Connectome-based prediction of global cognitive performance in people with HIV

Disconnection-Based Prediction of Poststroke Dysphagia

BACKGROUND AND PURPOSE

Dysphagia is a common deficit after a stroke and is associated with serious complications. It is not yet fully clear which brain regions are directly related to swallowing. Previous lesion symptom mapping studies may have overlooked structural disconnections that could be responsible for poststroke dysphagia. Here, we aimed to predict and explain the relationship between poststroke dysphagia and the topologic distribution of structural disconnection via a multivariate predictive framework.

MATERIALS AND METHODS

We enrolled first-ever ischemic stroke patients classified as full per-oral nutrition (71 patients) and nonoral nutrition necessary (43 patients). After propensity score matching, 43 patients for each group were enrolled (full per-oral nutrition group with 17 women, 68 ± 15 years; nonoral nutrition necessary group with 13 women, 75 ± 11 years). The structural disconnectome was estimated by using the lesion segmented from acute phase diffusion-weighted images. The prediction of poststroke dysphagia by using the structural disconnectome and demographics was performed in a leave-one-out manner.

RESULTS

Using both direct and indirect disconnection matrices of the motor network, the disconnectome-based prediction model could predict poststroke dysphagia above the level of chance (accuracy = 68.6%, permutation P = .001). When combined with demographic data, the classification accuracy reached 72.1%. The edges connecting the right insula and left motor strip were the most informative in prediction.

CONCLUSIONS

Poststroke dysphagia could be predicted by using the structural disconnectome derived from acute phase diffusion-weighted images. Specifically, the direct and indirect disconnection within the motor network was the most informative in predicting poststroke dysphagia.

Functional connectome fingerprinting and stability in multiple sclerosis

Background

Functional connectome fingerprinting can identify individuals based on their functional connectome. Previous studies relied mostly on short intervals between fMRI acquisitions.

Objective

This cohort study aimed to determine the stability of connectome-based identification and their underlying signatures in patients with multiple sclerosis and healthy individuals with long follow-up intervals.

Methods

We acquired resting-state fMRI in 70 patients with multiple sclerosis and 273 healthy individuals with long follow-up times (up to 4 and 9 years, respectively). Using functional connectome fingerprinting, we examined the stability of the connectome and additionally investigated which regions, connections and networks supported individual identification. Finally, we predicted cognitive and behavioural outcome based on functional connectivity.

Results

Multiple sclerosis patients showed connectome stability and identification accuracies similar to healthy individuals, with longer time delays between imaging sessions being associated with accuracies dropping from 89% to 76%. Lesion load, brain atrophy or cognitive impairment did not affect identification accuracies within the range of disease severity studied. Connections from the fronto-parietal and default mode network were consistently most distinctive, i.e., informative of identity. The functional connectivity also allowed the prediction of individual cognitive performances.

Conclusion

Our results demonstrate that discriminatory signatures in the functional connectome are stable over extended periods of time in multiple sclerosis, resulting in similar identification accuracies and distinctive long-lasting functional connectome fingerprinting signatures in patients and healthy individuals.

Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features

Objective

Headache is among the most frequent symptoms after coronavirus disease 2019 (COVID-19), so-called long COVID syndrome. Although distinct brain changes have been reported in patients with long COVID, such reported brain changes have not been used for predictions and interpretations in a multivariate manner. In this study, we applied machine learning to assess whether individual adolescents with long COVID can be accurately distinguished from those with primary headaches.

Methods

Twenty-three adolescents with long COVID headaches with the persistence of headache for at least 3 months and 23 age- and sex-matched adolescents with primary headaches (migraine, new daily persistent headache, and tension-type headache) were enrolled. Multivoxel pattern analysis (MVPA) was applied for disorder-specific predictions of headache etiology based on individual brain structural MRI. In addition, connectome-based predictive modeling (CPM) was also performed using a structural covariance network.

Results

MVPA correctly classified long COVID patients from primary headache patients, with an area under the curve of 0.73 (accuracy = 63.4%; permutation p = 0.001). The discriminating GM patterns exhibited lower classification weights for long COVID in the orbitofrontal and medial temporal lobes. The CPM using the structural covariance network achieved an area under the curve of 0.81 (accuracy = 69.5%; permutation p = 0.005). The edges that classified long COVID patients from primary headache were mainly comprising thalamic connections.

Conclusion

The results suggest the potential value of structural MRI-based features for classifying long COVID headaches from primary headaches. The identified features suggest that the distinct gray matter changes in the orbitofrontal and medial temporal lobes occurring after COVID, as well as altered thalamic connectivity, are predictive of headache etiology.

Connectome-based predictive modeling of trait forgiveness

Forgiveness is a positive, prosocial manner of reacting to transgressions and is strongly associated with mental health and well-being. Despite recent studies exploring the neural mechanisms underlying forgiveness, a model capable of predicting trait forgiveness at the individual level has not been developed. Herein, we applied a machine-learning approach, connectome-based predictive modeling (CPM), with whole-brain resting-state functional connectivity (rsFC) to predict individual differences in trait forgiveness in a training set (dataset 1, N = 100, 35 men, 17-24 years). As a result, CPM successfully predicted individual trait forgiveness based on whole-brain rsFC, especially via the functional connectivity of the limbic, prefrontal and temporal areas, which are key contributors to the prediction model comprising regions previously implicated in forgiveness. These regions include the retrosplenial cortex, temporal pole, dorsolateral prefrontal cortex (PFC), dorsal anterior cingulate cortex, precuneus and dorsal posterior cingulate cortex. Importantly, this predictive model could be successfully generalized to an independent sample (dataset 2, N = 71, 17 men, 16-25 years). These findings highlight the important roles of the limbic system, PFC and temporal region in trait forgiveness prediction and represent the initial steps toward establishing an individualized prediction model of forgiveness.

Cognitive performance and brain structural connectome alterations in major depressive disorder

BACKGROUND

Cognitive dysfunction and brain structural connectivity alterations have been observed in major depressive disorder (MDD). However, little is known about their interrelation. The present study follows a network approach to evaluate alterations in cognition-related brain structural networks.

METHODS

Cognitive performance of n = 805 healthy and n = 679 acutely depressed or remitted individuals was assessed using 14 cognitive tests aggregated into cognitive factors. The structural connectome was reconstructed from structural and diffusion-weighted magnetic resonance imaging. Associations between global connectivity strength and cognitive factors were established using linear regressions. Network-based statistics were applied to identify subnetworks of connections underlying these global-level associations. In exploratory analyses, effects of depression were assessed by evaluating remission status-related group differences in subnetwork-specific connectivity. Partial correlations were employed to directly test the complete triad of cognitive factors, depressive symptom severity, and subnetwork-specific connectivity strength.

RESULTS

All cognitive factors were associated with global connectivity strength. For each cognitive factor, network-based statistics identified a subnetwork of connections, revealing, for example, a subnetwork positively associated with processing speed. Within that subnetwork, acutely depressed patients showed significantly reduced connectivity strength compared to healthy controls. Moreover, connectivity strength in that subnetwork was associated to current depressive symptom severity independent of the previous disease course.

CONCLUSIONS

Our study is the first to identify cognition-related structural brain networks in MDD patients, thereby revealing associations between cognitive deficits, depressive symptoms, and reduced structural connectivity. This supports the hypothesis that structural connectome alterations may mediate the association of cognitive deficits and depression severity.

The impacts of HIV infection, age, and education on functional brain networks in adults with HIV

HIV-associated neurocognitive disorders (HAND) remain highly prevalent in people with HIV (PWH). Studies suggested that certain sociodemographic factors are associated with the risk of HAND in PWH. Here we investigated the impact of HIV infection and demographics on functional brain networks. One run of 8.5 min resting state functional MRI (fMRI) data was collected from 101 PWH (41-70 years old) and 40 demographically comparable controls. Functional connectivity (FC) was calculated using average wavelet coherence. The impact of demographic factors on FCs was investigated using canonical correlation analysis (CCA). Wavelet coherence analysis revealed a reduced within-network connectivity in the dorsal somatomotor network (dSMN), along with a reduced between-network connectivity between dSMN and medial temporal lobe (MTL) in PWH (compared to controls). Across all participants, CCA revealed that older age and HIV infection had negative impacts on network connectivity measures (mainly reduced within- and between-network FCs), whereas education had an opposite effect. In addition, being female at birth or a member of a minority ethnic/racial group was also associated with network disruptions. Our data suggested that advanced age and HIV infection are risk factors for functional brain network disruptions, whereas higher educational attainment was linked to better preserved functional network connectivity.