Functional Magnetic Resonance Imaging Activation During Unscripted Discourse in People With Poststroke Aphasia.

PURPOSE: The purpose of this project was to determine the feasibility of employing a functional magnetic resonance imaging (fMRI) task that captured activation associated with overt, unscripted (or free) discourse of people with aphasia (PWA), using a continuous scan paradigm. METHOD: Seven participants (six females, ages 48-70 years) with chronic poststroke aphasia underwent two fMRI scanning sessions that included a discourse fMRI paradigm that consisted of five 1-min picture description tasks, using personally relevant photographs, interspersed with two 30-s control periods where participants looked at a fixation cross. Audio during the continuous fMRI scan was collected and marked with speaking times and coded for correct information units. Activation maps from the fMRI data were generated for the contrast between speaking and control conditions. In order to show the effects of the multi-echo data analysis, we compared it to a single-echo analysis by using only the middle echo (echo time of 30 ms). RESULTS: Through the implementation of the free discourse fMRI task, we were able to elicit activation that included bilateral regions in the planum polare, central opercular cortex, precentral gyrus, superior temporal gyrus, middle temporal gyrus, superior temporal gyrus, Crus I of the cerebellum, as well as bilateral occipital regions. CONCLUSIONS: We describe a new tool for assessing discourse recovery in PWA. By demonstrating the feasibility of a natural language paradigm in patients with chronic, poststroke aphasia, we open a new area for future research.

Augmented pain-evoked primary sensorimotor cortex activation in adolescent girls with juvenile fibromyalgia.

ABSTRACT: Juvenile fibromyalgia (JFM) is a chronic widespread pain condition that primarily affects adolescent girls. Previous studies have found increased sensitivity to noxious pressure in adolescents with JFM. However, the underlying changes in brain systems remain unclear. The aim of this study was to characterize pain-evoked brain responses and identify brain mediators of pain hypersensitivity in adolescent girls with JFM. Thirty-three adolescent girls with JFM and 33 healthy adolescent girls underwent functional magnetic resonance imaging scans involving noxious pressure applied to the left thumbnail at an intensity of 2.5 or 4 kg/cm 2 and rated pain intensity and unpleasantness on a computerized Visual Analogue Scale. We conducted standard general linear model analyses and exploratory whole-brain mediation analyses. The JFM group reported significantly greater pain intensity and unpleasantness than the control group in response to noxious pressure stimuli at both intensities ( P < 0.05). The JFM group showed augmented right primary somatosensory cortex (S1) activation to 4 kg/cm 2 (Z > 3.1, cluster-corrected P < 0.05), and the peak S1 activation magnitudes significantly correlated with the scores on the Widespread Pain Index ( r = 0.35, P = 0.048) with higher activation associated with more widespread pain. We also found that greater primary sensorimotor cortex activation in response to 4 kg/cm 2 mediated the between-group differences in pain intensity ratings ( P < 0.001). In conclusion, we found heightened sensitivity to noxious pressure stimuli and augmented pain-evoked sensorimotor cortex responses in adolescent girls with JFM, which could reflect central sensitization or amplified nociceptive input.

ABCD_Harmonizer: An Open-source Tool for Mapping and Controlling for Scanner Induced Variance in the Adolescent Brain Cognitive Development Study.

Data from multisite magnetic resonance imaging (MRI) studies contain variance attributable to the scanner that can reduce statistical power and potentially bias results if not appropriately managed. The Adolescent Cognitive Brain Development (ABCD) study is an ongoing, longitudinal neuroimaging study acquiring data from over 11,000 children starting at 9-10 years of age. These scans are acquired on 29 different scanners of 5 different model types manufactured by 3 different vendors. Publicly available data from the ABCD study include structural MRI (sMRI) measures such as cortical thickness and diffusion MRI (dMRI) measures such as fractional anisotropy. In this work, we 1) quantify the variance attributable to scanner effects in the sMRI and dMRI datasets, 2) demonstrate the effectiveness of the data harmonization approach called ComBat to address scanner effects, and 3) present a simple, open-source tool for investigators to harmonize image features from the ABCD study. Scanner-induced variance was present in every image feature and varied in magnitude by feature type and brain location. For almost all features, scanner variance exceeded variability attributable to age and sex. ComBat harmonization was shown to effectively remove scanner induced variance from all image features while preserving the biological variability in the data. Moreover, we show that for studies examining relatively small subsamples of the ABCD dataset, the use of ComBat harmonized data provides more accurate estimates of effect sizes compared to controlling for scanner effects using ordinary least squares regression.

Examining reaction time variability on the stop-signal task in the ABCD study.

OBJECTIVE: Reaction time variability (RTV) has been estimated using Gaussian, ex-Gaussian, and diffusion model (DM) indices. Rarely have studies examined interrelationships among these performance indices in childhood, and the use of reaction time (RT) computational models has been slow to take hold in the developmental psychopathology literature. Here, we extend prior work in adults by examining the interrelationships among different model parameters in the ABCD sample and demonstrate how computational models of RT can clarify mechanisms of time-on-task effects and sex differences in RTs. METHOD: This study utilized trial-level data from the stop signal task from 8916 children (9-10 years old) to examine Gaussian, ex-Gaussian, and DM indicators of RTV. In addition to describing RTV patterns, we examined interrelations among these indicators, temporal patterns, and sex differences. RESULTS: There was no one-to-one correspondence between DM and ex-Gaussian parameters. Nonetheless, drift rate was most strongly associated with standard deviation of RT and tau, while nondecisional processes were most strongly associated with RT, mu, and sigma. Performance worsened across time with changes driven primarily by decreasing drift rate. Boys were faster and less variable than girls, likely attributable to girls' wide boundary separation. CONCLUSIONS: Intercorrelations among model parameters are similar in children as has been observed in adults. Computational approaches play a crucial role in understanding performance changes over time and can also clarify mechanisms of group differences. For example, standard RT models may incorrectly suggest slowed processing speed in girls that is actually attributable to other factors.

Processing of pain by the developing brain: evidence of differences between adolescent and adult females.

ABSTRACT: Adolescence is a sensitive period for both brain development and the emergence of chronic pain particularly in females. However, the brain mechanisms supporting pain perception during adolescence remain unclear. This study compares perceptual and brain responses to pain in female adolescents and adults to characterize pain processing in the developing brain. Thirty adolescent (ages 13-17 years) and 30 adult (ages 35-55 years) females underwent a functional magnetic resonance imaging scan involving acute pain. Participants received 12 ten-second noxious pressure stimuli that were applied to the left thumbnail at 2.5 and 4 kg/cm 2 , and rated pain intensity and unpleasantness on a visual analogue scale. We found a significant group-by-stimulus intensity interaction on pain ratings. Compared with adults, adolescents reported greater pain intensity and unpleasantness in response to 2.5 kg/cm 2 but not 4 kg/cm 2 . Adolescents showed greater medial-lateral prefrontal cortex and supramarginal gyrus activation in response to 2.5 kg/cm 2 and greater medial prefrontal cortex and rostral anterior cingulate responses to 4 kg/cm 2 . Adolescents showed greater pain-evoked responses in the neurologic pain signature and greater activation in the default mode and ventral attention networks. Also, the amygdala and associated regions played a stronger role in predicting pain intensity in adolescents, and activity in default mode and ventral attention regions more strongly mediated the relationship between stimulus intensity and pain ratings. This study provides first evidence of greater low-pain sensitivity and pain-evoked brain responses in female adolescents (vs adult women) in regions important for nociceptive, affective, and cognitive processing, which may be associated with differences in peripheral nociception.

Brain Structural Changes During Juvenile Fibromyalgia: Relationships With Pain, Fatigue, and Functional Disability.

OBJECTIVE: Juvenile fibromyalgia (FM) is a prevalent chronic pain condition affecting children and adolescents worldwide during a critical period of brain development. To date, no published studies have addressed the pathophysiology of juvenile FM. This study was undertaken to characterize gray matter volume (GMV) alterations in juvenile FM patients for the first time, and to investigate their functional and clinical relevance. METHODS: Thirty-four female adolescents with juvenile FM and 38 healthy adolescents underwent a structural magnetic resonance imaging examination and completed questionnaires assessing core juvenile FM symptoms. Using voxel-based morphometry, we assessed between-group GMV differences and associations between GMV and functional disability, fatigue, and pain interference in juvenile FM. We also studied whether validated brain patterns predicting pain, cognitive control, or negative emotion were amplified/attenuated in juvenile FM patients and whether structural alterations reported in adult FM were replicated in adolescents with juvenile FM. RESULTS: Compared to controls, juvenile FM patients showed GMV reductions in the anterior midcingulate cortex (aMCC) region (family-wise error corrected P [PFWE-corr ] = 0.04; estimated with threshold-free cluster enhancement [TFCE]; n = 72) associated with pain. Within the juvenile FM group, patients reporting higher functional disability had larger GMV in inferior frontal regions (PFWE-corr = 0.006; TFCE estimated; n = 34) linked to affective, self-referential, and language-related processes. Last, GMV reductions in juvenile FM showed partial overlap with findings in adult FM, specifically for the anterior/posterior cingulate cortices (P = 0.02 and P = 0.03, respectively; n = 72). CONCLUSION: Pain-related aMCC reductions may be a structural hallmark of juvenile FM, whereas alterations in regions involved in emotional, self-referential, and language-related processes may predict disease impact on patients' well-being. The partial overlap between juvenile and adult FM findings strengthens the importance of early symptom identification and intervention to prevent the transition to adult forms of the disease.

DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults.

BACKGROUND: Although MR elastography allows for quantitative evaluation of liver stiffness to assess chronic liver diseases, it has associated drawbacks related to additional scanning time, patient discomfort, and added costs. OBJECTIVE: To develop a machine learning model that can categorically classify the severity of liver stiffness using both anatomical T2-weighted MRI and clinical data for children and young adults with known or suspected pediatric chronic liver diseases. MATERIALS AND METHODS: We included 273 subjects with known or suspected chronic liver disease. We extracted data including axial T2-weighted fast spin-echo fat-suppressed images, clinical data (e.g., demographic/anthropomorphic data, particular medical diagnoses, laboratory values) and MR elastography liver stiffness measurements. We propose DeepLiverNet (a deep transfer learning model) to classify patients into one of two groups: no/mild liver stiffening (<3 kPa) or moderate/severe liver stiffening (≥3 kPa). We conducted internal cross-validation using 178 subjects, and external validation using an independent cohort of 95 subjects. We assessed diagnostic performance using accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AuROC). RESULTS: In the internal cross-validation experiment, the combination of clinical and imaging data produced the best performance (AuROC=0.86) compared to clinical (AuROC=0.83) or imaging (AuROC=0.80) data alone. Using both clinical and imaging data, the DeepLiverNet correctly classified patients with accuracy of 88.0%, sensitivity of 74.3% and specificity of 94.6%. In our external validation experiment, this same deep learning model achieved an accuracy of 80.0%, sensitivity of 61.1%, specificity of 91.5% and AuROC of 0.79. CONCLUSION: A deep learning model that incorporates clinical data and anatomical T2-weighted MR images might provide a means of risk-stratifying liver stiffness and directing the use of MR elastography.

Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data.

OBJECTIVE. The purpose of this study is to develop a machine learning model to categorically classify MR elastography (MRE)-derived liver stiffness using clinical and nonelastographic MRI radiomic features in pediatric and young adult patients with known or suspected liver disease. MATERIALS AND METHODS. Clinical data (27 demographic, anthropomorphic, medical history, and laboratory features), MRI presence of liver fat and chemical shift-encoded fat fraction, and MRE mean liver stiffness measurements were retrieved from electronic medical records. MRI radiomic data (105 features) were extracted from T2-weighted fast spin-echo images. Patients were categorized by mean liver stiffness (< 3 vs ≥ 3 kPa). Support vector machine (SVM) models were used to perform two-class classification using clinical features, radiomic features, and both clinical and radiomic features. Our proposed model was internally evaluated in 225 patients (mean age, 14.1 years) and externally evaluated in an independent cohort of 84 patients (mean age, 13.7 years). Diagnostic performance was assessed using ROC AUC values. RESULTS. In our internal cross-validation model, the combination of clinical and radiomic features produced the best performance (AUC = 0.84), compared with clinical (AUC = 0.77) or radiomic (AUC = 0.70) features alone. Using both clinical and radiomic features, the SVM model was able to correctly classify patients with accuracy of 81.8%, sensitivity of 72.2%, and specificity of 87.0%. In our external validation experiment, this SVM model achieved an accuracy of 75.0%, sensitivity of 63.6%, specificity of 82.4%, and AUC of 0.80. CONCLUSION. An SVM learning model incorporating clinical and T2-weighted radiomic features has fair-to-good diagnostic performance for categorically classifying liver stiffness.

Changes in functional organization and functional connectivity during story listening in children with benign childhood epilepsy with centro-temporal spikes.

Children with Benign Epilepsy with Centrotemporal Spikes (BECTS), despite high likelihood for seizure remission, are reported to have subtle difficulties in language and other cognitive skills. We used functional MRI and a story listening task to examine the effect of BECTS on patterns of activation and connectivity. Language and cognitive skills were assessed using standardized measures. Twenty-four children with recently diagnosed BECTS and 40 typically-developing children participated. In a functionally-defined region of interest in right inferior frontal gyrus, BECTS patients showed a lower level of activation. Across both groups combined, increased activation in superior/middle temporal regions of interest was associated with better language scores. Connectivity in the story processing network was similar between groups, but connectivity within left inferior frontal gyrus was decreased in children with BECTS. These results suggest that language networks are largely maintained in new-onset BECTS, but some subtle changes in activation and connectivity can be observed.

Impact of frequency and lateralization of interictal discharges on neuropsychological and fine motor status in children with benign epilepsy with centrotemporal spikes.

Despite a positive prognosis for seizure remission, children with benign epilepsy with centrotemporal spikes (BECTS) have been reported to exhibit subtle neuropsychological difficulties. We examined the relationship between patterns of centrotemporal spikes (the typical electroencephalography [EEG] finding in BECTS) and neuropsychological and motor outcomes in children with new-onset BECTS. Thirty-four patients with new-onset BECTS (not taking antiepileptic medication) and 48 typically developing children participated in the study. In BECTS patients, centrotemporal spikes (CTS) were evaluated in the first hour awake and first 2 h of sleep in a 24-h EEG recording and left or right-sided origin was noted. General intellectual function, language, visuospatial skill, processing speed, and fine motor skill were assessed in all participants. We found no significant difference between BECTS patients and controls on measures of general intellectual function, or visuospatial or language testing. There were significant differences in processing speed index and nondominant hand fine motor scores between groups. Significant negative relationships were observed between rates of left-sided CTS and right hand fine motor scores. This suggests that psychomotor and fine motor speed are affected in BECTS, but the extent of affected domains may be more limited than previously suggested, especially in untreated patients early in the course of their epilepsy.

Simultaneous Electroencephalography and Functional Magnetic Resonance Imaging and the Identification of Epileptic Networks in Children.

EEG/fMRI takes advantage of the high temporal resolution of EEG in combination with the high spatial resolution of fMRI. These features make it particularly applicable to the study of epilepsy in which the event duration (e.g., interictal epileptiform discharges) is short, typically less than 200 milliseconds. Interictal or ictal discharges can be identified on EEG and be used for source localization in fMRI analyses. The acquisition of simultaneous EEG/fMRI involves the use of specialized EEG hardware that is safe in the MR environment and comfortable to the participant. Advanced data analysis approaches such as independent component analysis conducted alone or sometimes combined with other, e.g., Granger Causality or "sliding window" analyses are currently thought to be most appropriate for EEG/fMRI data. These approaches make it possible to identify networks of brain regions associated with ictal and/or interictal events allowing examination of the mechanisms critical for generation and propagation through these networks. After initial evaluation in adults, EEG/fMRI has been applied to the examination of the pediatric epilepsy syndromes including Childhood Absence Epilepsy, Benign Epilepsy with Centrotemporal Spikes (BECTS), Dravet Syndrome, and Lennox-Gastaut Syndrome. Results of EEG/fMRI studies suggest that the hemodynamic response measured by fMRI may have a different shape in response to epileptic events compared to the response to external stimuli; this may be especially true in the developing brain. Thus, the main goal of this review is to provide an overview of the pediatric applications of EEG/fMRI and its associated findings up until this point.

Relationship between receptive vocabulary and the neural substrates for story processing in preschoolers.

A left-lateralized fronto-temporo-parietal language network has been well-characterized in adults; however, the neural basis of this fundamental network has hardly been explored in the preschool years, despite this being a time for rapid language development and vocabulary growth. We examined the functional imaging correlates associated with vocabulary ability and narrative comprehension in 30 preschool children ages 3 to 5. Bilateral auditory cortex and superior temporal activation as well as left angular and supramarginal gyrus activation were observed during a passive listening-to-stories task. Boys showed greater activation than girls in the right anterior cingulate and right superior frontal gyrus (SFG). Finally, children with higher vocabulary scores showed increased grey matter left-lateralization and greater activation in bilateral thalamus, hippocampus, and left angular gyrus. This study is novel in its approach to relate left-hemisphere language regions and vocabulary scores in preschool-aged children using fMRI.

Combined analysis of sMRI and fMRI imaging data provides accurate disease markers for hearing impairment.

In this research, we developed a robust two-layer classifier that can accurately classify normal hearing (NH) from hearing impaired (HI) infants with congenital sensori-neural hearing loss (SNHL) based on their Magnetic Resonance (MR) images. Unlike traditional methods that examine the intensity of each single voxel, we extracted high-level features to characterize the structural MR images (sMRI) and functional MR images (fMRI). The Scale Invariant Feature Transform (SIFT) algorithm was employed to detect and describe the local features in sMRI. For fMRI, we constructed contrast maps and detected the most activated/de-activated regions in each individual. Based on those salient regions occurring across individuals, the bag-of-words strategy was introduced to vectorize the contrast maps. We then used a two-layer model to integrate these two types of features together. With the leave-one-out cross-validation approach, this integrated model achieved an AUC score of 0.90. Additionally, our algorithm highlighted several important brain regions that differentiated between NH and HI children. Some of these regions, e.g. planum temporale and angular gyrus, were well known auditory and visual language association regions. Others, e.g. the anterior cingulate cortex (ACC), were not necessarily expected to play a role in differentiating HI from NH children and provided a new understanding of brain function and of the disorder itself. These important brain regions provided clues about neuroimaging markers that may be relevant to the future use of functional neuroimaging to guide predictions about speech and language outcomes in HI infants who receive a cochlear implant. This type of prognostic information could be extremely useful and is currently not available to clinicians by any other means.