Task-evoked Brain Activity Research Articles

Acute psychosocial stress modulates neural and behavioral substrates of cognitive control.

Acute psychosocial stress affects learning, memory, and attention, but the evidence for the influence of stress on the neural processes supporting cognitive control remains mixed. We investigated how acute psychosocial stress influences performance and neural processing during the Go/NoGo task-an established cognitive control task. The experimental group underwent the Trier Social Stress Test (TSST) acute stress induction, whereas the control group completed personality questionnaires. Then, participants completed a functional magnetic resonance imaging (fMRI) Go/NoGo task, with self-report, blood pressure and salivary cortisol measurements of induced stress taken intermittently throughout the experimental session. The TSST was successful in eliciting a stress response, as indicated by significant Stress > Control between-group differences in subjective stress ratings and systolic blood pressure. We did not identify significant differences in cortisol levels, however. The stress induction also impacted subsequent Go/NoGo task performance, with participants who underwent the TSST making fewer commission errors on trials requiring the most inhibitory control (NoGo Green) relative to the control group, suggesting increased vigilance. Univariate analysis of fMRI task-evoked brain activity revealed no differences between stress and control groups for any region. However, using multivariate pattern analysis, stress and control groups were reliably differentiated by activation patterns contrasting the most demanding NoGo trials (i.e., NoGo Green trials) versus baseline in the medial intraparietal area (mIPA, affiliated with the dorsal attention network) and subregions of the cerebellum (affiliated with the default mode network). These results align with prior reports linking the mIPA and the cerebellum to visuomotor coordination, a function central to cognitive control processes underlying goal-directed behavior. This suggests that stressor-induced hypervigilance may produce a facilitative effect on response inhibition which is represented neurally by the activation patterns of cognitive control regions.

A precision neuroscience approach to estimating reliability of neural responses during emotion processing: Implications for task-fMRI

Recent work demonstrating low test-retest reliability of neural activation during fMRI tasks raises questions about the utility of task-based fMRI for the study of individual variation in brain function. Two possible sources of the instability in task-based BOLD signal over time are noise or measurement error in the instrument, and meaningful variation across time within-individuals in the construct itself—brain activation elicited during fMRI tasks. Examining the contribution of these two sources of test-retest unreliability in task-evoked brain activity has far-reaching implications for cognitive neuroscience. If test-retest reliability largely reflects measurement error, it suggests that task-based fMRI has little utility in the study of either inter- or intra-individual differences. On the other hand, if task-evoked BOLD signal varies meaningfully over time, it would suggest that this tool may yet be well suited to studying intraindividual variation. We parse these sources of variance in BOLD signal in response to emotional cues over time and within-individuals in a longitudinal sample with 10 monthly fMRI scans. Test-retest reliability was low, reflecting a lack of stability in between-person differences across scans. In contrast, within-person, within-session internal consistency of the BOLD signal was higher, and within-person fluctuations across sessions explained almost half the variance in voxel-level neural responses. Additionally, monthly fluctuations in neural response to emotional cues were associated with intraindividual variation in mood, sleep, and exposure to stressors. Rather than reflecting trait-like differences across people, neural responses to emotional cues may be more reflective of intraindividual variation over time. These patterns suggest that task-based fMRI may be able to contribute to the study of individual variation in brain function if more attention is given to within-individual variation approaches, psychometrics—beginning with improving reliability beyond the modest estimates observed here, and the validity of task fMRI beyond the suggestive associations reported here.

Ketamine’s acute effects on negative brain states are mediated through distinct altered states of consciousness in humans

Ketamine commonly and rapidly induces dissociative and other altered states of consciousness (ASCs) in humans. However, the neural mechanisms that contribute to these experiences remain unknown. We used functional neuroimaging to engage key regions of the brain’s affective circuits during acute ketamine-induced ASCs within a randomized, multi-modal, placebo-controlled design examining placebo, 0.05 mg/kg ketamine, and 0.5 mg/kg ketamine in nonclinical adult participants (NCT03475277). Licensed clinicians monitored infusions for safety. Linear mixed effects models, analysis of variance, t-tests, and mediation models were used for statistical analyses. Our design enabled us to test our pre-specified primary and secondary endpoints, which were met: effects of ketamine across dose conditions on (1) emotional task-evoked brain activity, and (2) sub-components of dissociation and other ASCs. With this design, we also could disentangle which ketamine-induced affective brain states are dependent upon specific aspects of ASCs. Differently valenced ketamine-induced ASCs mediated opposing effects on right anterior insula activity. Participants experiencing relatively higher depersonalization induced by 0.5 mg/kg of ketamine showed relief from negative brain states (reduced task-evoked right anterior insula activity, 0.39 SD). In contrast, participants experiencing dissociative amnesia showed an exacerbation of insula activity (0.32 SD). These results in nonclinical participants may shed light on the mechanisms by which specific dissociative states predict response to ketamine in depressed individuals.

Revisiting deficits in threat and safety appraisal in obsessive-compulsive disorder.

Current behavioural treatment of obsessive-compulsive disorder (OCD) is informed by fear conditioning and involves iteratively re-evaluating previously threatening stimuli as safe. However, there is limited research investigating the neurobiological response to conditioning and reversal of threatening stimuli in individuals with OCD. A clinical sample of individuals with OCD (N = 45) and matched healthy controls (N = 45) underwent functional magnetic resonance imaging. While in the scanner, participants completed a well-validated fear reversal task and a resting-state scan. We found no evidence for group differences in task-evoked brain activation or functional connectivity in OCD. Multivariate analyses encompassing all participants in the clinical and control groups suggested that subjective appraisal of threatening and safe stimuli were associated with a larger difference in brain activity than the contribution of OCD symptoms. In particular, we observed a brain-behaviour continuum whereby heightened affective appraisal was related to increased bilateral insula activation during the task (r = 0.39, pFWE = .001). These findings suggest that changes in conditioned threat-related processes may not be a core neurobiological feature of OCD and encourage further research on the role of subjective experience in fear conditioning.

Generalizing prediction of task-evoked brain activity across datasets and populations

Predictions of task-based functional magnetic resonance imaging (fMRI) from task-free resting-state (rs) fMRI have gained popularity over the past decade. This method holds a great promise for studying individual variability in brain function without the need to perform highly demanding tasks. However, in order to be broadly used, prediction models must prove to generalize beyond the dataset they were trained on. In this work, we test the generalizability of prediction of task-fMRI from rs-fMRI across sites, MRI vendors and age-groups. Moreover, we investigate the data requirements for successful prediction. We use the Human Connectome Project (HCP) dataset to explore how different combinations of training sample sizes and number of fMRI datapoints affect prediction success in various cognitive tasks. We then apply models trained on HCP data to predict brain activations in data from a different site, a different MRI vendor (Phillips vs. Siemens scanners) and a different age group (children from the HCP-development project). We demonstrate that, depending on the task, a training set of approximately 20 participants with 100 fMRI timepoints each yields the largest gain in model performance. Nevertheless, further increasing sample size and number of timepoints results in significantly improved predictions, until reaching approximately 450–600 training participants and 800–1000 timepoints. Overall, the number of fMRI timepoints influences prediction success more than the sample size. We further show that models trained on adequate amounts of data successfully generalize across sites, vendors and age groups and provide predictions that are both accurate and individual-specific. These findings suggest that large-scale publicly available datasets may be utilized to study brain function in smaller, unique samples.

Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks.

Functional near-infrared spectroscopy (fNIRS) is a safe and non-invasive optical imaging technique that is being increasingly used in brain-computer interfaces (BCIs) to recognize mental tasks. Unlike electroencephalography (EEG) which directly measures neural activation, fNIRS signals reflect neurovascular-coupling inducing hemodynamic response that can be slow in time and varying in the pattern. The established classifiers extend the EEG-ones by mostly employing the feature based supervised models such as the support vector machine (SVM) and linear discriminant analysis (LDA), and fail to timely characterize the level-sensitive hemodynamic pattern. A dedicated classifier is desired for intentional activity recognition of fNIRS-BCI, including the adaptive acquisition of response relevant features and accurate discrimination of implied ideas. To this end, we herein propose a specifically-designed joint adaptive classification method that combines a Kalman filtering (KF) for robust level extraction and an adaptive Gaussian mixture model (a-GMM) for enhanced pattern recognition. The simulative investigations and paradigm experiments have shown that the proposed KF/a-GMM classification method can effectively track the random variations of task-evoked brain activation patterns, and improve the accuracy of single-trial classification task of mental arithmetic vs. mental singing, as compared to the conventional methods, e.g., those that employ combinations of the band-pass filtering (BPF) based feature extractors (mean, slope, and variance, etc.) and the classical recognizers (GMM, SVM, and LDA). The proposed approach paves a promising way for developing the real-time fNIRS-BCI technique.

Default mode network-associated intrinsic connectivity relates to individual learnability differences in errorless and trial-and-error learning

The intrinsic functional network architecture accounts for task-evoked brain activity changes and variabilities in cognitive performance. Relationships between the intrinsic functional network architecture and task performance or learning ability have been previously reported. However, the relationships between learning benefits and the characteristics of intrinsic functional network architecture for different types of learning methods remain unclear. In this study, we used graph theoretical analysis to examine the relationships between intrinsic functional network connectivity and learning benefits in two well-known learning methods in the field of cognitive rehabilitation—errorless learning (EL learning) and trial-and-error learning (T&E learning). We focused on the default mode network (DMN) as a task-relevant network, which can differentiate between EL and T&E learning and was found to be more important for T&E learning in a previous study. Participants performed a color–name association task with both learning methods. The graph metrics used were within-network connectivity and efficiency for the DMN. Within-DMN connectivity and DMN efficiency showed a significantly weak positive correlation with T&E scores but not with EL scores. These findings show that the intrinsic integration strength within the DMN relates to individuals’ learnability through the T&E method.

Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner

Modelling and predicting individual differences in task-fMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble learner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of task-fMRI scans, suggesting that it has potential to supplement traditional task localisers.

Divergences between resting state networks and meta-analytic maps of task-evoked brain activity

Divergences between resting state networks and meta-analytic maps of task-evoked brain activity

Looking for “fNIRS Signature” in Autism Spectrum: A Systematic Review Starting From Preschoolers

Accumulating evidence suggests that functional Near-Infrared Spectroscopy (fNIRS) can provide an essential bridge between our current understanding of neural circuit organization and cortical activity in the developing brain. Indeed, fNIRS allows studying brain functions through the measurement of neurovascular coupling that links neural activity to subsequent changes in cerebral blood flow and hemoglobin oxygenation levels. While the literature offers a multitude of fNIRS applications to typical development, only recently this tool has been extended to the study of neurodevelopmental disorders (NDDs). The exponential rise of scientific publications on this topic during the last years reflects the interest to identify a “fNIRS signature” as a biomarker of high translational value to support both early clinical diagnosis and treatment outcome. The purpose of this systematic review is to describe the updating clinical applications of fNIRS in NDDs, with a specific focus on preschool population. Starting from this rationale, a systematic search was conducted for relevant studies in different scientific databases (Pubmed, Scopus, and Web of Science) resulting in 13 published articles. In these studies, fNIRS was applied in individuals with Autism Spectrum Disorder (ASD) or infants at high risk of developing ASD. Both functional connectivity in resting-state conditions and task-evoked brain activation using multiple experimental paradigms were used in the selected investigations, suggesting that fNIRS might be considered a promising method for identifying early quantitative biomarkers in the autism field.

The Predictive Value of Individual Electric Field Modeling for Transcranial Alternating Current Stimulation Induced Brain Modulation.

There is considerable individual variability in the reported effectiveness of non-invasive brain stimulation. This variability has often been ascribed to differences in the neuroanatomy and resulting differences in the induced electric field inside the brain. In this study, we addressed the question whether individual differences in the induced electric field can predict the neurophysiological and behavioral consequences of gamma band tACS. In a within-subject experiment, bi-hemispheric gamma band tACS and sham stimulation was applied in alternating blocks to the participants’ superior temporal lobe, while task-evoked auditory brain activity was measured with concurrent functional magnetic resonance imaging (fMRI) and a dichotic listening task. Gamma tACS was applied with different interhemispheric phase lags. In a recent study, we could show that anti-phase tACS (180° interhemispheric phase lag), but not in-phase tACS (0° interhemispheric phase lag), selectively modulates interhemispheric brain connectivity. Using a T1 structural image of each participant’s brain, an individual simulation of the induced electric field was computed. From these simulations, we derived two predictor variables: maximal strength (average of the 10,000 voxels with largest electric field values) and precision of the electric field (spatial correlation between the electric field and the task evoked brain activity during sham stimulation). We found considerable variability in the individual strength and precision of the electric fields. Importantly, the strength of the electric field over the right hemisphere predicted individual differences of tACS induced brain connectivity changes. Moreover, we found in both hemispheres a statistical trend for the effect of electric field strength on tACS induced BOLD signal changes. In contrast, the precision of the electric field did not predict any neurophysiological measure. Further, neither strength, nor precision predicted interhemispheric integration. In conclusion, we found evidence for the dose-response relationship between individual differences in electric fields and tACS induced activity and connectivity changes in concurrent fMRI. However, the fact that this relationship was stronger in the right hemisphere suggests that the relationship between the electric field parameters, neurophysiology, and behavior may be more complex for bi-hemispheric tACS.

Behavioural and neuroplastic effects of a double-blind randomised controlled balance exercise trial in people with Parkinson\u2019s disease

Balance dysfunction is a disabling symptom in people with Parkinson’s disease (PD). Evidence suggests that exercise can improve balance performance and induce neuroplastic effects. We hypothesised that a 10-week balance intervention (HiBalance) would improve balance, other motor and cognitive symptoms, and alter task-evoked brain activity in people with PD. We performed a double-blind randomised controlled trial (RCT) where 95 participants with PD were randomised to either HiBalance (n = 48) or a control group (n = 47). We found no significant group by time effect on balance performance (b = 0.4 95% CI [−1, 1.9], p = 0.57) or on our secondary outcomes, including the measures of task-evoked brain activity. The findings of this well-powered, double-blind RCT contrast previous studies of the HiBalance programme but are congruent with other double-blind RCTs of physical exercise in PD. The divergent results raise important questions on how to optimise physical exercise interventions for people with PD.Preregistration clinicaltrials.gov: NCT03213873.

Enhancing task fMRI preprocessing via individualized model‐based filtering of intrinsic activity dynamics

Brain responses recorded during fMRI are thought to reflect both rapid, stimulus-evoked activity and the propagation of spontaneous activity through brain networks. In the current work, we describe a method to improve the estimation of task-evoked brain activity by first “filtering-out the intrinsic propagation of pre-event activity from the BOLD signal. We do so using Mesoscale Individualized NeuroDynamic (MINDy; Singh et al. 2020b) models built from individualized resting-state data to subtract the propagation of spontaneous activity from the task-fMRI signal (MINDy-based Filtering). After filtering, time-series are analyzed using conventional techniques. Results demonstrate that this simple operation significantly improves the statistical power and temporal precision of estimated group-level effects. Moreover, use of MINDy-based filtering increased the similarity of neural activation profiles and prediction accuracy of individual differences in behavior across tasks measuring the same construct (cognitive control). Thus, by subtracting the propagation of previous activity, we obtain better estimates of task-related neural effects.

Predicting individual variability in task-evoked brain activity in schizophrenia.

What goes wrong in a schizophrenia patient's brain that makes it so different from a healthy brain? In this study, we tested the hypothesis that the abnormal brain activity in schizophrenia is tightly related to alterations in brain connectivity. Using functional magnetic resonance imaging (fMRI), we demonstrated that both resting‐state functional connectivity and brain activity during the well‐validated N‐back task differed significantly between schizophrenia patients and healthy controls. Nevertheless, using a machine‐learning approach we were able to use resting‐state functional connectivity measures extracted from healthy controls to accurately predict individual variability in the task‐evoked brain activation in the schizophrenia patients. The predictions were highly accurate, sensitive, and specific, offering novel insights regarding the strong coupling between brain connectivity and activity in schizophrenia. On a practical perspective, these findings may allow to generate task activity maps for clinical populations without the need to actually perform any tasks, thereby reducing patients inconvenience while saving time and money.

Repetitive Transcranial Magnetic Stimulation (rTMS) Alters Variability in Brain Function in Schizophrenia: Data From a Double-Blind, Randomized, Sham-Controlled Trial

Recent evidence suggests that repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) in schizophrenia improves working memory (WM). To confirm pilot findings and understand mechanisms of efficacy of rTMS, we conducted a four-week RCT and recently reported rTMS-induced increases in DLPFC thickness. Part of this trial examined changes in WM task-evoked brain activity.

Functional Ultrasound (fUS) During Awake Brain Surgery: The Clinical Potential of Intra-Operative Functional and Vascular Brain Mapping.

Background and PurposeOncological neurosurgery relies heavily on making continuous, intra-operative tumor-brain delineations based on image-guidance. Limitations of currently available imaging techniques call for the development of real-time image-guided resection tools, which allow for reliable functional and anatomical information in an intra-operative setting. Functional ultrasound (fUS), is a new mobile neuro-imaging tool with unprecedented spatiotemporal resolution, which allows for the detection of small changes in blood dynamics that reflect changes in metabolic activity of activated neurons through neurovascular coupling. We have applied fUS during conventional awake brain surgery to determine its clinical potential for both intra-operative functional and vascular brain mapping, with the ultimate aim of achieving maximum safe tumor resection.MethodsDuring awake brain surgery, fUS was used to image tumor vasculature and task-evoked brain activation with electrocortical stimulation mapping (ESM) as a gold standard. For functional imaging, patients were presented with motor, language or visual tasks, while the probe was placed over (ESM-defined) functional brain areas. For tumor vascular imaging, tumor tissue (pre-resection) and tumor resection cavity (post-resection) were imaged by moving the hand-held probe along a continuous trajectory over the regions of interest.ResultsA total of 10 patients were included, with predominantly intra-parenchymal frontal and temporal lobe tumors of both low and higher histopathological grades. fUS was able to detect (ESM-defined) functional areas deep inside the brain for a range of functional tasks including language processing. Brain tissue could be imaged at a spatial and temporal resolution of 300 μm and 1.5–2.0 ms respectively, revealing real-time tumor-specific, and healthy vascular characteristics.ConclusionThe current study presents the potential of applying fUS during awake brain surgery. We illustrate the relevance of fUS for awake brain surgery based on its ability to capture both task-evoked functional cortical responses as well as differences in vascular characteristics between tumor and healthy tissue. As current neurosurgical practice is still pre-dominantly leaning on inherently limited pre-operative imaging techniques for tumor resection-guidance, fUS enters the scene as a promising alternative that is both anatomically and physiologically informative.

Effects of Exercise Modes on Neural Processing of Working Memory in Late Middle-Aged Adults: An fMRI Study.

Recent studies have highlighted the importance of regular exercise on cognitive function in aging populations, with aerobic exercise and cardiovascular fitness having received the largest amount of research attention. However, the relationship between exercise mode and cognitive function underlying behavioral modification and neural activation remains unknown. The present study, therefore, sought to examine the associations between different exercise modes and the working memory (WM) aspect of executive function as well as its task-evoked brain activation in the late middle-aged population. Seventy late middle-aged adults were classified into open-skill, closed-skill, or irregular exercise groups based on their participation in exercise activities prior to the study and then performed a spatial working memory (SWM) task while undergoing functional magnetic resonance imaging (fMRI) scanning. The results revealed that exercise groups, regardless of exercise modes, showed better SWM and physical fitness performance. Additionally, the open-skill group exhibited greater brain activation in the prefrontal lobe, anterior cingulate cortex/supplementary motor area (ACC/SMA), and hippocampus than those in the closed-skill group, suggesting a mode-sensitive compensatory mechanism in late middle-aged adults. These findings indicate that exercise promotes cognitive health, improves WM, and enhances neurocognitive scaffolding in late middle-aged adults and further suggest that various exercise modes can effectively modulate frontal and hippocampal function in the face of age-related neurocognitive declines, implications that may inform the development of exercise programs for the elderly.

Simultaneous task-based BOLD-fMRI and [18-F] FDG functional PET for measurement of neuronal metabolism in the human visual cortex

Studies of task-evoked brain activity are the cornerstone of cognitive neuroscience, and unravel the spatial and temporal brain dynamics of cognition in health and disease. Blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI) is one of the most common methods of studying brain function in humans. BOLD-fMRI indirectly infers neuronal activity from regional changes in blood oxygenation and is not a quantitative metric of brain function. Regional variation in glucose metabolism, measured using [18-F] fluorodeoxyglucose positron emission tomography (FDG-PET), provides a more direct and interpretable measure of neuronal activity. However, while the temporal resolution of BOLD-fMRI is in the order of seconds, standard FDG-PET protocols provide a static snapshot of glucose metabolism. Here, we develop a novel experimental design for measurement of task-evoked changes in regional blood oxygenation and glucose metabolism with high temporal resolution. Over a 90-min simultaneous BOLD-fMRI/FDG-PET scan, [18F] FDG was constantly infused to 10 healthy volunteers, who viewed a flickering checkerboard presented in a hierarchical block design. Dynamic task-related changes in blood oxygenation and glucose metabolism were examined with temporal resolution of 2.5sec and 1-min, respectively. Task-related, temporally coherent brain networks of haemodynamic and metabolic connectivity were jointly coupled in the visual cortex, as expected. Results demonstrate that the hierarchical block design, together with the infusion FDG-PET technique, enabled both modalities to track task-related neural responses with high temporal resolution. The simultaneous MR-PET approach has the potential to provide unique insights into the dynamic haemodynamic and metabolic interactions that underlie cognition in health and disease.

Increase of frontal neuronal activity in chronic neglect after training in virtual reality.

A third of patients with stroke acquire spatial neglect associated with poor rehabilitation outcome. New effective rehabilitation interventions are needed. Scanning training combined with multisensory stimulation to enhance the rehabilitation effect is suggested. In accordance, we have designed a virtual-reality based scanning training that combines visual, audio and sensori-motor stimulation called RehAtt® . Effects were shown in behavioural tests and activity of daily living. Here, we use fMRI to evaluate the change in brain activity during Posner's Cuing Task (attention task) after RehAtt® intervention, in patients with chronic neglect. Twelve patients (mean age=72.7years, SD=6.1) with chronic neglect (persistent symptoms >6months) performed the interventions 3 times/wk during 5weeks, in total 15hours. Training effects on brain activity were evaluated using fMRI task-evoked responses during the Posner's cuing task before and after the intervention. Patients improved their performance in the Posner fMRI task. In addition, patients increased their task-evoked brain activity after the VR interventions in an extended network including pre-frontal and temporal cortex during attentional cueing, but showed no training effects during target presentations. The current pilot study demonstrates that a novel multisensory VR intervention has the potential to benefit patients with chronic neglect in respect of behaviour and brain changes. Specifically, the fMRI results show that strategic processes (top-down control during attentional cuing) were enhanced by the intervention. The findings increase knowledge of the plasticity processes underlying positive rehabilitation effects from RehAtt® in chronic neglect.

Multiple pathways of reserve simultaneously present in cognitively normal older adults.

To examine neural correlates of intellectual activity underlying multiple pathways imparting reserve by testing that higher intellectual activity is associated with lower brain amyloid pathology, greater gray matter (GM) volume, and differential task-evoked brain activation levels as a function of amyloid positivity status among clinically intact older adults. Eighty-two cognitively normal older adults and 46 healthy young participants underwent fMRI during task switching. All older participants completed 18F-florbetaben-PET and an individual's amyloid positivity status was determined. To assess GM volume, T1-weighted high-resolution structural images were processed using voxel-based morphometry. As lifestyle factors, intellectual activity was estimated by a composite score of vocabulary, reading ability, and years of education. Across all older participants, intellectual activity was associated with lower amyloid deposition in lateral and medial frontoparietal and temporal lobes but higher amyloid deposition in superior frontal and parietal cortices, larger GM volume across widespread brain regions, and reduced brain activation during task switching. These patterns of associations, however, differed by amyloid positivity status. While the patterns of associations remained similar among amyloid-negative older adults, among amyloid-positive older adults, intellectual activity was associated with increased amyloid deposition in frontoparietal cortices and increased activation during task. Intellectual activity simultaneously exerts both neuroprotective and compensatory effects via multiple neural pathways that promote optimal brain aging and help maintain normal cognition during amyloid accumulation.