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Stern, Y.

Publications and source records attributed to Stern, Y..

At least 19 recordsLinked to original sources

Preserved implicit metacognitive sensitivity distinguishes psychosis risk from first-episode psychosis: A cross-sectional virtual reality-based study

Relatively preserved insight distinguishes individuals at risk for psychosis from those with full-blown psychosis. Metacognitive processes thought to support insight and uncertainty monitoring may therefore serve as early markers of illness progression. Yet findings have been inconsistent, perhaps partly due to reliance on explicit confidence ratings that introduce reflection and response biases. To address these limitations, we used a novel implicit confidence measure derived from post-decision gaze in a virtual-reality probabilistic learning task. Gaze-based confidence quantifies the alignment between spatial predictions and gaze direction. We assessed first-order learning and gaze-based metacognition in four groups: clinical high-risk for psychosis (CHR-P), first episode psychosis (FEP), help-seeking controls (HSC), and healthy controls (HC). We tested whether implicit metacognition differentiates psychosis risk from psychosis. Learning accuracy was reduced in both CHR-P and FEP compared to control groups. At the metacognitive level, CHR-P confidence levels were approximately commensurate with their reduced first-order performance, indicating preserved confidence calibration, along with preserved metacognitive sensitivity--the ability to distinguish correct from incorrect decisions. In contrast, FEP showed impaired confidence calibration and reduced metacognitive sensitivity. Metacognitive calibration and sensitivity distinguished CHR-P from FEP and provided predictive value in distinguishing CHR-P from FEP, whereas learning accuracy did not. These findings reveal a dissociation between first-order cognitive processes and distinct aspects of implicit metacognition, including confidence calibration and metacognitive sensitivity, across the psychosis continuum. This dissociation may refine early clinical characterization and improve identification of preserved insight-related mechanisms in psychosis risk.

neuroscience↗

Amyloid-beta relates to default and attention network connectivity during working memory retrieval

As one of the earliest pathological events in Alzheimers disease, amyloid-beta accumulation has been linked to alterations in large-scale brain networks, especially the default mode network. While most prior work has focused on resting-state functional connectivity or task-evoked activation, how amyloid-beta relates to task-modulated connectivity that directly supports behavior is less explored. Here, we examined the relationship between amyloid-beta burden and task-modulated connectivity during a Letter Sternberg verbal working-memory task in eighty-four cognitively normal older adults from an ongoing longitudinal cohort (age 56-71 years; 41 female). To further understand these connectivity alterations, we tested whether these connections were associated with task performance or with cognitive reserve factors. We found that higher amyloid-beta burden was associated with reduced connectivity specifically during the probe phase of working-memory retrieval. At the regional scale, lower node strength in posterior-medial regions of the default mode network was related to worse task performance and mediated the amyloid-behavior relationship. At the network scale, altered coupling between the default mode and dorsal attention networks was related to task performance but did not mediate the amyloid-behavior relationship. Moreover, physical activity was associated with network-level coupling but not with regional node strength. These findings suggest that amyloid-beta burden disrupts retrieval-related working-memory systems, particularly regions of the default mode network that are vulnerable to amyloid-beta, while coupling between the default mode and dorsal attention networks indexes resilience. Collectively, task-modulated connectivity provides insight into pathways through which early Alzheimers disease pathology influences working-memory performance and resilience and may inform targeted lifestyle or neuromodulation interventions for older adults.

neuroscience↗

Disrupted Energy Landscape in Individuals with Mild Cognitive Impairment: Insights from Network Control Theory

IntroductionPatients with mild cognitive impairment (MCI) have shown disruptions in both brain structure and function, often studied separately. However, understanding the relationship between brain structure and function can provide valuable insights into this early stage of cognitive decline for better treatment strategies to avoid its progression. Network Control Theory (NCT) is a multi-modal approach that captures the alterations in the brains energetic landscape by combining the brains functional activity and the structural connectome. Our study aims to explore the differences in the brains energetic landscape between people with MCI and healthy controls (HC). MethodsFour hundred ninety-nine HC and 55 MCI patients were included. First, k-means was applied to functional MRI (fMRI) time series to identify commonly recurring brain activity states. Second, NCT was used to calculate the minimum energy required to transition between these brain activity states, otherwise known as transition energy (TE). The entropy of the fMRI time series as well as PET-derived amyloid beta (A{beta}) and tau deposition were measured for each brain region. The TE and entropy were compared between MCI and HC at the network, regional, and global levels using linear models where age, sex, and intracranial volume were added as covariates. The association of TE and entropy with A{beta} and tau deposition was investigated in MCI patients using linear models where age, sex, and intracranial volume were controlled. ResultsCommonly recurring brain activity states included those with high and low amplitude activity in visual (+/-), default mode (+/-), and dorsal attention (+/-) networks. Compared to HC, MCI patients required lower transition energy in the limbic network (adjusted p = 0.028). Decreased global entropy was observed in MCI patients compared to HC (p = 7.29e-7). There was a positive association between TE and entropy in the frontoparietal network (p = 7.03e-3). Increased global A{beta} was associated with higher global entropy in MCI patients ({rho} = 0.632, p = 0.041). ConclusionLower TE in the limbic network in MCI patients may indicate either neurodegeneration-related neural loss and atrophy or a potential functional upregulation mechanism in this early stage of cognitive impairment. Future studies that include people with AD are needed to better characterize the changes in the energetic landscape in the later stages of cognitive impairment.

neuroscience↗

A Radiomic Approach to Clinical MRI Refines the Thalamus-Cognition Link in Multiple Sclerosis

Background and ObjectivesRadiomics extracts imaging features that may not be detectable through conventional volumetric analyses. Given their role in multiple sclerosis (MS), we applied radiomics to thalamic nuclei and examined their associations with cognitive performance. MethodsA total of 601 individuals were included (342 people with MS-PwMS from two cohorts, and 259 healthy controls-HC). Radiomic features (RF) and volumes were extracted from the whole thalamus, five thalamic nuclei, and the putamen segmented on 3D T1-weighted images. Cognitive performance was assessed using the Symbol Digit Modalities Test (SDMT) and Paced Auditory Serial Addition Test (PASAT) in PwMS, and the Digit Symbol Substitution Test (DSST) in HC. In the first MS cohort, multivariate linear regression in a discovery set (N=103) identified thalamus-derived RF associated with SDMT, which were retested in a replication set (N=63). Their associations with PASAT in a second MS cohort (N=176) and DSST in HC were also evaluated. We then tested whether the same RF, when extracted from the putamen, were associated with SDMT. LASSO models assessed the combined predictive value of RF and volumes. Figure 2 presents an overview of the involvement of different cohorts in the study, outlining the specific study objectives and the statistical analysis approaches employed. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/657698v1_fig2.gif" ALT="Figure 2"> View larger version (40K): org.highwire.dtl.DTLVardef@6f1dd8org.highwire.dtl.DTLVardef@11069c6org.highwire.dtl.DTLVardef@905512org.highwire.dtl.DTLVardef@1c12171_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO Overview of the involvement of different cohorts in the study, specific study objectives and the statistical analysis approaches employed. Abbreviations: SDMT: Symbol Digit Modalities Test, PASAT: Paced Auditory Serial Addition Test, DSST: Digit Symbol Substitution Test, LASSO: Least Absolute Shrinkage and Selection Operator. C_FIG ResultsTwenty-eight RF-ROI pairs were associated with SDMT in the replication set (FDR<0.05). Of these, 24 were also associated with PASAT (FDR[&le;]0.03), and 2 with DSST. Only ventral nuclei volume showed replicated associations among volumetrics. Only 4 putamen-derived pairs were associated with SDMT (FDR=0.04). LASSO results confirmed RF outperformed volumes. DiscussionRF extracted from the thalamus are strongly associated with cognitive performance in PwMS, outperforming volumetric measures and supporting their potential as sensitive imaging biomarkers.

neuroscience↗

Brain Signal Variability During Rest as a Neural Mechanism Underlying Cognitive Reserve

BackgroundResting-state brain signal variability has been found to vary with age and cognitive function. Neural flexibility has been suggested as a neural mechanism underlying cognitive reserve (CR), a construct that describes better than expected cognition given brain status. Thus, we examined the associations between age, resting-state brain signal variability, cognition, and CR. MethodAnalysis was based on resting-state functional neuroimaging data from 470 participants (aged 20-80 years) from the Reference Ability Neural Networks and the CR studies. Brain signal variability was quantified for each brain region as the log-transformed standard deviation of the time-varying blood-oxygen-dependent (BOLD) signal. We then derived variability patterns related to age, perceptual speed, fluid reasoning, episodic memory, and vocabulary using Scaled Subprofile Modelling principal component analysis. To perform the formal test whether these patterns fulfill the requirements for CR, we examined whether they explained additional variance in cognition beyond brain status, age, sex, and education, or moderated the brain status-cognition relationship. We additionally stratified all regression models by age (cutoff: 60 years) and sex. ResultsBOLD signal variability showed an age-related increase in subcortical/medial brain regions, and an age-related decrease in cortical regions. It also met the CR test for speed (standardized regression coefficient ({beta})=0.251, 95% confidence interval (CI): 0.118-0.384, pFDR<0.001), episodic memory ({beta}=0.344, CI: 0.200-0.489, pFDR<0.001), reasoning ({beta}=0.316, CI: 0.197-0.436, pFDR<0.001), and vocabulary ({beta}=0.270, CI: 0.167-0.373, pFDR<0.001). Associations were stronger in women for vocabulary and in young individuals for reasoning. ConclusionsBOLD signal variability plays a role in aging and cognition and underlies CR.

neuroscience↗

Blood mitochondrial health markers cf-mtDNA and GDF15 in human aging

Altered mitochondria biology can accelerate biological aging, but scalable biomarkers of mitochondrial health for population studies are lacking. We examined two potential candidates: 1) cell-free mitochondrial DNA (cf-mtDNA), a marker of mitochondrial signaling elevated with disease states accessible as distinct biological entities from plasma or serum; and 2) growth differentiation factor 15 (GDF15), an established biomarker of biological aging downstream of mitochondrial energy transformation defects and stress signaling. In a cohort of 430 participants aged 24-84 (54.2% women), we measured plasma and serum cf-mtDNA, and plasma GDF15 levels at two timepoints 5 years apart, then assessed their associations with age, BMI, diabetes, sex, health-related behaviors, and psychosocial factors. As expected, GDF15 showed a positive, exponential association with age (r=0.66, p<0.0001) and increased by 33% over five years. cf-mtDNA was not correlated with GDF15 or age. BMI and sex were also not related to cf-mtDNA nor GDF15. Type 2 diabetes was only positively associated with GDF15. Exploring potential drivers of systemic mitochondrial stress signaling, we report a novel association linking higher education to lower age-adjusted GDF15 (r=-0.14, p<0.0034), both at baseline and the 5-year follow up, highlighting a potential influence of psychosocial factors on mitochondrial health. Overall, our findings among adults spanning six decades of lifespan establish associations between age, diabetes and GDF15, an emerging marker of mitochondrial stress signaling. Further studies are needed to determine if the associations of blood GDF15 with age and metabolic stress can be moderated by psychosocial factors or health-related behaviors.

physiology↗

Network-based amyloid-beta pathology predicts subsequent cognitive decline in cognitively normal older adults

The deposition of amyloid-{beta} (A{beta}) protein in the human brain is a hallmark of Alzheimers disease and is related to cognitive decline. However, the relationship between early A{beta} deposition and future cognitive impairment remains poorly understood, particularly concerning its spatial distribution and network-level effects. Here, we employed a cross-validated machine learning approach and investigated whether integrating subject-specific brain connectome information with A{beta} burden measures improves predictive validity for subsequent cognitive decline. Baseline regional A{beta} pathology measures from positron emission tomography (PET) imaging predicted prospective cognitive decline. Incorporating structural connectome, but not functional connectome, information into the A{beta} measures improved predictive performance. We further identified a neuropathological signature pattern linked to future cognitive decline, which was validated in an independent cohort. These findings advance our understanding of how A{beta} pathology relates to brain networks and highlight the potential of network-based metrics for A{beta}-PET imaging to identify individuals at higher risk of cognitive decline.

neuroscience↗

Relation of CMV and brain atrophy to trajectories of immunosenescence in diverse populations

Immunosenescence (ISC), the aging of the immune system, has largely been studied in populations of European descent. Here, circulating immune cell cytometric data from African-American, Hispanic, and non-Hispanic White participants were generated. Known and novel age effects were identified using either a meta-analysis approach or a parallel genetic approach. Most results are consistent across the three populations, but some cell populations display evidence of heterogeneity, such as a PD-L1+CD56+ NK cell subset. The study estimated "Immunological Age" (IA) during physiologic aging. While we found no relation of IA to Multiple Sclerosis, IA is associated with entorhinal cortex atrophy, a presymptomatic feature of Alzheimers disease, linking neurodegeneration and peripheral immunity. ISC trajectories were also inferred, highlighting age, CMV status, and genetic ancestry as key influences. Our assessment offers reference ISC trajectories for personalization of assessments of immune function over the life course in diverse populations.

immunology↗

'Eye Know': Gaze reflects confidence in explicit predictions while relying on a distinct computational mechanism

Decisions are naturally accompanied by a feeling of confidence in their correctness. Typically measured via self-reported judgments, decision confidence is also expressed implicitly through sensorimotor behavior. In one exploratory and two preregistered experiments (total N = 115), we combined VR-based probabilistic-learning and spatial-cueing paradigms with computational modeling to examine whether post-decision gaze can serve as an implicit behavioral measure of decision confidence. Gaze direction generally aligned with predictive decisions about a targets upcoming location, while the degree of alignment tracked the model-based probability underlying the decision. Gaze-decision alignment also exhibited previously established statistical hallmarks of confidence. Under increased uncertainty, gaze and decisions tended to diverge, reflecting strategic "hedging" behavior. Gaze-decision alignment and explicit confidence judgments were modestly correlated, and remained correlated after controlling for the decisions probability, suggesting that the two are partially overlapping manifestations of internal confidence. Notably, reported confidence showed higher metacognitive sensitivity and increased serial dependence across trials, while gaze-based confidence expressed a more trial-specific readout of internal confidence. These findings suggest that post-decision gaze in visual-spatial learning tasks is an implicit, embodied manifestation of confidence. The complementary functional characteristics of implicit and explicit expressions of confidence may support adaptive behavior during learning.

neuroscience↗

Trauma Under Psychedelics: MDMA Shows Protective Effects During the Peritraumatic Period

Traumatic events play a causal role in the etiology of stress-related psychopathologies such as depression and posttraumatic stress disorder (PTSD). Recent research highlighted the therapeutic potential of psychoactive substances in alleviating trauma symptoms among chronic stress-related patients. This study is the first to investigate the impact of psychoactive substances consumed during actual trauma exposure. Our cohort includes 772 adult survivors (487 males, Mean{+/-}SD Age: 26.96{+/-}6.55) of the high-casualty attack that occurred at the Supernova festival in Israel on October 7th, 2023. Survivors completed the study during the peritraumatic period of one to four months following the attack. Primary outcomes include the PTSD Checklist for DSM-5 (PCL-5), the Kessler Psychological Distress Scale (K6), and a self-reported rating of feeling overwhelmed. Secondary outcomes include subjective experiences during the attack and reports on social interactions and sleep quality. All survivors reported being in direct danger of death during the attack. Approximately two-thirds of the sampled survivors were under the influence of psychoactive substances at the time of the attack, including LSD, MDMA, Ketamine, Cannabis, and Alcohol, creating a tragic and unique natural experiment to study the impact of psychoactive compounds on trauma processing. Analysis revealed that participants that were under the influence of MDMA during the attack (n=99) reported feeling less overwhelmed, having more social interactions, improved sleep quality, and reduced psychological distress compared to those not under the influence of any substance during the attack (n=216). In contrast, those consuming Cannabis and/or Alcohol during the attack (n=68) showed higher psychological distress, more PTSD symptoms, and worse sleep quality compared to those not under the influence of any substance during the attack. Together, these novel findings suggest that trauma exposure under the influence of MDMA is associated with reduced psychological distress and higher sociality, possibly mediated through MDMAs known effects of reducing negative emotions and elevating prosociality, while Cannabis and/or Alcohol consumption produces deleterious effects. Further research is needed to explore the cognitive and physiological mechanisms linking psychoactive substances to trauma recovery and establish the putative protective role of MDMA.

neuroscience↗

The Body Knows Better: Sensorimotor signals reveal "Suboptimal" inference of the Sense of Agency in the human mind

Sense of Agency (SoA) is the feeling of control over our actions. SoA has been suggested to arise from both implicit sensorimotor integration as well as higher-level decision processes. SoA is typically measured by collecting participants subjective judgments, conflating both implicit and explicit processing. Consequently, the interplay between implicit sensorimotor processing and explicit agency judgments is not well understood. Here, we evaluated in one exploratory and one preregistered experiment (N=60), using a machine learning approach, the relation between a well-known mechanism of implicit sensorimotor adaptation and explicit SoA judgments. Specifically, we examined whether subjective judgments of SoA and sensorimotor conflicts could be inferred from hand kinematics in a sensorimotor task using a virtual hand (VH). In both experiments participants performed a hand movement and viewed a virtual hand making a movement that could either be synchronous with their action or include a parametric temporal delay. After each movement, participants judged whether their actual movement was congruent with the movement they observed. Our results demonstrated that sensorimotor conflicts could be inferred from implicit motor kinematics on a trial by trial basis. Moreover, detection of sensorimotor conflicts from machine learning models of kinematic data provided more accurate classification of sensorimotor congruence than participants explicit judgments. These results were replicated in a second, preregistered, experiment. These findings show evidence of diverging implicit and explicit processing for SoA and suggest that the brain holds high-quality information on sensorimotor conflicts that is not fully utilized in the inference of conscious agency.

neuroscience↗

Cognitive Reserve Against Alzheimer's Pathology Is Linked to Brain Activity During Memory Formation

The cognitive reserve (CR) hypothesis posits that individuals can differ in how their brain function is disrupted by pathology associated with aging and neurodegeneration. Here, we tested this hypothesis in the Alzheimers disease continuum using longitudinal data from 490 participants of the DELCODE multicentric observational study. Brain function was measured using task fMRI of visual memory encoding. Using a multivariate moderation analysis we identified a CR-related activity pattern underlying successful memory encoding that moderated the detrimental effect of AD pathological load on cognitive performance. CR was mainly represented by a more pronounced expression of the task-active network encompassing the default mode network (DMN), anterior cingulate cortex (ACC) and inferior temporal regions including the fusiform gyrus. We devised personalized fMRI-based CR scores that moderated the impact of AD pathology on cognitive performance and were positively associated with years of education. Furthermore, higher CR scores were associated with slower cognitive decline over time. Our findings suggest maintenance of core cognitive circuits including the DMN and ACC as the primary mechanism of CR. Individual brain activity levels of these areas during memory encoding have prognostic value for future cognitive decline.

neuroscience↗

Fractions strategy differences in those born extremely preterm

IntroductionTo investigate the effects of different strategies and cognitive load we explored brain hemodynamic responses associated with the use of different strategies to solve subtraction of fractions. We focused on those born extremely preterm (EPT; <28 weeks gestation) as they are known to have cognitive challenges and struggle with mathematics. We also included a group of full-term (FT) peers for comparison. MethodsFunctional MRI was acquired while the participants mentally solved fraction equations using either a strategy based on improper or mixed fractions. Different fraction item types were given, which affected respective required cognitive loads per strategy. Diffusion and T1-weighted structural images were also acquired. ResultsThe EPT and FT groups differed in terms of task-related hemodynamic responses. Functional group differences were greatest when strategies were applied to item types that result in high cognitive load. Other findings showed reduced white and grey matter volume and reduced white matter connectivity in widespread areas in the EPT group compared to the FT group. ConclusionThe understanding of function and structure presented here may help inform pedagogical practices by allowing for tailoring of mathematical education through identifying suitable strategy adoption that depends on item type, to circumvent weaknesses in cognitive skills.

neuroscience↗

Persistent Homology-based Functional Connectivity Explains Cognitive Ability: Life-span Study

Brain-segregation attributes in resting-state functional networks have been widely investigated to understand cognition and cognitive aging using various approaches (e.g., average connectivity within/between networks and brain system segregation). While these approaches have assumed that resting-state functional networks operate in a modular structure, a complementary perspective assumes that a core-periphery or rich club structure accounts for brain functions where the hubs are tightly interconnected to each other to allow for integrated processing. We introduce a novel method, persistent homology (PH)-based functional connectivity, to quantify the pattern of information during the integrated processing. We also investigate whether PH-based functional connectivity explains cognitive performance and compare the amount of variability in explaining cognitive performance for three sets of independent variables: (1) PH-based functional connectivity, (2) graph theory-based measures, and (3) brain system segregation. Resting-state functional connectivity data were extracted from 279 healthy participants, and cognitive ability scores were generated in four domains (fluid reasoning, episodic memory, vocabulary, and processing speed). The results first highlight the pattern of brain-information flow over whole brain regions (i.e., integrated processing) accounts for more variance of cognitive abilities than either brain system segregation or the graph theory-based network topology measure. The results also show that fluid reasoning and vocabulary performance significantly decrease as the strength of the additional information flow on functional connectivity with the shortest path increases.

neuroscience↗

Connectome-based predictive modeling of cognitive reserve using task-based functional connectivity

Cognitive reserve supports cognitive function in the presence of pathology or atrophy. Functional neuroimaging may enable direct and accurate measurement of cognitive reserve which could have considerable clinical potential. The present study aimed to develop and validate a measure of cognitive reserve using task-based fMRI data that could then be applied to independent resting-state data. Connectome-based predictive modeling with leave-one-out cross-validation was applied to predict a residual measure of cognitive reserve using task-based functional connectivity from the Cognitive Reserve/Reference Ability Neural Network studies (n = 220, mean age = 51.91 years, SD = 17.04 years). Three network-strength predicted cognitive reserve measures were generated that accurately predicted the residual measures of unseen participants. The theoretical validity of these measures was established via a positive correlation with a socio-behavioural proxy of cognitive reserve (verbal intelligence) and a positive correlation with global cognition, independent of brain structure. This fitted model was then applied to external test data: resting-state functional connectivity data from The Irish Longitudinal Study on Ageing (TILDA, n = 294, mean age = 68.3 years, SD = 7.18 years). The network-strength predicted measures were not positively associated with a residual measure of cognitive reserve nor with measures of verbal intelligence and global cognition. The present study demonstrated that task-based functional connectivity data can be used to generate theoretically valid measures of cognitive reserve. Further work is needed to establish if, and how, measures of cognitive reserve derived from task-based functional connectivity can be applied to independent resting-state data.

neuroscience↗

Transfer Learning for Cognitive Reserve Quantification

Cognitive reserve (CR) has been introduced to explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or pathology. We developed a deep learning model to quantify the CR as residual variance in memory performance using the structural MRI data from a lifespan healthy cohort. The generalizability of the sMRI-based deep learning model was tested in two independent healthy and Alzheimers cohorts using transfer learning framework. Structural MRIs were collected from three cohorts: 495 healthy adults (initially aged 20-80) from RANN, 620 healthy participants (age 36-100) from lifespan Human Connectome Project Aging (HCPA), and 941 subjects from Alzheimers Disease Neuroimaging Initiative (ADNI). Region of interest (ROI)-specific cortical thickness and volume measures were extracted using the Desikan-Killiany Atlas. Cognitive reserve was quantified by residuals which subtract the predicted memory from the true memory. Cascade neural network (CNN) models were used to train RANN dataset for memory prediction. Transfer learning was applied to transfer the T1 imaging-based model from source domain (using RANN) to the target domain (HCPA or ADNI). The CNN model trained on the RANN dataset exhibited strong linear correlation between true and predicted memory based on the chosen T1 cortical thickness and volume predictors. In addition, the model generated from healthy lifespan data (RANN) was able to generalize to an independent healthy lifespan data (HCPA) and older demented participants (ADNI) across different scanner types. The estimated CR was correlated with CR proxies such education and IQ across all three datasets. The current findings suggest that the transfer learning approach is an effective way to generalize the residual-based CR estimation. It is applicable to various diseases and may flexibly incorporate different imaging modalities such as fMRI and PET, making it a promising tool for scientific and clinical purposes. HighlightO_LIQuantification of cognitive reserve using brain measures for pre-symptomatic Alzheimers patients can be estimated by leveraging lifespan data. C_LIO_LIMulti-center, multi-scanner, multi-sequence can affect the performance of the quantification. C_LIO_LILeveraging lifespan data from a single site can improve the performance. C_LIO_LITransfer learning allows the pre-trained network to successfully reconstruct the dataset acquired from different domains or age groups. C_LI

bioinformatics↗

Reduced synaptic activity and dysregulated extracellular matrix pathways are common phenotypes in midbrain neurons derived from sporadic and mutation-associated Parkinson's disease patients

Several mutations that cause Parkinsons disease (PD) have been identified over the past decade. These account for 15-25% of PD cases; the rest of the cases are considered sporadic. Currently, it is accepted that PD is not a single monolithic disease but rather a constellation of diseases with some common phenotypes. While rodent models exist for some of the PD-causing mutations, research on the sporadic forms of PD is lagging due to a lack of cellular models. In our study, we differentiated PD patient-derived dopaminergic (DA) neurons from induced pluripotent stem cells (iPSCs) of several PD-causing mutations as well as from sporadic PD patients. Strikingly, we observed a common neurophysiological phenotype: Neurons derived from PD patients had a severe reduction in the rate of synaptic currents compared to those derived from healthy controls. While the relationship between mutations in genes such as the SNCA and LRRK2 and a reduction in synaptic transmission has been investigated before, here we show evidence that the pathogenesis of the synapses in neurons is a general phenotype in PD. Analysis of RNA sequencing results displayed changes in gene expression in different synaptic mechanisms as well as other affected pathways such as extracellular matrix-related pathways. Some of these dysregulated pathways are common to all PD patients (monogenic or idiopathic). Our data, therefore, shows changes that are central and convergent to PD and suggests a strong involvement of the tetra-partite synapse in PD pathophysiology.

neuroscience↗

The role of neural flexibility in cognitive aging

Studies assessing relationships between brain and cognitive changes in healthy aging have shown that a variety of aspects of brain structure and function explain a significant portion of the variability in cognitive outcomes throughout adulthood. Many studies assessing relationships between brain function and cognition have utilized time-averaged, or static functional connectivity methods to explore ways in which brain network organization may contribute to aspects of cognitive aging. However, recent studies in this field have suggested that time-varying, or dynamic measures of functional connectivity, which assess changes in functional connectivity throughout a scan session, may play a stronger role in explaining cognitive outcomes in healthy young adults. Further, both static and dynamic functional connectivity studies suggest that there may be differences in patterns of brain-cognition relationships as a function of whether or not the participant is performing a task during the scan. Thus, the goals of the present study were threefold: (1) assess whether dynamic connectivity (neural flexibility) during both resting as well as task-based scans is related to participant age and cognitive performance in a lifespan aging sample, (2) determine whether neural flexibility moderates relationships between age and cognitive performance, and (3) explore differences in neural flexibility between rest and task. Participants in the study were 423 healthy adults between the ages of 20-80 who provided resting state and/or task-based (Matrix Reasoning) functional magnetic resonance imaging (fMRI) scan data as part of their participation in two ongoing studies of cognitive aging. Neural flexibility measures from both resting and task-based scans reflected the number of times each node changed network assignment, and were averaged both across the whole brain (global neural flexibility) as well as within nine somatosensory/cognitive networks. Results showed that neural flexibility during the task was higher in older adults, and that neural flexibility in Default Mode and Visual networks was negatively related to performance on the Matrix Reasoning task. Resting state neural flexibility was not significantly related to either participant age or cognitive performance. Additionally, no neural flexibility measures that significantly moderated relationships between participant age and cognitive outcomes. Further, neural flexibility differed as a function of scan type, with resting state neural flexibility exhibiting significantly more variability than task-based neural flexibility. Thus, neural flexibility measures computed during a cognitive task may be more strongly related to cognitive performance across the adult lifespan, and are more sensitive to the effects of participant age on brain organization.

neuroscience↗