The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling.

There exists a moderate correlation between MRI-measured brain size and the general factor of IQ performance (g), but the question of whether the association reflects a theoretically important causal relationship or spurious confounding remains somewhat open. Previous small studies (n < 100) looking for the persistence of this correlation within families failed to find a tendency for the sibling with the larger brain to obtain a higher test score. We studied the within-family relationship between brain volume and intelligence in the much larger sample provided by the Human Connectome Project (n = 1,022) and found a highly significant correlation (disattenuated ρ = 0.18, p < .001). We replicated this result in the Minnesota Center for Twin and Family Research (n = 2,698), finding a highly significant within-family correlation between head circumference and intelligence (disattenuated ρ = 0.19, p < .001). We also employed novel methods of causal inference relying on summary statistics from genome-wide association studies (GWAS) of head size (n ≈ 10,000) and measures of cognition (257,000 < n < 767,000). Using bivariate LD Score regression, we found a genetic correlation between intracranial volume (ICV) and years of education (EduYears) of 0.41 (p < .001). Using the Latent Causal Variable method, we found a genetic causality proportion of 0.72 (p < .001); thus the genetic correlation arises from an asymmetric pattern, extending to sub-significant loci, of genetic variants associated with ICV also being associated with EduYears but many genetic variants associated with EduYears not being associated with ICV. This is the pattern of genetic results expected from a causal effect of brain size on intelligence. These findings give reason to take up the hypothesis that the dramatic increase in brain volume over the course of human evolution has been the result of natural selection favoring general intelligence.

Test-retest reliability of dynamic functional connectivity in resting state fMRI.

While static functional connectivity (sFC) of resting state fMRI (rfMRI) measures the average functional connectivity (FC) over the entire rfMRI scan, dynamic FC (dFC) captures the temporal variations of FC at shorter time windows. Although numerous studies have implemented dFC analyses, only a few studies have investigated the reliability of dFC and this limits the biological interpretation of dFC. Here, we used a large cohort (N = 820) of subjects and four rfMRI scans from the Human Connectome Project to systematically explore the relationship between sFC, dFC and their test-retest reliabilities through intra-class correlation (ICC). dFC ICC was explored through the sliding window approach with three dFC statistics (standard deviation, ALFF, and excursion). Excursion demonstrated the highest dFC ICC and the highest age prediction accuracy. dFC ICC was generally higher at window sizes less than 40 s. sFC and dFC were negatively correlated. Compared to sFC, dFC was less reliable. While sFC and sFC ICC were positively correlated, dFC and dFC ICC were negatively correlated, indicating that FC that was more dynamic was less reliable. Intra-network FCs in the frontal-parietal, default mode, sensorimotor and visual networks demonstrated high sFC and low dFC. Moreover, ICCs of both sFC and dFC in these regions were higher. The above results were consistent across two brain atlases and independent component analysis-based networks, multiple window sizes and all three dFC statistics. In summary, dFC is less reliable than sFC and additional experiments are required to better understand the neurophysiological relevance of dFC.

Functional connectivity predicts gender: Evidence for gender differences in resting brain connectivity.

Prevalence of certain forms of psychopathology, such as autism and depression, differs between genders and understanding gender differences of the neurotypical brain may provide insights into risk and protective factors. In recent research, resting state functional magnetic resonance imaging (rfMRI) is widely used to map the inherent functional networks of the brain. Although previous studies have reported gender differences in rfMRI, the robustness of gender differences is not well characterized. In this study, we use a large data set to test whether rfMRI functional connectivity (FC) can be used to predict gender and identify FC features that are most predictive of gender. We utilized rfMRI data from 820 healthy controls from the Human Connectome Project. By applying a predefined functional template and partial least squares regression modeling, we achieved a gender prediction accuracy of 87% when multi-run rfMRI was used. Permutation tests confirmed that gender prediction was reliable ( p<.001). Effects of motion, age, handedness, blood pressure, weight, and brain volume on gender prediction are discussed. Further, we found that FC features within the default mode (DMN), fronto-parietal and sensorimotor networks contributed most to gender prediction. In the DMN, right fusiform gyrus and right ventromedial prefrontal cortex were important contributors. The above regions have been previously implicated in aspects of social functioning and this suggests potential gender differences in social cognition mediated by the DMN. Our findings demonstrate that gender can be reliably predicted using rfMRI data and highlight the importance of controlling for gender in brain imaging studies.

Automated quality assessment of structural magnetic resonance images in children: Comparison with visual inspection and surface-based reconstruction.

Motion-related artifacts are one of the major challenges associated with pediatric neuroimaging. Recent studies have shown a relationship between visual quality ratings of T1 images and cortical reconstruction measures. Automated algorithms offer more precision in quantifying movement-related artifacts compared to visual inspection. Thus, the goal of this study was to test three different automated quality assessment algorithms for structural MRI scans. The three algorithms included a Fourier-, integral-, and a gradient-based approach which were run on raw T1 -weighted imaging data collected from four different scanners. The four cohorts included a total of 6,662 MRI scans from two waves of the Generation R Study, the NIH NHGRI Study, and the GUSTO Study. Using receiver operating characteristics with visually inspected quality ratings of the T1 images, the area under the curve (AUC) for the gradient algorithm, which performed better than either the integral or Fourier approaches, was 0.95, 0.88, and 0.82 for the Generation R, NHGRI, and GUSTO studies, respectively. For scans of poor initial quality, repeating the scan often resulted in a better quality second image. Finally, we found that even minor differences in automated quality measurements were associated with FreeSurfer derived measures of cortical thickness and surface area, even in scans that were rated as good quality. Our findings suggest that the inclusion of automated quality assessment measures can augment visual inspection and may find use as a covariate in analyses or to identify thresholds to exclude poor quality data.

Dimensional assessment of schizotypal, psychotic, and other psychiatric traits in children and their parents: development and validation of the Childhood Oxford-Liverpool Inventory of Feelings and Experiences on a representative US sample.

BACKGROUND: Healthy functioning relies on a variety of perceptual, cognitive, emotional, and behavioral abilities that are distributed throughout the normal population. Variation in these traits define the wide range of neurodevelopmental (NDD) and neuropsychiatric (NPD) disorders. Here, we introduce a new measure for assessing these traits in typically developing children and children at risk for NDD and NPD from age 2 to 18 years. METHOD: The Childhood Oxford-Liverpool Inventory of Feelings and Experiences (CO-LIFE) was created as a dimensional, parent-report measure of schizotypal and psychotic traits in the general population. Parents of 2,786 children also self-reported on an adapted version of the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE-US). RESULTS: The CO-LIFE resulted in continuous distributions for the total score and for each of three factor analytically-derived subscales. Item response theory (IRT) analyses indicated strong reliability across the score range for the O-LIFE-US and the CO-LIFE. Internal consistency and test-retest reliability were high across all scales. Parent-child intraclass correlations were consistent with high heritability. The scales discriminated participants who reported a lifetime psychiatric diagnosis from those who reported no diagnosis. The O-LIFE-US and CO-LIFE scores correlated positively with the Social Responsiveness Scale 2 (SRS-2) indicating good convergent validity. CONCLUSIONS: Like the original O-LIFE, the O-LIFE-US and the CO-LIFE are valid and reliable tools that reflect the spectrum of psychiatric and schizotypal traits in the general population. Such scales are necessary for conducting family studies that aim to examine a range of psychological and behavioral traits in both children and adults and are well-suited for the Research Domain Criteria (RDoC) initiative of the NIMH.

Leveraging Clinical Imaging Archives for Radiomics: Reliability of Automated Methods for Brain Volume Measurement.

Purpose To validate the use of thick-section clinically acquired magnetic resonance (MR) imaging data for estimating total brain volume (TBV), gray matter (GM) volume (GMV), and white matter (WM) volume (WMV) by using three widely used automated toolboxes: SPM ( www.fil.ion.ucl.ac.uk/spm/ ), FreeSurfer ( surfer.nmr.mgh.harvard.edu ), and FSL (FMRIB software library; Oxford Centre for Functional MR Imaging of the Brain, Oxford, England, https://fsl.fmrib.ox.ac.uk/fsl ). Materials and Methods MR images from a clinical archive were used and data were deidentified. The three methods were applied to estimate brain volumes from thin-section research-quality brain MR images and routine thick-section clinical MR images acquired from the same 38 patients (age range, 1-71 years; mean age, 22 years; 11 women). By using these automated methods, TBV, GMV, and WMV were estimated. Thin- versus thick-section volume comparisons were made for each method by using intraclass correlation coefficients (ICCs). Results SPM exhibited excellent ICCs (0.97, 0.85, and 0.83 for TBV, GMV, and WMV, respectively). FSL exhibited ICCs of 0.69, 0.51, and 0.60 for TBV, GMV, and WMV, respectively, but they were lower than with SPM. FreeSurfer exhibited excellent ICC of 0.63 only for TBV. Application of SPM's voxel-based morphometry on the modulated images of thin-section images and interpolated thick-section images showed fair to excellent ICCs (0.37-0.98) for the majority of brain regions (88.47% [306924 of 346916 voxels] of WM and 80.35% [377 282 of 469 502 voxels] of GM). Conclusion Thick-section clinical-quality MR images can be reliably used for computing quantitative brain metrics such as TBV, GMV, and WMV by using SPM. © RSNA, 2017 Online supplemental material is available for this article.

A Comparison of Structural Brain Imaging Findings in Autism Spectrum Disorder and Attention-Deficit Hyperactivity Disorder.

ASD and ADHD are regarded as distinct disorders in the current DSM-5. However, recent research and the RDoC initiative are recognizing considerable overlap in the clinical presentation of ASD, ADHD, and other neurodevelopmental disorders. In spite of numerous neuroimaging findings in ASD and ADHD, the extent to which either of the above views are supported remains equivocal. Here we compare structural MRI and DTI literature in ASD and ADHD. Our main findings reveal both distinct and shared neural features. Distinct expressions were in total brain volume (ASD: increased volume, ADHD: decreased volume), amygdala (ASD: overgrowth, ADHD: normal), and internal capsule (ASD: unclear, ADHD: reduced FA in DTI). Considerable overlap was noted in the corpus callosum and cerebellum (lower volume in structural MRI and decreased FA in DTI), and superior longitudinal fasciculus (reduced FA in DTI). In addition, we identify brain regions which have not been studied in depth and require more research. We discuss relationships between brain features and symptomatology. We conclude by addressing limitations of current neuroimaging research and offer approaches that account for clinical heterogeneity to better distinguish brain-behavior relationships.

Enhanced motor network engagement during reward gain anticipation in fibromyalgia.

Reward motivation is essential in shaping human behavior and cognition. Both reward motivation and reward brain circuits are altered in chronic pain conditions, including fibromyalgia. In this study of fibromyalgia patients, we used a data-driven independent component analysis (ICA) approach to investigate how brain networks contribute to altered reward processing. From females with fibromyalgia (N = 24) and female healthy controls (N = 24), we acquired fMRI data while participants performed a monetary incentive delay (MID) reward task. After analyzing the task-based fMRI data using ICA to identify networks, we analyzed 3 networks of interest: motor network (left), value-driven attention network, and basal ganglia network. Then, we evaluated correlation coefficients between each network timecourse versus a task-based timecourse which modeled gain anticipation. Compared to controls, the fibromyalgia cohort demonstrated significantly stronger correlation between the left motor network timecourse and the gain anticipation timecourse, indicating the left motor network was more engaged with gain anticipation in fibromyalgia. In an exploratory analysis, we compared motor network engagement during early versus late phases of gain anticipation. Across cohorts, greater motor network engagement (i.e., stronger correlation between network and gain anticipation) occurred during the late timepoint, which reflected enhanced motor preparation immediately prior to response. Consistent with the main results, patients exhibited greater engagement of the motor network during both early and late phases compared with healthy controls. Visual-attention and basal ganglia networks revealed similar engagement in the task across groups. As indicated by post-hoc analyses, motor network engagement was positively related to anxiety and negatively related to reward responsiveness. In summary, we identified enhanced reward-task related engagement of the motor network in fibromyalgia using a novel data-driven ICA approach. Enhanced motor network engagement in fibromyalgia may relate to impaired reward motivation, heightened anxiety, and possibly to altered motor processing, such as restricted movement or dysregulated motor planning.

Altered Functional Networks during Gain Anticipation in Fibromyalgia.

Reward motivation is essential in shaping human behavior and cognition. Previous studies have shown altered reward motivation and reward brain circuitry in chronic pain conditions, including fibromyalgia. Fibromyalgia is a chronic disorder characterized by widespread musculoskeletal pain, fatigue, cognitive problems, and mood-related symptoms. In this study, we analyzed brain reward networks in patients with fibromyalgia by using a data-driven approach with task-based fMRI data. fMRI data from 24 patients with fibromyalgia and 24 healthy controls were acquired while subjects performed a monetary incentive delay (MID) reward task. Functional networks were derived using independent component analysis (ICA) focused on the gain anticipation phase of the reward task. Functional activity in the motor, value-driven attention, and basal ganglia networks was evaluated during gain anticipation in both patient and healthy control groups. Compared to controls, the motor network was more engaged during gain anticipation in patients with fibromyalgia. Our findings suggest that reward motivation may lead to hyperactivity in the motor network, possibly related to altered motor processing, such as restricted movement or dysregulated motor planning in fibromyalgia. As an exploratory analysis, we compared levels of motor network engagement during early and late timepoints of the gain anticipation phase. Both groups showed greater motor network engagement during the late timepoint (i.e., closer to response), which reflected motor preparation prior to target response. Importantly, compared to controls and consistent with the initial findings described above, patients exhibited greater engagement of the motor network during both early and late timepoints. In summary, by using a novel data-driven ICA approach to analyze task-based fMRI data, we identified elevated motor network engagement during gain anticipation in fibromyalgia.

Impact of Computerized Cognitive Training on Default Mode Network Connectivity in Subjects at Risk for Alzheimer's Disease: A 78-week Randomized Controlled Trial.

BACKGROUND: Mild cognitive impairment (MCI) represents a high risk group for Alzheimer's disease (AD). Computerized Cognitive Games Training (CCT) is an investigational strategy to improve targeted functions in MCI through the modulation of cognitive networks. OBJECTIVE: The goal of this study was to examine the effect of CCT versus a non-targeted active brain exercise on functional cognitive networks. METHODS: 107 patients with MCI were randomized to CCT or web-based crossword puzzles. Resting-state functional MRI (fMRI) was obtained at baseline and 18 months to evaluate differences in fMRI measured within- and between-network functional connectivity (FC) of the default mode network (DMN) and other large-scale brain networks: the executive control, salience, and sensorimotor networks. RESULTS: There were no differences between crosswords and games in the primary outcome, within-network DMN FC across all subjects. However, secondary analyses suggest differential effects on between-network connectivity involving the DMN and SLN, and within-network connectivity of the DMN in subjects with late MCI. Paradoxically, in both cases, there was a decrease in FC for games and an increase for the crosswords control (p < 0.05), accompanied by lesser cognitive decline in the crosswords group. CONCLUSION: Results do not support a differential impact on within-network DMN FC between games and crossword puzzle interventions. However, crossword puzzles might result in cognitively beneficial remodeling between the DMN and other networks in more severely impaired MCI subjects, parallel to the observed clinical benefits.

Adolescent alcohol use is linked to disruptions in age-appropriate cortical thinning: an unsupervised machine learning approach.

Cortical thickness changes dramatically during development and is associated with adolescent drinking. However, previous findings have been inconsistent and limited by region-of-interest approaches that are underpowered because they do not conform to the underlying spatially heterogeneous effects of alcohol. In this study, adolescents (n = 657; 12-22 years at baseline) from the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) study who endorsed little to no alcohol use at baseline were assessed with structural magnetic resonance imaging and followed longitudinally at four yearly intervals. Seven unique spatial patterns of covarying cortical thickness were obtained from the baseline scans by applying an unsupervised machine learning method called non-negative matrix factorization (NMF). The cortical thickness maps of all participants' longitudinal scans were projected onto vertex-level cortical patterns to obtain participant-specific coefficients for each pattern. Linear mixed-effects models were fit to each pattern to investigate longitudinal effects of alcohol consumption on cortical thickness. We found in six NMF-derived cortical thickness patterns, the longitudinal rate of decline in no/low drinkers was similar for all age cohorts. Among moderate drinkers the decline was faster in the younger adolescent cohort and slower in the older cohort. Among heavy drinkers the decline was fastest in the younger cohort and slowest in the older cohort. The findings suggested that unsupervised machine learning successfully delineated spatially coordinated patterns of vertex-level cortical thickness variation that are unconstrained by neuroanatomical features. Age-appropriate cortical thinning is more rapid in younger adolescent drinkers and slower in older adolescent drinkers, an effect that is strongest among heavy drinkers.

Multifaceted genomic risk for brain function in schizophrenia.

Recently, deriving candidate endophenotypes from brain imaging data has become a valuable approach to study genetic influences on schizophrenia (SZ), whose pathophysiology remains unclear. In this work we utilized a multivariate approach, parallel independent component analysis, to identify genomic risk components associated with brain function abnormalities in SZ. 5157 candidate single nucleotide polymorphisms (SNPs) were derived from genome-wide array based on their possible connections with SZ and further investigated for their associations with brain activations captured with functional magnetic resonance imaging (fMRI) during a sensorimotor task. Using data from 92 SZ patients and 116 healthy controls, we detected a significant correlation (r=0.29; p=2.41 × 10(-5)) between one fMRI component and one SNP component, both of which significantly differentiated patients from controls. The fMRI component mainly consisted of precentral and postcentral gyri, the major activated regions in the motor task. On average, higher activation in these regions was observed in participants with higher loadings of the linked SNP component, predominantly contributed to by 253 SNPs. 138 identified SNPs were from known coding regions of 100 unique genes. 31 identified SNPs did not differ between groups, but moderately correlated with some other group-discriminating SNPs, indicating interactions among alleles contributing toward elevated SZ susceptibility. The genes associated with the identified SNPs participated in four neurotransmitter pathways: GABA receptor signaling, dopamine receptor signaling, neuregulin signaling and glutamate receptor signaling. In summary, our work provides further evidence for the complexity of genomic risk to the functional brain abnormality in SZ and suggests a pathological role of interactions between SNPs, genes and multiple neurotransmitter pathways.

Effect of surgical mask on fMRI signals during task and rest.

Wearing a face mask has become essential to contain the spread of COVID-19 and has become mandatory when collecting fMRI data at most research institutions. Here, we investigate the effects of wearing a surgical mask on fMRI data in n = 37 healthy participants. Activations during finger tapping, emotional face matching, working memory tasks, and rest were examined. Preliminary fMRI analyses show that despite the different mask states, resting-state signals and task activations were relatively similar. Resting-state functional connectivity showed negligible attenuation patterns in mask-on compared with mask-off. Task-based ROI analysis also demonstrated no significant difference between the two mask states under each contrast investigated. Notwithstanding the overall insignificant effects, these results indicate that wearing a face mask during fMRI has little to no significant effect on resting-state and task activations.

Validity of the Web-Based, Self-Directed, NeuroCognitive Performance Test in Mild Cognitive Impairment.

BACKGROUND: Digital cognitive tests offer several potential advantages over established paper-pencil tests but have not yet been fully evaluated for the clinical evaluation of mild cognitive impairment. OBJECTIVE: The NeuroCognitive Performance Test (NCPT) is a web-based, self-directed, modular battery intended for repeated assessments of multiple cognitive domains. Our objective was to examine its relationship with the Alzheimer's Disease Assessment Scale-Cognition Subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) as well as with established paper-pencil tests of cognition and daily functioning in mild cognitive impairment (MCI). METHODS: We used Spearman correlations, regressions and principal components analysis followed by a factor analysis (varimax rotated) to examine our objectives. RESULTS: In MCI subjects, the NCPT composite is significantly correlated with both a composite measure of established tests (r = 0.78, p < 0.0001) as well as with the ADAS-Cog (r = -0.55, p < 0.0001). Both NCPT and paper-pencil test batteries had a similar factor structure that included a large "g" component with a high eigenvalue. The correlation for the analogous tests (e.g., Trails A and B, learning memory tests) were significant (p < 0.0001). Further, both the NCPT and established tests significantly (p < 0.0001) predicted the University of California San Diego Performance-Based Skills Assessment and Functional Activities Questionnaire, measures of daily functioning. CONCLUSION: The NCPT, a web-based, self-directed, computerized test, shows high concurrent validity with established tests and hence offers promise for use as a research or clinical tool in MCI. Despite limitations such as a relatively small sample, absence of control group and cross-sectional nature, these findings are consistent with the growing literature on the promise of self-directed, web-based cognitive assessments for MCI.

Computerized Games versus Crosswords Training in Mild Cognitive Impairment.

BACKGROUND: Mild cognitive impairment (MCI) increases the risk of dementia. The efficacy of cognitive training in patients with MCI is unclear. METHODS: In a two-site, single-blinded, 78-week trial, participants with MCI - stratified by age, severity (early/late MCI), and site - were randomly assigned to 12 weeks of intensive, home-based, computerized training with Web-based cognitive games or Web-based crossword puzzles, followed by six booster sessions. In mixed-model analyses, the primary outcome was change from baseline in the 11-item Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) score, a 70 point scale in which higher scores indicate greater cognitive impairment at 78 weeks, adjusted for baseline. Secondary outcomes included change from baseline in neuropsychological composite score, University of California San Diego Performance-Based Skills Assessment (functional outcome) score, and Functional Activities Questionnaire (functional outcome) score at 78 weeks, adjusted for baseline. Changes in hippocampal volume and cortical thickness on magnetic resonance imaging were assessed. RESULTS: Among 107 participants (n=51 [games]; n=56 [crosswords]), ADAS-Cog score worsened slightly for games and improved for crosswords at week 78 (least squares [LS] means difference, -1.44; 95% confidence interval [CI], -2.83 to -0.06; P=0.04). From baseline to week 78, mean ADAS-Cog score worsened for games (9.53 to 9.93) and improved for crosswords (9.59 to 8.61). The late MCI subgroup showed similar results (LS means difference, -2.45; SE, 0.89; 95% CI, -4.21 to -0.70). Among secondary outcomes, the Functional Activities Questionnaire score worsened more with games than with crosswords at week 78 (LS means difference, -1.08; 95% CI, -1.97 to -0.18). Other secondary outcomes showed no differences. Decreases in hippocampal volume and cortical thickness were greater for games than for crosswords (LS means difference, 34.07; SE, 17.12; 95% CI, 0.51 to 67.63 [hippocampal volume]; LS means difference, 0.02; SE, 0.01; 95% CI, 0.00 to 0.04 [cortical thickness]). CONCLUSIONS: Home-based computerized training with crosswords demonstrated superior efficacy to games for the primary outcome of baseline-adjusted change in ADAS-Cog score over 78 weeks. (Supported by the National Institutes of Health, National Institute on Aging; ClinicalTrials.gov number, NCT03205709.).

A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder.

Functional magnetic resonance imaging (fMRI) has been widely used to examine the relationships between brain function and phenotypic features in neurodevelopmental disorders. Techniques such as resting-state functional connectivity (FC) have enabled the identification of the primary networks of the brain. One fMRI network, in particular, the default mode network (DMN), has been implicated in social-cognitive deficits in autism spectrum disorders (ASD) and attentional deficits in attention deficit hyperactivity disorder (ADHD). Given the significant clinical and genetic overlap between ASD and ADHD, surprisingly, no reviews have compared the clinical, developmental, and genetic correlates of DMN in ASD and ADHD and here we address this knowledge gap. We find that, compared with matched controls, ASD studies show a mixed pattern of both stronger and weaker FC in the DMN and ADHD studies mostly show stronger FC. Factors such as age, intelligence quotient, medication status, and heredity affect DMN FC in both ASD and ADHD. We also note that most DMN studies make ASD versus ADHD group comparisons and fail to consider ASD+ADHD comorbidity. We conclude, by identifying areas for improvement and by discussing the importance of using transdiagnostic approaches such as the Research Domain Criteria (RDoC) to fully account for the phenotypic and genotypic heterogeneity and overlap of ASD and ADHD.

The Effects of Functionally Guided, Connectivity-Based rTMS on Amygdala Activation.

While repetitive transcranial magnetic stimulation (rTMS) is widely used to treat psychiatric disorders, innovations are needed to improve its efficacy. An important limitation is that while psychiatric disorders are associated with fronto-limbic dysregulation, rTMS does not have sufficient depth penetration to modulate affected subcortical structures. Recent advances in task-related functional connectivity provide a means to better link superficial and deeper cortical sources with the possibility of increasing fronto-limbic modulation to induce stronger therapeutic effects. The objective of this pilot study was to test whether task-related, connectivity-based rTMS could modulate amygdala activation through its connectivity with the medial prefrontal cortex (mPFC). fMRI was collected to identify a node in the mPFC showing the strongest connectivity with the amygdala, as defined by psychophysiological interaction analysis. To promote Hebbian-like plasticity, and potentially stronger modulation, 5 Hz rTMS was applied while participants viewed frightening video-clips that engaged the fronto-limbic network. Significant increases in both the mPFC and amygdala were found for active rTMS compared to sham, offering promising preliminary evidence that functional connectivity-based targeting may provide a useful approach to treat network dysregulation. Further research is needed to better understand connectivity influences on rTMS effects to leverage this information to improve therapeutic applications.

Network-based rTMS to modulate working memory: The difficult choice of effective parameters for online interventions.

BACKGROUND: Online repetitive transcranialmagnetic stimulation (rTMS) has been shown to modulate working memory (WM) performance in a site-specific manner, with behavioral improvements due to stimulation of the dorsolateral prefrontal cortex (DLPFC), and impairment from stimulation to the lateral parietal cortex (LPC). Neurobehavioral studies have demonstrated that subprocesses of WM allowing for the maintenance and manipulation of information in the mind involve unique cortical networks. Despite promising evidence of modulatory effects of rTMS on WM, no studies have yet demonstrated distinct modulatory control of these two subprocesses. The current study therefore sought to explore this possibility through site-specific stimulation during an online task invoking both skills. METHODS: Twenty-nine subjects completed a 4-day protocol, in which active or sham 5Hz rTMS was applied over the DLPFC and LPC in separate blocks of trials while participants performed tasks that required either maintenance alone, or both maintenance and manipulation (alphabetization) of information. Stimulation targets were defined individually based on fMRI activation and structural network properties. Stimulation amplitude was adjusted using electric field modeling to equate induced current in the target region across participants. RESULTS: Despite the use of advanced techniques, no significant differences or interactions between active and sham stimulation were found. Exploratory analyses testing stimulation amplitude, fMRI activation, and modal controllability showed nonsignificant but interesting trends with rTMS effects. CONCLUSION: While this study did not reveal any significant behavioral changes in WM, the results may point to parameters that contribute to positive effects, such as stimulation amplitude and functional activation.

WITHDRAWN: Erratum to "Does function follow form?: Methods to fuse structural and functional brain images show decreased linkage in schizophrenia" [NeuroImage 49 (2010) 2626-2637].

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Does function follow form?: methods to fuse structural and functional brain images show decreased linkage in schizophrenia.

When both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) data are collected they are typically analyzed separately and the joint information is not examined. Techniques that examine joint information can help to find hidden traits in complex disorders such as schizophrenia. The brain is vastly interconnected, and local brain morphology may influence functional activity at distant regions. In this paper we introduce three methods to identify inter-correlations among sMRI and fMRI voxels within the whole brain. We apply these methods to examine sMRI gray matter data and fMRI data derived from an auditory sensorimotor task from a large study of schizophrenia. In Method 1 the sMRI-fMRI cross-correlation matrix is reduced to a histogram and results show that healthy controls (HC) have stronger correlations than do patients with schizophrenia (SZ). In Method 2 the spatial information of sMRI-fMRI correlations is retained. Structural regions in the cerebellum and frontal regions show more positive and more negative correlations, respectively, with functional regions in HC than in SZ. In Method 3 significant sMRI-fMRI inter-regional links are detected, with regions in the cerebellum showing more significant positive correlations with functional regions in HC relative to SZ. Results from all three methods indicate that the linkage between gray matter and functional activation is stronger in HC than SZ. The methods introduced can be easily extended to comprehensively correlate large data sets.