Auditory feedback control mechanisms do not contribute to cortical hyperactivity within the voice production network in adductor spasmodic dysphonia

Adductor spasmodic dysphonia (ADSD), the most common form of spasmodic dysphonia, is a debilitating voice disorder characterized by hyperactivity and muscle spasms in the vocal folds during speech. Prior neuroimaging studies have noted excessive brain activity during speech in ADSD participants compared to controls. Speech involves an auditory feedback control mechanism that generates motor commands aimed at eliminating disparities between desired and actual auditory signals. Thus, excessive neural activity in ADSD during speech may reflect, at least in part, increased engagement of the auditory feedback control mechanism as it attempts to correct vocal production errors detected through audition. To test this possibility, functional magnetic resonance imaging was used to identify differences between ADSD participants and age-matched controls in (i) brain activity when producing speech under different auditory feedback conditions, and (ii) resting state functional connectivity within the cortical network responsible for vocalization. The ADSD group had significantly higher activity than the control group during speech (compared to a silent baseline task) in three left-hemisphere cortical regions: ventral Rolandic (sensorimotor) cortex, anterior planum temporale, and posterior superior temporal gyrus/planum temporale. This was true for speech while auditory feedback was masked with noise as well as for speech with normal auditory feedback, indicating that the excess activity was not the result of auditory feedback control mechanisms attempting to correct for perceived voicing errors in ADSD. Furthermore, the ADSD group had significantly higher resting state functional connectivity between sensorimotor and auditory cortical regions within the left hemisphere as well as between the left and right hemispheres, consistent with the view that excessive motor activity frequently co-occurs with increased auditory cortical activity in individuals with ADSD.First author draf

The mechanisms of tinnitus: perspectives from human functional neuroimaging

In this review, we highlight the contribution of advances in human neuroimaging to the current understanding of central mechanisms underpinning tinnitus and explain how interpretations of neuroimaging data have been guided by animal models. The primary motivation for studying the neural substrates of tinnitus in humans has been to demonstrate objectively its representation in the central auditory system and to develop a better understanding of its diverse pathophysiology and of the functional interplay between sensory, cognitive and affective systems. The ultimate goal of neuroimaging is to identify subtypes of tinnitus in order to better inform treatment strategies. The three neural mechanisms considered in this review may provide a basis for TI classification. While human neuroimaging evidence strongly implicates the central auditory system and emotional centres in TI, evidence for the precise contribution from the three mechanisms is unclear because the data are somewhat inconsistent. We consider a number of methodological issues limiting the field of human neuroimaging and recommend approaches to overcome potential inconsistency in results arising from poorly matched participants, lack of appropriate controls and low statistical power

Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI

Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method - support vector machine (SVM) classification - to test whether patterns in brain network activity, measured with resting state functional connectivity (RSFC) MRI, could predict diagnostic group membership for individuals. RSFC data from 42 children with TS (8-15 yrs) and 42 unaffected controls (age, IQ, in-scanner movement matched) were included. While univariate tests identified no significant group differences, SVM classified group membership with ~70% accuracy (p < .001). We also report a novel adaptation of SVM binary classification that, in addition to an overall accuracy rate for the SVM, provides a confidence measure for the accurate classification of each individual. Our results support the contention that multivariate methods can better capture the complexity of some brain disorders, and hold promise for predicting prognosis and treatment outcome for individuals with TS

Brain Imaging for Legal Thinkers: A Guide for the Perplexed

It has become increasingly common for brain images to be proffered as evidence in criminal and civil litigation. This Article - the collaborative product of scholars in law and neuroscience - provides three things. First, it provides the first introduction, specifically for legal thinkers, to brain imaging. It describes in accessible ways the new techniques and methods that the legal system increasingly encounters. Second, it provides a tutorial on how to read and understand a brain-imaging study. It does this by providing an annotated walk-through of the recently-published work (by three of the authors - Buckholtz, Jones, and Marois) that discovered the brain activity underlying a person\u27s decisions: a) whether to punish someone; and b) how much to punish. The annotation uses the \u27Comment\u27 feature of the Word software to supply contextual and step-by-step commentary on what unfamiliar terms mean, how and why brain imaging experiments are designed as they are, and how to interpret the results. Third, the Article offers some general guidelines about how to avoid misunderstanding brain images in legal contexts and how to identify when others are misusing brain images. The Article is a product of the \u27Law and Neuroscience Project\u27, supported by the MacArthur Foundation

Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders

Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate 15 (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demon- 20 strate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architec- 25 ture is modulated by local blood oxygen level-dependent activity and a-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. 30 Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be 35 potentially useful as a predictor for learning and neural rehabilitation

Long-term neural and physiological phenotyping of a single human

Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders

Quality control in resting-state fMRI: the benefits of visual inspection

Background: A variety of quality control (QC) approaches are employed in resting-state functional magnetic resonance imaging (rs-fMRI) to determine data quality and ultimately inclusion or exclusion of a fMRI data set in group analysis. Reliability of rs-fMRI data can be improved by censoring or “scrubbing” volumes affected by motion. While censoring preserves the integrity of participant-level data, including excessively censored data sets in group analyses may add noise. Quantitative motion-related metrics are frequently reported in the literature; however, qualitative visual inspection can sometimes catch errors or other issues that may be missed by quantitative metrics alone. In this paper, we describe our methods for performing QC of rs-fMRI data using software-generated quantitative and qualitative output and trained visual inspection. Results: The data provided for this QC paper had relatively low motion-censoring, thus quantitative QC resulted in no exclusions. Qualitative checks of the data resulted in limited exclusions due to potential incidental findings and failed pre-processing scripts. Conclusion: Visual inspection in addition to the review of quantitative QC metrics is an important component to ensure high quality and accuracy in rs-fMRI data analysis

Functional brain networks: intra and inter-subject variability in healthy individuals and patients with neurological or neuropsychiatric diseases.

The projects of this thesis sits at the intersection between classical neuroscience and aspects related to engineering, signals’ and neuroimaging processing. Each of the three years has been dedicated to specific projects carried out on distinct datasets, groups of individuals/patients and methods, putting great emphasis on multidisciplinarity and international mobility. The studies carried out in Cagliari were based on EEG (electroencephalography), and the one conducted abroad was developed on functional magnetic resonance imaging (fMRI) data. The common thread of the project concerns variability and stability of individuals' features related primarily to functional connectivity and network, as well as to the periodic and aperiodic components of EEG power spectra, and their possible use for clinical purposes. In the first study (Fraschini et al., 2019) we aimed to investigate the impact of some of the most commonly used metrics to estimate functional connectivity on the ability to unveil personal distinctive patterns of inter-channel interaction. In the second study (Demuru et al., 2020) we performed a comparison between power spectral density and some widely used nodal network metrics, both at scalp and source level, with the aim of evaluating their possible association. The first first-authored study (Pani et al., 2020)was dedicated to investigate how the variability due to subject, session and task affects electroencephalogram(EEG) power, connectivity and network features estimated using source-reconstructed EEG time-series of healthy subjects. In the study carried out with the supervision of Prof. Fornito (https://doi.org/10.1016/j.pscychresns.2020.111202) during the experience at the Brain, Mind and Society Research Hub of Monash University, partial least square analysis has been applied on fMRI data of an healthy cohort to evaluate how different specific aspects of psychosis-like experiences related to functional connectivity. Due to the pandemic of Sars-Cov-2 it was impossible to continue recording the patients affected by neurological diseases (Parkinson’s, Diskynesia) involved in the study we planned for the third year, that should have replicated the design of the first first-authored one, with the aim of investigate how individual variability/stability of functional brain networks is affected by diseases. For the aforementioned reason, we carried out the last study on a dataset we finished to record in February 2020. The analysis has the aim of investigate whether it is possible by using 19 channels sleep scalp EEG to highlight differences in the brain of patients affected by non-rem parasomnias and sleep-related hypermotor epilepsy, when considering the periodic and aperiodic component of EEG power spectra