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.

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.

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.

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.

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.

Longitudinal change in autonomic symptoms predicts activities of daily living and depression in Parkinson's disease.

PURPOSE: The primary objective of this study was to examine the relationship of longitudinal changes in autonomic symptom burden and longitudinal changes in activities of daily living (ADLs); a secondary analysis examined the impact of depressive symptoms in this relationship. METHODS: Data were retrieved from the Parkinson's Progression Markers Initiative (PPMI), a dataset documenting the natural history of newly diagnosed Parkinson's disease (PD). The analysis focused on data from baseline, visit 6 (24 months after enrollment), and visit 12 (60 months after enrollment). The impact of longitudinal changes in autonomic symptom burden on longitudinal changes in ADLs function was examined. A secondary mediation analysis was performed to investigate whether longitudinal changes in depressive symptoms mediate the relationship between longitudinal changes in autonomic symptom burden and ADLs function. RESULTS: Changes in autonomic symptom burden, cognitive function, depressive symptoms, and motor function all correlated with ADLs. Only changes in ADLs and depression were found to be associated with changes in autonomic symptom burden. We found that longitudinal change in autonomic symptoms was a significant predictor of change in ADLs at 24 and 60 months after enrollment, with the cardiovascular subscore being a major driver of this association. Mediation analysis revealed that the association between autonomic symptoms and ADLs is partially mediated by depressive symptoms. CONCLUSIONS: Longitudinal changes in autonomic symptoms impact ADLs function in patients with early signs of PD, both directly and indirectly through their impact on depressive symptoms. Future investigation into the influence of treatment of these symptoms on outcomes in PD is warranted.

Functional neuroimaging of the central autonomic network: recent developments and clinical implications.

PURPOSE: The central autonomic network (CAN) is an intricate system of brainstem, subcortical, and cortical structures that play key roles in the function of the autonomic nervous system. Prior to the advent of functional neuroimaging, in vivo studies of the human CAN were limited. The purpose of this review is to highlight the contribution of functional neuroimaging, specifically functional magnetic resonance imaging (fMRI), to the study of the CAN, and to discuss recent advances in this area. Additionally, we aim to emphasize exciting areas for future research. METHODS: We reviewed the existing literature in functional neuroimaging of the CAN. Here, we focus on fMRI research conducted in healthy human subjects, as well as research that has been done in disease states, to understand CAN function. To minimize confounding, papers examining CAN function in the context of cognition, emotion, pain, and affective disorders were excluded. RESULTS: fMRI has led to significant advances in the understanding of human CAN function. The CAN is composed of widespread brainstem and forebrain structures that are intricately connected and play key roles in reflexive and modulatory control of autonomic function. CONCLUSIONS: fMRI technology has contributed extensively to current knowledge of CAN function. It holds promise to serve as a biomarker in disease states. With ongoing advancements in fMRI technology, there is great opportunity and need for future research involving the CAN.

Neuromodulation of reinforced skill learning reveals the causal function of prefrontal cortex.

Accumulating evidence has suggested functional interactions between prefrontal cortex (PFC) and dissociable large-scale networks. However, how these networks interact in the human brain to enable complex behaviors is not well-understood. Here, using a combination of behavioral, brain stimulation and neuroimaging paradigms, we tested the hypothesis that human PFC is required for successful reinforced skill formation. We additionally tested the extent to which PFC-dependent skill formation is related to intrinsic functional communication with this region. We report that inhibitory noninvasive transcranial magnetic stimulation over lateral PFC, a hub region with a diverse connectivity profile, causally modulated effective reinforcement-based motor skill acquisition. Furthermore, PFC-dependent skill formation was strongly related to the strength of functional connectivity between the PFC and regions in the sensorimotor network. These results point to the involvement of lateral PFC in the neural architecture that underlies the acquisition of complex skills, and suggest that, in relation to skill acquisition, this region may be involved in functional interactions with sensorimotor networks.

Preferential encoding of movement amplitude and speed in the primary motor cortex and cerebellum.

Voluntary movements require control of multiple kinematic parameters, a task carried out by a distributed brain architecture. However, it remains unclear whether regions along the motor system encode single, or rather a mixture of, kinematic parameters during action execution. Here, rapid event-related functional magnetic resonance imaging was used to differentiate brain activity along the motor system during the encoding of movement amplitude, duration, and speed. We present cumulative evidence supporting preferential encoding of kinematic parameters along the motor system, based on blood-oxygenation-level dependent signal recorded in a well-controlled single-joint wrist-flexion task. Whereas activity in the left primary motor cortex (M1) showed preferential encoding of movement amplitude, the anterior lobe of the right cerebellum (primarily lobule V) showed preferential encoding of movement speed. Conversely, activity in the left supplementary motor area (SMA), basal ganglia (putamen), and anterior intraparietal sulcus was not preferentially modulated by any specific parameter. We found no preference in peak activation for duration encoding in any of the tested regions. Electromyographic data was mainly modulated by movement amplitude, restricting the distinction between amplitude and muscle force encoding. Together, these results suggest that during single-joint movements, distinct kinematic parameters are controlled by largely distinct brain-regions that work together to produce and control precise movements. Hum Brain Mapp 38:5970-5986, 2017. © 2017 Wiley Periodicals, Inc.

The Default Mode Network Differentiates Biological From Non-Biological Motion.

The default mode network (DMN) has been implicated in an array of social-cognitive functions, including self-referential processing, theory of mind, and mentalizing. Yet, the properties of the external stimuli that elicit DMN activity in relation to these domains remain unknown. Previous studies suggested that motion kinematics is utilized by the brain for social-cognitive processing. Here, we used functional MRI to examine whether the DMN is sensitive to parametric manipulations of observed motion kinematics. Preferential responses within core DMN structures differentiating non-biological from biological kinematics were observed for the motion of a realistically looking, human-like avatar, but not for an abstract object devoid of human form. Differences in connectivity patterns during the observation of biological versus non-biological kinematics were additionally observed. Finally, the results additionally suggest that the DMN is coupled more strongly with key nodes in the action observation network, namely the STS and the SMA, when the observed motion depicts human rather than abstract form. These findings are the first to implicate the DMN in the perception of biological motion. They may reflect the type of information used by the DMN in social-cognitive processing.

Alpha and beta band event-related desynchronization reflects kinematic regularities.

The short-lasting attenuation of brain oscillations is termed event-related desynchronization (ERD). It is frequently found in the alpha and beta bands in humans during generation, observation, and imagery of movement and is considered to reflect cortical motor activity and action-perception coupling. The shared information driving ERD in all these motor-related behaviors is unknown. We investigated whether particular laws governing production and perception of curved movement may account for the attenuation of alpha and beta rhythms. Human movement appears to be governed by relatively few kinematic laws of motion. One dominant law in biological motion kinematics is the 2/3 power law (PL), which imposes a strong dependency of movement speed on curvature and is prominent in action-perception coupling. Here we directly examined whether the 2/3 PL elicits ERD during motion observation by characterizing the spatiotemporal signature of ERD. ERDs were measured while human subjects observed a cloud of dots moving along elliptical trajectories either complying with or violating the 2/3 PL. We found that ERD within both frequency bands was consistently stronger, arose faster, and was more widespread while observing motion obeying the 2/3 PL. An activity pattern showing clear 2/3 PL preference and lying within the alpha band was observed exclusively above central motor areas, whereas 2/3 PL preference in the beta band was observed in additional prefrontal-central cortical sites. Our findings reveal that compliance with the 2/3 PL is sufficient to elicit a selective ERD response in the human brain.

Brain structural substrates of reward dependence during behavioral performance.

Interindividual differences in the effects of reward on performance are prevalent and poorly understood, with some individuals being more dependent than others on the rewarding outcomes of their actions. The origin of this variability in reward dependence is unknown. Here, we tested the relationship between reward dependence and brain structure in healthy humans. Subjects trained on a visuomotor skill-acquisition task and received performance feedback in the presence or absence of reward. Reward dependence was defined as the statistical trial-by-trial relation between reward and subsequent performance. We report a significant relationship between reward dependence and the lateral prefrontal cortex, where regional gray-matter volume predicted reward dependence but not feedback alone. Multivoxel pattern analysis confirmed the anatomical specificity of this relationship. These results identified a likely anatomical marker for the prospective influence of reward on performance, which may be of relevance in neurorehabilitative settings.

Practice structure improves unconscious transitional memories by increasing synchrony in a premotor network.

Sequence learning relies on formation of unconscious transitional and conscious ordinal memories. The influence of practice type on formation of these memories that compose skill and systems level neural substrates is not known. Here, we studied learning of transitional and ordinal memories in participants trained on motor sequences while scanned using fMRI. Practice structure was varied or grouped (mixing or grouping sequences during training, respectively). Memory was assessed 30 min and 1 week later. Varied practice improved transitional memory and enhanced coupling of the dorsal premotor cortex with thalamus, cerebellum, and lingual and cingulate regions and greater transitional memory correlated with this coupling. Thus, varied practice improves unconscious transitional memories in proportion to coupling within a cortico-subcortical network linked to premotor cortex. This result indicates that practice structure influences unconscious transitional memory formation and identifies underlying systems level mechanisms.

Stochastic reinforcement benefits skill acquisition.

Learning complex skills is driven by reinforcement, which facilitates both online within-session gains and retention of the acquired skills. Yet, in ecologically relevant situations, skills are often acquired when mapping between actions and rewarding outcomes is unknown to the learning agent, resulting in reinforcement schedules of a stochastic nature. Here we trained subjects on a visuomotor learning task, comparing reinforcement schedules with higher, lower, or no stochasticity. Training under higher levels of stochastic reinforcement benefited skill acquisition, enhancing both online gains and long-term retention. These findings indicate that the enhancing effects of reinforcement on skill acquisition depend on reinforcement schedules.

Baseline frontostriatal-limbic connectivity predicts reward-based memory formation.

Reward mediates the acquisition and long-term retention of procedural skills in humans. Yet, learning under rewarded conditions is highly variable across individuals and the mechanisms that determine interindividual variability in rewarded learning are not known. We postulated that baseline functional connectivity in a large-scale frontostriatal-limbic network could predict subsequent interindividual variability in rewarded learning. Resting-state functional MRI was acquired in two groups of subjects (n = 30) who then trained on a visuomotor procedural learning task with or without reward feedback. We then tested whether baseline functional connectivity within the frontostriatal-limbic network predicted memory strength measured immediately, 24 h and 1 month after training in both groups. We found that connectivity in the frontostriatal-limbic network predicted interindividual variability in the rewarded but not in the unrewarded learning group. Prediction was strongest for long-term memory. Similar links between connectivity and reward-based memory were absent in two control networks, a fronto-parieto-temporal language network and the dorsal attention network. The results indicate that baseline functional connectivity within the frontostriatal-limbic network successfully predicts long-term retention of rewarded learning.

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.

Redundancy protects processing speed in healthy individuals with accelerated brain aging.

Recent advancements in computational learning techniques have enabled the estimation of brain age (BA) from neuroimaging data. The difference between chronological age (CA) and BA, known as the BA gap, can potentially serve as a biomarker of brain health. Studies, however, have documented low correlations between BA gap and cognition in healthy aging. This suggests that protective mechanisms in the brain may help counter the effect of accelerated brain aging. Here, we investigated whether redundancy in brain networks may protect cognitive function in individuals with accelerated brain aging. First, we employed deep learning to estimate individual brain ages from structural magnetic resonance imaging (MRI). Next, we associated CA, BA, and BA gap, with cognitive measures and network topology derived from diffusion MRI and tractography. We found that CA and BA were both similarly related to cognitive measures and network topology, while BA gap did not show strong relationships in either domain. Despite observing no strong relationships between brain-age gap (BA gap) and demographic variables, cognitive measures, or topological features in healthy aging, individuals with accelerated aging (BA gap+) exhibited lower average degree and redundancy within the dorsal attention network compared to those with delayed aging (BA gap-). Furthermore, redundancy in the dorsal attention network was positively associated with processing speed in BA gap+ individuals. These results indicate a potential neuroprotective role of redundancy in structural brain networks for mitigating the impact of accelerated brain atrophy on cognitive performance in healthy aging.

Age-related differences 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 use diffusion MRI data from cognitively intact participants (n = 480, ages 40-90) to study age-associated differences in the average controllability of structural brain networks, topological features that could mitigate these differences, and the overall effect on cognitive function. We find age-associated declines in average controllability in control hubs and large-scale networks, particularly within the frontoparietal control and default mode networks. Further, we find that redundancy, a hypothesized mechanism of reserve, quantified via the assessment of multi-step paths within networks, mitigates the effects of topological differences on average network controllability. Lastly, we discover that average network controllability, redundancy, and grey matter volume, each uniquely contribute to predictive models of cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging.

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.