Monolingual and bilingual language networks in healthy subjects using functional MRI and graph theory

Bilingualism requires control of multiple language systems, and may lead to architectural differences in language networks obtained from clinical fMRI tasks. Emerging connectivity metrics such as k-core may capture these differences, highlighting crucial network components based on resiliency. We investigated the influence of bilingualism on clinical fMRI language tasks and characterized bilingual networks using connectivity metrics to provide a patient care benchmark. Sixteen right-handed subjects (mean age 42-years; nine males) without neurological history were included: eight native English-speaking monolinguals and eight native Spanish-speaking (L1) bilinguals with acquired English (L2). All subjects underwent fMRI with gold-standard clinical language tasks. Starting from active clusters on fMRI, we inferred the persistent functional network across subjects and ran centrality measures to characterize differences. Our results demonstrated a persistent network “core” consisting of Broca’s area, the pre-supplementary motor area, and the premotor area. K-core analysis showed that Wernicke’s area was engaged by the “core” with weaker connection in L2 than L1.

A quantitative approach for measuring laterality in clinical fMRI for preoperative language mapping

Purpose fMRI is increasingly used for presurgical language mapping, but lack of standard methodology has made it difficult to combine/compare data across institutions or determine the relative efficacy of different approaches. Here, we describe a quantitative analytic framework for determining language laterality in clinical fMRI that addresses these concerns. Methods We retrospectively analyzed fMRI data from 59 patients who underwent presurgical language mapping at our institution with identical imaging and behavioral protocols. First, we compared the efficacy of different regional masks in capturing language activations. Then, we systematically explored how laterality indices (LIs) computed from these masks vary as a function of task and activation threshold. Finally, we determined the percentile threshold that maximized the correlation between the results of our LI approach and the laterality assessments from the original clinical radiology reports. Results First, we found that a regional mask derived from a meta-analysis of the fMRI literature better captured language task activations than masks based on anatomically defined language areas. Then, we showed that an LI approach based on this functional mask and percentile thresholding of subject activation can quantify the relative ability of different language tasks to lateralize language function at the population level. Finally, we determined that the 92nd percentile of subject-level activation provides the optimal LI threshold with which to reproduce the original clinical reports. Conclusion A quantitative framework for determining language laterality that uses a functionally-derived language mask and percentile thresholding of subject activation can combine/compare results across tasks and patients and reproduce clinical assessments of language laterality.

Automatic identification of atypical clinical fMRI results

Purpose Functional MRI is not routinely used for neurosurgical planning despite potential important advantages, due to difficulty of determining quality. We introduce a novel method for objective evaluation of fMRI scan quality, based on activation maps. A template matching analysis (TMA) is presented and tested on data from two clinical fMRI protocols, performed by healthy controls in seven clinical centers. Preliminary clinical utility is tested with data from low-grade glioma patients. Methods Data were collected from 42 healthy subjects from seven centers, with standardized finger tapping (FT) and verb generation (VG) tasks. Copies of these “typical” data were deliberately analyzed incorrectly to assess feasibility of identifying them as “atypical.” Analyses of the VG task administered to 32 tumor patients assessed sensitivity of the TMA method to anatomical abnormalities. Results TMA identified all atypical activity maps for both tasks, at the cost of incorrectly classifying 3.6 (VG)–6.5% (FT) of typical maps as atypical. For patients, the average TMA was significantly higher than atypical healthy scans, despite localized anatomical abnormalities caused by a tumor. Conclusion This study supports feasibility of TMA for objective identification of atypical activation patterns for motor and verb generation fMRI protocols. TMA can facilitate the use and evaluation of clinical fMRI in hospital settings that have limited access to fMRI experts. In a clinical setting, this method could be applied to automatically flag fMRI scans showing atypical activation patterns for further investigation to determine whether atypicality is caused by poor scan data quality or abnormal functional topography.

Sample size evolution in neuroimaging research: an evaluation of highly-cited studies (1990-2012) and of latest practices (2017-2018) in high-impact journals

We evaluated 1038 of the most cited structural and functional (fMRI) magnetic resonance brain imaging papers (1161 studies) published during 1990-2012 and 273 papers (302 studies) published in top neuroimaging journals in 2017 and 2018. 96% of highly cited experimental fMRI studies had a single group of participants and these studies had median sample size of 12, highly cited clinical fMRI studies (with patient participants) had median sample size of 14.5, and clinical structural MRI studies had median sample size of 50. The sample size of highly cited experimental fMRI studies increased at a rate of 0.74 participant/year and this rate of increase was commensurate with the median sample sizes of neuroimaging studies published in top neuroimaging journals in 2017 (23 participants) and 2018 (24 participants). Only 4 of 131 papers in 2017 and 5 of 142 papers in 2018 had pre-study power calculations, most for single t-tests and correlations. Only 14% of highly cited papers reported the number of excluded participants whereas about 45% of papers in 2017 and 2018 reported excluded participants. Targeted interventions from publishers and funders could facilitate increase in sample sizes and adherence to better standards.

Neuroimaging of Emotion Dysregulation

Affective neuroscience, the study of neural mechanisms that give rise to emotional experiences in humans and animals, has a short but rich history. Almost three decades old, affective neuroscience has predominantly taken two theoretical approaches to understanding the brain bases of human emotions, and thus, two stances on the brain bases of emotion dysregulation. One approach, the traditional approach, argues that specific emotions are hardwired in human biology with specific neural underpinnings or signatures for said emotions. The second approach, a psychological constructionist approach, argues that each experienced emotion emerges not from a specific, dedicated anatomical circuit, but from an interplay of broad networks in the brain that are involved in general operations of the mind. This chapter provides an overview of these two theoretical approaches with a specific focus on functional magnetic resonance imaging (fMRI) findings. It concludes with evidence suggesting how emotion dysregulation may arise and links this work to clinical fMRI investigations of anxiety disorders. It closes by suggesting future directions affective neuroscience may take to better understand processes underlying dysregulated emotions.