Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI

Machine Learning Approaches to Identify Affected Brain Regions in Movement Disorders Using MRI Data: A Systematic Review and Diagnostic Meta-analysis

BACKGROUND

Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. Advanced magnetic resonance imaging (MRI) techniques combined with machine learning (ML) are promising tools for identifying imaging biomarkers and patterns associated with these disorders.

PURPOSE/HYPOTHESIS

We aimed to systematically identify the brain regions most commonly affected in movement disorders using ML approaches applied to structural and functional MRI data. We searched the PubMed and Scopus databases using relevant keywords up to June 2023 for studies that used ML approaches to detect brain regions associated with movement disorders using MRI data.

STUDY TYPE

A systematic review and diagnostic meta-analysis.

POPULATION/SUBJECTS

Sixty-seven studies with 6,285 patients were included.

FIELD STRENGTH/SEQUENCE

Studies utilizing 1.5T or 3T MR scanners and the acquisition of diffusion tensor imaging (DTI), structural MRI (sMRI), functional MRI (fMRI), or a combination of these were included.

ASSESSMENT

The authors independently assessed the study quality using the CLAIM and QUADAS-2 criteria and extracted data on diagnostic accuracy measures.

STATISTICAL TESTS

Sensitivity, specificity, accuracy, and area under the curve were pooled using random-effects models. Q statistics and the I2 index were used to evaluate heterogeneity, and Begg's funnel plot was used to identify publication bias.

RESULTS

sMRI showed the highest sensitivity (93%) and mixed modalities had the highest specificity (90%) for detecting regional abnormalities. sMRI had a 94% sensitivity for identifying subcortical changes. The support vector machine (93%) and logistic regression (91%) models exhibited high diagnostic accuracies.

DATA CONCLUSION

The combination of advanced MR neuroimaging techniques and ML is a promising approach for identifying brain biomarkers and affected regions in movement disorders with subcortical structures frequently implicated. Structural MRI, in particular, showed strong performance.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2.

Multivariate classification provides a neural signature of Tourette disorder

BACKGROUND

Tourette disorder (TD), hallmarks of which are motor and vocal tics, has been related to functional abnormalities in large-scale brain networks. Using a fully data driven approach in a prospective, case-control study, we tested the hypothesis that functional connectivity of these networks carries a neural signature of TD. Our aim was to investigate (i) the brain networks that distinguish adult patients with TD from controls, and (ii) the effects of antipsychotic medication on these networks.

METHODS

Using a multivariate analysis based on support vector machine (SVM), we developed a predictive model of resting state functional connectivity in 48 patients and 51 controls, and identified brain networks that were most affected by disease and pharmacological treatments. We also performed standard univariate analyses to identify differences in specific connections across groups.

RESULTS

SVM was able to identify TD with 67% accuracy (p = 0.004), based on the connectivity in widespread networks involving the striatum, fronto-parietal cortical areas and the cerebellum. Medicated and unmedicated patients were discriminated with 69% accuracy (p = 0.019), based on the connectivity among striatum, insular and cerebellar networks. Univariate approaches revealed differences in functional connectivity within the striatum in patients v. controls, and between the caudate and insular cortex in medicated v. unmedicated TD.

CONCLUSIONS

SVM was able to identify a neuronal network that distinguishes patients with TD from control, as well as medicated and unmedicated patients with TD, holding a promise to identify imaging-based biomarkers of TD for clinical use and evaluation of the effects of treatment.

Altered brain connectivity in hyperkinetic movement disorders: A review of resting-state fMRI

BACKGROUND

Hyperkinetic movement disorders (HMD) manifest as abnormal and uncontrollable movements. Despite reported involvement of several neural circuits, exact connectivity profiles remain elusive.

OBJECTIVES

Providing a comprehensive literature review of resting-state brain connectivity alterations using resting-state fMRI (rs-fMRI). We additionally discuss alterations from the perspective of brain networks, as well as correlations between connectivity and clinical measures.

METHODS

A systematic review was performed according to PRISMA guidelines and searching PubMed until October 2022. Rs-fMRI studies addressing ataxia, chorea, dystonia, myoclonus, tics, tremor, and functional movement disorders (FMD) were included. The standardized mean difference was used to summarize findings per region in the Automated Anatomical Labeling atlas for each phenotype. Furthermore, the activation likelihood estimation meta-analytic method was used to analyze convergence of significant between-group differences per phenotype. Finally, we conducted hierarchical cluster analysis to provide additional insights into commonalities and differences across HMD phenotypes.

RESULTS

Most articles concerned tremor (51), followed by dystonia (46), tics (19), chorea (12), myoclonus (11), FMD (11), and ataxia (8). Altered resting-state connectivity was found in several brain regions: in ataxia mainly cerebellar areas; for chorea, the caudate nucleus; for dystonia, sensorimotor and basal ganglia regions; for myoclonus, the thalamus and cingulate cortex; in tics, the basal ganglia, cerebellum, insula, and frontal cortex; for tremor, the cerebello-thalamo-cortical circuit; finally, in FMD, frontal, parietal, and cerebellar regions. Both decreased and increased connectivity were found for all HMD. Significant spatial convergence was found for dystonia, FMD, myoclonus, and tremor. Correlations between clinical measures and resting-state connectivity were frequently described.

CONCLUSION

Key brain regions contributing to functional connectivity changes across HMD often overlap. Possible increases and decreases of functional connections of a specific region emphasize that HMD should be viewed as a network disorder. Despite the complex interplay of physiological and methodological factors, this review serves to gain insight in brain connectivity profiles across HMD phenotypes.

Classification of tic disorders based on functional MRI by machine learning: a study protocol

INTRODUCTION

Tic disorder (TD) is a common neurodevelopmental disorder in children, and it can be categorised into three subtypes: provisional tic disorder (PTD), chronic motor or vocal TD (CMT or CVT), and Tourette syndrome (TS). An early diagnostic classification among these subtypes is not possible based on a new-onset tic symptom. Machine learning tools have been widely used for early diagnostic classification based on functional MRI (fMRI). However, few machine learning models have been built for the diagnostic classification of patients with TD. Therefore, in the present study, we will provide a study protocol that uses the machine learning model to make early classifications of the three different types of TD.

METHODS AND ANALYSIS

We planned to recruit 200 children aged 6-9 years with new-onset tic symptoms and 100 age-matched and sex-matched healthy controls under resting-state MRI scanning. Based on the neuroimaging data of resting-state fMRI, the support vector machine (SVM) model will be built. We planned to construct an SVM model based on functional connectivity for the early diagnosis classification of TD subtypes (including PTD, CMT/CVT, TS).

ETHICS AND DISSEMINATION

This study was approved by the ethics committee of Beijing Children's Hospital. The trial results will be submitted to peer-reviewed journals for publication.

TRIAL REGISTRATION NUMBER

ChiCTR2000033257.

Rigid motion-resolved B1+ prediction using deep learning for real-time parallel-transmission pulse design

PURPOSE

Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx B 1 + distributions following within-slice motion, which can then be used for tailored pTx pulse redesign.

METHODS

Using simulated data, conditional generative adversarial networks were trained to predict B 1 + distributions in the head following a displacement. Predictions were made for two virtual body models that were not included in training. Predicted maps were compared with ground-truth (simulated, following motion) B1 maps. Tailored pTx pulses were designed using B1 maps at the original position (simulated, no motion) and evaluated using simulated B1 maps at displaced position (ground-truth maps) to quantify motion-related excitation error. A second pulse was designed using predicted maps (also evaluated on ground-truth maps) to investigate improvement offered by the proposed method.

RESULTS

Predicted B 1 + maps corresponded well with ground-truth maps. Error in predicted maps was lower than motion-related error in 99% and 67% of magnitude and phase evaluations, respectively. Worst-case flip-angle normalized RMS error due to motion (76% of target flip angle) was reduced by 59% when pulses were redesigned using predicted maps.

CONCLUSION

We propose a framework for predicting B 1 + maps online with deep neural networks. Predicted maps can then be used for real-time tailored pulse redesign, helping to overcome head motion-related error in pTx.

Real-time motion monitoring improves functional MRI data quality in infants

Imaging the infant brain with MRI has improved our understanding of early neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are typically scanned while asleep, they commonly exhibit motion during scanning causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Here, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD ≤ 0.2 mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging.

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MRI studies of the infant brain are critical for studying early neurodevelopment.

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Head motion diminishes MRI data quality, which can adversely affect infant imaging.

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We show that real-time head motion monitoring improves fMRI scan quality in infants.

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Being able to monitor motion during fMRI acquisition improves scanning efficiency.

Enhancing neuroimaging genetics through meta-analysis for Tourette syndrome (ENIGMA-TS): A worldwide platform for collaboration

Tourette syndrome (TS) is characterized by multiple motor and vocal tics, and high-comorbidity rates with other neuropsychiatric disorders. Obsessive compulsive disorder (OCD), attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASDs), major depressive disorder (MDD), and anxiety disorders (AXDs) are among the most prevalent TS comorbidities. To date, studies on TS brain structure and function have been limited in size with efforts mostly fragmented. This leads to low-statistical power, discordant results due to differences in approaches, and hinders the ability to stratify patients according to clinical parameters and investigate comorbidity patterns. Here, we present the scientific premise, perspectives, and key goals that have motivated the establishment of the Enhancing Neuroimaging Genetics through Meta-Analysis for TS (ENIGMA-TS) working group. The ENIGMA-TS working group is an international collaborative effort bringing together a large network of investigators who aim to understand brain structure and function in TS and dissect the underlying neurobiology that leads to observed comorbidity patterns and clinical heterogeneity. Previously collected TS neuroimaging data will be analyzed jointly and integrated with TS genomic data, as well as equivalently large and already existing studies of highly comorbid OCD, ADHD, ASD, MDD, and AXD. Our work highlights the power of collaborative efforts and transdiagnostic approaches, and points to the existence of different TS subtypes. ENIGMA-TS will offer large-scale, high-powered studies that will lead to important insights toward understanding brain structure and function and genetic effects in TS and related disorders, and the identification of biomarkers that could help inform improved clinical practice.

What have we really learned from functional connectivity in clinical populations?

Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.

Standardizing human brain parcellations

Using brain atlases to localize regions of interest is a requirement for making neuroscientifically valid statistical inferences. These atlases, represented in volumetric or surface coordinate spaces, can describe brain topology from a variety of perspectives. Although many human brain atlases have circulated the field over the past fifty years, limited effort has been devoted to their standardization. Standardization can facilitate consistency and transparency with respect to orientation, resolution, labeling scheme, file storage format, and coordinate space designation. Our group has worked to consolidate an extensive selection of popular human brain atlases into a single, curated, open-source library, where they are stored following a standardized protocol with accompanying metadata, which can serve as the basis for future atlases. The repository containing the atlases, the specification, as well as relevant transformation functions is available in the neuroparc OSF registered repository or https://github.com/neurodata/neuroparc .

Abnormal information flow in postpartum depression: A resting-state functional magnetic resonance imaging study

BACKGROUND

Postpartum depression (PPD) is a common mental disorder among women. However, the brain information flow alteration in patients with PPD remains unclear. This study investigated the brain information flow characteristics of patients with PPD and their value for clinical evaluation by using support vector regression (SVR).

METHODS

Structural and resting-state functional magnetic resonance imaging data were acquired from 21 patients with PPD and 23 age-, educational level-, body mass index-, and menstruation-matched healthy controls. The preferred information flow direction between local brain regions and the preferred information flow direction index within local brain regions based on non-parametric multiplicative regression granger causality analysis were calculated to determine the global and local brain functional characteristics of the patients with PPD. Pearson's correlation analyses were performed to evaluate the relationship of the information flow characteristics with clinical scales. A predictive model for the mental state of the patients with PPD was established using SVR based on information flow characteristics.

RESULTS

The information flow patterns in the amygdala, cingulum gyrus, insula, hippocampus, frontal lobe, parietal lobe, and occipital lobe changed significantly in the patients with PPD. The preferred information flow direction between the amygdala and the temporal and frontal lobes significantly correlated with clinical scales. Prediction analysis shows that the information flow patterns can be used to assess depression in patients with PPD.

LIMITATION

This exploratory study has a small sample size with no longitudinal research.

CONCLUSION

The change in information flow pattern in the amygdala may play an important role in the neuropathological mechanism of PPD and may provide promising markers for clinical evaluation.

Discrimination of Tourette Syndrome Based on the Spatial Patterns of the Resting-State EEG Network

Tourette syndrome (TS) is a neuropsychiatric disorder with childhood onset characterized by chronic motor and vocal tics; however, the current diagnosis of TS patients is subjective, as it is mainly assessed based on the parents' description alongside specific evaluations. The early and accurate diagnosis of TS based on its potential symptoms in children would be of benefit in their future therapy, but reliable diagnoses are difficult due to the lack of objective knowledge of the etiology and pathogenesis of TS. In this study, resting-state electroencephalograms were first collected from 36 patients and 21 healthy controls (HCs); the corresponding resting-state functional networks were then constructed, and the potential differences in network topology between the two groups were extracted by using the topology of the spatial pattern of the network (SPN). Compared to the HCs, the TS patients exhibited decreased frontotemporal/occipital/parietal connectivity. When classifying the two groups, compared to the network properties, the derived SPN features achieved a much higher accuracy of 92.31%. The intrinsic long-range connectivity between the frontal and the temporal/occipital/parietal lobes was damaged in the patient group, and this dysfunctional network pattern might serve as a reliable biomarker to differentiate TS patients from HCs as well as to assess the severity of tic symptoms.

Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry

Studies comparing diverse groups have shown that many psychiatric diseases involve disruptions across distributed large-scale networks of the brain. There is hope that functional magnetic resonance imaging (fMRI) functional connectivity techniques will shed light on these disruptions, providing prognostic and diagnostic biomarkers as well as targets for therapeutic interventions. However, to date, progress on clinical translation of fMRI methods has been limited. Here, we argue that this limited translation is driven by a combination of intersubject heterogeneity and the relatively low reliability of standard fMRI techniques at the individual level. We review a potential solution to these limitations: the use of new "precision" fMRI approaches that shift the focus of analysis from groups to single individuals through the use of extended data acquisition strategies. We begin by discussing the potential advantages of fMRI functional connectivity methods for improving our understanding of functional neuroanatomy and disruptions in psychiatric disorders. We then discuss the budding field of precision fMRI and findings garnered from this work. We demonstrate that precision fMRI can improve the reliability of functional connectivity measures, while showing high stability and sensitivity to individual differences. We close by discussing the application of these approaches to clinical settings.

Bagging improves reproducibility of functional parcellation of the human brain

Increasing the reproducibility of neuroimaging measurement addresses a central impediment to the advancement of human neuroscience and its clinical applications. Recent efforts demonstrating variance in functional brain organization within and between individuals shows a need for improving reproducibility of functional parcellations without long scan times. We apply bootstrap aggregation, or bagging, to the problem of improving reproducibility in functional parcellation. We use two large datasets to demonstrate that compared to a standard clustering framework, bagging improves the reproducibility and test-retest reliability of both cortical and subcortical functional parcellations across a range of sites, scanners, samples, scan lengths, clustering algorithms, and clustering parameters (e.g., number of clusters, spatial constraints). With as little as 6 ​min of scan time, bagging creates more reproducible group and individual level parcellations than standard approaches with twice as much data. This suggests that regardless of the specific parcellation strategy employed, bagging may be a key method for improving functional parcellation and bringing functional neuroimaging-based measurement closer to clinical impact.

The New Tics study: A Novel Approach to Pathophysiology and Cause of Tic Disorders

We report on the ongoing project "The New Tics Study: A Novel Approach to Pathophysiology and Cause of Tic Disorders," describing the work completed to date, ongoing studies and long-term goals. The overall goals of this research are to study the pathophysiology of Provisional Tic Disorder, and to study tic remission (or improvement) in a prospective fashion. Preliminary data collection for the project began almost 10 years ago. The current study is nearing completion of its third year, and has already reported several novel and important results. First, surprisingly, at least 90% of children who had experienced tics for only a mean of 3 months still had tics at the 12-month anniversary of their first tic, though in some cases tics were seen only with remote video observation of the child sitting alone. Thus almost all of them now had a DSM-5 diagnosis of Tourette's Disorder or Persistent (Chronic) Tic Disorder. Baseline clinical features that predicted 12-month outcome included tic severity, subsyndromal autism spectrum symptoms, an anxiety disorder, and a history of 3 or more phonic tics. Second, we found that poorer tic suppression ability when immediately rewarded for suppression predicted greater tic severity at follow-up. Third, striatal volumes did not predict outcome as hypothesized, but a larger hippocampus at baseline predicted worse severity at follow-up. Enrollment and data collection continue, including functional connectivity MRI (fcMRI) imaging, and additional analyses are planned once the full sample is enrolled.

Specific absorption rate implications of within-scan patient head motion for ultra-high field MRI

PURPOSE

This study investigates the implications of all degrees of freedom of within-scan patient head motion on patient safety.

METHODS

Electromagnetic simulations were performed by displacing and/or rotating a virtual body model inside an 8-channel transmit array to simulate 6 degrees of freedom of motion. Rotations of up to 20° and displacements of up to 20 mm including off-axis axial/coronal translations were investigated, yielding 104 head positions. Quadrature excitation, RF shimming, and multi-spoke parallel-transmit excitation pulses were designed for axial slice-selection at 7T, for seven slices across the head. Variation of whole-head specific absorption rate (SAR) and 10-g averaged local SAR of the designed pulses, as well as the change in the maximum eigenvalue (worst-case pulse) were investigated by comparing off-center positions to the central position.

RESULTS

In their respective worst-cases, patient motion increased the eigenvalue-based local SAR by 42%, whole-head SAR by 60%, and the 10-g averaged local SAR by 210%. Local SAR was observed to be more sensitive to displacements along right-left and anterior-posterior directions than displacement in the superior-inferior direction and rotation.

CONCLUSION

This is the first study to investigate the effect of all 6 degrees of freedom of motion on safety of practical pulses. Although the results agree with the literature for overlapping cases, the results demonstrate higher increases (up to 3.1-fold) in local SAR for off-axis displacement in the axial plane, which had received less attention in the literature. This increase in local SAR could potentially affect the local SAR compliance of subjects, unless realistic within-scan patient motion is taken into account during pulse design.

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

An important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011, and (2) 333 cortical surface parcels reported in Gordon et al., 2016. However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.

Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity

Distinguishing individuals from brain connectivity, and studying the genetic influences on that identification across different ages, improves our basic understanding of functional brain network organization. We applied support vector machine classifiers to two datasets of twins (adult, pediatric) and two datasets of repeat-scan individuals (adult, pediatric). Classifiers were trained on resting state functional connectivity magnetic resonance imaging (rs-fcMRI) data and used to predict individuals and co-twin pairs from independent data. The classifiers successfully identified individuals from a previous scan with 100% accuracy, even when scans were separated by months. In twin samples, classifier accuracy decreased as genetic similarity decreased. Our results demonstrate that classification is stable within individuals, similar within families, and contains similar representations of functional connections over a few decades of life. Moreover, the degree to which these patterns of connections predict siblings' data varied by genetic relatedness, suggesting that genetic influences on rs-fcMRI connectivity are established early in life.

Portable, field-based neuroimaging using high-density diffuse optical tomography

Behavioral and cognitive tests in individuals who were malnourished as children have revealed malnutrition-related deficits that persist throughout the lifespan. These findings have motivated recent neuroimaging investigations that use highly portable functional near-infrared spectroscopy (fNIRS) instruments to meet the demands of brain imaging experiments in low-resource environments and enable longitudinal investigations of brain function in the context of long-term malnutrition. However, recent studies in healthy subjects have demonstrated that high-density diffuse optical tomography (HD-DOT) can significantly improve image quality over that obtained with sparse fNIRS imaging arrays. In studies of both task activations and resting state functional connectivity, HD-DOT is beginning to approach the data quality of fMRI for superficial cortical regions. In this work, we developed a customized HD-DOT system for use in malnutrition studies in Cali, Colombia. Our results evaluate the performance of the HD-DOT instrument for assessing brain function in a cohort of malnourished children. In addition to demonstrating portability and wearability, we show the HD-DOT instrument's sensitivity to distributed brain responses using a sensory processing task and measurements of homotopic functional connectivity. Task-evoked responses to the passive word listening task produce activations localized to bilateral superior temporal gyrus, replicating previously published work using this paradigm. Evaluating this localization performance across sparse and dense reconstruction schemes indicates that greater localization consistency is associated with a dense array of overlapping optical measurements. These results provide a foundation for additional avenues of investigation, including identifying and characterizing a child's individual malnutrition burden and eventually contributing to intervention development.

Atypical Functional Connectivity in Tourette Syndrome Differs Between Children and Adults

BACKGROUND

Tourette syndrome (TS) is a neuropsychiatric disorder with symptomatology that typically changes over development. Whether and how brain function in TS also differs across development has been largely understudied. Here, we used functional connectivity magnetic resonance imaging to examine whole-brain functional networks in children and adults with TS.

METHODS

Multivariate classification methods were used to find patterns among functional connections that distinguish individuals with TS from control subjects separately for children and adults (N = 202). We tested whether the patterns of connections that classify diagnosis in one age group (e.g., children) could classify diagnosis in another age group (e.g., adults). We also tested whether the developmental trajectory of these connections was altered in TS.

RESULTS

Diagnostic classification was successful in children and adults separately but expressly did not generalize across age groups, suggesting that the patterns of functional connections that best distinguished individuals with TS from control subjects were age specific. Developmental patterns among these functional connections used for diagnostic classification deviated from typical development. Brain networks in childhood TS appeared "older" and brain networks in adulthood TS appeared "younger" in comparison with typically developing individuals.

CONCLUSIONS

Our results demonstrate that brain networks are differentially altered in children and adults with TS. The observed developmental trajectory of affected connections is consistent with theories of accelerated and/or delayed maturation, but may also involve anomalous developmental pathways. These findings further our understanding of neurodevelopmental trajectories in TS and carry implications for future applications aimed at predicting the clinical course of TS in individuals over development.

ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI Data

Heterogeneous mental disorders such as Autism Spectrum Disorder (ASD) are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioral observation of symptomology (DSM-5/ICD-10) and may be prone to misdiagnosis. In order to move the field toward more quantitative diagnosis, we need advanced and scalable machine learning infrastructure that will allow us to identify reliable biomarkers of mental health disorders. In this paper, we propose a framework called ASD-DiagNet for classifying subjects with ASD from healthy subjects by using only fMRI data. We designed and implemented a joint learning procedure using an autoencoder and a single layer perceptron (SLP) which results in improved quality of extracted features and optimized parameters for the model. Further, we designed and implemented a data augmentation strategy, based on linear interpolation on available feature vectors, that allows us to produce synthetic datasets needed for training of machine learning models. The proposed approach is evaluated on a public dataset provided by Autism Brain Imaging Data Exchange including 1, 035 subjects coming from 17 different brain imaging centers. Our machine learning model outperforms other state of the art methods from 10 imaging centers with increase in classification accuracy up to 28% with maximum accuracy of 82%. The machine learning technique presented in this paper, in addition to yielding better quality, gives enormous advantages in terms of execution time (40 min vs. 7 h on other methods). The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/ASD-DiagNet).

Machine Learning With Neuroimaging: Evaluating Its Applications in Psychiatry

Psychiatric disorders are complex, involving heterogeneous symptomatology and neurobiology that rarely involves the disruption of single, isolated brain structures. In an attempt to better describe and understand the complexities of psychiatric disorders, investigators have increasingly applied multivariate pattern classification approaches to neuroimaging data and in particular supervised machine learning methods. However, supervised machine learning approaches also come with unique challenges and trade-offs, requiring additional study design and interpretation considerations. The goal of this review is to provide a set of best practices for evaluating machine learning applications to psychiatric disorders. We discuss how to evaluate two common efforts: 1) making predictions that have the potential to aid in diagnosis, prognosis, and treatment and 2) interrogating the complex neurophysiological mechanisms underlying psychopathology. We focus here on machine learning as applied to functional connectivity with magnetic resonance imaging, as an example to ground discussion. We argue that for machine learning classification to have translational utility for individual-level predictions, investigators must ensure that the classification is clinically informative, independent of confounding variables, and appropriately assessed for both performance and generalizability. We contend that shedding light on the complex mechanisms underlying psychiatric disorders will require consideration of the unique utility, interpretability, and reliability of the neuroimaging features (e.g., regions, networks, connections) identified from machine learning approaches. Finally, we discuss how the rise of large, multisite, publicly available datasets may contribute to the utility of machine learning approaches in psychiatry.

Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation

The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine.

Evaluating the Prediction of Brain Maturity From Functional Connectivity After Motion Artifact Denoising

The ability to make individual-level predictions from neuroanatomy has the potential to be particularly useful in child development. Previously, resting-state functional connectivity (RSFC) MRI has been used to successfully predict maturity and diagnosis of typically and atypically developing individuals. Unfortunately, submillimeter head motion in the scanner produces systematic, distance-dependent differences in RSFC and may contaminate, and potentially facilitate, these predictions. Here, we evaluated individual age prediction with RSFC after stringent motion denoising. Using multivariate machine learning, we found that 57% of the variance in individual RSFC after motion artifact denoising was explained by age, while 4% was explained by residual effects of head motion. When RSFC data were not adequately denoised, 50% of the variance was explained by motion. Reducing motion-related artifact also revealed that prediction did not depend upon characteristics of functional connections previously hypothesized to mediate development (e.g., connection distance). Instead, successful age prediction relied upon sampling functional connections across multiple functional systems with strong, reliable RSFC within an individual. Our results demonstrate that RSFC across the brain is sufficiently robust to make individual-level predictions of maturity in typical development, and hence, may have clinical utility for the diagnosis and prognosis of individuals with atypical developmental trajectories.

Provisional Tic Disorder is not so transient

Motor and vocal tics are common in childhood. The received wisdom among clinicians is that for most children the tics are temporary, disappearing within a few months. However, that common clinical teaching is based largely on biased and incomplete data. The present study was designed to prospectively assess outcome of children with what the current nomenclature calls Provisional Tic Disorder. We identified 43 children with recent onset tics (mean 3.3 months since tic onset) and re-examined 39 of them on the 12-month anniversary of their first tic. Tic symptoms improved on a group level at the 12-month follow-up, and only two children had more than minimal impairment due to tics. Remarkably, however, tics were present in all children at follow-up, although in several cases tics were apparent only when the child was observed remotely by video. Our results suggest that remission of Provisional Tic Disorder is the exception rather than the rule. We also identified several clinical features present at the first examination that predict one-year outcome; these include baseline tic severity, subsyndromal autism spectrum symptoms, and the presence of an anxiety disorder.

Aberrant structural and functional connectivity and neurodevelopmental impairment in preterm children

Background

Despite advances in antenatal and neonatal care, preterm birth remains a leading cause of neurological disabilities in children. Infants born prematurely, particularly those delivered at the earliest gestational ages, commonly demonstrate increased rates of impairment across multiple neurodevelopmental domains. Indeed, the current literature establishes that preterm birth is a leading risk factor for cerebral palsy, is associated with executive function deficits, increases risk for impaired receptive and expressive language skills, and is linked with higher rates of co-occurring attention deficit hyperactivity disorder, anxiety, and autism spectrum disorders. These same infants also demonstrate elevated rates of aberrant cerebral structural and functional connectivity, with persistent changes evident across advanced magnetic resonance imaging modalities as early as the neonatal period. Emerging findings from cross-sectional and longitudinal investigations increasingly suggest that aberrant connectivity within key functional networks and white matter tracts may underlie the neurodevelopmental impairments common in this population.

Main body

This review begins by highlighting the elevated rates of neurodevelopmental disorders across domains in this clinical population, describes the patterns of aberrant structural and functional connectivity common in prematurely-born infants and children, and then reviews the increasingly established body of literature delineating the relationship between these brain abnormalities and adverse neurodevelopmental outcomes. We also detail important, typically understudied, clinical, and social variables that may influence these relationships among preterm children, including heritability and psychosocial risks.

Conclusion

Future work in this domain should continue to leverage longitudinal evaluations of preterm infants which include both neuroimaging and detailed serial neurodevelopmental assessments to further characterize relationships between imaging measures and impairment, information necessary for advancing our understanding of modifiable risk factors underlying these disorders and best practices for improving neurodevelopmental trajectories in this high-risk clinical population.

Spatial and Temporal Organization of the Individual Human Cerebellum

The cerebellum contains the majority of neurons in the human brain and is unique for its uniform cytoarchitecture, absence of aerobic glycolysis, and role in adaptive plasticity. Despite anatomical and physiological differences between the cerebellum and cerebral cortex, group-average functional connectivity studies have identified networks related to specific functions in both structures. Recently, precision functional mapping of individuals revealed that functional networks in the cerebral cortex exhibit measurable individual specificity. Using the highly sampled Midnight Scan Club (MSC) dataset, we found the cerebellum contains reliable, individual-specific network organization that is significantly more variable than the cerebral cortex. The frontoparietal network, thought to support adaptive control, was the only network overrepresented in the cerebellum compared to the cerebral cortex (2.3-fold). Temporally, all cerebellar resting state signals lagged behind the cerebral cortex (125-380 ms), supporting the hypothesis that the cerebellum engages in a domain-general function in the adaptive control of all cortical processes.

Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social deficits and repetitive behaviors that typically emerge by 24 months of age. To develop effective early interventions that can potentially ameliorate the defining deficits of ASD and improve long-term outcomes, early detection is essential. Using prospective neuroimaging of 59 6-month-old infants with a high familial risk for ASD, we show that functional connectivity magnetic resonance imaging correctly identified which individual children would receive a research clinical best-estimate diagnosis of ASD at 24 months of age. Functional brain connections were defined in 6-month-old infants that correlated with 24-month scores on measures of social behavior, language, motor development, and repetitive behavior, which are all features common to the diagnosis of ASD. A fully cross-validated machine learning algorithm applied at age 6 months had a positive predictive value of 100% [95% confidence interval (CI), 62.9 to 100], correctly predicting 9 of 11 infants who received a diagnosis of ASD at 24 months (sensitivity, 81.8%; 95% CI, 47.8 to 96.8). All 48 6-month-old infants who were not diagnosed with ASD were correctly classified [specificity, 100% (95% CI, 90.8 to 100); negative predictive value, 96.0% (95% CI, 85.1 to 99.3)]. These findings have clinical implications for early risk assessment and the feasibility of developing early preventative interventions for ASD.

Behavioral interventions for reducing head motion during MRI scans in children

A major limitation to structural and functional MRI (fMRI) scans is their susceptibility to head motion artifacts. Even submillimeter movements can systematically distort functional connectivity, morphometric, and diffusion imaging results. In patient care, sedation is often used to minimize head motion, but it incurs increased costs and risks. In research settings, sedation is typically not an ethical option. Therefore, safe methods that reduce head motion are critical for improving MRI quality, especially in high movement individuals such as children and neuropsychiatric patients. We investigated the effects of (1) viewing movies and (2) receiving real-time visual feedback about head movement in 24 children (5-15 years old). Children completed fMRI scans during which they viewed a fixation cross (i.e., rest) or a cartoon movie clip, and during some of the scans they also received real-time visual feedback about head motion. Head motion was significantly reduced during movie watching compared to rest and when receiving feedback compared to receiving no feedback. However, these results depended on age, such that the effects were largely driven by the younger children. Children older than 10 years showed no significant benefit. We also found that viewing movies significantly altered the functional connectivity of fMRI data, suggesting that fMRI scans during movies cannot be equated to standard resting-state fMRI scans. The implications of these results are twofold: (1) given the reduction in head motion with behavioral interventions, these methods should be tried first for all clinical and structural MRIs in lieu of sedation; and (2) for fMRI research scans, these methods can reduce head motion in certain groups, but investigators must keep in mind the effects on functional MRI data.

PAIR Comparison between Two Within-Group Conditions of Resting-State fMRI Improves Classification Accuracy

Classification approaches have been increasingly applied to differentiate patients and normal controls using resting-state functional magnetic resonance imaging data (RS-fMRI). Although most previous classification studies have reported promising accuracy within individual datasets, achieving high levels of accuracy with multiple datasets remains challenging for two main reasons: high dimensionality, and high variability across subjects. We used two independent RS-fMRI datasets (n = 31, 46, respectively) both with eyes closed (EC) and eyes open (EO) conditions. For each dataset, we first reduced the number of features to a small number of brain regions with paired t-tests, using the amplitude of low frequency fluctuation (ALFF) as a metric. Second, we employed a new method for feature extraction, named the PAIR method, examining EC and EO as paired conditions rather than independent conditions. Specifically, for each dataset, we obtained EC minus EO (EC—EO) maps of ALFF from half of subjects (n = 15 for dataset-1, n = 23 for dataset-2) and obtained EO—EC maps from the other half (n = 16 for dataset-1, n = 23 for dataset-2). A support vector machine (SVM) method was used for classification of EC RS-fMRI mapping and EO mapping. The mean classification accuracy of the PAIR method was 91.40% for dataset-1, and 92.75% for dataset-2 in the conventional frequency band of 0.01–0.08 Hz. For cross-dataset validation, we applied the classifier from dataset-1 directly to dataset-2, and vice versa. The mean accuracy of cross-dataset validation was 94.93% for dataset-1 to dataset-2 and 90.32% for dataset-2 to dataset-1 in the 0.01–0.08 Hz range. For the UNPAIR method, classification accuracy was substantially lower (mean 69.89% for dataset-1 and 82.97% for dataset-2), and was much lower for cross-dataset validation (64.69% for dataset-1 to dataset-2 and 64.98% for dataset-2 to dataset-1) in the 0.01–0.08 Hz range. In conclusion, for within-group design studies (e.g., paired conditions or follow-up studies), we recommend the PAIR method for feature extraction. In addition, dimensionality reduction with strong prior knowledge of specific brain regions should also be considered for feature selection in neuroimaging studies.

Real-time motion analytics during brain MRI improve data quality and reduce costs

Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more.

Precision Functional Mapping of Individual Human Brains

Human functional MRI (fMRI) research primarily focuses on analyzing data averaged across groups, which limits the detail, specificity, and clinical utility of fMRI resting-state functional connectivity (RSFC) and task-activation maps. To push our understanding of functional brain organization to the level of individual humans, we assembled a novel MRI dataset containing 5 hr of RSFC data, 6 hr of task fMRI, multiple structural MRIs, and neuropsychological tests from each of ten adults. Using these data, we generated ten high-fidelity, individual-specific functional connectomes. This individual-connectome approach revealed several new types of spatial and organizational variability in brain networks, including unique network features and topologies that corresponded with structural and task-derived brain features. We are releasing this highly sampled, individual-focused dataset as a resource for neuroscientists, and we propose precision individual connectomics as a model for future work examining the organization of healthy and diseased individual human brains.

Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. Although previous TS studies revealed structural abnormalities in distinct corticobasal ganglia circuits, the topological alterations of the whole-brain white matter (WM) structural networks remain poorly understood. Here, we used diffusion MRI probabilistic tractography and graph theoretical analysis to investigate the topological organization of WM networks in 44 drug-naive TS children and 41 age- and gender-matched healthy children. The WM networks were constructed by estimating inter-regional connectivity probability and the topological properties were characterized using graph theory. We found that both TS and control groups showed an efficient small-world organization in WM networks. However, compared to controls, TS children exhibited decreased global and local efficiency, increased shortest path length and small worldness, indicating a disrupted balance between local specialization and global integration in structural networks. Although both TS and control groups showed highly similar hub distributions, TS children exhibited significant decreased nodal efficiency, mainly distributed in the default mode, language, visual, and sensorimotor systems. Furthermore, two separate networks showing significantly decreased connectivity in TS group were identified using network-based statistical (NBS) analysis, primarily composed of the parieto-occipital cortex, precuneus, and paracentral lobule. Importantly, we combined support vector machine and multiple kernel learning frameworks to fuse multiple levels of network topological features for classification of individuals, achieving high accuracy of 86.47%. Together, our study revealed the disrupted topological organization of structural networks related to pathophysiology of TS, and the discriminative topological features for classification are potential quantitative neuroimaging biomarkers for clinical TS diagnosis. Hum Brain Mapp 38:3988-4008, 2017. © 2017 Wiley Periodicals, Inc.

Combining Disrupted and Discriminative Topological Properties of Functional Connectivity Networks as Neuroimaging Biomarkers for Accurate Diagnosis of Early Tourette Syndrome Children

Tourette syndrome (TS) is a childhood-onset neurological disorder. To date, accurate TS diagnosis remains challenging due to its varied clinical expressions and dependency on qualitative description of symptoms. Therefore, identifying accurate and objective neuroimaging biomarkers may help improve early TS diagnosis. As resting-state functional MRI (rs-fMRI) has been demonstrated as a promising neuroimaging tool for TS diagnosis, previous rs-fMRI studies on TS revealed functional connectivity (FC) changes in a few local brain networks or circuits. However, no study explored the disrupted topological organization of whole-brain FC networks in TS children. Meanwhile, very few studies have examined brain functional networks using machine-learning methods for diagnostics. In this study, we construct individual whole-brain, ROI-level FC networks for 29 drug-naive TS children and 37 healthy children. Then, we use graph theory analysis to investigate the topological disruptions between groups. The identified disrupted regions in FC networks not only involved the sensorimotor association regions but also the visual, default-mode and language areas, all highly related to TS. Furthermore, we propose a novel classification framework based on similarity network fusion (SNF) algorithm, to both diagnose an individual subject and explore the discriminative power of FC network topological properties in distinguishing between TS children and controls. We achieved a high accuracy of 88.79%, and the involved discriminative regions for classification were also highly related to TS. Together, both the disrupted topological properties between groups and the discriminative topological features for classification may be considered as comprehensive and helpful neuroimaging biomarkers for assisting the clinical TS diagnosis.

The Fallacy of Univariate Solutions to Complex Systems Problems

Complex biological systems, by definition, are composed of multiple components that interact non-linearly. The human brain constitutes, arguably, the most complex biological system known. Yet most investigation of the brain and its function is carried out using assumptions appropriate for simple systems—univariate design and linear statistical approaches. This heuristic must change before we can hope to discover and test interventions to improve the lives of individuals with complex disorders of brain development and function. Indeed, a movement away from simplistic models of biological systems will benefit essentially all domains of biology and medicine. The present brief essay lays the foundation for this argument.

Provisional Tic Disorder: What to tell parents when their child first starts ticcing

The child with recent onset of tics is a common patient in a pediatrics or child neurology practice. If the child’s first tic was less than a year in the past, the diagnosis is usually Provisional Tic Disorder (PTD). Published reviews by experts reveal substantial consensus on prognosis in this situation: the tics will almost always disappear in a few months, having remained mild while they lasted. Surprisingly, however, the sparse existing data may not support these opinions.

PTD may have just as much importance for science as for clinical care. It provides an opportunity to prospectively observe the spontaneous remission of tics. Such prospective studies may aid identification of genes or biomarkers specifically associated with remission rather than onset of tics. A better understanding of tic remission may also suggest novel treatment strategies for Tourette syndrome, or may lead to secondary prevention of tic disorders.

This review summarizes the limited existing data on the epidemiology, phenomenology, and outcome of PTD, highlights areas in which prospective study is sorely needed, and proposes that tic disorders may completely remit much less often than is generally believed.

Considerations for MRI study design and implementation in pediatric and clinical populations

Human neuroimaging, specifically magnetic resonance imaging (MRI), is being used with increasing popularity to study brain structure and function in development and disease. When applying these methods to developmental and clinical populations, careful consideration must be taken with regard to study design and implementation. In this article, we discuss two major considerations particularly pertinent to brain research in special populations. First, we discuss considerations for subject selection and characterization, including issues related to comorbid conditions, medication status, and clinical assessment. Second, we discuss methods and considerations for acquisition of adequate, useable MRI data. Given that children and patients may experience anxiety with the scanner environment, preventing participation, and that they have a higher risk of motion artifact, resulting in data loss, successful subject compliance and data acquisition are not trivial tasks. We conclude that, as researchers, we must consider a number of issues when using neuroimaging tools to study children and patients, and we should thoughtfully justify our choices of methods and study design.

Neuroimaging in Tourette Syndrome: Research Highlights From 2014-2015

Tourette Syndrome (ts) is a developmental neuropsychiatric disorder of the central nervous system defined by the presence of chronic tics. While investigations of the underlying brain mechanisms have provided valuable information, a complete understanding of the pathophysiology of ts remains elusive. Neuroimaging methods provide remarkable tools for examining the human brain, and have been used to study brain structure and function in ts. In this article, we review ts neuroimaging studies published in 2014-2015. We highlight a number of noteworthy studies due to their innovative methods and interesting findings. Yet, we note that many of the recent studies share common concerns, specifically susceptibility to motion artifacts and modest sample sizes. Thus, we encourage future work to carefully address potential methodological confounds and to study larger samples to increase the potential for replicable results.