Biology subjects

Reutens, D. C.

Publications and source records attributed to Reutens, D. C..

4 recordsLinked to original sources

bioRxiv2024

Cell-type-informed genotyping of mosaic focal epilepsies reveals cell-autonomous and non-cell-autonomous disease-associated transcriptional programs

Recent studies demonstrate growing roles for genetic mosaicism in neurodevelopmental and neuropsychiatric disorders, with the paradigm being drug-resistant pediatric focal epilepsy related to activating somatic variants in the PI3K-mTOR pathway. While identifying the genotype-associated changes at the single-cell level is fundamental to understanding disease pathophysiology, this remains technically challenging in human tissue samples with existing methods. Here, we performed single-nucleus RNA-sequencing (snRNA-seq) of 20 focal cortical dysplasia (FCD) samples removed surgically for treatment of drug-resistant epilepsy, and 10 non-FCD controls, and we developed a new approach, Genotyping Of Transcriptomes Enhanced with Nanopore sequencing (GO-TEN), that combines targeted complementary (c)DNA sequencing with snRNA-seq to perform concurrent single-nucleus genotyping and transcriptional analysis. We find that mosaic pathogenic variants in FCD do not produce a detectable novel cell identity, but instead we observe conserved cell types present both in FCD cases and non-FCD control specimens. Similarly, GO-TEN analysis shows that most pathogenic variant-carrying cells have well-differentiated neuronal or glial identities and are enriched for layer II-III excitatory neurons. We identify cell-intrinsic disruption of glutamate and GABA-A signaling pathways in variant-carrying neurons and altered intercellular signaling, making potential mechanisms for epileptogenesis in FCD. In summary, by addressing genotype-specific changes in mosaic epilepsy-associated lesions, our study highlights new potential disease mechanisms and therapeutic targets.

genetics↗

bioRxiv2019

A new role of hippocampus during logical reasoning and belief bias in aging

Reasoning requires initial encoding of the semantic association between premises or assumptions, retrieval of these semantic associations from memory, and recombination of information to draw a logical conclusion. Currently-held beliefs can interfere with the content of the assumptions if not congruent and inhibited. This study aimed to investigate the role of the hippocampus and hippocampal networks during logical reasoning tasks in which the congruence between currently-held beliefs and assumptions varies. Participants of younger and older age completed a series of syllogistic reasoning tasks in which two premises and one conclusion were presented and they were required to decide if the conclusion logically followed the premises. The belief load of premises was manipulated to be either congruent or incongruent with currently-held beliefs. Our whole-brain results showed that older adults recruited the hippocampus during the premise integration stage more than their younger counterparts. Functional connectivity using a hippocampal seed revealed that older, but not younger, adults recruited a hippocampal network that included anterior cingulate and inferior frontal regions when premises were believable. Importantly, this network contributed to better performance in believable inferences, only in older adults group. Further analyses suggested that, in older adults group, the integrity of the left cingulum bundle was associated with the higher correct rejection of believable premises more than unbelievable ones. Using multimodal imaging, this study highlights the importance of the hippocampus during premise integration and supports the compensatory role of the hippocampal network during a logical reasoning task among older adults.

neuroscience↗

bioRxiv2019

Spatiotemporal Feature Selection Improves Prediction Accuracy of Multi-Voxel Pattern Classification

The importance of spatiotemporal feature selection in fMRI decoding studies has not been studied exhaustively. Temporal embedding of features allows the incorporation of brain activity dynamics into multivariate pattern classification, and may provide enriched information about stimulus-specific response patterns and potentially improve prediction accuracy. This study investigates the possibility of enhancing the classification performance by exploring spatial and temporal (spatiotemporal) domain, to identify the optimum combination of the spatiotemporal features based on the classification performance. We investigated the importance of spatiotemporal feature selection using a slow event-related design adapted from the classic Haxby et al. (2001) study. Data were collected using a multiband fMRI sequence with temporal resolution of 0.568 seconds. A wide range of spatiotemporal observations was created as various combinations of spatiotemporal features. Using both random forest, and support vector machine, classifiers, prediction accuracies for these combinations were then compared with the single time-point spatial multivariate pattern approach that uses only a single temporal observation. The results showed that on average spatiotemporal feature selection improved prediction accuracy. Moreover, the random forest algorithm outperformed the support vector machine and benefitted from temporal information to a greater extent. As expected, the most influential temporal durations were found to be around the peak of the hemodynamic response function, a few seconds after the stimuli onset until [~]4 seconds after the peak of the hemodynamic response function. The superiority of spatiotemporal feature selection over single time-point spatial approaches invites future work to design systematic and optimal approaches to the incorporation of spatiotemporal dependencies into feature selection for decoding.\n\nHighlightsO_LISpatiotemporal feature selection effect on MVPC was assessed in slow event-related fMRI\nC_LIO_LISpatiotemporal feature selection improved brain decoding accuracy\nC_LIO_LIFrom [~]2-11 seconds after stimuli onset were the most informative part of each trial\nC_LIO_LIRandom forest outperformed support vector machines\nC_LIO_LIRandom forest benefited more from temporal changes compared with support vector machine\nC_LI

neuroscience↗

bioRxiv2017

Presurgical language fMRI: Current practices and patient outcomes in epilepsy surgical planning

The goal of this study was to document current clinical practice and report patient outcomes in presurgical language functional MRI (fMRI) for epilepsy surgery. Epilepsy surgical programs worldwide were surveyed as to the utility, implementation, and efficacy of language fMRI in the clinic; 82 programs responded between July 2015 and January 2016. Respondents were predominantly from the US (61%), were academic programs (85%), and predominantly evaluated adults (44%), both adults and children (40%), or children only (16%). Nearly all (96%) respondents reported using language fMRI. fMRI is used for language lateralization (100%) and for localizing (44%) language cortex to guide surgical margins. While typically considered useful, programs often reported at least one instance of disagreement with other measures (56%). When used to localize language cortex, direct brain stimulation typically confirmed fMRI findings (74%) but instances of unpredicted decline were reported by 17% of programs and unexpected preservation of function were reported by 54%. Programs reporting unexpected decline did not clearly differ from those which did not. Clinicians using fMRI to guide surgical margins typically map Brocas and Wernickes areas but not other known language areas. Language fMRI is widely used for lateralizing language cortex in patients with medically intractable epilepsy. It is also frequently used to localize language cortex though it is not yet well validated for this purpose. Many centers report cases of unexpected language preservation when fMRI activation is resected, and cases of language decline when it is not. Care will almost certainly be improved by standardizing protocols and accurately detailing the relationship between fMRI-positive areas and post-surgical language decline.

neuroscience↗