Denoising the speaking brain: toward a robust technique for correcting artifact-contaminated fMRI data under severe motion

When remediating one artifact results in another: control, confounders, and correction

Scientists aim to remediate artifacts in their experimental datasets. However, the remediation of one artifact can result in another. Why might this happen, and what does this consequence tell us about how we should account for artifacts and their control? In this paper, I explore a case in functional neuroimaging where remediation appears to have caused this problem. I argue that remediation amounts to a change to an experimental arrangement. These changes need not be surgical, and the arrangement need not satisfy the criterion of causal modularity. Thus, remediation can affect more than just the factor responsible for the artifact. However, if researchers can determine the consequences of their remediation, they can make adjustments that control for the present artifact as well as for previously controlled ones. Current philosophical accounts of artifacts and the factors responsible for them cannot adequately address this issue, as they do not account for what is needed for artifact remediation (and specifically correction). I support my argument by paralleling it with ongoing concerns regarding the transparency of complex computational systems, as near future remediation across the experimental life sciences will likely make greater use of AI tools to correct for artifacts.

Multilayer network analysis of dynamic network reconfiguration in patients with moderate-to-severe obstructive sleep apnea and its association with neurocognitive function

BACKGROUND

Brain functional network disruption and neurocognitive dysfunction have been reported in obstructive sleep apnea (OSA) patients. Nevertheless, most research studies static networks, while brain evolution continues dynamically.

PURPOSE

To investigate the characteristics of dynamical networks in moderate-to-severe OSA patients using multilayer network analysis of dynamic networks and compare their association with neurocognitive function.

METHODS

Twenty-seven moderate-to-severe OSA patients and twenty-five matched healthy controls (HCs) who completed the examination of the Epworth sleepiness scale (ESS), neurocognitive function, polysomnography, and functional magnetic resonance imaging (fMRI) were prospectively included. The dynamic variations of resting-state functional networks in both groups were described via network switching rate. Switching rates and their correlation with clinical parameters were analyzed.

RESULTS

At the global level, network switching rates were notably lower in the OSA group than in the HCs group (p = 0.002). More specifically, the differences include the default mode network (DMN), auditory network, and ventral attention network at the subnetwork level, and the right rolandic operculum, left middle temporal gyrus, and right precentral gyrus at the nodal level. Furthermore, these altered switching rates have a close correlation with ESS, sleep parameters, and neurocognitive function.

CONCLUSION

Patients with moderate-to-severe OSA showed lower network switching rates, especially in the DMN, auditory network, and ventral attention network. The disruption of dynamic functional networks may be a potentially crucial mechanism of neurocognitive dysfunction in moderate-to-severe OSA patients.

Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired BOLD signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example in a chronic stroke cohort with varying stroke location and degree of tissue damage.

Brain activity during the preparation and production of spontaneous speech in children with persistent stuttering

Speech production forms the basis for human verbal communication. Though fluent speech production is effortless and automatic for most people, it is disrupted in speakers who stutter, who experience difficulties especially during spontaneous speech and at utterance onsets. Brain areas comprising the basal ganglia thalamocortical (BGTC) motor loop have been a focus of interest in the context of stuttering, given this circuit's critical role in initiating and sequencing connected speech. Despite the importance of better understanding the role of the BGTC motor loop in supporting overt, spontaneous speech production, capturing brain activity during speech has been challenging to date, due to fMRI artifacts associated with severe head motions during speech production. Here, using an advanced technique that removes speech-related artifacts from fMRI signals, we examined brain activity occurring immediately before, and during, overt spontaneous speech production in 22 children with persistent stuttering (CWS) and 18 children who do not stutter (controls) in the 5-to-12-year age range. Brain activity during speech production was compared in two conditions: spontaneous speech (i.e., requiring language formulation) and automatic speech (i.e., overlearned word sequences). Compared to controls, CWS exhibited significantly reduced left premotor activation during spontaneous speech production but not during automatic speech. Moreover, CWS showed an age-related reduction in left putamen and thalamus activation during speech preparation. These results provide further evidence that stuttering is associated with functional deficits in the BGTC motor loop, which are exacerbated during spontaneous speech production.

Neural activity during solo and choral reading: A functional magnetic resonance imaging study of overt continuous speech production in adults who stutter

Previous neuroimaging investigations of overt speech production in adults who stutter (AWS) found increased motor and decreased auditory activity compared to controls. Activity in the auditory cortex is heightened, however, under fluency-inducing conditions in which AWS temporarily become fluent while synchronizing their speech with an external rhythm, such as a metronome or another speaker. These findings suggest that stuttering is associated with disrupted auditory motor integration. Technical challenges in acquiring neuroimaging data during continuous overt speech production have limited experimental paradigms to short or covert speech tasks. Such paradigms are not ideal, as stuttering primarily occurs during longer speaking tasks. To address this gap, we used a validated spatial ICA technique designed to address speech movement artifacts during functional magnetic resonance imaging (fMRI) scanning. We compared brain activity and functional connectivity of the left auditory cortex during continuous speech production in two conditions: solo (stutter-prone) and choral (fluency-inducing) reading tasks. Overall, brain activity differences in AWS relative to controls in the two conditions were similar, showing expected patterns of hyperactivity in premotor/motor regions but underactivity in auditory regions. Functional connectivity of the left auditory cortex (STG) showed that within the AWS group there was increased correlated activity with the right insula and inferior frontal area during choral speech. The AWS also exhibited heightened connectivity between left STG and key regions of the default mode network (DMN) during solo speech. These findings indicate possible interference by the DMN during natural, stuttering-prone speech in AWS, and that enhanced coordination between auditory and motor regions may support fluent speech.

The effects of a simulated fMRI environment on voice intensity in individuals with Parkinson's disease hypophonia and older healthy adults

PURPOSE

Functional magnetic resonance imaging (fMRI) has promise for understanding neural mechanisms of neurogenic speech and voice disorders. However, performing vocal tasks within the fMRI environment may not always be analogous to performance outside of the scanner. Using a mock MRI scanner, this study examines the effects of a simulated scanning environment on vowel intensity in individuals with Parkinson's disease (PD) and hypophonia and older healthy control (OHC) participants.

METHOD

Thirty participants (15 PD, 15 OHC) performed a sustained /ɑ/ vowel production task in three conditions: 1) Upright, 2) Mock Scanner + No Noise, and 3) Mock Scanner + MRI noise. We used a linear mixed-effects (multi-level) model to evaluate the contributions of group and recording environment to vowel intensity. A second linear mixed-effects model was also used to evaluate the contributions of PD medication state (On vs. Off) to voice intensity.

RESULTS

Vowel intensity was significantly lower for PD compared to the OHC group. The intensity of vowels produced in the Upright condition was significantly lower compared to the Mock Scanner + No Noise condition, while vowel intensity in the Mock Scanner + MRI Noise condition was significantly higher compared to the Mock Scanner + No Noise condition. A group by condition interaction also indicated that the addition of scanner noise had a greater impact on the PD group. A second analysis conducted within the PD group showed no effects of medication state on vowel intensity.

CONCLUSION

Our findings demonstrate that performance on voice production tasks is altered for PD and OHC groups when translated into the fMRI environment, even in the absence of acoustic scanner noise. For fMRI studies of voice in PD hypophonia, careful thought should be given to how the presence of acoustic noise may differentially affect PD and OHC, for both group and task comparisons.

Into the Unknown: Examining Neural Representations of Parent-Adolescent Interactions

The parent-adolescent relationship is important for adolescents' emotion regulation (ER), yet little is known regarding the neural patterns of dyadic ER that occur during parent-adolescent interactions. A novel measure that can be used to examine such patterns is cross-brain connectivity (CBC)-concurrent and time-lagged connectivity between two individuals' brain regions. This study sought to provide evidence of CBC and explore associations between CBC, parenting, and adolescent internalizing symptoms. Thirty-five adolescents (mean age = 15 years, 69% female, 72% Non-Hispanic White, 17% Black, 11% Hispanic or Latino) and one biological parent (94% female) completed an fMRI hyperscanning conflict discussion task. Results revealed CBC between emotion-related brain regions. Exploratory analyses indicated CBC is associated with parenting and adolescent depressive symptoms.

Beyond synchrony: the capacity of fMRI hyperscanning for the study of human social interaction

Hyperscanning-simultaneous brain scanning of two or more individuals-holds great promise in elucidating the neurobiological underpinnings of social cognitive functions. This article focuses on functional magnetic resonance imaging (fMRI) hyperscanning and identifies promising targets for studying the neuroscience of social interaction with fMRI hyperscanning. Specifically, we present applications of fMRI hyperscanning in the study of social interaction along with promising analysis approaches for fMRI hyperscanning, with its high spatial and low temporal resolution. We first review fMRI hyperscanning studies in social neuroscience and evaluate the premise of using this costly neuroimaging paradigm. Many second-person social neuroscience studies are possible without fMRI hyperscanning. However, certain fundamental aspects of social cognition in real-life social interactions, including different roles of interactors, shared intention emerging through interaction and history of interaction, can be addressed only with hyperscanning. We argue that these fundamental aspects have not often been investigated in fMRI hyperscanning studies. We then discuss the implication of the signal coupling found in fMRI hyperscanning and consider analysis approaches that make fair use of it. With fMRI hyperscanning, we can explore not only synchronous brain activations but whole-brain asymmetric activation patterns with a lagged association between interacting individuals.

Modeling motor task activation from resting-state fMRI using machine learning in individual subjects

Resting-state functional MRI (rs-fMRI) has provided important insights into brain physiology. It has become an increasingly popular method for presurgical mapping, as an alternative to task-based functional MRI wherein the subject performs a task while being scanned. However, there is no commonly acknowledged gold standard approach for detecting eloquent brain areas using rs-fMRI data in clinical settings. In this study, a general linear model-based machine learning (GLM-ML) approach was tested to predict individual motor task activation based on rs-fMRI data. Its accuracy was then compared to a conventional independent component analysis (ICA) approach. 47 healthy subjects were scanned using resting state, active and passive motor task fMRI experiments using a clinically applicable low-resolution fMRI protocol. The model was trained to associate rs-fMRI network maps with that of hand movement task fMRI, then used to predict task activation maps for unseen subjects solely based on their rs-fMRI data. Our results showed that the GLM-ML approach can accurately predict individual differences in task activation using rs-fMRI data and outperform conventional ICA to detect task activation in the primary sensorimotor region. Furthermore, the predicted activation maps using the GLM -ML model matched well with the activation of passive hand movement fMRI on an individual basis. These results suggest that GLM-ML approach can robustly predict individual differences of task activation based on conventional low-resolution rs-fMRI data and has important implications for future clinical applications.

ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI

Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.

Whole brain dynamics during optogenetic self-stimulation of the medial prefrontal cortex in mice

Intracranial self-stimulation, in which an animal performs an operant response to receive regional brain electrical stimulation, is a widely used procedure to study motivated behavior. While local neuronal activity has long been measured immediately before or after the operant, imaging the whole brain in real-time remains a challenge. Herein we report a method that permits functional MRI (fMRI) of brain dynamics while mice are cued to perform an operant task: licking a spout to receive optogenetic stimulation to the medial prefrontal cortex (MPFC) during a cue ON, but not cue OFF. Licking during cue ON results in activation of a widely distributed network consistent with underlying MPFC projections, while licking during cue OFF (without optogenetic stimulation) leads to negative fMRI signal in brain regions involved in acute extinction. Noninvasive whole brain readout combined with circuit-specific neuromodulation opens an avenue for investigating adaptive behavior in both healthy and disease models.

A method to mitigate spatio-temporally varying task-correlated motion artifacts from overt-speech fMRI paradigms in aphasia

Quantifying accurate functional magnetic resonance imaging (fMRI) activation maps can be dampened by spatio‐temporally varying task‐correlated motion (TCM) artifacts in certain task paradigms (e.g., overt speech). Such real‐world tasks are relevant to characterize longitudinal brain reorganization poststroke, and removal of TCM artifacts is vital for improved clinical interpretation and translation. In this study, we developed a novel independent component analysis (ICA)‐based approach to denoise spatio‐temporally varying TCM artifacts in 14 persons with aphasia who participated in an overt language fMRI paradigm. We compared the new methodology with other existing approaches such as “standard” volume registration, nonselective motion correction ICA packages (i.e., AROMA), and combining the novel approach with AROMA. Results show that the proposed methodology outperforms other approaches in removing TCM‐related false positive activity (i.e., improved detectability power) with high spatial specificity. The proposed method was also effective in maintaining a balance between removal of TCM‐related trial‐by‐trial variability and signal retention. Finally, we show that the TCM artifact is related to clinical metrics, such as speech fluency and aphasia severity, and the implication of TCM denoising on such relationship is also discussed. Overall, our work suggests that routine bulkhead motion based denoising packages cannot effectively account for spatio‐temporally varying TCM. Further, the proposed TCM denoising approach requires a one‐time front‐end effort to hand label and train the classifiers that can be cost‐effectively utilized to denoise large clinical data sets.

Stuttering Severity Modulates Effects of Non-invasive Brain Stimulation in Adults Who Stutter

Stuttering is a neurodevelopmental disorder that manifests as frequent disruptions in the flow of speech, affecting 1% of adults. Treatments are limited to behavioral interventions with variable success and high relapse rates, particularly in adults. However, even in severe cases, fluency can be temporarily induced during conditions in which the speaker synchronizes his speech with external rhythmic cues, such as when reading in unison (choral speech) or with a metronome. Non-invasive neuromodulation techniques such as transcranial direct current stimulation (tDCS) have shown promise in augmenting the effects of behavioral treatment during motor and speech/language rehabilitation, but only one study to date has examined behavioral modulatory effects of tDCS in the context of stuttering. Using high-definition (HD)-tDCS electrodes, which improves focality of stimulation relative to conventional tDCS, we investigated the effects of tDCS on speech fluency and brain activation in 14 adults who stutter (AWS). Either anodal or sham stimulation was delivered on separate days over left supplementary motor area (SMA). During stimulation, participants read aloud in sync with a metronome. Measures of speech fluency and brain activity functional magnetic resonance imaging (fMRI) were collected before and after stimulation. No significant differences in brain activity or speech fluency were found when comparing active and sham stimulation. However, stuttering severity significantly modulated the effect of stimulation: active stimulation attenuated the atypically strong association between stuttering severity and right thalamocortical network activity, especially in more severe speakers. These preliminary results warrant additional research into potential application of HD-tDCS to modulate speech motor networks to enhance fluency in stuttering.

Hypoconnectivity of insular resting-state networks in adolescents with Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is characterized by deficits in social interaction and communication. The anterior insula (AI) participates in emotional salience detection; and the posterior insula (PI) participates in sensorimotor integration and response selection. Meta-analyses have noted insula-based aberrant connectivity within ASD. Given the observed social impairments in ASD and the role of the insula in social information processing (SIP), investigating functional organization of this structure in ASD is important. We investigated differences in resting-state functional connectivity (RSFC) using fMRI in male youths with (N=13; mean=14.6 years; range: 10.2-18.0 years) and without ASD (N=17; mean=14.5 years; range: 10.0-17.5 years). With seed-based FC measures, we compared RSFC in insular networks. Hypoconnectivity was observed in ASD (AI-superior frontal gyrus (SFG); AI-thalamus; PI-inferior parietal lobule (IPL); PI-fusiform gyrus (FG); PI-lentiform nucleus/putamen). Using the Social Communication Questionnaire (SCQ) to assess social functioning, regression analyses yielded negative correlations between SCQ scores and RSFC (AI-SFG; AI-thalamus; PI-FG; PI-IPL). Given the insula's connections to limbic regions, and its role in integrating external sensory stimuli with internal states, atypical activity in this structure may be associated with social deficits characterizing ASD. Our results suggest further investigation of the insula's role in SIP across a continuum of social abilities is needed.

Longitudinal Alterations in Prefrontal Resting Brain Connectivity in Non-Treatment-Seeking Young Adults With Cannabis Use Disorder

Background: Cannabis is increasingly perceived as a harmless drug by recreational users, yet chronic use may impact brain changes into adulthood. Repeated cannabis exposure has been associated with enduring synaptic changes in executive control and reward networks. It is important to determine whether there are brain functional alterations within these networks in individuals that do not seek treatment for chronic cannabis abuse. Methods: This longitudinal study compared resting-state functional connectivity changes in executive control and reward networks between 23 non-treatment-seeking young adults with cannabis use disorder (6 females; baseline age M = 19.3 ± 1.18) and 21 age-matched controls (10 females; baseline age M = 19.4 ± 0.65) to determine group differences in the temporal trajectories of resting-state functional connectivity across a 2-year span. Results: Results showed i) significant increases in resting-state functional connectivity between the caudal anterior cingulate cortex and precentral and parietal regions over time in the control group, but not in the cannabis use disorder group, and ii) sustained lower resting-state functional connectivity of anterior cingulate cortex seeds with frontal and thalamic regions in the cannabis use disorder group vs. the age-matched controls. Resting-state functional connectivity strength was correlated with cannabis use patterns in the cannabis use disorder sample. Conclusion: Longitudinal alterations in intrinsic functional organization of executive control networks found in non-treatment-seeking young adults with cannabis use disorder (when compared to age-matched controls) may impact regulatory control over substance use behavior. Current findings were limited to examining executive control and reward networks seeded in ACC and NAcc, respectively. Future studies with larger sample sizes and enough power are needed to conduct exploratory analyses examining rsFC of other networks beyond those within the scope of the current study.

Data Driven Classification Using fMRI Network Measures: Application to Schizophrenia

Using classification to identify biomarkers for various brain disorders has become a common practice among the functional MR imaging community. Typical classification pipeline includes taking the time series, extracting features from them, and using them to classify a set of patients and healthy controls. The most informative features are then presented as novel biomarkers. In this paper, we compared the results of single and double cross validation schemes on a cohort of 170 subjects with schizophrenia and healthy control subjects. We used graph theoretic measures as our features, comparing the use of functional and anatomical atlases to define nodes and the effect of prewhitening to remove autocorrelation trends. We found that double cross validation resulted in a 20% decrease in classification performance compared to single cross validation. The anatomical atlas resulted in higher classification results. Prewhitening resulted in a 10% boost in classification performance. Overall, a classification performance of 80% was obtained with a double-cross validation scheme using prewhitened time series and an anatomical brain atlas. However, reproducibility of classification within subjects across scans was surprisingly low and comparable to across subject classification rates, indicating that subject state during the short scan significantly influences the estimated features and classification performance.

Anomalous network architecture of the resting brain in children who stutter

PURPOSE

We combined a large longitudinal neuroimaging dataset that includes children who do and do not stutter and a whole-brain network analysis in order to examine the intra- and inter-network connectivity changes associated with stuttering. Additionally, we asked whether whole brain connectivity patterns observed at the initial year of scanning could predict persistent stuttering in later years.

METHODS

A total of 224 high-quality resting state fMRI scans collected from 84 children (42 stuttering, 42 controls) were entered into an independent component analysis (ICA), yielding a number of distinct network connectivity maps ("components") as well as expression scores for each component that quantified the degree to which it is expressed for each child. These expression scores were compared between stuttering and control groups' first scans. In a second analysis, we examined whether the components that were most predictive of stuttering status also predicted persistence in stuttering.

RESULTS

Stuttering status, as well as stuttering persistence, were associated with aberrant network connectivity involving the default mode network and its connectivity with attention, somatomotor, and frontoparietal networks. The results suggest developmental alterations in the balance of integration and segregation of large-scale neural networks that support proficient task performance including fluent speech motor control.

CONCLUSIONS

This study supports the view that stuttering is a complex neurodevelopmental disorder and provides comprehensive brain network maps that substantiate past theories emphasizing the importance of considering situational, emotional, attentional and linguistic factors in explaining the basis for stuttering onset, persistence, and recovery.

Reduced Functional Brain Activation and Connectivity During a Working Memory Task in Childhood-Onset Schizophrenia

OBJECTIVE

Working memory (WM) deficits are consistently reported in schizophrenia and are related to poor functional outcomes. Functional magnetic resonance imaging studies of adult-onset schizophrenia have reported decreased functional activations and connectivity in the WM network, but no prior functional magnetic resonance imaging study has examined WM in childhood-onset schizophrenia (COS). The aim of this study was to examine the neural correlates of WM in COS.

METHOD

Adult patients with COS (n = 32, 21.3 ± 1.1 years), nonpsychotic siblings of patients with COS (n = 30, 19.4 ± 0.8 years), and healthy controls (n = 39, 20.0 ± 0.7 years) completed 1- and 2-back WM tasks during 3-T functional magnetic resonance imaging. Functional activation and connectivity analyses were conducted. A separate group of 23 younger patients with COS (17.9 ± 7.4 years) could not perform the tasks after twice completing a standard training and are not included in this report.

RESULTS

Patients with COS who were included scored significantly lower than controls on all tasks (p < .001). Patients with COS showed significantly lower activations in the dorsolateral prefrontal cortices, posterior parietal cortices, cerebellum, and caudate and decreased frontoparietal and corticostriatal functional connectivity compared with controls (p < .05, corrected). Siblings had functional activations and connectivity intermediate between those of patients and controls in a similar set of regions (p < .05, corrected). In patients, functional connectivity strength in the left frontoparietal network correlated positively with accuracy scores during the 1-back task (p = .0023, corrected).

CONCLUSION

Decreased functional activation and connectivity in the WM network in COS supports pathophysiologic continuity with adult-onset schizophrenia. The low participation rate and accuracy of the patients highlights the disease severity of COS. Hypo-activations and hypo-connectivity were shared by siblings of patients with COS, suggesting COS as a potential endophenotype.

CLINICAL TRIAL REGISTRATION INFORMATION

Evaluating Genetic Risk Factors for Childhood-Onset Schizophrenia; http://ClinicalTrials.gov;NCT00001198.

Methods for cleaning the BOLD fMRI signal

Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.

Cohesion and Joint Speech: Right Hemisphere Contributions to Synchronized Vocal Production

Synchronized behavior (chanting, singing, praying, dancing) is found in all human cultures and is central to religious, military, and political activities, which require people to act collaboratively and cohesively; however, we know little about the neural underpinnings of many kinds of synchronous behavior (e.g., vocal behavior) or its role in establishing and maintaining group cohesion. In the present study, we measured neural activity using fMRI while participants spoke simultaneously with another person. We manipulated whether the couple spoke the same sentence (allowing synchrony) or different sentences (preventing synchrony), and also whether the voice the participant heard was "live" (allowing rich reciprocal interaction) or prerecorded (with no such mutual influence). Synchronous speech was associated with increased activity in posterior and anterior auditory fields. When, and only when, participants spoke with a partner who was both synchronous and "live," we observed a lack of the suppression of auditory cortex, which is commonly seen as a neural correlate of speech production. Instead, auditory cortex responded as though it were processing another talker's speech. Our results suggest that detecting synchrony leads to a change in the perceptual consequences of one's own actions: they are processed as though they were other-, rather than self-produced. This may contribute to our understanding of synchronized behavior as a group-bonding tool.

SIGNIFICANCE STATEMENT

Synchronized human behavior, such as chanting, dancing, and singing, are cultural universals with functional significance: these activities increase group cohesion and cause participants to like each other and behave more prosocially toward each other. Here we use fMRI brain imaging to investigate the neural basis of one common form of cohesive synchronized behavior: joint speaking (e.g., the synchronous speech seen in chants, prayers, pledges). Results showed that joint speech recruits additional right hemisphere regions outside the classic speech production network. Additionally, we found that a neural marker of self-produced speech, suppression of sensory cortices, did not occur during joint synchronized speech, suggesting that joint synchronized behavior may alter self-other distinctions in sensory processing.

Adverse Effects of Cannabis on Adolescent Brain Development: A Longitudinal Study

Cannabis is widely perceived as a safe recreational drug and its use is increasing in youth. It is important to understand the implications of cannabis use during childhood and adolescence on brain development. This is the first longitudinal study that compared resting functional connectivity of frontally mediated networks between 43 healthy controls (HCs; 20 females; age M = 16.5 ± 2.7) and 22 treatment-seeking adolescents with cannabis use disorder (CUD; 8 females; age M = 17.6 ± 2.4). Increases in resting functional connectivity between caudal anterior cingulate cortex (ACC) and superior frontal gyrus across time were found in HC, but not in CUD. CUD showed a decrease in functional connectivity between caudal ACC and dorsolateral and orbitofrontal cortices across time. Lower functional connectivity between caudal ACC cortex and orbitofrontal cortex at baseline predicted higher amounts of cannabis use during the following 18 months. Finally, high amounts of cannabis use during the 18-month interval predicted lower intelligence quotient and slower cognitive function measured at follow-up. These data provide compelling longitudinal evidence suggesting that repeated exposure to cannabis during adolescence may have detrimental effects on brain resting functional connectivity, intelligence, and cognitive function.

Reduction of Motion Artifacts and Noise Using Independent Component Analysis in Task-Based Functional MRI for Preoperative Planning in Patients with Brain Tumor

BACKGROUND AND PURPOSE

Although it is a potentially powerful presurgical tool, fMRI can be fraught with artifacts, leading to interpretive errors, many of which are not fully accounted for in routinely applied correction methods. The purpose of this investigation was to evaluate the effects of data denoising by independent component analysis in patients undergoing preoperative evaluation for glioma resection compared with more routinely applied correction methods such as realignment or motion scrubbing.

MATERIALS AND METHODS

Thirty-five functional runs (both motor and language) in 12 consecutive patients with glioma were analyzed retrospectively by double-blind review. Data were processed and compared with the following: 1) realignment alone, 2) motion scrubbing, 3) independent component analysis denoising, and 4) both independent component analysis denoising and motion scrubbing. Primary outcome measures included a change in false-positives, false-negatives, z score, and diagnostic rating.

RESULTS

Independent component analysis denoising reduced false-positives in 63% of studies versus realignment alone. There was also an increase in the z score in areas of true activation in 71.4% of studies. Areas of new expected activation (previous false-negatives) were revealed in 34.4% of cases with independent component analysis denoising versus motion scrubbing or realignment alone. Of studies deemed nondiagnostic with realignment or motion scrubbing alone, 65% were considered diagnostic after independent component analysis denoising.

CONCLUSIONS

The addition of independent component analysis denoising of fMRI data in preoperative patients with glioma has a significant impact on data quality, resulting in reduced false-positives and an increase in true-positives compared with more commonly applied motion scrubbing or simple realignment methods.

Relation between functional connectivity and rhythm discrimination in children who do and do not stutter

Our ability to perceive and produce rhythmic patterns in the environment supports fundamental human capacities ranging from music and language processing to the coordination of action. This article considers whether spontaneous correlated brain activity within a basal ganglia-thalamocortical (rhythm) network is associated with individual differences in auditory rhythm discrimination. Moreover, do children who stutter with demonstrated deficits in rhythm perception have weaker links between rhythm network functional connectivity and rhythm discrimination? All children in the study underwent a resting-state fMRI session, from which functional connectivity measures within the rhythm network were extracted from spontaneous brain activity. In a separate session, the same children completed an auditory rhythm-discrimination task, where behavioral performance was assessed using signal detection analysis. We hypothesized that in typically developing children, rhythm network functional connectivity would be associated with behavioral performance on the rhythm discrimination task, but that this relationship would be attenuated in children who stutter. Results supported our hypotheses, lending strong support for the view that (1) children who stutter have weaker rhythm network connectivity and (2) the lack of a relation between rhythm network connectivity and rhythm discrimination in children who stutter may be an important contributing factor to the etiology of stuttering.

Head Motion and Correction Methods in Resting-state Functional MRI

Resting-state functional magnetic resonance imaging (RS-fMRI) is used to investigate brain functional connectivity at rest. However, noise from human physiological motion is an unresolved problem associated with this technique. Following the unexpected previous result that group differences in head motion between control and patient groups caused group differences in the resting-state network with RS-fMRI, we reviewed the effects of human physiological noise caused by subject motion, especially motion of the head, on functional connectivity at rest detected with RS-fMRI. The aim of the present study was to review head motion artifact with RS-fMRI, individual and patient population differences in head motion, and correction methods for head motion artifact with RS-fMRI. Numerous reports have described new methods [e.g., scrubbing, regional displacement interaction (RDI)] for motion correction on RS-fMRI, many of which have been successful in reducing this negative influence. However, the influence of head motion could not be entirely excluded by any of these published techniques. Therefore, in performing RS-fMRI studies, head motion of the participants should be quantified with measurement technique (e.g., framewise displacement). Development of a more effective correction method would improve the accuracy of RS-fMRI analysis.

Brain activity and connectivity during poetry composition: Toward a multidimensional model of the creative process

Creativity, a multifaceted construct, can be studied in various ways, for example, investigating phases of the creative process, quality of the creative product, or the impact of expertise. Previous neuroimaging studies have assessed these individually. Believing that each of these interacting features must be examined simultaneously to develop a comprehensive understanding of creative behavior, we examined poetry composition, assessing process, product, and expertise in a single experiment. Distinct activation patterns were associated with generation and revision, two major phases of the creative process. Medial prefrontal cortex (MPFC) was active during both phases, yet responses in dorsolateral prefrontal and parietal executive systems (DLPFC/IPS) were phase‐dependent, indicating that while motivation remains unchanged, cognitive control is attenuated during generation and re‐engaged during revision. Experts showed significantly stronger deactivation of DLPFC/IPS during generation, suggesting that they may more effectively suspend cognitive control. Importantly however, similar overall patterns were observed in both groups, indicating the same cognitive resources are available to experts and novices alike. Quality of poetry, assessed by an independent panel, was associated with divergent connectivity patterns in experts and novices, centered upon MPFC (for technical facility) and DLPFC/IPS (for innovation), suggesting a mechanism by which experts produce higher quality poetry. Crucially, each of these three key features can be understood in the context of a single neurocognitive model characterized by dynamic interactions between medial prefrontal areas regulating motivation, dorsolateral prefrontal, and parietal areas regulating cognitive control and the association of these regions with language, sensorimotor, limbic, and subcortical areas distributed throughout the brain. Hum Brain Mapp 36:3351–3372, 2015. © 2015 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc..

Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM

Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p < 0.01) features were identified by general linear modeling and used to generate a classification model for the framework. This feature-optimized classification of ICs with SVM (FOCIS) framework was used to classify both group and single subject ICA results. The classification results obtained using FOCIS and previously published FSL-FIX were compared against manually evaluated results. On average the false negative rate in identifying artifact contaminated ICs for FOCIS and FSL-FIX were 98.27 and 92.34%, respectively. The number of artifact and functional network components increased almost linearly with the input NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC < 33, whereas only a few limited ICs are affected by direct splitting when NIC is incremented beyond NIC > 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data.

Personal experience with narrated events modulates functional connectivity within visual and motor systems during story comprehension

Past experience of everyday life activities, which forms the basis of our knowledge about the world, greatly affects how we understand stories. Yet, little is known about how this influence is instantiated in the human brain. Here, we used functional magnetic resonance imaging to investigate how past experience facilitates functional connectivity during the comprehension of stories rich in perceptual and motor details. We found that comprehenders' past experience with the scenes and actions described in the narratives selectively modulated functional connectivity between lower- and higher-level areas within the neural systems for visual and motor processing, respectively. These intramodal interactions may play an important role in integrating personal knowledge about a narrated situation with an evolving discourse representation. This study provides empirical evidence consistent with the idea that regions related to visual and motor processing are involved in the reenactment of experience as proposed by theories of embodied cognition.