Janelle E. Letzen

Visual attention to dynamic faces and objects is linked to face processing skills: a combined study of children with autism and controls

Although the extant literature on face recognition skills in Autism Spectrum Disorder (ASD) shows clear impairments compared to typically developing controls (TDC) at the group level, the distribution of scores within ASD is broad. In the present research, we take a dimensional approach and explore how differences in social attention during an eye tracking experiment correlate with face recognition skills across ASD and TDC. Emotional discrimination and person identity perception face processing skills were assessed using the Lets Face It! Skills Battery in 110 children with and without ASD. Social attention was assessed using infrared eye gaze tracking during passive viewing of movies of facial expressions and objects displayed together on a computer screen. Face processing skills were significantly correlated with measures of attention to faces and with social skills as measured by the Social Communication Questionnaire (SCQ). Consistent with prior research, children with ASD scored significantly lower on face processing skills tests but, unexpectedly, group differences in amount of attention to faces (vs. objects) were not found. We discuss possible methodological contributions to this null finding. We also highlight the importance of a dimensional approach for understanding the developmental origins of reduced face perception skills, and emphasize the need for longitudinal research to truly understand how social motivation and social attention influence the development of social perceptual skills.

Abnormal resting state functional connectivity in patients with chronic fatigue syndrome: an arterial spin-labeling fMRI study ☆

BACKGROUND Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disorder characterized by severe fatigue and neurocognitive dysfunction. Recent work from our laboratory and others utilizing arterial spin labeling functional magnetic resonance imaging (ASL) indicated that ME/CFS patients have lower resting state regional cerebral blood flow (rCBF) in several brain areas associated with memory, cognitive, affective, and motor function. This hypoperfusion may underlie ME/CFS pathogenesis and may result in alterations of functional relationships between brain regions. The current report used ASL to compare functional connectivity of regions implicated in ME/CFS between patients and healthy controls (HC). METHODS Participants were 17 ME/CFS patients (Mage=48.88years, SD=12) fulfilling the 1994 CDC criteria and 17 age/sex matched HC (Mage=49.82years, SD=11.32). All participants underwent T1-weighted structural MRI as well as a 6-min pseudo-continuous arterial spin labeling (pCASL) sequence, which quantifies CBF by magnetically labeling blood as it enters the brain. Imaging data were preprocessed using SPM 12 and ASL tbx, and seed-to-voxel functional connectivity analysis was conducted using the CONN toolbox. All effects noted below are significant at p<0.05 with cluster-wise FDR correction for multiple comparisons. RESULTS ME/CFS patients demonstrated greater functional connectivity relative to HC in bilateral superior frontal gyrus, ACC, precuneus, and right angular gyrus to regions including precuneus, right postcentral gyrus, supplementary motor area, posterior cingulate gyrus, and thalamus. In contrast, HC patients had greater functional connectivity than ME/CFS in ACC, left parahippocampal gyrus, and bilateral pallidum to regions including right insula, right precentral gyrus, and hippocampus. Connectivity of the left parahippocampal gyrus correlated strongly with overall clinical fatigue of ME/CFS patients. CONCLUSION This is the first ASL based connectivity analysis of patients with ME/CFS. Our results demonstrate altered functional connectivity of several regions associated with cognitive, affective, memory, and higher cognitive function in ME/CFS patients. Connectivity to memory related brain areas (parahippocampal gyrus) was correlated with clinical fatigue ratings, providing supporting evidence that brain network abnormalities may contribute to ME/CFS pathogenesis.

Functional connectivity of the default mode network and its association with pain networks in irritable bowel patients assessed via lidocaine treatment.

UNLABELLED The default mode network (DMN), a group of brain regions implicated in passive thought processes, has been proposed as a potentially informative neural marker to aid in novel treatment development. However, the DMNs internal connectivity and its temporal relationship (ie, functional network connectivity) with pain-related neural networks in chronic pain conditions is poorly understood, as is the DMNs sensitivity to analgesic effects. The current study assessed how DMN functional connectivity and its temporal association with 3 pain-related networks changed after rectal lidocaine treatment in irritable bowel syndrome patients. Eleven females with irritable bowel syndrome underwent a rectal balloon distension paradigm during functional magnetic resonance imaging in 2 conditions: natural history (ie, baseline) and lidocaine. Results showed increased DMN connectivity with pain-related regions during natural history and increased within-network connectivity of DMN structures under lidocaine. Further, there was a significantly greater lag time between 2 of the pain networks, those involved in cognitive and in affective pain processes, comparing lidocaine to natural history. These findings suggest that 1) DMN plasticity is sensitive to analgesic effects, and 2) reduced pain ratings via analgesia reflect DMN connectivity more similar to pain-free individuals. Findings show potential implications of this network as an approach for understanding clinical pain management techniques. PERSPECTIVE This study shows that lidocaine, a peripheral analgesic, significantly altered DMN connectivity and affected its relationship with pain-related networks. These findings suggest that the DMN, which is hypothesized to represent non-goal-oriented activity, is sensitive to analgesic effects and could be useful to understand pain treatment mechanisms.

Effective connectivity predicts future placebo analgesic response: A dynamic causal modeling study of pain processing in healthy controls.

A better understanding of the neural mechanisms underlying pain processing and analgesia may aid in the development and personalization of effective treatments for chronic pain. Clarification of the neural predictors of individual variability in placebo analgesia (PA) could aid in this process. The present study examined whether the strength of effective connectivity (EC) among pain-related brain regions could predict future placebo analgesic response in healthy individuals. In Visit 1, fMRI data were collected from 24 healthy subjects (13 females, mean age=22.56, SD=2.94) while experiencing painful thermal stimuli. During Visit 2, subjects were conditioned to expect less pain via a surreptitiously lowered temperature applied at two of the four sites on their feet. They were subsequently scanned again using the Visit 1 (painful) temperature. Subjects used an electronic VAS to rate their pain following each stimulus. Differences in ratings at conditioned and unconditioned sites were used to measure placebo response (PA scores). Dynamic causal modeling was used to estimate the EC among a set of brain regions related to pain processing at Visit 1 (periaqueductal gray, thalamus, rostral anterior cingulate cortex, dorsolateral prefrontal cortex). Individual PA scores from Visit 2 were regressed on salient EC parameter estimates from Visit 1. Results indicate that both greater left hemisphere modulatory DLPFC➔PAG connectivity and right hemisphere, endogenous thalamus➔DLPFC connectivity were significantly predictive of future placebo response (R(2)=0.82). To our knowledge, this is the first study to identify the value of EC in understanding individual differences in PA, and may suggest the potential modifiability of endogenous pain modulation.

Test-retest reliability of pain-related functional brain connectivity compared with pain self-report.

Abstract Test-retest reliability, or reproducibility of results over time, is poorly established for functional brain connectivity (fcMRI) during painful stimulation. As reliability informs the validity of research findings, it is imperative to examine, especially given recent emphasis on using functional neuroimaging as a tool for biomarker development. Although proposed pain neural signatures have been derived using complex, multivariate algorithms, even the reliability of less complex fcMRI findings has yet to be reported. This study examined the test-retest reliability for fcMRI of pain-related brain regions, and self-reported pain (through visual analogue scales [VASs]). Thirty-two healthy individuals completed 3 consecutive fMRI runs of a thermal pain task. Functional connectivity analyses were completed on pain-related brain regions. Intraclass correlations were conducted on fcMRI values and VAS scores across the fMRI runs. Intraclass correlations coefficients for fcMRI values varied widely (range = −.174-.766), with fcMRI between right nucleus accumbens and medial prefrontal cortex showing the highest reliability (range = .649-.766). Intraclass correlations coefficients for VAS scores ranged from .906 to .947. Overall, self-reported pain was more reliable than fcMRI data. These results highlight that fMRI findings might be less reliable than inherently assumed and have implications for future studies proposing pain markers.

Static and dynamic functional connectivity in patients with chronic fatigue syndrome: use of arterial spin labelling fMRI.

Studies using arterial spin labelling (ASL) have shown that individuals with chronic fatigue syndrome (CFS) have decreased regional cerebral blood flow, which may be associated with changes in functional neural networks. Indeed, recent studies indicate disruptions in functional connectivity (FC) at rest in chronically fatigued patients including perturbations in static FC (sFC), that is average FC at rest between several brain regions subserving neurocognitive, motor and affect‐related networks. Whereas sFC often provides information of functional network reorganization in chronic illnesses, investigations of temporal changes in functional connectivity between multiple brain areas may shed light on the dynamic characteristics of brain network activation associated with such maladies. We used ASL fMRI in 19 patients with CFS and 15 healthy controls (HC) to examine both static and dynamic changes in FC among several a priori selected brain regions during a fatiguing cognitive task. HC showed greater increases than CFS in static FC (sFC) between insula and temporo‐occipital structures and between precuneus and thalamus/striatum. Furthermore, inferior frontal gyrus connectivity to cerebellum, occipital and temporal structures declined in HC but increased in CFS. Patients also showed lower dynamic FC (dFC) between hippocampus and right superior parietal lobule. Both sFC and dFC correlated with task‐related fatigue increases. These data provide the first evidence that perturbations in static and dynamic FC may underlie chronically fatigued patients’ report of task‐induced fatigue. Further research will determine whether such changes in sFC and dFC are also characteristic for other fatigued individuals, including patients with chronic pain, cancer and multiple sclerosis.

Negative mood influences default mode network functional connectivity in patients with chronic low back pain: implications for functional neuroimaging biomarkers.

Abstract The default mode network (DMN) has been proposed as a biomarker for several chronic pain conditions. Default mode network functional connectivity (FC) is typically examined during resting-state functional neuroimaging, in which participants are instructed to let thoughts wander. However, factors at the time of data collection (eg, negative mood) that might systematically impact pain perception and its brain activity, influencing the application of the DMN as a pain biomarker, are rarely reported. This study measured whether positive and negative moods altered DMN FC patterns in patients with chronic low back pain (CLBP), specifically focusing on negative mood because of its clinical relevance. Thirty-three participants (CLBP = 17) underwent resting-state functional magnetic resonance imaging scanning before and after sad and happy mood inductions, and rated levels of mood and pain intensity at the time of scanning. Two-way repeated-measures analysis of variances were conducted on resting-state functional connectivity data. Significant group (CLBP > healthy controls) × condition (sadness > baseline) interaction effects were identified in clusters spanning parietal operculum/postcentral gyrus, insular cortices, anterior cingulate cortex, frontal pole, and a portion of the cerebellum (PFDR < 0.05). However, only 1 significant cluster covering a portion of the cerebellum was identified examining a two-way repeated-measures analysis of variance for happiness > baseline (PFDR < 0.05). Overall, these findings suggest that DMN FC is affected by negative mood in individuals with and without CLBP. It is possible that DMN FC seen in patients with chronic pain is related to an affective dimension of pain, which is important to consider in future neuroimaging biomarker development and implementation.

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70–92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Interhemispheric Dorsolateral Prefrontal Cortex Connectivity is Associated with Individual Differences in Pain Sensitivity in Healthy Controls.

The dorsolateral prefrontal cortex (DLPFC) is implicated in pain modulation through multiple psychological processes. Recent noninvasive brain stimulation studies suggest that interhemispheric DLPFC connectivity influences pain tolerance and discomfort by altering interhemispheric inhibition. The structure and role of interhemispheric DLPFC connectivity in pain processing have not been investigated. The present study used dynamic causal modeling (DCM) for fMRI to investigate transcallosal DLPFC connectivity during painful stimulation in healthy volunteers. DCM parameters were used to predict individual differences in sensitivity to noxious heat stimuli. Bayesian model selection results indicated that influences among the right DLPFC (rDLPFC) and left DLPFC (lDLPFC) are modulated during painful stimuli. Regression analyses revealed that greater rDLPFC→lDLPFC couplings were associated with higher suprathreshold pain temperatures. These results highlight the role of interhemispheric connectivity in pain modulation and support the preferential role of the right hemisphere in pain processing. Knowledge of these mechanisms may improve understanding of abnormal pain modulation in chronic pain populations.

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.