized with AT8-bearing neurofibrillary tangles (NFTs). FC pS5-RNA pol II, PreC U1-70K, Pre pS5,2-RNA pol II, and PCC hnRNPA2B1 and SRSF2 protein levels were associated with cognitive decline but not neuropathology across clinical groups. By contrast, splicing protein nuclear OD measures, size, counts, and mislocalized U1-70K and U1A NFT-like structures were not correlated with NFT or plaque density, cognitive domains, and neuropathological criteria in DMN hubs. Findings suggest that RNA splicing protein alterations and U1 mislocalization contribute differentially to DMN pathogenesis and cognitive deterioration in AD.

Applications and advances of combined fMRI-fNIRs techniques in brain functional research

Understanding the intricate functions of the human brain requires multimodal approaches that integrate complementary neuroimaging techniques. This review systematically examines the integration of functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRs) in brain functional research, addressing their synergistic potential, methodological advancements, clinical and neuroscientific applications, and persistent challenges. We conducted a comprehensive literature review of 63 studies (from PubMed and Web of Science up to September 2024) using keyword combinations such as fMRI, fNIRs, and multimodal imaging. Our analysis reveals three key findings: (1) Methodological Synergy: Combining fMRI's high spatial resolution with fNIRs's superior temporal resolution and portability enables robust spatiotemporal mapping of neural activity, validated across motor, cognitive, and clinical tasks. Additionally, this study examines experimental paradigms and data processing techniques essential for effective multimodal neuroimaging. (2) Applications: The review categorizes integration methodologies into synchronous and asynchronous detection modes, highlighting their respective applications in spatial localization, validation of efficacy, and mechanism discovery. Synchronous and asynchronous integration modes have advanced research in neurological disorders (e.g., stroke, Alzheimer's), social cognition, and neuroplasticity, while novel hyperscanning paradigms extend applications to naturalistic, interactive settings. (3) Challenges: Hardware incompatibilities (e.g., electromagnetic interference in MRI environments), experimental limitations (e.g., restricted motion paradigms), and data fusion complexities hinder widespread adoption. The future direction emphasizes hardware innovation (such as fNIR probe compatible with MRI), standardized protocol and data integration driven by machine learning, etc. to solve the depth limitation of fNIR and infer subcortical activities. This synthesis underscores the transformative potential of fMRI-fNIRs integration in bridging spatial and temporal gaps in neuroimaging, while enhancing diagnostic and therapeutic strategies and paving the way for future innovations in brain research.

Early prediction of Alzheimer's disease using artificial intelligence and cortical features on T1WI sequences

Background

Accurately predicting the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is a challenging task, which is crucial for helping develop personalized treatment plans to improve prognosis.

Purpose

To develop new technology for the early prediction of AD using artificial intelligence and cortical features on MRI.

Methods

A total of 162 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. By using a 3D-MPRAGE sequence, T1W images for each patient were acquired. All patients were randomly divided into a training set (n = 112) and a validation set (n = 50) at a ratio of 7:3. Morphological features of the cerebral cortex were extracted with FreeSurfer software. Network features were extracted from gray matter with the GRETNA toolbox. The network, morphology, network-clinical, morphology-clinical, morphology-network and morphology-network-clinical models were developed by multivariate Cox proportional hazard model. The performance of each model was assessed by the concordance index (C-index).

Results

In the training group, the C-indexes of the network, morphology, network-clinical, morphology-clinical, morphology-network and morphology-network-clinical models were 0.834, 0.926, 0.915, 0.949, 0.928, and 0.951, respectively. The C-indexes of those models in the validation group were 0.765, 0.784, 0.849, 0.877, 0.884, and 0.880, respectively. The morphology-network-clinical model performed the best. A multi-predictor nomogram with high accuracy for individual AD prediction (C-index = 0.951) was established.

Conclusion

The early occurrence of AD could be accurately predicted using our morphology-network-clinical model and the multi-predictor nomogram. This could help doctors make early and personalized treatment decisions in clinical practice, which showed important clinical significance.

Alpha-frequency stimulation strengthens coupling between temporal fluctuations in alpha oscillation power and default mode network connectivity

Alpha (8-12 Hz) oscillations and default mode network (DMN) activity dominate the brain's intrinsic activity in the temporal and spatial domains, respectively. They are thought to play crucial roles in the spatiotemporal organization of the complex brain system. Relatedly, both have been implicated, often concurrently, in diverse neuropsychiatric disorders, with accruing electroencephalogram/magnetoencephalogram (EEG/MEG) and functional magnetic resonance imaging (fMRI) data linking these two neural activities both at rest and during key cognitive operations. Prominent theories and extant findings thus converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Here, we leveraged simultaneous EEG-fMRI data acquired before and after alpha-frequency transcranial alternating current stimulation (α-tACS) and observed that α-tACS tightened the dynamic coupling between spontaneous fluctuations in alpha power and DMN connectivity (especially, in the posterior DMN, between the posterior cingulate cortex and the bilateral angular gyrus). In comparison, no significant changes were observed for temporal correlations between power in other oscillatory frequencies and connectivity in other major networks. These results thus suggest an inherent coupling between alpha and DMN activity in humans. Importantly, these findings highlight the efficacy of α-tACS in regulating the DMN, a clinically significant network that is challenging to target directly with non-invasive methods.Significance Statement Alpha (8-12 Hz) oscillations and the default mode network (DMN) represent two major intrinsic activities of the brain. Prominent theories and extant findings converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Combining simultaneous electroencephalogram-functional-magnetic-resonance imaging (EEG-fMRI) with alpha-frequency transcranial alternating current stimulation (α-tACS), we demonstrated tightened coupling between alpha oscillations and DMN connectivity. These results lend credence to an inherent alpha-DMN link. Given DMN dysfunctions in multiple major neuropsychiatric conditions, the findings also highlight potential utility of α-tACS in clinical interventions by regulating the DMN.

Alzheimer's disease-like features in resting state EEG/fMRI of cognitively intact and healthy middle-aged APOE/PICALM risk carriers

BackgroundGenetic susceptibility is a primary factor contributing to etiology of late-onset Alzheimer's disease (LOAD). The exact mechanisms and timeline through which APOE/PICALM influence brain functions and contribute to LOAD remain unidentified. This includes their effects on individuals prior to the development of the disease.ObjectiveTo investigate the effects of APOE and PICALM risk genes on brain health and function in non-demented individuals. This study aims to differentiate the combined risk effects of both genes from the risk associated solely with APOE, and to examine how PICALM alleles influence the risk linked to APOE.MethodsAPOE/PICALM alleles were assessed to determine the genetic risk of LOAD in 79 healthy, middle-aged participants who underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings. The resting-state signal was analyzed to estimate relative spectral power, complexity (Higuchi's algorithm), and connectivity (coherence in EEG and independent component analysis-based connectivity in fMRI).ResultsThe main findings indicated that individuals at risk for LOAD exhibited reduced signal complexity and the so-called "slowing of EEG" which are well-known EEG markers of Alzheimer's disease. Additionally, these individuals showed altered functional connectivity in fMRI (within attention-related areas).ConclusionsRisk alleles of APOE/PICALM may affect brain integrity and function prior to the clinical onset of the disease.

Variation in brain aging: A review and perspective on the utility of individualized approaches to the study of functional networks in aging

Healthy aging is associated with cognitive decline across multiple domains, including executive function, memory, and attention. These cognitive changes can often influence an individual's ability to function and quality of life. However, the degree to which individuals experience cognitive decline, as well as the trajectory of these changes, exhibits wide variability across people. These cognitive abilities are thought to depend on the coordinated activity of large-scale networks. Like behavioral effects, large variation can be seen in brain structure and function with aging, including in large-scale functional networks. However, tracking this variation requires methods that reliably measure individual brain networks and their changes over time. Here, we review the literature on age-related cognitive decline and on age-related differences in brain structure and function. We focus particularly on functional networks and the individual variation that exists in these measures. We propose that novel individual-centered fMRI approaches can shed new light on patterns of inter- and intra-individual variability in aging. These approaches may be instrumental in understanding the neural bases of cognitive decline.

Direct access to specific autobiographical memories is lower in healthy middle-aged to older adult Apolipoprotein E ε4 carriers compared to non-carriers

Recent research suggests that the retrieval of autobiographical memories among cognitively healthy middle-aged and older adults is sensitive to the Apolipoprotein E ε4 (APOE4) allele, a genetic marker that increases the risk of Alzheimer's disease (AD) dementia. However, whether the APOE4-associated alteration in autobiographical memory retrieval encompasses rapid (i.e. direct retrieval) or iterative (i.e. generative retrieval) processes remains unclear. In the present study, 39 APOE4 carriers and 45 non-carriers (ages 60-80) who scored within normal limits on neuropsychological testing were cued to generate specific autobiographical events. We examined group differences in direct and generative retrieval and correlated direct and generative retrieval rates with performance on neuropsychological tests. Direct retrieval rates were lower in the APOE4 carriers compared to non-carriers. Episodic memory positively correlated with direct retrieval rates across the sample, though this relationship became non-significant when factoring in age and sex. There were no significant findings related to successful generative retrieval rates and its efficiency. In summary, compared to non-carriers, cognitively unimpaired middle-aged to older adult APOE4 carriers demonstrated greater difficulty, rapidly reconstructing specific autobiographical events without the support of semantic memory, suggesting that early autobiographical memory retrieval processes demonstrate vulnerability to AD-related risk factors.

Within-Individual Precision Mapping of Brain Networks Exclusively Using Task Data

Precision mapping of brain networks within individuals has become a widely used tool that prevailingly relies on functional connectivity analysis of resting-state data. Here we explored whether networks could be precisely estimated solely using data acquired during active task paradigms. The straightforward strategy involved extracting residualized data after application of a task-based general linear model (GLM) and then applying standard functional connectivity analysis. Functional correlation matrices estimated from task data were highly similar to those derived from traditional resting-state fixation data. The largest factor affecting similarity between correlation matrices was the amount of data. Networks estimated within-individual from task data displayed strong spatial overlap with those estimated from resting-state fixation data and predicted the same triple functional dissociation in independent data. The implications of these findings are that (1) existing task data can be reanalyzed to estimate within-individual network organization, (2) resting-state fixation and task data can be pooled to increase statistical power, and (3) future studies can exclusively acquire task data to both estimate networks and extract task responses. Most broadly, the present results suggest that there is an underlying, stable network architecture that is idiosyncratic to the individual and persists across task states.

Resting-state functional MRI in pediatric epilepsy: a narrative review

The role of connectivity in the function and development of the human brain has been intensely studied over the last two decades. These findings have begun to be translated to the clinical setting, particularly in the context of epilepsy. Determining connectivity in the epileptic brain can be challenging and is even more difficult in the pediatric patient. In pediatric epilepsy, resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful method for determining connectivity. Resting-state fMRI is a non-invasive method of determining correlated activity (functional connectivity) between brain regions in a task-free manner. This modality is especially useful in the pediatric population as it can be done under sedation and requires minimal cooperation from the patient. Over the last decade, rs-fMRI has been increasingly used and studied in pediatric epilepsy. In this article, we review this recent work and discuss the current state of rs-fMRI in the diagnosis and management of the different pediatric epilepsy syndromes. We first provide an overview of rs-fMRI in practice, including the different methods of analysis. We then describe the connectivity findings in pediatric epilepsy that have been revealed by rs-fMRI and the current state of rs-fMRI use in practice. Finally, we discuss what rs-fMRI has revealed about postoperative changes in connectivity and provide several recommendations for future research.

Neuropsychological profile associated with financial exploitation vulnerability in older adults without dementia

Objective: Reports of financial exploitation have steadily increased among older adults. Few studies have examined neuropsychological profiles for individuals vulnerable to financial exploitation, and existing studies have focused on susceptibility to scams, one specific type of financial exploitation. The current study therefore examines whether a general measure of financial exploitation vulnerability is associated with neuropsychological performance in a community sample. Methods: A sample (n = 116) of adults aged 50 or older without dementia completed a laboratory visit that measures physical and psychological functioning and a neuropsychological assessment, the Uniform Data Set-3 (UDS-3) and California Verbal Learning Test-II. Results: After covarying for demographics, current medical problems, financial literacy, and a global cognition screen, financial exploitation vulnerability was negatively associated with scores on the Multilingual Naming Test, Craft Story Recall and Delayed Recall, California Verbal Learning Test-II Delayed Recall and Recognition Discriminability, Phonemic Fluency, and Trails B. Financial exploitation vulnerability was not associated with performance on Digit Span, Semantic Fluency, Benson Complex Figure Recall, or Trails A. Conclusions: Among older adults without dementia, individuals at higher risk for financial exploitation demonstrated worse verbal memory, confrontation naming, phonemic fluency, and set-shifting. These tests are generally sensitive to Default Mode Network functioning and Alzheimer's Disease neuropathology. Longitudinal studies in more impaired samples are warranted to further corroborate and elucidate these relationships.

Multi-domain Online Therapeutic Investigation Of Neurocognition (MOTION) - A randomized comparative-effectiveness study of two remotely delivered mind-body interventions for older adults with cognitive decline

BACKGROUND

Research suggest that mind-body movement programs have beneficial effects on cognitive outcomes for older adults with cognitive decline. However, few studies have directly compared specific approaches to mind-body movement or studied the impact of remote program delivery.

METHODS

In a 3-arm randomized controlled trial (RCT) for older adults with cognitive impairment, we are comparing a multidomain mind-body program that emphasizes movement, body awareness, personal meaningfulness, and social connection, and a traditional Chinese mind-body exercise (Tai Chi) to a health and wellness education control condition. All 3 interventions are delivered remotely two times per week (onehour per session) for 12 weeks. The two active interventions are live-streamed. Outcomes are assessed prior to, after, and 6-months after the interventions. The co-primary outcomes are changes on the Alzheimer's Disease Assessment Scale - Cognitive Subscale (ADAS-cog) and brain functional connectivity in the Default Mode Network (DMN). Secondary outcomes include measures of specific cognitive domains (e.g., executive function, attention), mobility, and self-report measures of general well-being, quality of life, social engagement, self- and attention-regulation.

CONCLUSION

This RCT will directly compare the effects of two mind-body movement programs versus an education control delivered remotely over 12 weeks on cognitive, neuroimaging, and participant-reported outcomes. If successful, these programs may provide scalable strategies for slowing cognitive decline, which could potentially delay dementia onset in some individuals.

TRIAL REGISTRATION ID

NCT05217849.

A review of the application of exercise intervention on improving cognition in patients with Alzheimer's disease: mechanisms and clinical studies

Alzheimer's disease (AD) is the most common form of dementia, leading to sustained cognitive decline. An increasing number of studies suggest that exercise is an effective strategy to promote the improvement of cognition in AD. Mechanisms of the benefits of exercise intervention on cognitive function may include modulation of vascular factors by affecting cardiovascular risk factors, regulating cardiorespiratory health, and enhancing cerebral blood flow. Exercise also promotes neurogenesis by stimulating neurotrophic factors, affecting neuroplasticity in the brain. Additionally, regular exercise improves the neuropathological characteristics of AD by improving mitochondrial function, and the brain redox status. More and more attention has been paid to the effect of Aβ and tau pathology as well as sleep disorders on cognitive function in persons diagnosed with AD. Besides, there are various forms of exercise intervention in cognitive improvement in patients with AD, including aerobic exercise, resistance exercise, and multi-component exercise. Consequently, the purpose of this review is to summarize the findings of the mechanisms of exercise intervention on cognitive function in patients with AD, and also discuss the application of different exercise interventions in cognitive impairment in AD to provide a theoretical basis and reference for the selection of exercise intervention in cognitive rehabilitation in AD.

Alpha-frequency stimulation strengthens coupling between temporal fluctuations in alpha oscillation power and default mode network connectivity

Alpha (8-12 Hz) oscillations and default mode network (DMN) activity dominate the brain's intrinsic activity in the temporal and spatial domains, respectively. They are thought to play crucial roles in the spatiotemporal organization of the complex brain system. Relatedly, both have been implicated, often concurrently, in diverse neuropsychiatric disorders, with accruing electroencephalogram/magnetoencephalogram (EEG/MEG) and functional magnetic resonance imaging (fMRI) data linking these two neural activities both at rest and during key cognitive operations. Prominent theories and extant findings thus converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Here, we leveraged simultaneous EEG-fMRI data acquired before and after alpha-frequency transcranial alternating current stimulation (α-tACS) and observed that α-tACS tightened the dynamic coupling between spontaneous fluctuations in alpha power and DMN connectivity (especially, in the posterior DMN, between the posterior cingulate cortex and the bilateral angular gyrus). In comparison, no significant changes were observed for temporal correlations between power in other oscillatory frequencies and connectivity in other major networks. These results thus suggest an inherent coupling between alpha and DMN activity in humans. Importantly, these findings highlight the efficacy of α-tACS in regulating the DMN, a clinically significant network that is challenging to target directly with non-invasive methods.

Specialization of the human hippocampal long axis revisited

The hippocampus possesses anatomical differences along its long axis. Here, we explored the functional specialization of the human hippocampal long axis using network-anchored precision functional MRI in two independent datasets (N = 11 and N = 9) paired with behavioral analysis (N = 266 and N = 238). Functional connectivity analyses demonstrated that the anterior hippocampus was preferentially correlated with a cerebral network associated with remembering, while the posterior hippocampus selectively contained a region correlated with a distinct network associated with behavioral salience. Seed regions placed within the hippocampus recapitulated the distinct cerebral networks. Functional characterization of the anterior and posterior hippocampal regions using task data identified and replicated a functional double dissociation. The anterior hippocampal region was sensitive to remembering and imagining the future, specifically tracking the process of scene construction, while the posterior hippocampal region displayed transient responses to targets in an oddball detection task and to transitions between task blocks. These findings suggest an unexpected specialization along the long axis of the human hippocampus with differential responses reflecting the functional properties of the partner cerebral networks.

Revealing excitation-inhibition imbalance in Alzheimer's disease using multiscale neural model inversion of resting-state functional MRI

BACKGROUND

Alzheimer's disease (AD) is a serious neurodegenerative disorder without a clear understanding of pathophysiology. Recent experimental data have suggested neuronal excitation-inhibition (E-I) imbalance as an essential element of AD pathology, but E-I imbalance has not been systematically mapped out for either local or large-scale neuronal circuits in AD, precluding precise targeting of E-I imbalance in AD treatment.

METHOD

In this work, we apply a Multiscale Neural Model Inversion (MNMI) framework to the resting-state functional MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to identify brain regions with disrupted E-I balance in a large network during AD progression.

RESULTS

We observe that both intra-regional and inter-regional E-I balance is progressively disrupted from cognitively normal individuals, to mild cognitive impairment (MCI) and to AD. Also, we find that local inhibitory connections are more significantly impaired than excitatory ones and the strengths of most connections are reduced in MCI and AD, leading to gradual decoupling of neural populations. Moreover, we reveal a core AD network comprised mainly of limbic and cingulate regions. These brain regions exhibit consistent E-I alterations across MCI and AD, and thus may represent important AD biomarkers and therapeutic targets. Lastly, the E-I balance of multiple brain regions in the core AD network is found to be significantly correlated with the cognitive test score.

CONCLUSIONS

Our study constitutes an important attempt to delineate E-I imbalance in large-scale neuronal circuits during AD progression, which may facilitate the development of new treatment paradigms to restore physiological E-I balance in AD.

Neuromark PET: A multivariate method for estimating whole brain fMRI guided intrinsic networks and connectomes from fMRI and PET data

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are both widely used neuroimaging techniques to study brain functional and molecular connectivity. Although whole brain resting functional MRI (fMRI) connectomes (a matrix describing the inter-regional connectivity patterns) are widely used, the integration or association of whole brain molecular connectomes with PET data are rarely done. This likely stems from the fact that PET data is typically analyzed by using a region of interest approach, while whole brain spatial networks and their connectivity (covariation) receive much less attention. As a result, to date, there have been little focus on directly comparing whole brain PET and fMRI connectomes. In this study, we present a method that uses spatially constrained independent component analysis (scICA) (utilizing fMRI components as spatial priors) to estimate corresponding (Amyloid) PET and fMRI connectomes and examine the relationship between them using datasets that include individuals with mild cognitive impairment (MCI). Our results demonstrate highly modularized PET connectome patterns that complement those identified from resting fMRI. In particular, fMRI showed strong intra-domain connectivity with interdomain anticorrelation in sensorimotor and visual domains as well as default mode network. PET amyloid data showed similar strong intra-domain effects, but showed much higher correlations within cognitive control and default mode domains, as well as anticorrelation between cerebellum and other domains. The estimated fMRI informed PET networks have similar, but not identical, network spatial patterns to the resting fMRI networks, with the fMRI informed PET networks being slightly smoother and, in some cases, showing variations in subnodes. To further compare the two modalities, we also analyzed the differences between individuals with MCI receiving medication versus a placebo. Results show both common and modality specific treatment effects on fMRI and PET connectomes. From our fMRI analysis, we observed higher connectivity differences in various regions, such as the connection between the thalamus and middle occipital gyrus, as well as the insula and right middle occipital gyrus. Meanwhile, the PET analysis revealed increased activation between the anterior cingulate cortex and the left inferior parietal lobe, along with other regions, in individuals who received medication versus placebo. In sum, our novel approach identifies corresponding whole-brain fMRI informed PET and fMRI networks and connectomes. While we observed common patterns of network connectivity, our analysis of the MCI treatment and placebo groups revealed that each modality captures modality and group specific information about brain networks, highlighting differences between the two groups in both network expression and network connectivity.

The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review

INTRODUCTION

The prevalence of neurodegenerative diseases has significantly increased, necessitating a deeper understanding of their symptoms, diagnostic processes, and prevention strategies. Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are two prominent neurodegenerative conditions that present diagnostic challenges due to overlapping symptoms. To address these challenges, experts utilize a range of imaging techniques, including magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). These techniques facilitate a detailed examination of the manifestations of these diseases. Recent research has demonstrated the potential of artificial intelligence (AI) in automating the diagnostic process, generating significant interest in this field.

MATERIALS AND METHODS

This narrative review aims to compile and analyze articles related to the AI-assisted diagnosis of FTD and AD. We reviewed 31 articles published between 2012 and 2024, with 23 focusing on machine learning techniques and 8 on deep learning techniques. The studies utilized features extracted from both single imaging modalities and multi-modal approaches, and evaluated the performance of various classification models.

RESULTS

Among the machine learning studies, Support Vector Machines (SVM) exhibited the most favorable performance in classifying FTD and AD. In deep learning studies, the ResNet convolutional neural network outperformed other networks.

CONCLUSION

This review highlights the utility of different imaging modalities as diagnostic aids in distinguishing between FTD and AD. However, it emphasizes the importance of incorporating clinical examinations and patient symptom evaluations to ensure comprehensive and accurate diagnoses.

Social Determinants of Health and Functional Brain Connectivity Predict Long-Term Physical Activity in Older Adults with a New Cardiovascular Diagnosis

Background

Physical activity is essential for preventing cognitive decline, stroke and dementia in older adults. A new cardiovascular diagnosis offers a critical window for positive lifestyle changes. However, sustaining physical activity behavior change remains challenging and the underlying mechanisms are poorly understood.

Methods

To identify the neural, behavioral and contextual predictors of successful longer-term behavior change after a new cardiovascular diagnosis, we used support vector machine learning to predict changes in moderate-to-vigorous physical activity over four years in 295 cognitively unimpaired older adults from the UK Biobank, testing three models that incorporated baseline: (i) demographic, cognitive, and contextual factors, (ii) baseline resting-state functional connectivity alone, and (iii) combined multimodal features across all predictors.

Results

The combined multi-modal model had the highest predictive power (r=0.28, p=0.001). Key predictors included greenspace access, social support, retirement status, executive function, and between-network functional connectivity within the default mode, frontoparietal control and salience/ventral attention networks.

Conclusions

These findings underscore the importance of social and structural determinants of health and uncover neural mechanisms that may support lifestyle modifications. In addition to furthering our understanding of the mechanisms underlying successful physical activity behavior change, these findings help to guide the design of interventions and health policy with the ultimate goal of preventing cardiovascular disease burden and late-life cognitive decline.

Association of player position and functional connectivity alterations in collegiate American football players: an fMRI study

Introduction

Resting state-fMRI, provides a sensitive method for detecting changes in brain functional integrity, both with respect to regional oxygenated blood flow and whole network connectivity. The primary goal of this report was to examine alterations in functional connectivity in collegiate American football players after a season of repetitive head impact exposure.

Methods

Collegiate football players completed a rs-fMRI at pre-season and 1 week into post-season. A seed-based functional connectivity method, isolating the posterior cingulate cortex (PCC), was utilized to create individual functional connectivity maps. During group analysis, first, voxel-wise paired sample t-tests identified significant changes in connectivity from pre- to post-season, by player, and previous concussion history. Second, 10 DMN ROIs were constructed by overlaying an anatomical map over regions of positive correlation from one-sample t-tests of pre-season and post-season. These ROIs, plus the LpCun, were included in linear mix-effect modeling, with position or concussion history as covariates.

Results

66 players were included (mean age 20.6 years; 100% male; 34 (51.5%) non-speed position players). The 10 DMN ROIs showed no alterations from pre-season to post-season. By concussion history, the right temporal ROI demonstrated a significant effect on baseline functional connectivity (p = 0.03). Speed players, but not non-speed players, demonstrated a significant decrease in functional connectivity in the precuneus from pre- to post-season (p < 0.001).

Discussion

There are region-specific differences functional connectivity related to both position and concussion history in American collegiate football players. Player position affected functional connectivity across a season of football. Position-specific differences in head impact exposure rate and magnitude plays a crucial role in functional connectivity alterations.

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.

Effects of intermittent theta burst stimulation on cognitive and swallowing function in patients with MCI and dysphagia risk: a randomized controlled trial

BACKGROUND

Mild cognitive impairment (MCI) is a high-risk factor for dementia and dysphagia; therefore, early intervention is vital. The effectiveness of intermittent theta burst stimulation (iTBS) targeting the right dorsal lateral prefrontal cortex (rDLPFC) remains unclear.

METHODS

Thirty-six participants with MCI were randomly allocated to receive real (n = 18) or sham (n = 18) iTBS. Global cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), and executive function was evaluated with the Trail Making Test (TMT), Digital span test (DST) and Stroop color word test (SCWT). Quantitative swallowing measurements were obtained using temporal and kinetic parameters based on the videofluoroscopic swallowing study (VFSS). Resting-state functional magnetic imaging (fMRI) was performed to observe brain plasticity, functional connectivity (FC) values were calculated. All assessments were completed at baseline and two weeks after treatment. Participants received 10 sessions of daily robotic navigated iTBS.

RESULTS

The MoCA score and the SCWT duration of the real group improved significantly compared with that of the sham group. Temporal parameters of VFSS included 5-ml oral transit time (OTT), 5-ml soft palate elevation time (SET) and 10-ml OTT showed a decreasing trend. However, there was significant improvement in 10-ml OTT when choosing patients with OTT exceeding 1000 ms. FC value between the left middle frontal gyrus and the rDLPFC increased significantly in real stimulation group (p < 0.05 with false discovery rate corrected). We found that baseline FC scores were negatively correlated with the SCWT task duration (r = -0.554, p = 0.017) and with the 10-ml OTT (rho = -0.442, p = 0.027) across all participants. Among those in the iTBS group with a pre-10-ml OTT greater than 1000 ms, we observed a positive correlation between changes in MoCA scores and changes in FC values (r = 0.789, p = 0.035). Furthermore, changes in MoCA scores were positively correlated with changes in 10-ml OTT (r = 0.648, p = 0.031), as determined by Pearson analysis.

CONCLUSIONS

Navigated iTBS over the rDLPFC has the potential to improve global cognition, response inhibition ability, and certain aspects of swallowing function for patients with MCI at high risk for dysphagia. Changes in FC between right and left DLPFC may underlie the neural mechanisms responsible for the effectiveness of iTBS targeting the right DLPFC.

Non-Drug and Non-Invasive Therapeutic Options in Alzheimer's Disease

Despite the massive efforts of modern medicine to stop the evolution of Alzheimer's disease (AD), it affects an increasing number of people, changing individual lives and imposing itself as a burden on families and the health systems. Considering that the vast majority of conventional drug therapies did not lead to the expected results, this review will discuss the newly developing therapies as an alternative in the effort to stop or slow AD. Focused Ultrasound (FUS) and its derived Transcranial Pulse Stimulation (TPS) are non-invasive therapeutic approaches. Singly or as an applied technique to change the permeability of the blood-brain-barrier (BBB), FUS and TPS have demonstrated the benefits of use in treating AD in animal and human studies. Adipose-derived stem Cells (ADSCs), gene therapy, and many other alternative methods (diet, sleep pattern, physical exercise, nanoparticle delivery) are also new potential treatments since multimodal approaches represent the modern trend in this disorder research therapies.

Neurovascular coupling over cortical brain areas and resting state network connectivity with and without rigidified carotid artery

Significance

Neurovascular coupling (NVC) is key to research as hemodynamics can reflect neuronal activation and is often used in studies regarding the resting state network (RSN). However, several circumstances, including diseases that reduce blood vessel elasticity, can diminish NVC. In these cases, hemodynamic proxies might not accurately reflect the neuronal RSN.

Aim

We aim to investigate in resting state if (1) NVC differs over brain regions, (2) NVC remains intact with a mild rigidification of the carotid artery, (3) hemodynamic-based RSN reflects neuronal-based RSN, and (4) RSN differs with a mildly rigidified artery.

Approach

We rigidified the right common carotid artery of mice ( n = 15 ) by applying aCaCl 2 -soaked cloth to it (NaCl for Sham, n = 17 ). With simultaneous GCaMP and intrinsic optical imaging, we compared neuronal activation to hemodynamic changes over the entire cortex.

Results

NVC parameters did not differ between the CaCl and Sham groups. Likewise, GCaMP and hemodynamic RSN showed similar connections in both groups. However, the parameters of NVC differed over brain regions. Retrosplenial regions had a slower response and a higher HbR peak than sensory and visual regions, and the motor cortex showed less HbO influx than sensory and visual regions.

Conclusions

NVC in a resting state differs over brain regions but is not altered by mild rigidification of the carotid artery.

Aberrant functional connectivity between the retrosplenial cortex and hippocampal subregions in amnestic mild cognitive impairment and Alzheimer's disease

The posterior cingulate cortex and hippocampus are the core regions involved in episodic memory, and they exhibit functional connectivity changes in the development and progression of Alzheimer's disease. Previous studies have demonstrated that the posterior cingulate cortex and hippocampus are both cytoarchitectonically heterogeneous regions. Specifically, the retrosplenial cortex, typically subsumed under the posterior cingulate cortex, is an area functionally and anatomically distinct from the posterior cingulate cortex, and the hippocampus is composed of several subregions that participate in multiple cognitive processes. However, little is known about the functional connectivity patterns of the retrosplenial cortex or other parts of the posterior cingulate cortex with hippocampal subregions and their differential vulnerability to Alzheimer's disease pathology. Demographic data, neuropsychological assessments, and resting-state functional magnetic resonance imaging data were collected from 60 Alzheimer's disease participants, 60 participants with amnestic mild cognitive impairment, and 60 sex-matched normal controls. The bilateral retrosplenial cortex, other parts of the posterior cingulate cortex, and hippocampus subregions (including the bilateral anterior hippocampus and posterior hippocampus) were selected to investigate functional connectivity alterations in amnestic mild cognitive impairment and Alzheimer's disease. Resting-state functional connectivity analysis demonstrated heterogeneity in the degree of connectivity between the hippocampus and different parts of the total posterior cingulate cortex, with considerably greater functional connectivity of the retrosplenial cortex with the hippocampus compared with other parts of the posterior cingulate cortex. Furthermore, the bilateral retrosplenial cortex exhibited widespread intrinsic functional connectivity with all anterior-posterior hippocampus subregions. Compared to the normal controls, the amnestic mild cognitive impairment and Alzheimer's disease groups showed different magnitudes of decreased functional connectivity between the retrosplenial cortex and the contralateral posterior hippocampus. Additionally, diminished functional connectivity between the left retrosplenial cortex and right posterior hippocampus was correlated with clinical disease severity in amnestic mild cognitive impairment subjects, and the combination of multiple functional connectivity indicators of the retrosplenial cortex can discriminate the three groups from each other. These findings confirm and extend previous studies suggesting that the retrosplenial cortex is extensively and functionally connected with hippocampus subregions and that these functional connections are selectively affected in the Alzheimer's disease continuum, with prominent disruptions in functional connectivity between the retrosplenial cortex and contralateral posterior hippocampus underpinning episodic memory impairment associated with the disease.

Perceptual learning and neural correlates of virtual navigation in subjective cognitive decline: A pilot study

Spatial navigation deficits in age-related diseases involve brain changes affecting spatial memory and verbal cognition. Studies in blind and blindfolded individuals show that multisensory training can induce neuroplasticity through visual cortex recruitment. This proof-of-concept study introduces a digital navigation training protocol, integrating egocentric and allocentric strategies with multisensory stimulation and visual masking to enhance spatial cognition and brain connectivity in 17 individuals (mean age 57.2 years) with subjective cognitive decline. Results indicate improved spatial memory performance correlated with recruitment of the visual area 6-thalamic pathway and enhanced connectivity between memory, executive frontal areas, and default mode network (DMN) regions. Additionally, increased connectivity between allocentric and egocentric navigation areas via the retrosplenial complex (RSC) hub was observed. These findings suggest that this training has the potential to induce perceptual learning and neuroplasticity through key functional connectivity hubs, offering potential widespread cognitive benefits by enhancing critical brain network functions.

Auditory brainstem responses as a biomarker for cognition

A non-invasive, accessible and effective biomarker is critical to the diagnosis, monitoring and treatment of age-related cognitive decline. Recent work has suggested a strong association between auditory brainstem responses (ABR) and cognitive function in aging macaques. Here we show in 118 human participants (66 females; age range=18-92 years; hearing loss = -5 to 70 dB HL) that cognition is associated with both age and hearing level, but this triad relationship is mainly driven by the age factor. After adjusting for age, cognition is still significantly associated with both the ABR wave V amplitude (B, 0.110, 95% CI, 0.018- 0.202; p = 0.020) and latency (B, -0.101, 95% CI, -0.186- -0.016; p = 0.021). Importantly, this age-adjusted ABR-cognition association is primarily driven by older individuals and language-dependent cognitive functions. We also perform the area under the curve (AUC) of the receiver-operating-characteristic analysis and find that the ABR wave V amplitude is best for detecting good cognitive performers (AUC = 0.96) whereas the wave V latency is best for detecting poor ones (AUC = 0.86). The present result not only confirms the previous animal work in humans but also shows the clinical potential of using auditory brainstem responses to improve diagnosis and treatment of age-related cognitive decline.

Shared and disorder-specific large-scale intrinsic and effective functional network connectivities in postpartum depression with and without anxiety

Postpartum depression and postpartum depression with anxiety, which are highly prevalent and debilitating disorders, become a growing public concern. The high overlap on the symptomatic and neurobiological levels led to ongoing debates about their diagnostic and neurobiological uniqueness. Delineating the shared and disorder-specific intrinsic functional connectivities and their causal interactions is fundamental to precision diagnosis and treatment. In this study, we recruited 138 participants including 45 postpartum depression, 31 postpartum depression comorbid with anxiety patients, and 62 healthy postnatal women with age ranging from 23 to 40 years. We combined independent component analysis, resting-state functional connectivity, and Granger causality analysis to reveal the abnormal intrinsic functional couplings and their causal interactions in postpartum depression and postpartum depression comorbid with anxiety from a large-scale brain network perspective. We found that they exhibited widespread abnormalities in intrinsic and effective functional network connectivities. Importantly, the intrinsic and effective functional network connectivities within or between the fronto-parietal network, default model network, ventral and dorsal attention network, sensorimotor network, and visual network, especially the functional imbalances between primary and association cortices could serve as effective neural markers to differentiate postpartum depression, postpartum depression comorbid with anxiety, and healthy controls. Our findings provide the initial evidence for shared and disorder-specific intrinsic and effective functional network connectivities for postpartum depression and postpartum depression comorbid with anxiety, which provide an underlying neuropathological basis for postpartum depression or postpartum depression comorbid with anxiety to facilitate precision diagnosis and therapy in future studies.

Medial Amygdalar Tau Is Associated With Mood Symptoms in Preclinical Alzheimer's Disease

BACKGROUND

While the amygdala receives early tau deposition in Alzheimer's disease (AD) and is involved in social and emotional processing, the relationship between amygdalar tau and early neuropsychiatric symptoms in AD is unknown. We sought to determine whether focal tau binding in the amygdala and abnormal amygdalar connectivity were detectable in a preclinical AD cohort and identify relationships between these and self-reported mood symptoms.

METHODS

We examined 598 individuals (347 amyloid positive [58% female], 251 amyloid negative [62% female] subset in tau positron emission tomography and functional magnetic resonance imaging cohorts) from the A4 (Anti-Amyloid Treatment in Asymptomatic AD) Study. In the tau positron emission tomography cohort, we used amygdalar segmentations to examine representative nuclei from 3 functional divisions of the amygdala. We analyzed between-group differences in division-specific tau binding in the amygdala in preclinical AD. We conducted seed-based functional connectivity analyses from each division in the functional magnetic resonance imaging cohort. Finally, we conducted exploratory post hoc correlation analyses between neuroimaging biomarkers of interest and anxiety and depression scores.

RESULTS

Amyloid-positive individuals demonstrated increased tau binding in the medial and lateral amygdala, and tau binding in these regions was associated with mood symptoms. Across amygdalar divisions, amyloid-positive individuals had relatively higher regional connectivity from the amygdala to other temporal regions, the insula, and the orbitofrontal cortex, but medial amygdala to retrosplenial cortex connectivity was lower. Medial amygdala to retrosplenial connectivity was negatively associated with anxiety symptoms, as was retrosplenial tau.

CONCLUSIONS

Our findings suggest that preclinical tau deposition in the amygdala and associated changes in functional connectivity may be related to early mood symptoms in AD.

Facing healthy and pathological aging: A systematic review of fMRI task-based studies to understand the neural mechanisms of cognitive reserve

Cognitive reserve (CR) explains the varying trajectories of cognitive decline in healthy and pathological ageing. CR is often operationalized in terms of socio-behavioural proxies that modulate cognitive performance. Individuals with higher CR are known to maintain better cognitive functions, but evidence on the underlying brain activity remains scattered. Here we review CR studies using functional MRI in young, healthy and pathologically elderly individuals. We focus on the two potential neural mechanisms of CR, neural reserve (efficiency of brain networks) and neural compensation (recruitment of additional brain regions), and the effect of different proxies on them. The results suggest increased task-related activity in different cognitive domains with age and compensation in case of difficult task and pathology. The effects of proxies lead to increased neural reserve (reduced brain activity) in both older and younger individuals. Their relationship with compensation remains unclear, largely due to the lack of young adult samples, particularly in clinical studies. These findings underscore the critical role of lifelong engagement in mentally enriching activities for preserving cognitive function during aging. New studies are encouraged to refine the CR theoretical and empirical framework, particularly regarding the measurement of socio-behavioral proxies and their relationship with cognitive decline and neural underpinning.

Sex-specific topological structure associated with dementia via latent space estimation

INTRODUCTION

We investigate sex-specific topological structures associated with typical Alzheimer's disease (AD) dementia using a novel state-of-the-art latent space estimation technique.

METHODS

This study applies a probabilistic approach for latent space estimation that extends current multiplex network modeling approaches and captures the higher-order dependence in functional connectomes by preserving transitivity and modularity structures.

RESULTS

We find sex differences in network topology with females showing more default mode network (DMN)-centered hyperactivity and males showing more limbic system (LS)-centered hyperactivity, while both show DMN-centered hypoactivity. We find that centrality plays an important role in dementia-related dysfunction with stronger association between connectivity changes and regional centrality in females than in males.

DISCUSSION

The study contributes to the current literature by providing a more comprehensive picture of dementia-related neurodegeneration linking centrality, network segregation, and DMN-centered changes in functional connectomes, and how these components of neurodegeneration differ between the sexes.

HIGHLIGHTS

We find evidence supporting the active role network topology plays in neurodegeneration with an imbalance between the excitatory and inhibitory mechanisms that can lead to whole-brain destabilization in dementia patients. We find sex-based differences in network topology with females showing more default mode network (DMN)-centered hyperactivity, males showing more limbic system (LS)-centered hyperactivity, while both show DMN-centered hypoactivity. We find that brain region centrality plays an important role in dementia-related dysfunction with a stronger association between connectivity changes and regional centrality in females than in males. Females, compared to males, tend to exhibit stronger dementia-related changes in regions that are the central actors of the brain networks. Taken together, this research uniquely contributes to the current literature by providing a more comprehensive picture of dementia-related neurodegeneration linking centrality, network segregation, and DMN-centered changes in functional connectomes, and how these components of neurodegeneration differ between the sexes.

The links among age, sex, and glutathione: A cross-sectional magnetic resonance spectroscopy study

Glutathione (GSH) is a brain marker for oxidative stress and has previously been associated with cerebral amyloid deposition and memory decline. However, to date, no study has examined the links among GSH, sex, age, amyloid, and Apolipoprotein E (APOE) genotype in a large non-clinical cohort of older adults. We performed APOE genotyping, magnetic resonance spectroscopy (MRS) as well as simultaneous positron emission tomography with the radiotracer Flutemetamol (Amyloid-PET), in a group of older adults. The final analysis set comprised 140 healthy older adults (mean age: 64.7 years) and 49 participants with mild cognitive impairment (mean age: 71.4 years). We recorded metabolites in the posterior cingulate cortex (PCC) by a GSH-edited MEGAPRESS sequence. Structural equation modeling revealed that higher GSH levels were associated with female sex, but neither APOE- epsilon 4 carrier status nor age showed significant associations with GSH. Conversely, older age and the presence of an APOE4 allele, but not sex, are linked to higher global amyloid load. Our results suggest that the PCC shows sex-specific GSH alterations in older adults.

Histamine H3 receptor inverse agonists/antagonists influence intra-regional cortical activity and inter-regional synchronization during resting state: an exploratory cortex-wide imaging study in mice

The histaminergic system plays a key role in modulating learning and memory, wakefulness, and energy balance. Histamine H3 receptors constitutively inhibit the synthesis and release of histamine and other neurotransmitters. Therefore, H3 receptor inverse agonists/antagonists increase the synthesis and release of these neurotransmitters, enhancing cognitive functions, including memory consolidation and retrieval. Spontaneous neural activity across the cerebral cortex is essential for cognitive function, including memory consolidation. Abnormal spontaneous activity has, in fact, been associated with cognitive dysfunctions and psychiatric disorders. Given the cognitive improvement achieved with the use of H3 receptor inverse agonists/antagonists, we examined the effects of two inverse agonists/antagonists - thioperamide and pitolisant - on spontaneous cortical activity, using in vivo wide-field Ca2+ imaging. Changes in cortical activity, across multiple cortical regions and in inter-regional connectivity, from pre- to post-administration were evaluated using a linear support vector machine decoder. Thioperamide and pitolisant both modified the amplitude distribution of calcium events across multiple cortical regions, including a reduction in the frequency of low-amplitude calcium events in the somatosensory cortex. Graph theory analysis revealed increases in centrality measures in the somatosensory cortex with the use of both thioperamide and pitolisant, indicative of their importance in the organization of cortical networks. These findings indicate that H3 receptor inverse agonists/antagonists influence intra-regional cortical activity and inter-regional synchronization of activity in the cerebral cortex during the resting state.

Disconnection of alpha oscillations within default mode network associated with memory dysfunction in amnestic MCI

OBJECTIVE

Episodic memory dysfunction and alterations of functional connectivity (FC) in default mode network (DMN) were found in patients with amnestic mild cognitive impairment (aMCI). However, previous studies were limited in probing certain oscillations within the DMN. This study employed measures of resting-state FC across various oscillations within the DMN to comprehensively examine the FC and its association with episodic memory performance in aMCI.

METHODS

Twenty-six healthy controls (HC) and 30 patients with aMCI were recruited to perform resting-state magnetoencephalographic recordings. We compared the spectral powers and peak frequency values in each frequency band and FC within the DMN between these two groups. The associations of FC values with memory performance were also examined.

RESULTS

No significant between-group differences in spectral powers and peak frequency values were observed in the regional nodes. Patients with aMCI exhibited diminished alpha-band FC as compared to HC. Furthermore, lower alpha-band FC between the medial temporal cortex - and the posterior cingulate cortex/precuneus was correlated with poorer memory performance.

CONCLUSIONS

Aberrant DMN connectivity, particularly in the alpha frequency range, might be a neural correlate of episodic memory impairment.

SIGNIFICANCE

Our results inform the potential development of brain stimulation in managing memory impairments in aMCI.