The spatial distribution of coupling between tau and neurodegeneration in amyloid-ß positive mild cognitive impairment.

Synergies between amyloid-ß (Aß), tau, and neurodegeneration persist along the Alzheimer's disease (AD) continuum. This study aimed to evaluate the extent of spatial coupling between tau and neurodegeneration (atrophy) and its relation to Aß positivity in mild cognitive impairment (MCI). Data from 409 participants were included (95 cognitively normal controls, 158 Aß positive (Aß+) MCI, and 156 Aß negative (Aß-) MCI). Florbetapir PET, Flortaucipir PET, and structural MRI were used as biomarkers for Aß, tau and atrophy, respectively. Individual correlation matrices for tau load and atrophy were used to layer a multilayer network, with separate layers for tau and atrophy. A measure of coupling between corresponding regions of interest (ROIs) in the tau and atrophy layers was computed, as a function of Aß positivity. Fewer than 25% of the ROIs across the brain showed heightened coupling between tau and atrophy in Aß+ , relative to Aß- MCI. Coupling strengths in the right rostral middle frontal and right paracentral gyri, in particular, mediated the association between Aß burden and cognition in this sample.

Evidence of Implicit and Explicit Motor Learning during Gait Training with Distorted Rhythmic Auditory Cues.

Gait training with rhythmic auditory cues contains motor learning mechanisms that are weighted more explicitly than implicitly. However, various clinical populations may benefit from a shift to gait training with greater implicit motor learning mechanisms. To investigate the ability to incorporate more implicit-weighted motor learning processes during rhythmic auditory cueing, we attempted to induce error-based recalibration using a subtly varying metronome cue for naïve unimpaired young adults. We assessed the extent of implicit and explicit retention after both an isochronous metronome and subtly varying metronome frequency during treadmill and overground walking. Despite 90% of participants remaining unaware of the changing metronome frequency, participants adjusted their cadence and step length to the subtly changing metronome, both on a treadmill and overground (p < 0.05). However, despite evidence of both implicit and explicit processes involved with each metronome (i.e., isochronous and varying), there were no between-condition differences in implicit or explicit retention for cadence, step length, or gait speed, and thus no increased implicit learning advantage with the addition of error-based recalibration for young, unimpaired adults.

The spatial distribution of coupling between tau and neurodegeneration in amyloid-ß positive mild cognitive impairment.

Synergies between amyloid-ß (Aß), tau, and neurodegeneration persist along the Alzheimer's disease (AD) continuum. This study aimed to evaluate the extent of spatial coupling between tau and neurodegeneration (atrophy) and its relation to Aß positivity in mild cognitive impairment (MCI). Data from 409 subjects were included (95 cognitively normal controls, 158 Aß positive (Aß+) MCI, and 156 Aß negative (Aß-) MCI) Florbetapir PET, Flortaucipir PET, and structural MRI were used as biomarkers for Aß, tau and atrophy, respectively. Individual correlation matrices for tau load and atrophy were used to layer a multilayer network, with separate layers for tau and atrophy. A measure of coupling between corresponding regions of interest/nodes in the tau and atrophy layers was computed, as a function of Aß positivity. The extent to which tau-atrophy coupling mediated associations between Aß burden and cognitive decline was also evaluated. Heightened coupling between tau and atrophy in Aß+ MCI was found primarily in the entorhinal and hippocampal regions (i.e., in regions corresponding to Braak stages I/II), and to a lesser extent in limbic and neocortical regions (i.e., corresponding to later Braak stages). Coupling strengths in the right middle temporal and inferior temporal gyri mediated the association between Aß burden and cognition in this sample. Higher coupling between tau and atrophy in Aß+ MCI is primarily evident in regions corresponding to early Braak stages and relates to overall cognitive decline. Coupling in neocortical regions is more restricted in MCI.

Age-related changes in network controllability are mitigated by redundancy in large-scale brain networks.

The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we used diffusion MRI data from cognitively intact participants (n=480, ages 40-90) to study age-associated changes in the controllability of structural brain networks, features that could mitigate these changes, and the overall effect on cognitive function. We found age-associated declines in controllability in control hubs and large-scale networks, particularly within the and frontoparietal control and default mode networks. Redundancy, quantified via the assessment of multi-step paths within networks, mitigated the effects of changes in topology on network controllability. Lastly, network controllability, redundancy, and grey matter volume each played important complementary roles in cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging.

Elevated integration within the reward network underlies vulnerability to distress.

Distress tolerance (DT), the capability to persist under negative circumstances, underlies a range of psychopathologies. It has been proposed that DT may originate from the activity and connectivity in diverse neural networks integrated by the reward system. To test this hypothesis, we examined the link between DT and integration and segregation in the reward network as derived from resting-state functional connectivity data. DT was measured in 147 participants from a large community sample using the Behavioral Indicator of Resiliency to Distress task. Prior to DT evaluation, participants underwent a resting-state functional magnetic resonance imaging scan. For each participant, we constructed a whole-brain functional connectivity network and calculated the degree of reward network integration and segregation based on the extent to which reward network nodes showed functional connections within and outside their network. We found that distress-intolerant participants demonstrated heightened reward network integration relative to the distress-tolerant participants. In addition, these differences in integration were higher relative to the rest of the brain and, more specifically, the somatomotor network, which has been implicated in impulsive behavior. These findings support the notion that increased integration in large-scale brain networks may constitute a risk for distress intolerance and its psychopathological correlates.

A robust core architecture of functional brain networks supports topological resilience and cognitive performance in middle- and old-aged adults.

Aging is associated with gradual changes in cognition, yet some individuals exhibit protection against age-related cognitive decline. The topological characteristics of brain networks that promote protection against cognitive decline in aging are unknown. Here, we investigated whether the robustness and resilience of brain networks, queried via the delineation of the brain's core network structure, relate to age and cognitive performance in a cross-sectional dataset of healthy middle- and old-aged adults (n = 478, ages 40 to 90 y). First, we decomposed each subject's functional brain network using k-shell decomposition and found that age was negatively associated with robust core network structures. Next, we perturbed these networks, via attack simulations, and found that resilience of core brain network nodes also declined in relationship to age. We then partitioned our dataset into middle- (ages 40 to 65 y, n = 300) and old- (ages 65 to 90 y, n = 178) aged subjects and observed that older individuals had less robust core connectivity and resilience. Following these analyses, we found that episodic memory was positively related to robust connectivity and core resilience, particularly within the default node, limbic, and frontoparietal control networks. Importantly, we found that age-related differences in episodic memory were positively related to core resilience, which indicates a potential role for core resilience in protection against cognitive decline. Together, these findings suggest that robust core connectivity and resilience of brain networks could facilitate high cognitive performance in aging.

Identifying the regional substrates predictive of Alzheimer's disease progression through a convolutional neural network model and occlusion.

Progressive brain atrophy is a key neuropathological hallmark of Alzheimer's disease (AD) dementia. However, atrophy patterns along the progression of AD dementia are diffuse and variable and are often missed by univariate methods. Consequently, identifying the major regional atrophy patterns underlying AD dementia progression is challenging. In the current study, we propose a method that evaluates the degree to which specific regional atrophy patterns are predictive of AD dementia progression, while holding all other atrophy changes constant using a total sample of 334 subjects. We first trained a dense convolutional neural network model to differentiate individuals with mild cognitive impairment (MCI) who progress to AD dementia versus those with a stable MCI diagnosis. Then, we retested the model multiple times, each time occluding different regions of interest (ROIs) from the model's testing set's input. We also validated this approach by occluding ROIs based on Braak's staging scheme. We found that the hippocampus, fusiform, and inferior temporal gyri were the strongest predictors of AD dementia progression, in agreement with established staging models. We also found that occlusion of limbic ROIs defined according to Braak stage III had the largest impact on the performance of the model. Our predictive model reveals the major regional patterns of atrophy predictive of AD dementia progression. These results highlight the potential for early diagnosis and stratification of individuals with prodromal AD dementia based on patterns of cortical atrophy, prior to interventional clinical trials.

Breaking the boundaries of interacting with the human brain using adaptive closed-loop stimulation.

The human brain is arguably one of the most complex systems in nature. To understand how it operates, it is essential to understand the link between neural activity and behavior. Experimental investigation of that link requires tools to interact with neural activity during behavior. Human neuroscience, however, has been severely bottlenecked by the limitations of these tools. While invasive methods can support highly specific interaction with brain activity during behavior, their applicability in human neuroscience is limited. Despite extensive development in the last decades, noninvasive alternatives have lacked spatial specificity and yielded results that are commonly fraught with variability and replicability issues, along with relatively limited understanding of the neural mechanisms involved. Here we provide a comprehensive review of the state-of-the-art in interacting with human brain activity and highlight current limitations and recent efforts to overcome these limitations. Beyond crucial technical and scientific advancements in electromagnetic brain stimulation, new frontiers in interacting with human brain activity such as task-irrelevant sensory stimulation and focal ultrasound stimulation are introduced. Finally, we argue that, along with technological improvements and breakthroughs in noninvasive methods, a paradigm shift towards adaptive closed-loop stimulation will be a critical step for advancing human neuroscience.

Autonomic and Depression Symptoms in Parkinson's Disease: Clinical Evidence for Overlapping Physiology.

BACKGROUND: Autonomic dysfunction and depression are common non-motor symptoms of Parkinson's disease (PD) that confer poorer prognosis. These PD symptoms may have overlapping pathophysiologic underpinnings. OBJECTIVE: To investigate associations between autonomic and depression symptoms in early PD, and their evolution over time. METHODS: We obtained data from the Parkinson's Progression Markers Initiative, a prospective open-access database of early PD. Regression analyses were used to model effects of depression on autonomic symptoms in controls and in PD at baseline, visit 6 (24 months after baseline), and visit 12 (60 months after baseline), correcting for multiple comparisons. RESULTS: Data from 421 people with PD at baseline, 360 at visit 6, 300 at visit 12, and 193 controls were included. When controlling for age, depression, and anti-hypertensive medications, depression predicted autonomic symptoms in all groups. Accounting for motor symptoms did not alter these associations. When comparing groups, the influence of depression on autonomic symptoms was stronger in all PD groups compared to controls, and strongest in PD at visit 12. Depression predicted the presence of orthostatic hypotension only in the PD group at visit 12. CONCLUSION: We demonstrated the important impact of depression on autonomic symptoms in early and middle stages of PD, which are independent of motor symptoms. Though the physiologic basis of these two PD symptoms are not fully understood, our findings add to pathologic evidence of a shared mechanistic substrate, separate from that responsible for PD motor symptoms. These findings may influence clinical management and development of novel therapies.

Model-based stratification of progression along the Alzheimer disease continuum highlights the centrality of biomarker synergies.

BACKGROUND: The progression rates of Alzheimer's disease (AD) are variable and dynamic, yet the mechanisms that contribute to heterogeneity in progression rates remain ill-understood. Particularly, the role of synergies in pathological processes reflected by biomarkers for amyloid-beta ('A'), tau ('T'), and neurodegeneration ('N') in progression along the AD continuum is not fully understood. METHODS: Here, we used a combination of model and data-driven approaches to address this question. Working with a large dataset (N = 321 across the training and testing cohorts), we first applied unsupervised clustering on longitudinal cognitive assessments to divide individuals on the AD continuum into those showing fast vs. moderate decline. Next, we developed a deep learning model that differentiated fast vs. moderate decline using baseline AT(N) biomarkers. RESULTS: Training the model with AT(N) biomarker combination revealed more prognostic utility than any individual biomarkers alone. We additionally found little overlap between the model-driven progression phenotypes and established atrophy-based AD subtypes. Our model showed that the combination of all AT(N) biomarkers had the most prognostic utility in predicting progression along the AD continuum. A comprehensive AT(N) model showed better predictive performance than biomarker pairs (A(N) and T(N)) and individual biomarkers (A, T, or N). CONCLUSIONS: This study combined data and model-driven methods to uncover the role of AT(N) biomarker synergies in the progression of cognitive decline along the AD continuum. The results suggest a synergistic relationship between AT(N) biomarkers in determining this progression, extending previous evidence of A-T synergistic mechanisms.

Differential Role for Hippocampal Subfields in Alzheimer's Disease Progression Revealed with Deep Learning.

Mild cognitive impairment (MCI) is often considered the precursor of Alzheimer's disease. However, MCI is associated with substantially variable progression rates, which are not well understood. Attempts to identify the mechanisms that underlie MCI progression have often focused on the hippocampus but have mostly overlooked its intricate structure and subdivisions. Here, we utilized deep learning to delineate the contribution of hippocampal subfields to MCI progression. We propose a dense convolutional neural network architecture that differentiates stable and progressive MCI based on hippocampal morphometry with an accuracy of 75.85%. A novel implementation of occlusion analysis revealed marked differences in the contribution of hippocampal subfields to the performance of the model, with presubiculum, CA1, subiculum, and molecular layer showing the most central role. Moreover, the analysis reveals that 10.5% of the volume of the hippocampus was redundant in the differentiation between stable and progressive MCI.

Crowdsourcing in Cognitive and Systems Neuroscience.

Behavioral research in cognitive and human systems neuroscience has been largely carried out in-person in laboratory settings. Underpowering and lack of reproducibility due to small sample sizes have weakened conclusions of these investigations. In other disciplines, such as neuroeconomics and social sciences, crowdsourcing has been extensively utilized as a data collection tool, and a means to increase sample sizes. Recent methodological advances allow scientists, for the first time, to test online more complex cognitive, perceptual, and motor tasks. Here we review the nascent literature on the use of online crowdsourcing in cognitive and human systems neuroscience. These investigations take advantage of the ability to reliably track the activity of a participant's computer keyboard, mouse, and eye gaze in the context of large-scale studies online that involve diverse research participant pools. Crowdsourcing allows for testing the generalizability of behavioral hypotheses in real-life environments that are less accessible to lab-designed investigations. Crowdsourcing is further useful when in-laboratory studies are limited, for example during the current COVID-19 pandemic. We also discuss current limitations of crowdsourcing research, and suggest pathways to address them. We conclude that online crowdsourcing is likely to widen the scope and strengthen conclusions of cognitive and human systems neuroscience investigations.

The association between hippocampal volume and memory in pathological aging is mediated by functional redundancy.

Hippocampal neurodegeneration, a primary component of Alzheimer's disease pathology, relates to poor cognition; however, the mechanisms underlying this relationship are not well understood. Using a sample of cognitively normal older adults and individuals with mild cognitive impairment, this study aims to determine the topological properties of functional networks accompanying hippocampal atrophy in aging, along with their association to cognition and clinical progression. We considered two conceptually differing topological properties: redundancy (the existence of alternative channels of functional commutation) and local efficiency (the efficiency of local information exchange). Hippocampal redundancy, but not local efficiency, mediated the association between low hippocampal volume and low memory in both the whole sample and in ß-amyloid positive participants. Additionally, participants with high hippocampal volume, redundancy, and memory clustered separately from those with low values on all three measures, with the latter group showing higher conversion rates to dementia within three years. Together, these results demonstrate that reduced hippocampal redundancy is one mechanism through which hippocampal atrophy associates with memory impairment in healthy and pathological aging.

Intrinsic Functional Connectivity of the Anterior Cingulate Cortex Is Associated with Tolerance to Distress.

The ability to adapt under significant adversity, defined as psychological resilience, is instrumental in preventing stress-related disorders. An important aspect of resilience is the capacity to endure affective distress when in pursuit of goals, also known as distress tolerance. Evidence that links intrinsic baseline interactions within large-scale functional networks with performance under distress remains missing. We hypothesized that the anterior cingulate cortex (ACC) may engage in distress tolerance because of its involvement in attention and emotion regulation. Accordingly, we tested whether behavioral performance under distress is associated with baseline resting-state ACC functional connectivity (FC). Distress tolerance was measured in 97 participants using the behavioral indicator of resiliency to distress (BIRD) task. Analyses contrasted participants who quit the task before its designated termination (n = 51) with those who persisted throughout it (n = 46). Seed-based FC analysis indicated greater connectivity between the ACC and dorsolateral prefrontal cortex (DLPFC) in subjects who persisted throughout the task, along with greater FC between the ACC and the precentral gyrus in those who quit before its termination. The results shed light on the mechanisms underlying interindividual differences in the ability to handle distress.

Frontoparietal network resilience is associated with protection against cognitive decline in Parkinson's disease.

Though Parkinson's disease is primarily defined as a movement disorder, it is also characterized by a range of non-motor symptoms, including cognitive decline. The onset and progression of cognitive decline in individuals with Parkinson's disease is variable, and the neurobiological mechanisms that contribute to, or protect against, cognitive decline in Parkinson's disease are poorly understood. Using resting-state functional magnetic resonance imaging data collected from individuals with Parkinson's disease with and without cognitive decline, we examined the relationship between topological brain-network resilience and cognition in Parkinson's disease. By leveraging network attack analyses, we demonstrate that relative to individuals with Parkinson's disease experiencing cognitive decline, the frontoparietal network in cognitively stable individuals with Parkinson's disease is significantly more resilient to network perturbation. Our findings suggest that the topological robustness of the frontoparietal network is associated with the absence of cognitive decline in individuals with Parkinson's disease.

Autonomic disorders in Parkinson disease: Disrupted hypothalamic connectivity as revealed from resting-state functional magnetic resonance imaging.

Converging evidence from diverse methodologies implicate the hypothalamus in the pathophysiology of Parkinson's disease (PD). Pathology in the hypothalamus and in hypothalamic pathways has been linked primarily to autonomic dysfunction, routinely experienced by individuals with PD throughout the course of the disease, sometimes predating onset of motor symptoms. Postmortem and molecular imaging studies have delineated pathologic changes in the hypothalamus and demonstrated alterations in neurotransmitter systems within this structure and associated pathways, which track the progression of the disease. More recently, functional interactions between the hypothalamus, thalamus, and striatum, as assessed using resting-state functional magnetic resonance imaging, were shown to be reduced in PD patients with high in comparison to those with low autonomic symptom burden. These functional changes may relate to micro- and macrostructural alterations which are also observed in PD. An examination of the hypothalamus and hypothalamic pathways can also shed light on atypical parkinsonian disorders and their distinct pathophysiologic characteristics relative to idiopathic PD. Altogether, the current state of knowledge on the involvement of the hypothalamus in PD is profound, yet emerging methodological advances are likely to move our understanding of hypothalamic pathology in PD significantly forward.

Accrual of functional redundancy along the lifespan and its effects on cognition.

Despite the necessity to understand how the brain endures the initial stages of age-associated cognitive decline, no brain mechanism has been quantitatively specified to date. The brain may withstand the effects of cognitive aging through redundancy, a design feature in engineered and biological systems, which entails the presence of substitute elements to protect it against failure. Here, we investigated the relationship between functional network redundancy and age over the human lifespan and their interaction with cognition, analyzing resting-state functional MRI images and cognitive measures from 579 subjects. Network-wide redundancy was significantly associated with age, showing a stronger link with age than other major topological measures, presenting a pattern of accumulation followed by old-age decline. Critically, redundancy significantly mediated the association between age and executive function, with lower anti-correlation between age and cognition in subjects with high redundancy. The results suggest that functional redundancy accrues throughout the lifespan, mitigating the effects of age on cognition.

Lower functional hippocampal redundancy in mild cognitive impairment.

With an increasing prevalence of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in response to an aging population, it is critical to identify and understand neuroprotective mechanisms against cognitive decline. One potential mechanism is redundancy: the existence of duplicate elements within a system that provide alternative functionality in case of failure. As the hippocampus is one of the earliest sites affected by AD pathology, we hypothesized that functional hippocampal redundancy is protective against cognitive decline. We compared hippocampal functional redundancy derived from resting-state functional MRI networks in cognitively normal older adults, with individuals with early and late MCI, as well as the relationship between redundancy and cognition. Posterior hippocampal redundancy was reduced between cognitively normal and MCI groups, plateauing across early and late MCI. Higher hippocampal redundancy was related to better memory performance only for cognitively normal individuals. Critically, functional hippocampal redundancy did not come at the expense of network efficiency. Our results provide support that hippocampal redundancy protects against cognitive decline in aging.

Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns.

Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date, subtyping of MCI has been based primarily on cognitive measures, often resulting in indistinct boundaries between subgroups and limited validity. Here, we introduce a subtyping method for MCI based solely upon brain atrophy. We train a deep learning model to differentiate between Alzheimer's disease (AD) and cognitively normal (CN) subjects based on whole-brain MRI features. We then deploy the trained model to classify MCI subjects based on whole-brain gray matter resemblance to AD-like or CN-like patterns. We subsequently validate the subtyping approach using cognitive, clinical, fluid biomarker, and molecular imaging data. Overall, the results suggest that atrophy patterns in MCI are sufficiently heterogeneous and can thus be used to subtype individuals into biologically and clinically meaningful subgroups.

Explaining Individual Differences in Motor Behavior by Intrinsic Functional Connectivity and Corticospinal Excitability.

Motor performance varies substantially between individuals. This variance is rooted in individuals' innate motor abilities, and should thus have a neural signature underlying these differences in behavior. Could these individual differences be detectable with neural measurements acquired at rest? Here, we tested the hypothesis that motor performance can be predicted by resting motor-system functional connectivity and motor-evoked-potentials (MEPs) induced by non-invasive brain stimulation. Twenty healthy right handed subjects performed structural and resting-state fMRI scans. On a separate day, MEPs were measured using transcranial magnetic stimulation (TMS) over the contrateral primary motor cortex (M1). At the end of the session, participants performed a finger-tapping task using their left non-dominant hand. Resting-state functional connectivity between the contralateral M1 and the supplementary motor area (SMA) predicted motor task performance, indicating that individuals with stronger resting M1-SMA functional connectivity exhibit better motor performance. This prediction was neither improved nor reduced by the addition of corticospinal excitability to the model. These results confirm that motor behavior can be predicted from neural measurements acquired prior to task performance, primarily relying on resting functional connectivity rather than corticospinal excitability. The ability to predict motor performance from resting neural markers, provides an opportunity to identify the extent of successful rehabilitation following neurological damage.