Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting

Advances in artificial intelligence have cultivated a strong interest in developing and validating the clinical utilities of computer-aided diagnostic models. Machine learning for diagnostic neuroimaging has often been applied to detect psychological and neurological disorders, typically on small-scale datasets or data collected in a research setting. With the collection and collation of an ever-growing number of public datasets that researchers can freely access, much work has been done in adapting machine learning models to classify these neuroimages by diseases such as Alzheimer's, ADHD, autism, bipolar disorder, and so on. These studies often come with the promise of being implemented clinically, but despite intense interest in this topic in the laboratory, limited progress has been made in clinical implementation. In this review, we analyze challenges specific to the clinical implementation of diagnostic AI models for neuroimaging data, looking at the differences between laboratory and clinical settings, the inherent limitations of diagnostic AI, and the different incentives and skill sets between research institutions, technology companies, and hospitals. These complexities need to be recognized in the translation of diagnostic AI for neuroimaging from the laboratory to the clinic.

BOston Neonatal Brain Injury Dataset for Hypoxic Ischemic Encephalopathy (BONBID-HIE): Part I. MRI and Manual Lesion Annotation

Hypoxic ischemic encephalopathy (HIE) is a brain injury that occurs in 1 ~ 5/1000 term neonates. Accurate identification and segmentation of HIE-related lesions in neonatal brain magnetic resonance images (MRIs) is the first step toward predicting prognosis, identifying high-risk patients, and evaluating treatment effects. It will lead to a more accurate estimation of prognosis, a better understanding of neurological symptoms, and a timely prediction of response to therapy. We release the first public dataset containing neonatal brain diffusion MRI and expert annotation of lesions from 133 patients diagnosed with HIE. HIE-related lesions in brain MRI are often diffuse (i.e., multi-focal), and small (over half the patients in our data having lesions occupying <1% of brain volume). Segmentation for HIE MRI data is remarkably different from, and arguably more challenging than, other segmentation tasks such as brain tumors with focal and relatively large lesions. We hope that this dataset can help fuel the development of MRI lesion segmentation methods for HIE and small diffuse lesions in general.

Uncovering and mitigating bias in large, automated MRI analyses of brain development

Large, population-based MRI studies of adolescents promise transformational insights into neurodevelopment and mental illness risk 1,2. However, MRI studies of youth are especially susceptible to motion and other artifacts 3,4. These artifacts may go undetected by automated quality control (QC) methods that are preferred in high-throughput imaging studies, 5 and can potentially introduce non-random noise into clinical association analyses. Here we demonstrate bias in structural MRI analyses of children due to inclusion of lower quality images, as identified through rigorous visual quality control of 11,263 T1 MRI scans obtained at age 9-10 through the Adolescent Brain Cognitive Development (ABCD) Study6. Compared to the best-rated images (44.9% of the sample), lower-quality images generally associated with decreased cortical thickness and increased cortical surface area measures (Cohen's d 0.14-2.84). Variable image quality led to counterintuitive patterns in analyses that associated structural MRI and clinical measures, as inclusion of lower-quality scans altered apparent effect sizes in ways that increased risk for both false positives and negatives. Quality-related biases were partially mitigated by controlling for surface hole number, an automated index of topological complexity that differentiated lower-quality scans with good specificity at Baseline (0.81-0.93) and in 1,000 Year 2 scans (0.88-1.00). However, even among the highest-rated images, subtle topological errors occurred during image preprocessing, and their correction through manual edits significantly and reproducibly changed thickness measurements across much of the cortex (d 0.15-0.92). These findings demonstrate that inadequate QC of youth structural MRI scans can undermine advantages of large sample size to detect meaningful associations.

Genetic Association of Attention-Deficit/Hyperactivity Disorder and Major Depression With Suicidal Ideation and Attempts in Children: The Adolescent Brain Cognitive Development Study

BACKGROUND

Suicide is among the leading causes of death in children and adolescents. There are well-known risk factors of suicide, including childhood abuse, family conflicts, social adversity, and psychopathology. While suicide risk is also known to be heritable, few studies have investigated genetic risk in younger individuals.

METHODS

Using polygenic risk score analysis, we examined whether genetic susceptibility to major psychiatric disorders is associated with suicidal behaviors among 11,878 children enrolled in the ABCD (Adolescent Brain Cognitive Development) Study. Suicidal ideation and suicide attempt data were assessed using the youth report of the Kiddie Schedule for Affective Disorders and Schizophrenia for DSM-5. After performing robust quality control of genotype data, unrelated individuals of European descent were included in analyses (n = 4344).

RESULTS

Among 8 psychiatric disorders we examined, depression polygenic risk scores were associated with lifetime suicide attempts both in the baseline (odds ratio = 1.55, 95% CI = 1.10-2.18, p = 1.27 × 10-2) and in the follow-up year (odds ratio = 1.38, 95% CI = 1.08-1.77, p = 1.05 × 10-2), after adjusting for children's age, sex, socioeconomic backgrounds, family history of suicide, and psychopathology. In contrast, attention-deficit/hyperactivity disorder polygenic risk scores were associated with lifetime suicidal ideation (odds ratio = 1.15, 95% CI = 1.05-1.26, p = 3.71 × 10-3), suggesting a distinct contribution of the genetic risk underlying attention-deficit/hyperactivity disorder and depression on suicidal behaviors of children.

CONCLUSIONS

The largest genetic sample of suicide risk data in U.S. children suggests a significant genetic basis of suicide risk related to attention-deficit/hyperactivity disorder and depression. Further research is warranted to examine whether incorporation of genomic risk may facilitate more targeted screening and intervention efforts.

How Machine Learning is Powering Neuroimaging to Improve Brain Health

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

Perturbing fMRI brain dynamics using transcranial direct current stimulation

The dynamic nature of resting-state functional magnetic resonance imaging (fMRI) brain activity and connectivity has drawn great interest in the past decade. Specific temporal properties of fMRI brain dynamics, including metrics such as occurrence rate and transitions, have been associated with cognition and behaviors, indicating the existence of mechanism distruption in neuropsychiatric disorders. The development of new methods to manipulate fMRI brain dynamics will advance our understanding of these pathophysiological mechanisms from native observation to experimental mechanistic manipulation. In the present study, we applied repeated transcranial direct current stimulation (tDCS) to the right dorsolateral prefrontal cortex (rDLPFC) and the left orbitofrontal cortex (lOFC), during multiple simultaneous tDCS-fMRI sessions from 81 healthy participants to assess the modulatory effects of stimulating target brain regions on fMRI brain dynamics. Using the rDLPFC and the lOFC as seeds, respectively, we first identified two reoccurring co-activation patterns (CAPs) and calculated their temporal properties (e.g., occurrence rate and transitions) before administering tDCS. The spatial maps of CAPs were associated with different cognitive and disease domains using meta-analytical decoding analysis. We then investigated how active tDCS compared to sham tDCS in the modulation of the occurrence rates of these different CAPs and perturbations of transitions between CAPs. We found that by enhancing neuronal excitability of the rDLPFC and the lOFC, the occurrence rate of one CAP was significantly decreased while that of another CAP was significantly increased during the first 6 min of stimulation. Furthermore, these tDCS-associated changes persisted over subsequent testing sessions (both during and before/after tDCS) across three consecutive days. Active tDCS could perturb transitions between CAPs and a non-CAP state (when the rDLPFC and the lOFC were not activated), but not the transitions within CAPs. These results demonstrate the feasibility of modulating fMRI brain dynamics, and open new possibilities for discovering stimulation targets and dynamic connectivity patterns that can ensure the propagation of tDCS-induced neuronal excitability, which may facilitate the development of new treatments for disorders with altered dynamics.

Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan

Brain age estimated by machine learning from T1-weighted magnetic resonance images (T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early detection of such disorders. A fundamental step is to build an accurate age estimator from healthy brain MRIs. We focus on this step, and propose a framework to improve the accuracy, generality, and interpretation of age estimation in healthy brain MRIs. For accuracy, we used one of the largest sample sizes (N = 16,705). For each subject, our proposed algorithm first explicitly splits the T1w image, which has been commonly treated as a single-channel 3D image in other studies, into two 3D image channels representing contrast and morphometry information. We further proposed a "fusion-with-attention" deep learning convolutional neural network (FiA-Net) to learn how to best fuse the contrast and morphometry image channels. FiA-Net recognizes varying contributions across image channels at different brain anatomy and different feature layers. In contrast, multi-channel fusion does not exist for brain age estimation, and is mostly attention-free in other medical image analysis tasks (e.g., image synthesis, or segmentation), where treating channels equally may not be optimal. For generality, we used lifespan data 0-97 years of age for real-world utility; and we thoroughly tested FiA-Net for multi-site and multi-scanner generality by two phases of cross-validations in discovery and replication data, compared to most other studies with only one phase of cross-validation. For interpretation, we directly measured each artificial neuron's correlation with the chronological age, compared to other studies looking at the saliency of features where salient features may or may not predict age. Overall, FiA-Net achieved a mean absolute error (MAE) of 3.00 years and Pearson correlation r=0.9840 with known chronological ages in healthy brain MRIs 0-97 years of age, comparing favorably with state-of-the-art algorithms and studies for accuracy and generality across sites and datasets. We also provided interpretations on how different artificial neurons and real neuroanatomy contribute to the age estimation.

Voxelwise and Regional Brain Apparent Diffusion Coefficient Changes on MRI from Birth to 6 Years of Age

Background A framework for understanding rapid diffusion changes from 0 to 6 years of age is important in the detection of neurodevelopmental disorders. Purpose To quantify patterns of normal apparent diffusion coefficient (ADC) development from 0 to 6 years of age. Materials and Methods Previously constructed age-specific ADC atlases from 201 healthy full-term children (108 male; age range, 0-6 years) with MRI scans acquired from 2006 to 2013 at one large academic hospital were analyzed to quantify four patterns: ADC trajectory, rate of ADC change, age of ADC maturation, and hemispheric asymmetries of maturation ages. Patterns were quantified in whole-brain, segmented regional, and voxelwise levels by fitting a two-term exponential model. Hemispheric asymmetries in ADC maturation ages were assessed using t tests with Bonferroni correction. Results The posterior limb of the internal capsule (mean ADC: left hemisphere, 1.18 ×10³μm²/sec; right hemisphere, 1.17 ×10³μm²/sec), anterior limb of the internal capsule (left, 1.11 ×10³μm²/sec; right, 1.09 ×10³μm²/sec), vermis (1.26 ×10³μm²/sec), thalami (left, 1.17 ×10³μm²/sec; right, 1.15 ×10³μm²/sec), and basal ganglia (left, 1.26 ×10³μm²/sec; right, 1.23 ×10³μm²/sec) demonstrate low initial ADC values, indicating an earlier prenatal time course of development. ADC maturation was completed between 1.3 and 2.4 years of age, depending on the region. The vermis and left thalamus matured earliest (1.3 years). The frontolateral gray matter matured latest (right, 2.3 years; left, 2.4 years). ADC maturation occurred earlier in the left hemisphere (P < .001) in several regions, including the frontal (mean ± standard deviation) (left, 2.16 years ± 0.29; right, 2.19 years ± 0.31), temporal (left, 1.93 years ± 0.22; right, 1.99 years ± 0.22), and parietal (left, 1.92 years ± 0.30; right, 2.03 years ± 0.28) white matter. Maturation occurred earlier in the right hemisphere (P < .001) in several regions, including the thalami (left, 1.63 years ± 0.32; right, 1.45 years ± 0.33), basal ganglia (left, 1.79 years ± 0.31; right, 1.70 years ± 0.37), and hippocampi (left, 1.93 years ± 0.34; right, 1.78 years ± 0.33). Conclusion Normative apparent diffusion coefficient developmental patterns on diffusion-weighted MRI scans were quantified in children aged 0 to 6 years. This work provides knowledge about early brain development and may guide the detection of abnormal patterns of maturation. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Rollins in this issue.

Acupuncture Treatment Modulates the Connectivity of Key Regions of the Descending Pain Modulation and Reward Systems in Patients with Chronic Low Back Pain

Chronic low back pain (cLBP) is a common disorder with unsatisfactory treatment options. Acupuncture has emerged as a promising method for treating cLBP. However, the mechanism underlying acupuncture remains unclear. In this study, we investigated the modulation effects of acupuncture on resting state functional connectivity (rsFC) of the periaqueductal gray (PAG) and ventral tegmental area (VTA) in patients with cLBP. Seventy-nine cLBP patients were recruited and assigned to four weeks of real or sham acupuncture. Resting state functional magnetic resonance imaging data were collected before the first and after the last treatment. Fifty patients completed the study. We found remission of pain bothersomeness in all treatment groups after four weeks, with greater pain relief after real acupuncture compared to sham acupuncture. We also found that real acupuncture can increase VTA/PAG rsFC with the amygdala, and the increased rsFC was associated with decreased pain bothersomeness scores. Baseline PAG-amygdala rsFC could predict four-week treatment response. Our results suggest that acupuncture may simultaneously modulate the rsFC of key regions in the descending pain modulation (PAG) and reward systems (VTA), and the amygdala may be a key node linking the two systems to produce antinociceptive effects. Our findings highlight the potential of acupuncture for chronic low back pain management.

BRAIN AGE ESTIMATION USING LSTM ON CHILDREN'S BRAIN MRI

Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis. In this paper, we consider the 3D brain MRI volume as a sequence of 2D images and propose a new framework using the recurrent neural network for brain age estimation. The proposed method is named as 2D-ResNet18+Long short-term memory (LSTM), which consists of four parts: 2D ResNet18 for feature extraction on 2D images, a pooling layer for feature reduction over the sequences, an LSTM layer, and a final regression layer. We apply the proposed method on a public multisite NIH-PD dataset and evaluate generalization on a second multisite dataset, which shows that the proposed 2D-ResNet18+LSTM method provides better results than traditional 3D based neural network for brain age estimation.

Impaired mesocorticolimbic connectivity underlies increased pain sensitivity in chronic low back pain

Chronic low back pain (cLBP) is a prevalent disorder. A growing body of evidence linking the pathology of the reward network to chronic pain suggests that pain sensitization may contribute to cLBP chronification via disruptions of mesocortical and mesolimbic circuits in the reward system. Resting-state (RS) functional magnetic resonance imaging (fMRI) data was acquired from 90 patients with cLBP and 74 matched pain-free controls (HCs) at baseline and after a manipulation for back pain intensification. The ventral tegmental area (VTA) was chosen as a seed region to perform RS functional connectivity (FC) analysis. Baseline rsFC of both the mesocortical (between the VTA and bilateral rostral anterior cingulate cortex (rACC)/and medial prefrontal cortex (mPFC)) and mesolimbic (between the VTA and bilateral hippocampus/parahippocampus) pathways was reduced in patients with cLBP (vs. HCs). In addition, patients exhibiting higher back pain intensity (compared to the relatively lower back pain intensity condition) also showed increases in both mesocortical and mesolimbic connectivity, implicating these pathways in pain downregulation in cLBP. Mediation analysis further isolated the mesolimbic (VTA-hippocampus/parahippocampus) dysconnectivity as a neural mechanism mediating the association between mechanical pain sensitivity (indexed by P40 pressure) and cLBP severity. In sum, the current study demonstrates deficient mesocorticolimbic connectivity in cLBP, with mesolimbic dysconnectivity potentially mediating the contribution of pain sensitization to pain chronification. These reward network dysfunctions and purportedly, dopaminergic dysregulations, may help us to identify key brain targets of neuromodulation in the treatment of cLBP.

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.

Reward and empathy in the treating clinician: the neural correlates of successful doctor-patient interactions

The goal of this study was to determine the neural correlates of successful doctor-patient interactions. We performed an experimental neuroimaging study where medical doctors (MDs) performed a treatment task while their brain activation pattern was measured, using functional magnetic resonance imaging (fMRI). MDs (25-37 years old) first performed a standardized clinical exam of a "professional patient". Unbeknownst to the doctors, the professional patient was a confederate that rated the doctors' clinical examination using the Consultation And Relational Empathy (CARE) questionnaire, a standardized protocol assessing a clinician's social interaction during a consultation. After the clinical exam, MDs were placed inside a brain scanner and the patient was placed on a chair next to the MD. MDs performed a treatment task where an analgesic device was used to alleviate the patient's pain (experimentally induced), while the MD's brain activity was measured with fMRI. MDs rated their own empathic concern (equivalent of compassion) and personal distress using the Interpersonal Reactivity Index questionnaire. The patient's rating of CARE was robustly related to the MD's own ratings of trait empathic concern and to compassion-related and reward-related activation of medial frontal brain regions during treatment. In contrast, there was no relation with MD's personal distress, nor with activation in regions associated with the aversive component of experiencing empathy. We conclude that a patient's positive experience of a medical examination is reflected in doctors' empathic concern and reward-related brain activations during treatment, suggesting that compassion and pleasure are key factors for successful doctor-patient interactions.

Between placebo and nocebo: Response to control treatment is mediated by amygdala activity and connectivity

BACKGROUND

In experimental placebo and nocebo studies, neutral control treatments are often administered for comparison with active treatments, but are of little interest, as, on average, they result in little change. Yet, when considered at an individual level, they fluctuate between baseline and subsequent measurements and may reveal important information about participants' placebo/nocebo responding tendencies.

METHODS

In a paradigm involving application of creams paired with positive, negative and neutral expectations, some subjects rated identical stimuli in the neutral condition as more painful while others as less painful after treatment with inert cream. We divided subjects into two groups based on the median split in these pre-post responses in the neutral control condition, and investigated (a) fMRI signal differences (post minus pre) between the two groups in neutral condition, and (b) seed-based resting state connectivity of the bilateral amygdala, known to be involved in emotional self-regulation, as well as ambiguous stimulus processing and aversive learning.

RESULTS

The results suggested that subjects who rated the same pain stimuli after treatment with explicitly neutral cream as more painful showed stronger fMRI activation of the amygdala during the experiment and had higher connectivity between the left amygdala and the striatum at rest. Neutral pre-post changes predicted behavioural placebo/nocebo response in this and two independent datasets.

CONCLUSION

These findings suggest that measuring pre-post change in the neutral control condition might provide important information about subjects' individual differences in placebo/nocebo response.

SIGNIFICANCE

Pre-post changes in pain ratings in neutral conditions are modulated by amygdala activity and connectivity and can be used to predict placebo/nocebo responses.

Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy

BACKGROUND

Secondary and retrospective use of hospital-hosted clinical data provides a time- and cost-efficient alternative to prospective clinical trials for biomarker development. This study aims to create a retrospective clinical dataset of Magnetic Resonance Images (MRI) and clinical records of neonatal hypoxic ischemic encephalopathy (HIE), from which clinically-relevant analytic algorithms can be developed for MRI-based HIE lesion detection and outcome prediction.

METHODS

This retrospective study will use clinical registries and big data informatics tools to build a multi-site dataset that contains structural and diffusion MRI, clinical information including hospital course, short-term outcomes (during infancy), and long-term outcomes (~ 2 years of age) for at least 300 patients from multiple hospitals.

DISCUSSION

Within machine learning frameworks, we will test whether the quantified deviation from our recently-developed normative brain atlases can detect abnormal regions and predict outcomes for individual patients as accurately as, or even more accurately, than human experts. Trial Registration Not applicable. This study protocol mines existing clinical data thus does not meet the ICMJE definition of a clinical trial that requires registration.

Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain

Despite the high prevalence and socioeconomic impact of chronic low back pain (cLBP), treatments for cLBP are often unsatisfactory, and effectiveness varies widely across patients. Recent neuroimaging studies have demonstrated abnormal resting-state functional connectivity (rsFC) of the default mode, salience, central executive, and sensorimotor networks in chronic pain patients, but their role as predictors of treatment responsiveness has not yet been explored. In this study, we used machine learning approaches to test if pre-treatment rsFC can predict responses to both real and sham acupuncture treatments in cLBP patients. Fifty cLBP patients participated in 4 weeks of either real (N = 24, age = 39.0 ± 12.6, 16 females) or sham acupuncture (N = 26, age = 40.0 ± 13.7, 15 females) treatment in a single-blinded trial, and a resting-state fMRI scan prior to treatment was used in data analysis. Both real and sham acupuncture can produce significant pain reduction, with those receiving real treatment experiencing greater pain relief than those receiving sham treatment. We found that pre-treatment rsFC could predict symptom changes with up to 34% and 29% variances for real and sham treatment, respectively, and the rsFC characteristics that were significantly predictive for real and sham treatment differed. These results suggest a potential way to predict treatment responses and may facilitate the development of treatment plans that optimize time, cost, and available resources.

Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study

Chronic low back pain (cLBP) is associated with widespread functional and structural changes in the brain. This study aims to investigate the resting state functional connectivity (rsFC) changes of visual networks in cLBP patients and the feasibility of distinguishing cLBP patients from healthy controls using machine learning methods. cLBP (n = 90) and control individuals (n = 74) were enrolled and underwent resting-state BOLD fMRI scans. Primary, dorsal, and ventral visual networks derived from independent component analysis were used as regions of interest to compare resting state functional connectivity changes between the cLBP patients and healthy controls. We then applied a support vector machine classifier to distinguish the cLBP patients and control individuals. These results were further verified in a new cohort of subjects. We found that the functional connectivity between the primary visual network and the somatosensory/motor areas were significantly enhanced in cLBP patients. The rsFC between the primary visual network and S1 was negatively associated with duration of cLBP. In addition, we found that the rsFC of the visual network could achieve a classification accuracy of 79.3% in distinguishing cLBP patients from HCs, and these results were further validated in an independent cohort of subjects (accuracy = 66.7%). Our results demonstrate significant changes in the rsFC of the visual networks in cLBP patients. We speculate these alterations may represent an adaptation/self-adjustment mechanism and cross-model interaction between the visual, somatosensory, motor, attention, and salient networks in response to cLBP. Elucidating the role of the visual networks in cLBP may shed light on the pathophysiology and development of the disorder.

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We investigated rsFC changes of visual networks in cLBP patients.

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rsFC of the primary visual network with S1 and M1 increased in cLBP patients.

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rsFC of the visual networks can differentiate cLBP patients from controls (with 79.3% accuracy).

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Classification results can be validated in an independent cohort (with 66.7% accuracy).

Field of View Normalization in Multi-Site Brain MRI

Multi-site brain MRI analysis is needed in big data neuroimaging studies, but challenging. The challenges lie in almost every analysis step including skull stripping. The diversities in multi-site brain MR images make it difficult to tune parameters specific to subjects or imaging protocols. Alternatively, using constant parameter settings often leads to inaccurate, inconsistent and even failed skull stripping results. One reason is that images scanned at different sites, under different scanners or protocols, and/or by different technicians often have very different fields of view (FOVs). Normalizing FOV is currently done manually or using ad hoc pre-processing steps, which do not always generalize well to multi-site diverse images. In this paper, we show that (a) a generic FOV normalization approach is possible in multi-site diverse images; we show experiments on images acquired from Philips, GE, Siemens scanners, from 1.0T, 1.5T, 3.0T field of strengths, and from subjects 0-90 years of ages; and (b) generic FOV normalization improves skull stripping accuracy and consistency for multiple skull stripping algorithms; we show this effect for 5 skull stripping algorithms including FSL's BET, AFNI's 3dSkullStrip, FreeSurfer's HWA, BrainSuite's BSE, and MASS. We have released our FOV normalization software at http://www.nitrc.org/projects/normalizefov .

Association of Prenatal Exposure to Population-Wide Folic Acid Fortification With Altered Cerebral Cortex Maturation in Youths

IMPORTANCE

Presently, 81 countries mandate the fortification of grain products with folic acid to lessen the risk of neural tube defects in the developing fetus. Epidemiologic data on severe mental illness suggest potentially broader effects of prenatal folate exposure on postnatal brain development, but this link remains unsubstantiated by biological evidence.

OBJECTIVE

To evaluate associations among fetal folic acid exposure, cortical maturation, and psychiatric risk in youths.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective, observational clinical cohort study was conducted at Massachusetts General Hospital (MGH) among 292 youths 8 to 18 years of age born between January 1993 and December 2001 (inclusive of folic acid fortification rollout ±3.5 years) with normative results of clinical magnetic resonance imaging, divided into 3 age-matched groups based on birthdate and related level of prenatal folic acid fortification exposure (none, partial, or full). Magnetic resonance imaging was performed between January 2005 and March 2015. Two independent, observational, community-based cohorts (Philadelphia Neurodevelopmental Cohort [PNC] and National Institutes of Health Magnetic Resonance Imaging Study of Normal Brain Development [NIH]) comprising 1078 youths 8 to 18 years of age born throughout (PNC, 1992-2003) or before (NIH, 1983-1995) the rollout of folic acid fortification were studied for replication, clinical extension, and specificity. Statistical analysis was conducted from 2015 to 2018.

EXPOSURES

United States-mandated grain product fortification with folic acid, introduced in late 1996 and fully in effect by mid-1997.

MAIN OUTCOMES AND MEASURES

Differences in cortical thickness among nonexposed, partially exposed, and fully exposed youths (MGH) and underlying associations between age and cortical thickness (all cohorts). Analysis of the PNC cohort also examined the association of age-cortical thickness slopes with the odds of psychotic symptoms.

RESULTS

The MGH cohort (139 girls and 153 boys; mean [SD] age, 13.3 [2.3] years) demonstrated exposure-associated cortical thickness increases in bilateral frontal and temporal regions (9.9% to 11.6%; corrected P < .001 to P = .03) and emergence of quadratic (delayed) age-associated thinning in temporal and parietal regions (β = -11.1 to -13.9; corrected P = .002). The contemporaneous PNC cohort (417 girls and 444 boys; mean [SD] age, 13.5 [2.7] years) also exhibited exposure-associated delays of cortical thinning (β = -1.59 to -1.73; corrected P < .001 to P = .02), located in similar regions and with similar durations of delay as in the MGH cohort. Flatter thinning profiles in frontal, temporal, and parietal regions were associated with lower odds of psychosis spectrum symptoms in the PNC cohort (odds ratio, 0.37-0.59; corrected P < .05). All identified regions displayed earlier thinning in the nonexposed NIH cohort (118 girls and 99 boys; mean [SD] age, 13.3 [2.6] years).

CONCLUSIONS AND RELEVANCE

The results of this study suggest an association between gestational exposure to fortification of grain products with folic acid and altered cortical development and, in turn, with reduction in the risk of psychosis in youths.

The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) consortium: A collaborative cognitive and neuroimaging genetics project

BACKGROUND

Schizophrenia has a large genetic component, and the pathways from genes to illness manifestation are beginning to be identified. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium aims to clarify the role of genetic variation in brain abnormalities underlying schizophrenia. This article describes the GENUS Consortium sample collection.

METHODS

We identified existing samples collected for schizophrenia studies consisting of patients, controls, and/or individuals at familial high-risk (FHR) for schizophrenia. Samples had single nucleotide polymorphism (SNP) array data or genomic DNA, clinical and demographic data, and neuropsychological and/or brain magnetic resonance imaging (MRI) data. Data were subjected to quality control procedures at a central site.

RESULTS

Sixteen research groups contributed data from 5199 psychosis patients, 4877 controls, and 725 FHR individuals. All participants have relevant demographic data and all patients have relevant clinical data. The sex ratio is 56.5% male and 43.5% female. Significant differences exist between diagnostic groups for premorbid and current IQ (both p<1×10^-10). Data from a diversity of neuropsychological tests are available for 92% of participants, and 30% have structural MRI scans (half also have diffusion-weighted MRI scans). SNP data are available for 76% of participants. The ancestry composition is 70% European, 20% East Asian, 7% African, and 3% other.

CONCLUSIONS

The Consortium is investigating the genetic contribution to brain phenotypes in a schizophrenia sample collection of >10,000 participants. The breadth of data across clinical, genetic, neuropsychological, and MRI modalities provides an important opportunity for elucidating the genetic basis of neural processes underlying schizophrenia.

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13-30 Hz) and gamma (31-80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development.

A Functional Neuroimaging Study of Expectancy Effects on Pain Response in Patients With Knee Osteoarthritis

Placebo treatments and healing rituals share much in common, such as the effects of expectancy, and have been used since the beginning of human history to treat pain. Previous mechanistic neuroimaging studies investigating the effects of expectancy on placebo analgesia have used young, healthy volunteers. Using functional magnetic resonance imaging (fMRI), we aimed to investigate the neural mechanisms by which expectancy evokes analgesia in older adults living with a chronic pain disorder and determine whether there are interactions with active treatment. In this fMRI study, we investigated the brain networks underlying expectancy in participants with chronic pain due to knee osteoarthritis (OA) after verum (genuine) and sham electroacupuncture treatment before and after experiencing calibrated experimental heat pain using a well tested expectancy manipulation model. We found that expectancy significantly and similarly modulates the pain experience in knee OA patients in both verum (n = 21, 11 female; mean ± SD age 57 ± 7 years) and sham (n = 22, 15 female; mean ± SD age 59 ± 7 years) acupuncture treatment groups. However, there were different patterns of changes in fMRI indices of brain activity associated with verum and sham treatment modalities specifically in the lateral prefrontal cortex. We also found that continuous electroacupuncture in knee OA patients can evoke significant regional coherence decreases in pain associated brain regions. Our results suggest that expectancy modulates the experience of pain in knee OA patients but may work through different pathways depending on the treatment modality and, we speculate, on pathophysiological states of the participants.

PERSPECTIVE

To investigate the neural mechanisms underlying pain modulation, we used an expectancy manipulation model and fMRI to study response to heat pain stimuli before and after verum or sham acupuncture treatment in chronic pain patients. Both relieve pain and each is each associated with a distinct pattern of brain activation.

Using clinically acquired MRI to construct age-specific ADC atlases: Quantifying spatiotemporal ADC changes from birth to 6-year old

Diffusion imaging is critical for detecting acute brain injury. However, normal apparent diffusion coefficient (ADC) maps change rapidly in early childhood, making abnormality detection difficult. In this article, we explored clinical PACS and electronic healthcare records (EHR) to create age-specific ADC atlases for clinical radiology reference. Using the EHR and three rounds of multiexpert reviews, we found ADC maps from 201 children 0-6 years of age scanned between 2006 and 2013 who had brain MRIs with no reported abnormalities and normal clinical evaluations 2+ years later. These images were grouped in 10 age bins, densely sampling the first 1 year of life (5 bins, including neonates and 4 quarters) and representing the 1-6 year age range (an age bin per year). Unbiased group-wise registration was used to construct ADC atlases for 10 age bins. We used the atlases to quantify (a) cross-sectional normative ADC variations; (b) spatiotemporal heterogeneous ADC changes; and (c) spatiotemporal heterogeneous volumetric changes. The quantified age-specific whole-brain and region-wise ADC values were compared to those from age-matched individual subjects in our study and in multiple existing independent studies. The significance of this study is that we have shown that clinically acquired images can be used to construct normative age-specific atlases. These first of their kind age-specific normative ADC atlases quantitatively characterize changes of myelination-related water diffusion in the first 6 years of life. The quantified voxel-wise spatiotemporal ADC variations provide standard references to assist radiologists toward more objective interpretation of abnormalities in clinical images. Our atlases are available at https://www.nitrc.org/projects/mgh_adcatlases. Hum Brain Mapp 38:3052-3068, 2017. © 2017 Wiley Periodicals, Inc.

Reusable Client-Side JavaScript Modules for Immersive Web-Based Real-Time Collaborative Neuroimage Visualization

In this paper we present a web-based software solution to the problem of implementing real-time collaborative neuroimage visualization. In both clinical and research settings, simple and powerful access to imaging technologies across multiple devices is becoming increasingly useful. Prior technical solutions have used a server-side rendering and push-to-client model wherein only the server has the full image dataset. We propose a rich client solution in which each client has all the data and uses the Google Drive Realtime API for state synchronization. We have developed a small set of reusable client-side object-oriented JavaScript modules that make use of the XTK toolkit, a popular open-source JavaScript library also developed by our team, for the in-browser rendering and visualization of brain image volumes. Efficient realtime communication among the remote instances is achieved by using just a small JSON object, comprising a representation of the XTK image renderers' state, as the Google Drive Realtime collaborative data model. The developed open-source JavaScript modules have already been instantiated in a web-app called MedView, a distributed collaborative neuroimage visualization application that is delivered to the users over the web without requiring the installation of any extra software or browser plugin. This responsive application allows multiple physically distant physicians or researchers to cooperate in real time to reach a diagnosis or scientific conclusion. It also serves as a proof of concept for the capabilities of the presented technological solution.

Certainty of genuine treatment increases drug responses among intellectually disabled patients

Objective:

To determine the placebo component of treatment responses in patients with intellectual disability (ID).

Methods:

A statistical meta-analysis comparing bias-corrected effect sizes (Hedges g) of drug responses in open-label vs placebo-controlled clinical trials was performed, as these trial types represent different certainty of receiving genuine treatment (100% vs 50%). Studies in fragile X, Down, Prader-Willi, and Williams syndrome published before June 2015 were considered.

Results:

Seventeen open-label trials (n = 261, 65% male; mean age 23.6 years; mean trial duration 38 weeks) and 22 placebo-controlled trials (n = 721, 62% male; mean age 17.1 years; mean trial duration 35 weeks) were included. The overall effect size from pre to post treatment in open-label studies was g = 0.602 (p = 0.001). The effect of trial type was statistically significant (p = 0.001), and revealed higher effect sizes in studies with 100% likelihood of getting active drug, compared to both the drug and placebo arm of placebo-controlled trials. We thus provide evidence for genuine placebo effects, not explainable by natural history or regression toward the mean, among patients with ID.

Conclusions:

Our data suggest that clinical trials in patients with severe cognitive deficits are influenced by the certainty of receiving genuine medication, and open-label design should thus not be used to evaluate the effect of pharmacologic treatments in ID, as the results will be biased by an enhanced placebo component.

A Preliminary Study of the Opioid System and Personality Traits Using Positron Emission Tomography

BACKGROUND

Personality traits, such as Neuroticism and Extraversion, have been implicated in the processing of emotion. The neural correlates most often associated with Neuroticism and Extraversion are the insular cortex, orbitofrontal cortex, amygdala, and ventral striatum.

OBJECTIVE

The aim of the current study was to explore neurotransmitter systems underlying those neural correlates and investigate the relationship between personality traits and opioid receptor binding potential.

METHOD

Twelve healthy participants completed an [11C]diprenorphine positron emission tomography scan at rest. Endogenous opioid levels as indicated by opioid receptor binding potential was examined in relation to personality phenotype.

RESULTS

A high score of Neuroticism, a personality trait characterized by negative affect, was found to be associated with high opioid receptor binding in the right anterior insula. Conversely, a high score of Extraversion, a personality trait characterized by positive affect, was found to be associated with low opioid receptor binding in the left posterior insula.

CONCLUSIONS

While preliminary, the results of this study suggest that the expression of Neuroticism and Extraversion is related to baseline function of the opioid neurotransmitter system in the insular cortex. These findings may help elucidate the neural mechanisms underlying the expression of personality traits, particularly those implicated in affective processing.

Novel genetic loci associated with hippocampal volume

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg=-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.

Novel genetic loci underlying human intracranial volume identified through genome-wide association

Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (ρgenetic = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (Ncombined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.

High throughput tools to access images from clinical archives for research

Historically, medical images collected in the course of clinical care have been difficult to access for secondary research studies. While there is a tremendous potential value in the large volume of studies contained in clinical image archives, Picture Archiving and Communication Systems (PACS) are designed to optimize clinical operations and workflow. Search capabilities in PACS are basic, limiting their use for population studies, and duplication of archives for research is costly. To address this need, we augment the Informatics for Integrating Biology and the Bedside (i2b2) open source software, providing investigators with the tools necessary to query and integrate medical record and clinical research data. Over 100 healthcare institutions have installed this suite of software tools that allows investigators to search medical record metadata including images for specific types of patients. In this report, we describe a new Medical Imaging Informatics Bench to Bedside (mi2b2) module (www.mi2b2.org), available now as an open source addition to the i2b2 software platform that allows medical imaging examinations collected during routine clinical care to be made available to translational investigators directly from their institution’s clinical PACS for research and educational use in compliance with the Health Insurance Portability and Accountability Act (HIPAA) Omnibus Rule. Access governance within the mi2b2 module is customizable per institution and PACS minimizing impact on clinical systems. Currently in active use at our institutions, this new technology has already been used to facilitate access to thousands of clinical MRI brain studies representing specific patient phenotypes for use in research.

What Have We Learned From Brain Functional Connectivity Studies in Migraine Headache?

Over the past 20 years, headache syndromes, especially migraine, have benefited significantly from the knowledge gained through neuroimaging studies. This article is focused on the neuroimaging studies of the functional organization and connectivity of the migraine brain. First, data sources and the study design elements in functional neuroimaging studies of the brain connectivity in migraine headaches are discussed. Then, the article reviews the findings to date and discusses how functional connectivity studies have contributed to a better understanding of the mechanisms of the migraine disease by extending the focus from a single region or structure to a network of regions and structures and the interactions among them. Finally, the potential scenarios for the translation of connectivity knowledge to the benefit for patients are discussed.

Not seeing or feeling is still believing: conscious and non-conscious pain modulation after direct and observational learning

Our experience with the world is shaped not only directly through personal exposure but also indirectly through observing others and learning from their experiences. Using a conditioning paradigm, we investigated how directly and observationally learned information can affect pain perception, both consciously and non-consciously. Differences between direct and observed cues were manifest in higher pain ratings and larger skin conductance responses to directly experienced cues. However, the pain modulation effects produced by conditioning were of comparable magnitude for direct and observational learning. These results suggest that social observation can induce positive and negative pain modulation. Importantly, the fact that cues learned by observation and activated non-consciously still produced a robust conditioning effect that withstood extinction highlights the role of indirect exposure in placebo and nocebo effects.

Brain extraction in pediatric ADC maps, toward characterizing neuro-development in multi-platform and multi-institution clinical images

Apparent Diffusion Coefficient (ADC) maps can be used to characterize myelination and to detect abnormalities in the developing brain. However, given the normal variation in regional ADC with myelination, detection of abnormalities is difficult when based on visual assessment. Quantitative and automated analysis of pediatric ADC maps is thus desired but requires accurate brain extraction as the first step. Currently, most existing brain extraction methods are optimized for structural T1-weighted MR images of fully myelinated brains. Due to differences in age and image contrast, these approaches do not translate well to pediatric ADC maps. To address this problem, we present a multi-atlas brain extraction framework that has 1) specificity: designed and optimized specifically for pediatric ADC maps; 2) generality: applicable to multi-platform and multi-institution data, and to subjects at various neuro-developmental stages across the first 6 years of life; 3) accuracy: highly accurate compared to expert annotations; and 4) consistency: consistently accurate regardless of sources of data and ages of subjects. We show how we achieve these goals, via optimizing major components in a multi-atlas brain extraction framework, and via developing and evaluating new criteria for its atlas ranking component. Moreover, we demonstrate that these goals can be achieved with a fixed set of atlases and a fixed set of parameters, which opens doors for our optimized framework to be used in large-scale and multi-institution neuro-developmental and clinical studies. In a pilot study, we use this framework in a dataset containing scanner-generated ADC maps from 308 pediatric patients collected during the course of routine clinical care. Our framework leads to successful quantifications of the changes in whole-brain volumes and mean ADC values across the first 6 years of life.

Brain structure and function correlates of cognitive subtypes in schizophrenia

Stable neuropsychological deficits may provide a reliable basis for identifying etiological subtypes of schizophrenia. The aim of this study was to identify clusters of individuals with schizophrenia based on dimensions of neuropsychological performance, and to characterize their neural correlates. We acquired neuropsychological data as well as structural and functional magnetic resonance imaging from 129 patients with schizophrenia and 165 healthy controls. We derived eight cognitive dimensions and subsequently applied a cluster analysis to identify possible schizophrenia subtypes. Analyses suggested the following four cognitive clusters of schizophrenia: (1) Diminished Verbal Fluency, (2) Diminished Verbal Memory and Poor Motor Control, (3) Diminished Face Memory and Slowed Processing, and (4) Diminished Intellectual Function. The clusters were characterized by a specific pattern of structural brain changes in areas such as Wernicke's area, lingual gyrus and occipital face area, and hippocampus as well as differences in working memory-elicited neural activity in several fronto-parietal brain regions. Separable measures of cognitive function appear to provide a method for deriving cognitive subtypes meaningfully related to brain structure and function. Because the present study identified brain-based neural correlates of the cognitive clusters, the proposed groups of individuals with schizophrenia have some external validity.

The modulation effect of longitudinal acupuncture on resting state functional connectivity in knee osteoarthritis patients

Recent advances in brain imaging have contributed to our understanding of the neural activity associated with acupuncture treatment. In this study, we investigated functional connectivity across longitudinal acupuncture treatments in older patients with knee osteoarthritis (OA). Over a period of 4 weeks (six treatments), we collected resting state functional magnetic resonance imaging (fMRI) scans from 30 patients before and after their first, third and sixth treatments. Clinical outcome showed a significantly greater pain subscore on the Knee Injury and Osteoarthritis Outcome Score (KOOS) (indicative of improvement) with verum acupuncture than with sham acupuncture. Independent component analysis (ICA) of the resting state fMRI data showed that the right frontoparietal network (rFPN) and the executive control network (ECN) showed enhanced functional connectivity (FC) with the rostral anterior cingulate cortex/medial prefrontal cortex, a key region in the descending pain modulatory system, in the verum groups as compared to the sham group after treatments. We also found that the rFPN connectivity with the left insula is (1) significantly associated with changes in KOOS pain score after treatments, and (2) significantly enhanced after verum acupuncture treatments as compared to sham treatment. Analysis of the acupuncture needle stimulation scan showed that compared with sham treatment, verum acupuncture activated the left operculum/insula, which also overlaps with findings observed in resting state analysis. Our results suggest that acupuncture may achieve its therapeutic effect on knee OA pain by modulating functional connectivity between the rFPN, ECN and the descending pain modulatory pathway.

Clinical trial number: NCT01079390

Repeated verum but not placebo acupuncture normalizes connectivity in brain regions dysregulated in chronic pain

Acupuncture, an ancient East Asian therapy, is aimed at rectifying the imbalance within the body caused by disease. Studies evaluating the efficacy of acupuncture with neuroimaging tend to concentrate on brain regions within the pain matrix, associated with acute pain. We, however, focused on the effect of repeated acupuncture treatment specifically on brain regions known to support functions dysregulated in chronic pain disorders. Transition to chronic pain is associated with increased attention to pain, emotional rumination, nociceptive memory and avoidance learning, resulting in brain connectivity changes, specifically affecting the periaqueductal gray (PAG), medial frontal cortex (MFC) and bilateral hippocampus (Hpc). We demonstrate that the PAG-MFC and PAG-Hpc connectivity in patients with chronic pain due to knee osteoarthritis indeed correlates with clinical severity scores and further show that verum acupuncture-induced improvement in pain scores (compared to sham) is related to the modulation of PAG-MFC and PAG-Hpc connectivity in the predicted direction. This study shows that repeated verum acupuncture might act by restoring the balance in the connectivity of the key pain brain regions, altering pain-related attention and memory.

Patterns of Gray Matter Abnormalities in Schizophrenia Based on an International Mega-analysis

Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both source-based morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over analyses of data subsets. VBM analyses identified overall cortical GMC loss and one small cluster of increased GMC in Sz, which overlapped with the SBM brainstem component. We found no significant association between the component loadings and symptom severity in either analysis. This mega-analysis confirms that the commonly found GMC loss in Sz in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset. The separation of GMC loss into robust, repeatable spatial patterns across multiple datasets paves the way for the application of these methods to identify subtle genetic and clinical cohort effects.

Genetic underpinnings of left superior temporal gyrus thickness in patients with schizophrenia

OBJECTIVES

Schizophrenia is a highly disabling psychiatric disorder with a heterogeneous phenotypic appearance. We aimed to further the understanding of some of the underlying genetics of schizophrenia, using left superior temporal gyrus (STG) grey matter thickness reduction as an endophenoptype in a genome-wide association (GWA) study.

METHODS

Structural magnetic resonance imaging (MRI) and genetic data of the Mind Clinical Imaging Consortium (MCIC) study of schizophrenia were used to analyse the interaction effects between 1,067,955 single nucleotide polymorphisms (SNPs) and disease status on left STG thickness in 126 healthy controls and 113 patients with schizophrenia. We next used a pathway approach to detect underlying pathophysiological pathways that may be related to schizophrenia.

RESULTS

No SNP by diagnosis interaction effect reached genome-wide significance (5 × 10-8) in our GWA study, but 10 SNPs reached P-values less than 10-6. The most prominent pathways included those involved in insulin, calcium, PI3K-Akt and MAPK signalling.

CONCLUSIONS

Our strongest findings in the GWA study and pathway analysis point towards an involvement of glucose metabolism in left STG thickness reduction in patients with schizophrenia only. These results are in line with recently published studies, which showed an increased prevalence of psychosis among patients with metabolic syndrome-related illnesses including diabetes.

Placebo Responses in Genetically Determined Intellectual Disability: A Meta-Analysis

Background

Genetically determined Intellectual Disability (ID) is an intractable condition that involves severe impairment of mental abilities such as learning, reasoning and predicting the future. As of today, little is known about the placebo response in patients with ID.

Objective

To determine if placebo response exists in patients with genetically determined ID.

Data sources and Study selection

We searched Medline/PubMed, EMBASE, CENTRAL and PsycINFO to find all placebo-controlled double-blind randomized clinical trials (RCTs) in patients with genetically determined ID, published up to April 2013, focusing on core ID symptoms.

Data extraction and synthesis

Two investigators extracted outcome data independently.

Main outcomes and measures

Bias-corrected standardized mean difference (Hedge’s g) was computed for each outcome measure, using the Comprehensive Meta-Analysis software. A priori defined patient sub-groups were analyzed using a mixed-effect model. The relationship between pre-defined continuous variable moderators (age, IQ, year of publication and trial duration) and effect size was analyzed using meta-regression

Results

Twenty-two placebo-controlled double-blind RCTs met the inclusion criteria (n = 721, mean age = 17.1 years, 62% men, mean trial duration = 35 weeks). There was a significant overall placebo response from pre- to post-treatment in patients with ID (g = 0.468, p = 0.002), both for “subjective outcomes” (a third-person’s evaluation of the patient) (g = 0.563, p = 0.022) and “objective outcomes” (direct evaluation of the patient’s abilities) (g = 0.434, p = 0.036). Individuals with higher IQ had higher response to placebo (p = 0.02) and no placebo response was observed in ID patients with comorbid dementia. A significant effect of age (p = 0.02) was found, indicating higher placebo responses in treatment of younger patients.

Conclusions and relevance

Results suggest that patients with genetically determined ID improve in the placebo arm of RCTs. Several mechanisms may contribute to placebo effects in ID, including expectancy, implicit learning and “placebo-by-proxy” induced by clinicians/family members. As the condition is refractory, there is little risk that improvements are explained by spontaneous remission. While new avenues for treatment of genetically determined ID are emerging, our results demonstrate how contextual factors can affect clinical outcomes and emphasize the importance of being vigilant on the role of placebos when testing novel treatments in ID.

Associations between DNA methylation and schizophrenia-related intermediate phenotypes - a gene set enrichment analysis

Multiple genetic approaches have identified microRNAs as key effectors in psychiatric disorders as they post-transcriptionally regulate expression of thousands of target genes. However, their role in specific psychiatric diseases remains poorly understood. In addition, epigenetic mechanisms such as DNA methylation, which affect the expression of both microRNAs and coding genes, are critical for our understanding of molecular mechanisms in schizophrenia. Using clinical, imaging, genetic, and epigenetic data of 103 patients with schizophrenia and 111 healthy controls of the Mind Clinical Imaging Consortium (MCIC) study of schizophrenia, we conducted gene set enrichment analysis to identify markers for schizophrenia-associated intermediate phenotypes. Genes were ranked based on the correlation between DNA methylation patterns and each phenotype, and then searched for enrichment in 221 predicted microRNA target gene sets. We found the predicted hsa-miR-219a-5p target gene set to be significantly enriched for genes (EPHA4, PKNOX1, ESR1, among others) whose methylation status is correlated with hippocampal volume independent of disease status. Our results were strengthened by significant associations between hsa-miR-219a-5p target gene methylation patterns and hippocampus-related neuropsychological variables. IPA pathway analysis of the respective predicted hsa-miR-219a-5p target genes revealed associated network functions in behavior and developmental disorders. Altered methylation patterns of predicted hsa-miR-219a-5p target genes are associated with a structural aberration of the brain that has been proposed as a possible biomarker for schizophrenia. The (dys)regulation of microRNA target genes by epigenetic mechanisms may confer additional risk for developing psychiatric symptoms. Further study is needed to understand possible interactions between microRNAs and epigenetic changes and their impact on risk for brain-based disorders such as schizophrenia.

Myelination-related genes are associated with decreased white matter integrity in schizophrenia

Disruptions in white matter (WM) tract structures have been implicated consistently in the pathophysiology of schizophrenia. Global WM integrity--as measured by fractional anisotropy (FA)--is highly heritable and may provide a good endophenotype for genetic studies of schizophrenia. WM abnormalities in schizophrenia are not localized to one specific brain region but instead reflect global low-level decreases in FA coupled with focal abnormalities. In this study, we sought to investigate whether functional gene sets associated with schizophrenia are also associated with WM integrity. We analyzed FA and genetic data from the Mind Research Network Clinical Imaging Consortium to study the effect of multiple oligodendrocyte gene sets on schizophrenia and WM integrity using a functional gene set analysis in 77 subjects with schizophrenia and 104 healthy controls. We found that a gene set involved in myelination was significantly associated with schizophrenia and FA. This gene set includes 17 genes that are expressed in oligodendrocytes and one neuronal gene (NRG1) that is known to regulate myelination. None of the genes within the gene set were associated with schizophrenia or FA individually, suggesting that no single gene was driving the association of the gene set. Our findings support the hypothesis that multiple genetic variants in myelination-related genes contribute to the observed correlation between schizophrenia and decreased WM integrity as measured by FA.

The lateral prefrontal cortex mediates the hyperalgesic effects of negative cognitions in chronic pain patients

Although high levels of negative affect and cognitions have been associated with greater pain sensitivity in chronic pain conditions, the neural mechanisms mediating the hyperalgesic effect of psychological factors in patients with pain disorders are largely unknown. In this cross-sectional study, we hypothesized that 1) catastrophizing modulates brain responses to pain anticipation and 2) anticipatory brain activity mediates the hyperalgesic effect of different levels of catastrophizing in fibromyalgia (FM) patients. Using functional magnetic resonance imaging, we scanned the brains of 31 FM patients exposed to visual cues anticipating the onset of moderately intense deep-tissue pain stimuli. Our results indicated the existence of a negative association between catastrophizing and pain-anticipatory brain activity, including in the right lateral prefrontal cortex. A bootstrapped mediation analysis revealed that pain-anticipatory activity in the lateral prefrontal cortex mediates the association between catastrophizing and pain sensitivity. These findings highlight the role of the lateral prefrontal cortex in the pathophysiology of FM-related hyperalgesia and suggest that deficits in the recruitment of pain-inhibitory brain circuitry during pain-anticipatory periods may play an important contributory role in the association between various degrees of widespread hyperalgesia in FM and levels of catastrophizing, a well-validated measure of negative cognitions and psychological distress.

PERSPECTIVE

This article highlights the presence of alterations in pain-anticipatory brain activity in FM. These findings provide the rationale for the development of psychological or neurofeedback-based techniques aimed at modifying patients' negative affect and cognitions toward pain.

Distinct neural representations of placebo and nocebo effects

Expectations shape the way we experience the world. In this study, we used fMRI to investigate how positive and negative expectation can change pain experiences in the same cohort of subjects. We first manipulated subjects' treatment expectation of the effectiveness of three inert creams, with one cream labeled "Lidocaine" (positive expectancy), one labeled "Capsaicin" (negative expectancy) and one labeled "Neutral" by surreptitiously decreasing, increasing, or not changing respectively, the intensity of the noxious stimuli administered following cream application. We then used fMRI to investigate the signal changes associated with administration of identical pain stimuli before and after the treatment and control creams. Twenty-four healthy adults completed the study. Results showed that expectancy significantly modulated subjective pain ratings. After controlling for changes in the neutral condition, the subjective pain rating changes evoked by positive and negative expectancies were significantly associated. fMRI results showed that the expectation of an increase in pain induced significant fMRI signal changes in the insula, orbitofrontal cortex, and periaqueductal gray, whereas the expectation of pain relief evoked significant fMRI signal changes in the striatum. No brain regions were identified as common to both "Capsaicin" and "Lidocaine" conditioning. There was also no significant association between the brain response to identical noxious stimuli in the pain matrix evoked by positive and negative expectancies. Our findings suggest that positive and negative expectancies engage different brain networks to modulate our pain experiences, but, overall, these distinct patterns of neural activation result in a correlated placebo and nocebo behavioral response.

Common genetic variants influence human subcortical brain structures

The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10(-33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

A Neural Mechanism for Nonconscious Activation of Conditioned Placebo and Nocebo Responses

Fundamental aspects of human behavior operate outside of conscious awareness. Yet, theories of conditioned responses in humans, such as placebo and nocebo effects on pain, have a strong emphasis on conscious recognition of contextual cues that trigger the response. Here, we investigated the neural pathways involved in nonconscious activation of conditioned pain responses, using functional magnetic resonance imaging in healthy participants. Nonconscious compared with conscious activation of conditioned placebo analgesia was associated with increased activation of the orbitofrontal cortex, a structure with direct connections to affective brain regions and basic reward processing. During nonconscious nocebo, there was increased activation of the thalamus, amygdala, and hippocampus. In contrast to previous assumptions about conditioning in humans, our results show that conditioned pain responses can be elicited independently of conscious awareness and our results suggest a hierarchical activation of neural pathways for nonconscious and conscious conditioned responses. Demonstrating that the human brain has a nonconscious mechanism for responding to conditioned cues has major implications for the role of associative learning in behavioral medicine and psychiatry. Our results may also open up for novel approaches to translational animal-to-human research since human consciousness and animal cognition is an inherent paradox in all behavioral science.

Association between atrial fibrillation and silent cerebral infarctions: a systematic review and meta-analysis

BACKGROUND

Atrial fibrillation (AF) is a common cause of stroke. Silent cerebral infarctions (SCIs) are known to occur in the presence and absence of AF, but the association between these disorders has not been well-defined.

PURPOSE

To estimate the association between AF and SCIs and the prevalence of SCIs in stroke-free patients with AF.

DATA SOURCES

Searches of MEDLINE, PsycINFO, Cochrane Library, CINAHL, and EMBASE from inception to 8 May 2014 without language restrictions and manual screening of article references.

STUDY SELECTION

Observational studies involving adults with AF and no clinical history of stroke or prosthetic valves who reported SCIs.

DATA EXTRACTION

Study characteristics and study quality were assessed in duplicate.

DATA SYNTHESIS

Eleven studies including 5317 patients with mean ages from 50.0 to 83.6 years reported on the association between AF and SCIs. Autopsy studies were heterogeneous and low-quality; therefore, they were excluded from the meta-analysis of the risk estimates. When computed tomography (CT) and magnetic resonance imaging (MRI) studies were combined, AF was associated with SCIs in patients with no history of symptomatic stroke (odds ratio, 2.62 [95% CI, 1.81 to 3.80]; I(2) = 32.12%; P for heterogeneity = 0.118). This association was independent of AF type (paroxysmal vs. persistent). The results were not altered significantly when the analysis was restricted to studies that met at least 70% of the maximum possible quality score (odds ratio, 3.06 [CI, 2.24 to 4.19]). Seventeen studies reported the prevalence of SCIs. The overall prevalence of SCI lesions on MRI and CT among patients with AF was 40% and 22%, respectively.

LIMITATION

Most studies were cross-sectional, and autopsy studies were heterogeneous and not sufficiently sensitive to detect small lesions.

CONCLUSION

Atrial fibrillation is associated with more than a 2-fold increase in the odds for SCI.

PRIMARY FUNDING SOURCE

Deane Institute for Integrative Research in Atrial Fibrillation and Stroke, Massachusetts General Hospital.

Prefrontal inefficiency is associated with polygenic risk for schizophrenia

Considering the diverse clinical presentation and likely polygenic etiology of schizophrenia, this investigation examined the effect of polygenic risk on a well-established intermediate phenotype for schizophrenia. We hypothesized that a measure of cumulative genetic risk based on additive effects of many genetic susceptibility loci for schizophrenia would predict prefrontal cortical inefficiency during working memory, a brain-based biomarker for the disorder. The present study combined imaging, genetic and behavioral data obtained by the Mind Clinical Imaging Consortium study of schizophrenia (n = 255). For each participant, we derived a polygenic risk score (PGRS), which was based on over 600 nominally significant single nucleotide polymorphisms, associated with schizophrenia in a separate discovery sample comprising 3322 schizophrenia patients and 3587 control participants. Increased polygenic risk for schizophrenia was associated with neural inefficiency in the left dorsolateral prefrontal cortex after covarying for the effects of acquisition site, diagnosis, and population stratification. We also provide additional supporting evidence for our original findings using scores based on results from the Psychiatric Genomics Consortium study. Gene ontology analysis of the PGRS highlighted genetic loci involved in brain development and several other processes possibly contributing to disease etiology. Our study permits new insights into the additive effect of hundreds of genetic susceptibility loci on a brain-based intermediate phenotype for schizophrenia. The combined impact of many common genetic variants of small effect are likely to better reveal etiologic mechanisms of the disorder than the study of single common genetic variants.

Patterns of brain activation when mothers view their own child and dog: an fMRI study

Neural substrates underlying the human-pet relationship are largely unknown. We examined fMRI brain activation patterns as mothers viewed images of their own child and dog and an unfamiliar child and dog. There was a common network of brain regions involved in emotion, reward, affiliation, visual processing and social cognition when mothers viewed images of both their child and dog. Viewing images of their child resulted in brain activity in the midbrain (ventral tegmental area/substantia nigra involved in reward/affiliation), while a more posterior cortical brain activation pattern involving fusiform gyrus (visual processing of faces and social cognition) characterized a mother's response to her dog. Mothers also rated images of their child and dog as eliciting similar levels of excitement (arousal) and pleasantness (valence), although the difference in the own vs. unfamiliar child comparison was larger than the own vs. unfamiliar dog comparison for arousal. Valence ratings of their dog were also positively correlated with ratings of the attachment to their dog. Although there are similarities in the perceived emotional experience and brain function associated with the mother-child and mother-dog bond, there are also key differences that may reflect variance in the evolutionary course and function of these relationships.