Dataset Information

Dynamic functional connectome predicts individual working memory performance across diagnostic categories.

ABSTRACT: Working memory impairment is a common feature of psychiatric disorders. Although its neural mechanisms have been extensively examined in healthy subjects or individuals with a certain clinical condition, studies investigating neural predictors of working memory in a transdiagnostic sample are scarce. The objective of this study was to create a transdiagnostic predictive working memory model from whole-brain functional connectivity using connectome-based predictive modeling (CPM), a recently developed machine learning approach. Resting-state functional MRI data from 242 subjects across 4 diagnostic categories (healthy controls and individuals with schizophrenia, bipolar disorder, and attention deficit/hyperactivity) were used to construct dynamic and static functional connectomes. Spatial working memory was assessed by the spatial capacity task. CPM was conducted to predict individual working memory from dynamic and static functional connectivity patterns. Results showed that dynamic connectivity-based CPM models successfully predicted overall working memory capacity and accuracy as well as mean reaction time, yet their static counterparts fell short in the prediction. At the neural level, we found that dynamic connectivity of the frontoparietal and somato-motor networks were negatively correlated with working memory capacity and accuracy, and those of the default mode and visual networks were positively associated with mean reaction time. Moreover, different feature selection thresholds, parcellation strategies and model validation methods as well as diagnostic categories did not significantly influence the prediction results. Our findings not only are coherent with prior reports that dynamic functional connectivity encodes more behavioral information than static connectivity, but also help advance the translation of cognitive "connectome fingerprinting" into real-world application.

SUBMITTER: Zhu J

PROVIDER: S-EPMC7930367 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Dynamic functional connectome predicts individual working memory performance across diagnostic categories.

Zhu Jiajia J Li Yating Y Fang Qian Q Shen Yuhao Y Qian Yinfeng Y Cai Huanhuan H Yu Yongqiang Y

NeuroImage. Clinical 20210223

Working memory impairment is a common feature of psychiatric disorders. Although its neural mechanisms have been extensively examined in healthy subjects or individuals with a certain clinical condition, studies investigating neural predictors of working memory in a transdiagnostic sample are scarce. The objective of this study was to create a transdiagnostic predictive working memory model from whole-brain functional connectivity using connectome-based predictive modeling (CPM), a recently deve ...[more]

PMID: 33647810

Similar Datasets

Project description:Episodic memory is essential for forming and retaining personal experiences, representing a fundamental aspect of human cognition. Traditional studies of episodic memory have typically used static analysis methods, viewing the brain as an unchanging entity and overlooking its dynamic properties over time. In this study, we utilized dynamic functional connectivity analysis on fMRI data from healthy adults performing an episodic memory task. We quantified integration and recruitment metrics and examined their correlation with memory performance using Pearson correlation. During encoding, integration across the entire brain, especially within the frontoparietal subnetwork, was significantly correlated with memory performance. During retrieval, recruitment becomes significantly associated with memory performance in visual subnetwork, somatomotor subnetwork, and ventral attention subnetwork. At the nodal level, a significant negative correlation was observed between memory scores and integration of the anterior cingulate gyrus, precentral gyrus, and inferior frontal gyrus within the frontoparietal network during encoding task. During retrieval task, a significant negative correlation was found between memory scores and recruitment in the left progranular cortex and right transverse gyral ventral, whereas positive correlations were seen in the right posterior inferior temporal, left middle temporal, right frontal operculum, and left operculum nodes. Moreover, the dynamic reconfiguration of the functional network was predictive of predict memory performance, as demonstrated by a significant correlation between actual and predicted memory scores. These findings advance our understanding network mechanisms underlying memory processes and developing intervention approaches for memory-related disorders as they shed light on critical factors involved in cognitive processes and provide a deeper understanding of the underlying mechanisms driving cognitive function.

Project description:Background: Normal aging is associated with working memory decline. A decrease in working memory performance is associated with age-related changes in functional activation patterns in the dorsolateral prefrontal cortex (DLPFC). Cognitive training can improve cognitive performance in healthy older adults. We implemented a cognitive training study to assess determinants of generalization of training gains to untrained tasks, a key indicator for the effectiveness of cognitive training. We aimed to investigate the association of resting-state functional connectivity (FC) of DLPFC with working memory performance improvement and cognitive gains after the training. Method: A sample of 60 healthy older adults (mean age: 68 years) underwent a 4-week neuropsychological training, entailing a working memory task. Baseline resting-state functional MRI (rs-fMRI) images were acquired in order to investigate the FC of DLPFC. To evaluate training effects, participants underwent a neuropsychological assessment before and after the training. A second follow-up assessment was applied 12 weeks after the training. We used cognitive scores of digit span backward and visual block span backward tasks representing working memory function. The training group was divided into subjects who had and who did not have training gains, which was defined as a higher improvement in working memory tasks than the control group (N = 19). Results: A high FC of DLPFC of the right hemisphere was significantly associated with training gains and performance improvement in the visuospatial task. The maintenance of cognitive gains was restricted to the time period directly after the training. The training group showed performance improvement in the digit span backward task. Conclusion: Functional activation patterns of the DLPFC were associated with the degree of working memory training gains and visuospatial performance improvement. Although improvement through cognitive training and acquisition of training gains are possible in aging, they remain limited.

Project description:Sentence comprehension involves maintaining and continuously integrating linguistic information and, thus, makes demands on working memory (WM). Past research has demonstrated that semantic WM, but not phonological WM, is critical for integrating word meanings across some distance and resolving semantic interference in sentence comprehension. Here, we examined the relation between phonological and semantic WM and the comprehension of center-embedded relative clause sentences, often argued to make heavy demands on WM. Additionally, we examined the relation between phonological and semantic WM and the comprehension of transitive and dative active and passive sentences, which may also draw on WM resources depending on the number of propositions that must be maintained and the difficulty of processing passive clauses. In a large sample of individuals with aphasia (N = 56), we assessed whether comprehension performance on more complex vs. simpler active-passive or embedded relative clause sentences would be predicted by semantic but not phonological WM when controlling for single word comprehension. For performance on the active-passive comprehension task, we found that semantic WM, but not phonological WM, predicted comprehension of dative sentences when controlling for comprehension of transitive sentences. We also found that phonological WM, but not semantic WM, predicted mean comprehension for reversible active-passive sentences when controlling for trials with lexical distractors. On the relative clause comprehension task, consistent with prior results, we found that semantic WM, but not phonological WM, predicted comprehension of object relative clause sentences and relative clause sentences with a passive construction. However, both phonological WM and semantic WM predicted mean comprehension across all relative clause types for reversible trials when controlling for trials with lexical distractors. While we found evidence of semantic WM’s role in comprehension, we also observed unpredicted relations between phonological WM and comprehension in some conditions. Post-hoc analyses provided preliminary evidence that phonological WM maintains a backup phonological representation of the sentence that may be accessed when sentence comprehension processing is less efficient. Future work should investigate possible roles that phonological WM may play across sentence types.

Dataset Information

Dynamic functional connectome predicts individual working memory performance across diagnostic categories.

Publications

Dynamic functional connectome predicts individual working memory performance across diagnostic categories.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets