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Inter-individual variation in fronto-temporal connectivity predicts the ability to learn different types of associations.

ABSTRACT: The uncinate fasciculus connects portions of the anterior and medial temporal lobes to the lateral orbitofrontal cortex, so it has long been thought that this limbic fiber pathway plays an important role in episodic memory. Some types of episodic memory are impaired after damage to the uncinate, while others remain intact. Because of this, the specific role played by the uncinate fasciculus in episodic memory remains undetermined. In the present study, we tested the hypothesis that the uncinate fasciculus is involved in episodic memory tasks that have high competition between representations at retrieval. To test this hypothesis, healthy young adults performed three tasks: Experiment 1 in which they learned to associate names with faces through feedback provided at the end of each trial; Experiment 2 in which they learned to associate fractals with cued locations through feedback provided at the end of each trial; and Experiment 3 in which unique faces were remembered in a paradigm with low retrieval competition. Diffusion tensor imaging and deterministic tractography methods were used to extract measures of uncinate fasciculus microstructure. Results revealed that microstructural properties of the uncinate, but not a control tract, the inferior longitudinal fasciculus, significantly predicted individual differences in performance on the face-name and fractal-location tasks. However, no relationship was observed for simple face memory (Experiment 3). These findings suggest that the uncinate fasciculus may be important for adjudicating between competing memory representations at the time of episodic retrieval.

SUBMITTER: Alm KH

PROVIDER: S-EPMC4851884 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Inter-individual variation in fronto-temporal connectivity predicts the ability to learn different types of associations.

Alm Kylie H KH Rolheiser Tyler T Olson Ingrid R IR

NeuroImage 20160222

The uncinate fasciculus connects portions of the anterior and medial temporal lobes to the lateral orbitofrontal cortex, so it has long been thought that this limbic fiber pathway plays an important role in episodic memory. Some types of episodic memory are impaired after damage to the uncinate, while others remain intact. Because of this, the specific role played by the uncinate fasciculus in episodic memory remains undetermined. In the present study, we tested the hypothesis that the uncinate ...[more]

PMID: 26908315

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Project description:Background:Electroconvulsive therapy (ECT) is arguably the most effective available treatment for severe depression. Recent studies have used MRI data to predict clinical outcome to ECT and other antidepressant therapies. One challenge facing such studies is selecting from among the many available metrics, which characterize complementary and sometimes non-overlapping aspects of brain function and connectomics. Here, we assessed the ability of aggregated, functional MRI metrics of basal brain activity and connectivity to predict antidepressant response to ECT using machine learning. Methods:A radial support vector machine was trained using arterial spin labeling (ASL) and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) metrics from n?=?46 (26 female, mean age 42) depressed patients prior to ECT (majority right-unilateral stimulation). Image preprocessing was applied using standard procedures, and metrics included cerebral blood flow in ASL, and regional homogeneity, fractional amplitude of low-frequency modulations, and graph theory metrics (strength, local efficiency, and clustering) in BOLD data. A 5-repeated 5-fold cross-validation procedure with nested feature-selection validated model performance. Linear regressions were applied post hoc to aid interpretation of discriminative features. Results:The range of balanced accuracy in models performing statistically above chance was 58-68%. Here, prediction of non-responders was slightly higher than for responders (maximum performance 74 and 64%, respectively). Several features were consistently selected across cross-validation folds, mostly within frontal and temporal regions. Among these were connectivity strength among: a fronto-parietal network [including left dorsolateral prefrontal cortex (DLPFC)], motor and temporal networks (near ECT electrodes), and/or subgenual anterior cingulate cortex (sgACC). Conclusion:Our data indicate that pattern classification of multimodal fMRI metrics can successfully predict ECT outcome, particularly for individuals who will not respond to treatment. Notably, connectivity with networks highly relevant to ECT and depression were consistently selected as important predictive features. These included the left DLPFC and the sgACC, which are both targets of other neurostimulation therapies for depression, as well as connectivity between motor and right temporal cortices near electrode sites. Future studies that probe additional functional and structural MRI metrics and other patient characteristics may further improve the predictive power of these and similar models.

Dataset Information

Inter-individual variation in fronto-temporal connectivity predicts the ability to learn different types of associations.

Publications

Inter-individual variation in fronto-temporal connectivity predicts the ability to learn different types of associations.

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