The Interaction of Dynamic Cerebral Autoregulation and Neurovascular Coupling in Cognitive Impairment

Background: Dynamic cerebral autoregulation (dCA) remains intact in both ageing and dementia, but studies of neurovascular coupling (NVC) have produced mixed findings. Objective: We investigated the effects of task-activation on dCA in healthy older adults (HOA), and patients with mild cognitive impairment (MCI) and Alzheimer’s Disease (AD). Methods: Resting and task-activated data from thirty HOA, twenty-two MCI, and thirty-four AD were extracted from a database. The autoregulation index (ARI) was determined at rest and during five cognitive tasks from transfer function analysis. NVC responses were present where group-specific thresholds of cross-correlation peak function and variance ratio were exceeded. Cumulative response rate (CRR) was the total number of positive responses across five tasks and two hemispheres. Results: ARI differed between groups in dominant (p=0.012) and non-dominant (p=0.042) hemispheres at rest but not during task-activation (p=0.33). ARI decreased during language and memory tasks in HOA (p=0.002) but not in MCI or AD (p=0.40). There was a significant positive correlation between baseline ARI and CRR in all groups (r=0.26, p=0.018), but not within sub-groups. Conclusion: dCA efficiency was reduced in task-activation in healthy but not cognitively impaired participants. These results indicate differences in neurovascular processing in healthy older adults relative to cognitively impaired individuals.

Optimizing EEG source reconstruction with concurrent fMRI-derived spatial priors

Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by also including fMRI-derived spatial priors in the inverse models. However, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity (dFC) fluctuations. Moreover, there is no consensus regarding the inversion algorithm of choice, nor a systematic comparison between different sets of spatial priors. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (MN, LORETA, EBB and MSP) under a Bayesian framework, each with three different sets of priors consisting of: 1) those specific to the algorithm (S1); 2) S1 plus fMRI task activation maps and RSNs (S2); and 3) S2 plus network modules of task-related dFC states estimated from the dFC fluctuations (S3). The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the free-energy and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP+S1 exhibiting the best performance. However, optimal overlap/proportion values were found using EBB+S2 or MSP+S3, respectively, indicating that fMRI spatial priors, including dFC state modules, are crucial for the EEG source components to reflect neuronal activity of interest. Our results pave the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be crucial in future studies.

Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

Resting-state functional MRI (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, i.e., should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple non-contiguous components, therefore we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10min of data generalized better than other approaches using 150min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity (RSFC) derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM (cMS-HBM) exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM (gMS-HBM) was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. Multi-resolution trained models and parcellations are publicly available (GITHUB_LINK).

Structural brain imaging predicts individual-level task activation maps using deep learning

Accurate individual functional mapping of task activations is a potential tool for biomarker discovery and is critically important for clinical care. While structural imaging does not directly map task activation, we hypothesized that structural imaging contains information that can accurately predict variations in task activation between individuals. To this end, we trained a convolutional neural network to use structural imaging (T1-weighted, T2-weighted, and diffusion tensor imaging) to predict 47 different functional MRI task activation volumes across seven task domains. The U-Net model was trained on 654 subjects and then subsequently tested on 122 unrelated subjects. The predicted activation maps correlated more strongly with their actual maps than with the maps of the other test subjects. An ablation study revealed that a model using the shape of the cortex alone or the shape of the subcortical matter alone was sufficient to predict individual-level differences in task activation maps, but a model using the shape of the whole brain resulted in markedly decreased performance. The ablation study also showed that the additional information provided by the T2-weighted and diffusion tensor imaging strengthened the predictions as compared to using the T1-weighted imaging alone. These results indicate that structural imaging contains information that is predictive of inter-subject variability in task activation mapping and cortical folding patterns as well as microstructural features may be a key component to linking brain structure to brain function.

Brain network mechanisms of visual shape completion

Visual shape completion represents object shape, size, and number from spatially segregated edges. Despite being extensively investigated, the process’s underlying brain regions, networks, and functional connections are still not well understood. To shed light on the topic, we scanned (fMRI) healthy adults during rest and during a task in which they discriminated pac-man configurations that formed or failed to form visually completed shapes (illusory and fragmented condition, respectively). Task activation differences (illusory-fragmented), resting-state functional connectivity, and multivariate pattern differences were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping (ActFlow) was used to evaluate the likely involvement of resting-state connections for shape completion. We identified 34 differentially-active parcels including a posterior temporal region, PH, whose activity was consistent across all 20 observers. Significant task regions primarily occupied the secondary visual network but also incorporated the frontoparietal, dorsal attention, default mode, and cingulo-opercular networks. Each parcel’s task activation difference could be modeled via its resting-state connections with the remaining parcels (r=.62, p<10-9), suggesting that such connections undergird shape completion. Functional connections from the dorsal attention network were key in modeling activation differences in the secondary visual network and across all remaining networks. Taken together, these results suggest that shape completion relies upon a distributed but densely interconnected network coalition that is centered in the secondary visual network, coordinated by the dorsal attention network, and inclusive of at least three other networks.HighlightsShape completion differentially activates regions distributed across five networksThe secondary visual network plays the clearest role in shape completionDorsal attention functional connections likely coordinate activity across networksPosterior temporal region, PH, played a highly consistent role in completion

S146. ASSOCIATION OF IMPAIRED MODEL-FREE DECISION-MAKING WITH ABERRANT STRIATAL DOPAMINE, BRAIN ACTIVATION, AND COGNITIVE DIFFICULTIES IN PATIENTS WITH SCHIZOPHRENIA DURING PSYCHOTIC REMISSION

Background Human decision-making ranges between the extremes of automatic and fast model-free behavior (i.e., relying only on previous outcomes) and more flexible, but computationally demanding model-based behavior (i.e., implementing cognitive models). Model-based/model-free decision-making can be investigated using sequential decision tasks and has been shown to be associated with presynaptic striatal dopamine synthesis. During phases of psychotic remission in schizophrenia, dopamine synthesis in the dorsal striatum is reduced. We hypothesized that particularly model-free decision-making is impaired in schizophrenia during psychotic remission and is associated with (i) abnormal dopamine synthesis in dorsal striatum, (ii) aberrant task-activation in dorsal striatum, and (iii) cognitive difficulties in patients (e.g., reduced speed). Methods 26 patients with chronic schizophrenia, currently in psychotic remission, and 22 healthy controls (matched by age and gender) were enrolled in the study. Model-based/model-free decision-making was evaluated with a two-stage Markov decision task, followed by computational modeling of subjects’ learning behavior. Presynaptic dopamine synthesis was assessed by 18F-DOPA positron emission tomography and subsequent graphical Patlak analysis. Task-activation was measured by functional magnetic resonance imaging. Cognitive impairments were quantified by Trail-Making-Test A (among others). Associations between decision-making parameters, dopamine synthesis, task-activation, and cognitive impairments were tested by correlation analyses. Results Patients with schizophrenia showed selectively impaired model-free decision-making. 18F-DOPA uptake (i.e., presynaptic dopamine synthesis capacity) in the dorsal striatum was decreased in patients. Impaired model-free decision-making in patients correlated with (i) decreased dopamine synthesis in dorsal striatum, (ii) abnormal task-activation in dorsal striatum, and (iii) lower speed in Trail-Making-Test A. Discussion Results demonstrate an association of reduced dorsal striatal dopamine synthesis and brain activity with impaired model-free decision-making in schizophrenia, which potentially contributes to cognitive difficulties.