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Introduction: The false discovery rate (FDR) procedure does not incorporate the geometry of the random field and requires high statistical power at each voxel, a requirement not satisfied by the limited number of participants in imaging studies. Topological FDR, threshold free cluster enhancement (TFCE), and probabilistic TFCE improve statistical power by incorporating local geometry. However, topological FDR requires specifying a cluster defining threshold and TFCE requires specifying transformation weights., Methods: Geometry-derived statistical significance (GDSS) procedure overcomes these limitations by combining voxelwise p-values for the test statistic with the probabilities computed from the local geometry for the random field, thereby providing substantially greater statistical power than the procedures currently used to control for multiple comparisons. We use synthetic data and real-world data to compare its performance against the performance of these other, previously developed procedures., Results: GDSS provided substantially greater statistical power relative to the comparator procedures, which was less variable to the number of participants. GDSS was more conservative than TFCE: that is, it rejected null hypotheses at voxels with much higher effect sizes than TFCE. Our experiments also showed that the Cohen's D effect size decreases as the number of participants increases. Therefore, sample size calculations from small studies may underestimate the participants required in larger studies. Our findings also suggest effect size maps should be presented along with p-value maps for correct interpretation of findings., Conclusions: GDSS compared with the other procedures provides considerably greater statistical power for detecting true positives while limiting false positives, especially in small sized (<40 participants) imaging cohorts., (© 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

We propose a method for segmenting brain tissue as either gray matter or white matter in the presence of varying tissue contrast, which can derive from either differential changes in tissue water content or increasing myelin content of white matter. Our method models the spatial distribution of intensities as a Markov Random Field (MRF) and estimates the parameters for the MRF model using a maximum likelihood approach. Although previously described methods have used similar models to segment brain tissue, accurate model of the conditional probabilities of tissue intensities and adaptive estimates of tissue properties to local intensities generates tissue definitions that are accurate and robust to variations in tissue contrast with age and across illnesses. Robustness to variations in tissue contrast is important to understand normal brain development and to identify the brain bases of neurological and psychiatric illnesses. We used simulated brains of varying tissue contrast to compare both visually and quantitatively the performance of our method with the performance of prior methods. We assessed validity of the cortical definitions by associating cortical thickness with various demographic features, clinical measures, and medication use in our three large cohorts of participants who were either healthy or who had Bipolar Disorder (BD), Autism Spectrum Disorder (ASD), or familial risk for Major Depressive Disorder (MDD). We assessed validity of the tissue definitions using synthetic brains and data for three large cohort of individuals with various neuropsychiatric disorders. Visual inspection and quantitative analyses showed that our method accurately and robustly defined the cortical mantle in brain images with varying contrast. Furthermore, associating the thickness with various demographic and clinical measures generated findings that were novel and supported by histological analyses or were supported by previous MRI studies, thereby validating the cortical definitions generated by the proposed method: (1) Although cortical thickness decreased with age in adolescents, in adults cortical thickness did not correlate significantly with age. Our synthetic data showed that the previously reported thinning of cortex in adults is likely due to decease in tissue contrast, thereby suggesting that the method generated cortical definitions in adults that were invariant to tissue contrast. In adolescents, cortical thinning with age was preserved likely due to widespread dendritic and synaptic pruning, even though the effects of decreasing tissue contrast were minimized. (3) The method generated novel finding of both localized increases and decreases in thickness of males compared to females after controlling for the differing brain sizes, which are supported by the histological analyses of brain tissue in males and females. (4) The proposed method, unlike prior methods, defined thicker cortex in BD individuals using lithium. The novel finding is supported by the studies that showed lithium treatment increased dendritic arborization and neurogenesis, thereby leading to thickening of cortex. (5) In both BD and ASD participants, associations of more severe symptoms with thinner cortex showed that correcting for the effects of tissue contrast preserved the biological consequences of illnesses. Therefore, consistency of the findings across the three large cohorts of participants, in images acquired on either 1.5T or 3T MRI scanners, and with findings from prior histological analyses provides strong evidence that the proposed method generated valid and accurate definitions of the cortex while controlling for the effects of tissue contrast., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Many investigators have tried to apply machine learning techniques to magnetic resonance images (MRIs) of the brain in order to diagnose neuropsychiatric disorders. Usually the number of brain imaging measures (such as measures of cortical thickness and measures of local surface morphology) derived from the MRIs (i.e., their dimensionality) has been large (e.g. >10) relative to the number of participants who provide the MRI data (<100). Sparse data in a high dimensional space increase the variability of the classification rules that machine learning algorithms generate, thereby limiting the validity, reproducibility, and generalizability of those classifiers. The accuracy and stability of the classifiers can improve significantly if the multivariate distributions of the imaging measures can be estimated accurately. To accurately estimate the multivariate distributions using sparse data, we propose to estimate first the univariate distributions of imaging data and then combine them using a Copula to generate more accurate estimates of their multivariate distributions. We then sample the estimated Copula distributions to generate dense sets of imaging measures and use those measures to train classifiers. We hypothesize that the dense sets of brain imaging measures will generate classifiers that are stable to variations in brain imaging measures, thereby improving the reproducibility, validity, and generalizability of diagnostic classification algorithms in imaging datasets from clinical populations. In our experiments, we used both computer-generated and real-world brain imaging datasets to assess the accuracy of multivariate Copula distributions in estimating the corresponding multivariate distributions of real-world imaging data. Our experiments showed that diagnostic classifiers generated using imaging measures sampled from the Copula were significantly more accurate and more reproducible than were the classifiers generated using either the real-world imaging measures or their multivariate Gaussian distributions. Thus, our findings demonstrate that estimated multivariate Copula distributions can generate dense sets of brain imaging measures that can in turn be used to train classifiers, and those classifiers are significantly more accurate and more reproducible than are those generated using real-world imaging measures alone., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Background: The widespread use of Magnetic Resonance Imaging (MRI) in the study of child- and adult-onset developmental psychopathologies has generated many investigations that have measured brain structure and function in vivo throughout development, often generating great excitement over our ability to visualize the living, developing brain using the attractive, even seductive images that these studies produce. Often lost in this excitement is the recognition that brain imaging generally, and MRI in particular, is simply a technology, one that does not fundamentally differ from any other technology, be it a blood test, a genotyping assay, a biochemical assay, or behavioral test. No technology alone can generate valid scientific findings. Rather, it is only technology coupled with a strong experimental design that can generate valid and reproducible findings that lead to new insights into the mechanisms of disease and therapeutic response., Methods: In this review we discuss selected studies to illustrate the most common and important limitations of MRI study designs as most commonly implemented thus far, as well as the misunderstanding that the interpretations of findings from those studies can create for our theories of developmental psychopathologies., Results: Common limitations of MRI study designs are in large part responsible thus far for the generally poor reproducibility of findings across studies, poor generalizability to the larger population, failure to identify developmental trajectories, inability to distinguish causes from effects of illness, and poor ability to infer causal mechanisms in most MRI studies of developmental psychopathologies. For each of these limitations in study design and the difficulties they entail for the interpretation of findings, we discuss various approaches that numerous laboratories are now taking to address those difficulties, which have in common the yoking of brain imaging technologies to studies with inherently stronger designs that permit more valid and more powerful causal inferences. Those study designs include epidemiological, longitudinal, high-risk, clinical trials, and multimodal imaging studies., Conclusions: We highlight several studies that have yoked brain imaging technologies to these stronger designs to illustrate how doing so can aid our understanding of disease mechanisms and in the foreseeable future can improve clinical diagnosis, prevention, and treatment planning for developmental psychopathologies., (© 2014 The Authors. Journal of Child Psychology and Psychiatry © 2014 Association for Child and Adolescent Mental Health.)

Potential sources of multisensory influences on low-level sensory cortices include direct projections from sensory cortices of different modalities, as well as more indirect feedback inputs from higher order multisensory cortical regions. These multiple architectures may be functionally complementary, but the exact roles and inter-relationships of the circuits are unknown. Using a fully balanced context manipulation, we tested the hypotheses that: (1) feedforward and lateral pathways subserve speed functions, such as detecting peripheral stimuli. Multisensory integration effects in this context are predicted in peripheral fields of low-level sensory cortices. (2) Slower feedback pathways underpin accuracy functions, such as object discrimination. Integration effects in this context are predicted in higher-order association cortices and central/foveal fields of low-level sensory cortex. We used functional magnetic resonance imaging to compare the effects of central versus peripheral stimulation on audiovisual integration, while varying speed and accuracy requirements for behavioral responses. We found that interactions of task demands and stimulus eccentricity in low-level sensory cortices are more complex than would be predicted by a simple dichotomy such as our hypothesized peripheral/speed and foveal/accuracy functions. Additionally, our findings point to individual differences in integration that may be related to skills and strategy. Overall, our findings suggest that instead of using fixed, specialized pathways, the exact circuits and mechanisms that are used for low-level multisensory integration are much more flexible and contingent upon both individual and contextual factors than previously assumed., (Copyright © 2013 Wiley Periodicals, Inc.)

Dysfunctional learning systems are thought to be central to the pathogenesis of and impair recovery from addictions. The functioning of the brain circuits for episodic memory or learning that support goal-directed behavior has not been studied previously in persons with cocaine dependence (CD). Thirteen abstinent CD and 13 healthy participants underwent MRI scanning while performing a task that requires the use of spatial cues to navigate a virtual-reality environment and find monetary rewards, allowing the functional assessment of the brain systems for spatial learning, a form of episodic memory. Whereas both groups performed similarly on the reward-based spatial learning task, we identified disturbances in brain regions involved in learning and reward in CD participants. In particular, CD was associated with impaired functioning of medial temporal lobe (MTL), a brain region that is crucial for spatial learning (and episodic memory) with concomitant recruitment of striatum (which normally participates in stimulus-response, or habit, learning), and prefrontal cortex. CD was also associated with enhanced sensitivity of the ventral striatum to unexpected rewards but not to expected rewards earned during spatial learning. We provide evidence that spatial learning in CD is characterized by disturbances in functioning of an MTL-based system for episodic memory and a striatum-based system for stimulus-response learning and reward. We have found additional abnormalities in distributed cortical regions. Consistent with findings from animal studies, we provide the first evidence in humans describing the disruptive effects of cocaine on the coordinated functioning of multiple neural systems for learning and memory.

The cerebellum is emerging as a key anatomical structure underlying normal attentional and cognitive control mechanisms. Dysregulation within cerebellar circuits may contribute to the core symptoms of Attention Deficit/Hyperactivity Disorder (ADHD). In the present study we aimed to characterize surface morphological features of the cerebellum in ADHD and healthy comparison youths. Further, we studied the association of cerebellar morphology with the severity of ADHD symptoms and the effects of stimulant treatment. We examined 46 youths with ADHD and 59 comparison youths 8-18 years of age in a cross-sectional, case-control study using magnetic resonance imaging. Measures of cerebellar surface morphology were the primary outcome. Relative to comparison participants, youths with ADHD exhibited smaller regional volumes corresponding to the lateral surface of the left anterior and the right posterior cerebellar hemispheres. Stimulant medication was associated with larger regional volumes over the left cerebellar surface, whereas more severe ADHD symptoms were associated with smaller regional volumes in the vermis. We used optimized measures of morphology to detect alterations in cerebellar anatomy specific to ADHD, dimensions of symptomology, and stimulant treatment. Duration of treatment correlated positively with volumes of specific cerebellar subregions, supporting a model whereby compensatory morphological changes support the effects of stimulant treatment.

Emotions elicited by interpersonal versus non-interpersonal experiences have different effects on neurobiological functioning in both animals and humans. However, the extent to which the brain circuits underlying interpersonal and non-interpersonal emotions are distinct still remains unclear. The goal of our study was to assess whether different neural circuits are implicated in the processing of arousal and valence of interpersonal versus non-interpersonal emotions. During functional magnetic resonance imaging, participants imagined themselves in emotion-eliciting interpersonal or non-interpersonal situations and then rated the arousal and valence of emotions they experienced. We identified (1) separate neural circuits that are implicated in the arousal and valence dimensions of interpersonal versus non-interpersonal emotions, (2) circuits that are implicated in arousal and valence for both types of emotion, and (3) circuits that are responsive to the type of emotion, regardless of the valence or arousal level of the emotion. We found extensive recruitment of limbic (for arousal) and temporal-parietal (for valence) systems associated with processing of specifically interpersonal emotions compared to non-interpersonal ones. The neural bases of interpersonal and non-interpersonal emotions may, therefore, be largely distinct.

Behavioral interference elicited by competing response tendencies adapts to contextual changes. Recent nonhuman primate research suggests a key mnemonic role of distinct prefrontal cells in supporting such context-driven behavioral adjustments by maintaining conflict information across trials, but corresponding prefrontal functions have yet to be probed in humans. Using event-related functional magnetic resonance imaging, we investigated the human neural substrates of contextual adaptations to conflict. We found that a neural system comprising the rostral dorsomedial prefrontal cortex and portions of the dorsolateral prefrontal cortex specifically encodes the history of previously experienced conflict and influences subsequent adaptation to conflict on a trial-by-trial basis. This neural system became active in anticipation of stimulus onsets during preparatory periods and interacted with a second neural system engaged during the processing of conflict. Our findings suggest that a dynamic interaction between a system that represents conflict history and a system that resolves conflict underlies the contextual adaptation to conflict.

Objective: Velocardiofacial syndrome (VCFS; 22q11.2 deletion syndrome) represents one of the highest known risk factors for schizophrenia. Insofar as up to 30% of individuals with this genetic disorder develop schizophrenia, VCFS constitutes a unique, etiologically homogeneous model for understanding the pathogenesis of schizophrenia., Method: Using a longitudinal, case-control design, anatomic magnetic resonance images were acquired to investigate cross-sectional and longitudinal alterations in surface cortical morphology in a cohort of adolescents with VCFS and age-matched typical controls. All participants were scanned at two time points., Results: Compared with controls, youth with VCFS exhibited alterations in inferior frontal, dorsal frontal, occipital, and cerebellar brain regions at both time points. Little change was observed over time in surface morphology of either study group. However, within the VCFS group only, worsening psychosocial functioning over time was associated with time 2 surface contractions in left middle and inferior temporal gyri. Further, prodromal symptoms at time 2 were associated with surface contractions in the left and right orbitofrontal, temporal, and cerebellar regions and surface protrusions of the supramarginal gyrus., Conclusions: These findings advance the understanding of cortical disturbances in VCFS that produce vulnerability for psychosis in this high-risk population., (Copyright © 2011 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.)

Although temporo-parietal cortices mediate spatial navigation in animals and humans, the neural correlates of reward-based spatial learning are less well known. Twenty-five healthy adults performed a virtual reality fMRI task that required learning to use extra-maze cues to navigate an 8-arm radial maze and find hidden rewards. Searching the maze in the spatial learning condition compared to the control conditions was associated with activation of temporo-parietal regions, albeit not including the hippocampus. The receipt of rewards was associated with activation of the hippocampus in a control condition when using the extra-maze cues for navigation was rendered impossible by randomizing the spatial location of cues. Our novel experimental design allowed us to assess the differential contributions of the hippocampus and other temporo-parietal areas to searching and reward processing during reward-based spatial learning. This translational research will permit parallel studies in animals and humans to establish the functional similarity of learning systems across species; cellular and molecular studies in animals may then inform the effects of manipulations on these systems in humans, and fMRI studies in humans may inform the interpretation and relevance of findings in animals., (Published by Elsevier Ltd.)

Purpose: To develop, implement, and evaluate a novel postprocessing method for enhancing the spectral resolution of in vivo MR spectroscopic imaging (MRSI) data., Materials and Methods: Magnetic field inhomogeneity across the imaging volume was determined by acquiring MRI datasets with two differing echo times. The lineshapes of the MRSI spectra were derived from these field maps by simulating an MRSI scan of a virtual sample whose resonance frequencies varied according to the observed variations in the magnetic field. By deconvolving the lineshapes from the measured MRSI spectra, the linebroadening effects of the field inhomogeneities were reduced significantly., Results: Both phantom and in vivo proton MRSI spectra exhibited significantly enhanced spectral resolutions and improved spectral lineshapes following application of our method. Quantitative studies on a phantom show that, on average, the full width at half maximum of water peaks was reduced 42%, the full width at tenth maximum was reduced 38%, and the asymmetries of the peaks were reduced 86%., Conclusion: Our method reduces the linebroadening and lineshape distortions caused by magnetic field inhomogeneities. It substantially improves the spectral resolution and lineshape of MRSI data.

Objective: We aimed to study the neural processing of emotion-denoting words based on a circumplex model of affect, which posits that all emotions can be described as a linear combination of two neurophysiological dimensions, valence and arousal. Based on the circumplex model, we predicted a linear relationship between neural activity and incremental changes in these two affective dimensions., Methods: Using functional magnetic resonance imaging, we assessed in 10 subjects the correlations of BOLD (blood oxygen level dependent) signal with ratings of valence and arousal during the presentation of emotion-denoting words., Results: Valence ratings correlated positively with neural activity in the left insular cortex and inversely with neural activity in the right dorsolateral prefrontal and precuneus cortices. The absolute value of valence ratings (reflecting the positive and negative extremes of valence) correlated positively with neural activity in the left dorsolateral and medial prefrontal cortex (PFC), dorsal anterior cingulate cortex, posterior cingulate cortex, and right dorsal PFC, and inversely with neural activity in the left medial temporal cortex and right amygdala. Arousal ratings and neural activity correlated positively in the left parahippocampus and dorsal anterior cingulate cortex, and inversely in the left dorsolateral PFC and dorsal cerebellum., Conclusion: We found evidence for two neural networks subserving the affective dimensions of valence and arousal. These findings clarify inconsistencies from prior imaging studies of affect by suggesting that two underlying neurophysiological systems, valence and arousal, may subserve the processing of affective stimuli, consistent with the circumplex model of affect.

Previous studies have investigated patterns of volumetric covariance (i.e. intercorrelation) among brain regions. Methodological issues, however, have limited the validity and generalizability of findings from these prior studies. Additionally, patterns of volumetric covariance have often been assumed to reflect the presence of structural networks, but this assumption has never been tested formally. We identified patterns of volumetric covariance, correlated these patterns with behavioral measures, and tested the hypothesis that the observed patterns of covariance reflect the presence of underlying networks. Specifically, we performed factor analysis on regional brain volumes of 99 healthy children and adults, and we correlated factor scores with scores on the Stroop Word-Color Interference Test. We identified four latent volumetric systems in each hemisphere: dorsal cortical, limbic, posterior, and basal ganglia. The positive correlation of the right posterior system with Stroop scores suggested that larger latent volumes are detrimental to inhibitory control. We also applied Structural Equation Modeling (SEM) to our dataset (n = 107) to test whether a model based on the anatomical pathways within cortico-striatal-thalamic-cortical (CSTC) circuits accounts for the covariances observed in our sample. The degree to which SEM predicted volumetric covariance in the CSTC circuit depended on whether we controlled for age and whole brain volume in the analyses. Removing the effects of age worsened the fit of the model, pointing to a possible developmental component in establishing connections within CSTC circuits. These modeling techniques may prove useful in the future for the study of structural networks in disease populations.

The basal ganglia portions of cortico-striato-thalamo-cortical (CSTC) circuits have consistently been implicated in the pathogenesis of Tourette syndrome, whereas motor and sensorimotor cortices in these circuits have been relatively overlooked. Using magnetic resonance imaging, we detected cortical thinning in frontal and parietal lobes in groups of Tourette syndrome children relative to controls. This thinning was most prominent in ventral portions of the sensory and motor homunculi that control the facial, orolingual and laryngeal musculature that is commonly involved in tic symptoms. Correlations of cortical thickness in sensorimotor regions with tic symptoms suggest that these brain regions are important in the pathogenesis of Tourette syndrome.

Several prior imaging studies of healthy adults have correlated volumes of the hippocampus and amygdala with measures of general intelligence (IQ), with variable results. In this study, we assessed correlations between volumes of the hippocampus and amygdala and full-scale IQ scores (FSIQ) using a method of image analysis that permits detailed regional mapping of this correlation throughout the surface contour of these brain structures. We delineated the hippocampus and amygdala in high-resolution magnetic resonance images of the brain from 34 healthy individuals. We then correlated FSIQ with overall volumes and with the surface morphologies of each of these structures. Hippocampus volumes correlated significantly and inversely with FSIQ independently of gender, age, socioeconomic status, and whole brain volume. Left and right hippocampus volumes correlated respectively with verbal and performance IQ subscales. Higher IQs were significantly associated with large inward deformations of the surface of the anterior hippocampus bilaterally. These findings suggest that a smaller anterior hippocampus contributes to an increased efficiency of neural processing that subserves overall intelligence.

Increasing evidence supports the existence of distinct neural systems that subserve two dimensions of affect--arousal and valence. Ten adult participants underwent functional magnetic resonance imaging during which they were presented a range of standardized faces and then asked, during the scan, to rate the emotional expressions of the faces along the dimensions of arousal and valence. Lower ratings of arousal accompanied greater activity in the amygdala complex, cerebellum, dorsal pons, and right medial prefrontal cortex (mPFC). More negative ratings of valence accompanied greater activity in the dorsal anterior cingulate (dACC) and parietal cortices. Extreme ratings of valence (highly positive and highly negative ratings) accompanied greater activity in the temporal cortex and fusiform gyrus. Building on an empirical literature which suggests that the amygdala serves as a salience and ambiguity detector, we interpret our findings as showing that a face rated as having low arousal is more ambiguous and a face rated as having extreme valence is more personally salient. This explains how both low arousal and extreme valence lead to greater activation of an ambiguity/salience system subserved by the amygdala, cerebellum, and dorsal pons. In addition, the right medial prefrontal cortex appears to down-regulate individual ratings of arousal, whereas the fusiform and related temporal cortices seem to up-regulate individual assessments of extreme valence when individual ratings are studied relative to group reference ratings for each stimulus. The simultaneous assessment of the effects of arousal and valence proved essential for the identification of neural systems contributing to the processing of emotional faces.

We used magnetic resonance imaging and cortical matching algorithms to map gray matter density (GMD) in 176 normal individuals ranging in age from 7 to 87 years. We found a significant, nonlinear decline in GMD with age, which was most rapid between 7 and about 60 years, over dorsal frontal and parietal association cortices on both the lateral and interhemispheric surfaces. Age effects were inverted in the left posterior temporal region, where GMD gain continued up to age 30 and then rapidly declined. The trajectory of maturational and aging effects varied considerably over the cortex. Visual, auditory and limbic cortices, which are known to myelinate early, showed a more linear pattern of aging than the frontal and parietal neocortices, which continue myelination into adulthood. Our findings also indicate that the posterior temporal cortices, primarily in the left hemisphere, which typically support language functions, have a more protracted course of maturation than any other cortical region.

Here we report on detailed three-dimensional quantitative maps of brain surface and gray matter density asymmetry patterns during normal adolescent development and show how these anatomical features of the brain are disrupted as a result of prenatal exposure to large quantities of alcohol. We studied two independent samples of normally developing children, adolescents, and young adults, totaling 83 subjects from two different research groups, and compared them to 21 individuals with heavy prenatal alcohol exposure. Surface-based image analysis techniques allowed us to match cortical anatomy across subjects and between hemispheres based on manually delineated sulcal landmarks. Quantitative maps of brain surface asymmetry reveal prominent peri-Sylvian hemispheric differences in which the superior temporal and inferior parietal cortices are shifted backward in the left relative to the right hemisphere in both normal and alcohol-exposed subjects. Cortical surface gray matter asymmetry, mapped here in adolescent populations, is most prominent in the posterior inferior temporal lobes (right greater than left), and this effect does not differ between groups of normally developing children, adolescents, or young adults. Alcohol-exposed individuals show a significant reduction in this asymmetry, whether studied with surface-based or more traditional volumetric region of interest analyses. This region of cortex, near the junction of Brodmann's areas 21, 22, and 37, primarily subserves language functions that are known to be impaired on average in the alcohol-exposed subjects. Our findings elucidate regional patterns of brain surface and gray matter asymmetry during normal development and may contribute to a more comprehensive understanding of the neural substrates of cognitive dysfunction after heavy prenatal alcohol exposure.

Background: To assess whether patients with Persistent Depressive Disorder (PDD) have abnormal levels of N-acetyl-aspartate (NAA) and whether those levels normalize following treatment with the antidepressant medication duloxetine. Furthermore, we conducted post hoc analyses of other important brain metabolites to understand better the cellular and physiological determinants for changes in NAA levels. Methods: We acquired proton (1H) magnetic resonance spectroscopic imaging (MRSI) data on a 3 Tesla (3T), GE Magnetic Resonance Imaging (MRI) scanner in 41 patients (39.9±10.4 years, 22 males) with PDD at two time points: before the start and at the end of a 10-week, placebo-controlled, double-blind, randomized controlled trial (RCT) of the antidepressant medication duloxetine. Patients were randomized such that 21 patients received the active medication and 20 patients received placebo during the 10 week period of the trial. In addition, we acquire 1H MRSI data once in 29 healthy controls (37.7±11.2 years, 17 males). Findings: Patients had significantly higher baseline concentrations of NAA across white matter (WM) pathways and subcortical gray matter, and in direct proportion to the severity of depressive symptoms. NAA concentrations declined in duloxetine-treated patients over the duration of the trial in the direction toward healthy values, whereas concentrations increased in placebo-treated patients, deviating even further away from healthy values. Changes in NAA concentration did not mediate medication effects on reducing symptom severity, however; instead, changes in symptom severity partially mediated the effects of medication on NAA concentration, especially in the caudate and putamen. Interpretation: These findings, taken together, suggest that PDD is not a direct consequence of elevated NAA concentrations, but that a more fundamental pathophysiological process likely causes PDD and determines the severity of its symptoms. The findings also suggest that although duloxetine normalized NAA concentrations in patients, it did so by modulating the severity of depressive symptoms. Medication presumably reduced depressive symptoms through other, as yet unidentified, brain processes. Trial registration: ClinicalTrials.gov . [ABSTRACT FROM AUTHOR]

The aim of this study was to identify differences in functional and effective brain connectivity between persons who stutter (PWS) and typically developing (TD) fluent speakers, and to assess whether those differences can serve as biomarkers to distinguish PWS from TD controls. We acquired resting-state functional magnetic resonance imaging data in 44 PWS and 50 TD controls. We then used Independent Component Analysis (ICA) together with Hierarchical Partner Matching (HPM) to identify networks of robust, functionally connected brain regions that were highly reproducible across participants, and we assessed whether connectivity differed significantly across diagnostic groups. We then used Granger Causality (GC) to study the causal interactions (effective connectivity) between the regions that ICA and HPM identified. Finally, we used a kernel support vector machine to assess how well these measures of functional connectivity and granger causality discriminate PWS from TD controls. Functional connectivity was stronger in PWS compared with TD controls in the supplementary motor area (SMA) and primary motor cortices, but weaker in inferior frontal cortex (IFG, Broca’s area), caudate, putamen, and thalamus. Additionally, causal influences were significantly weaker in PWS from the IFG to SMA, and from the basal ganglia to IFG through the thalamus, compared to TD controls. ICA and GC indices together yielded an accuracy of 92.7% in classifying PWS from TD controls. Our findings suggest the presence of dysfunctional circuits that support speech planning and timing cues for the initiation and execution of motor sequences in PWS. Our high accuracy of classification further suggests that these aberrant brain features may serve as robust biomarkers for PWS. [ABSTRACT FROM AUTHOR]

Importance: Developmental stuttering is a neuropsychiatric condition of incompletely understood brain origin. Our recent functional magnetic resonance imaging study indicates a possible partial basis of stuttering in circuits enacting self-regulation of motor activity, attention, and emotion.Objective: To further characterize the neurophysiology of stuttering through in vivo assay of neurometabolites in suspect brain regions.Design, Setting, and Participants: Proton chemical shift imaging of the brain was performed in a case-control study of children and adults with and without stuttering. Recruitment, assessment, and magnetic resonance imaging were performed in an academic research setting.Main Outcomes and Measures: Ratios of N-acetyl-aspartate plus N-acetyl-aspartyl-glutamate (NAA) to creatine (Cr) and choline compounds (Cho) to Cr in widespread cerebral cortical, white matter, and subcortical regions were analyzed using region of interest and data-driven voxel-based approaches.Results: Forty-seven children and adolescents aged 5 to 17 years (22 with stuttering and 25 without) and 47 adults aged 21 to 51 years (20 with stuttering and 27 without) were recruited between June 2008 and March 2013. The mean (SD) ages of those in the stuttering and control groups were 12.2 (4.2) years and 13.4 (3.2) years, respectively, for the pediatric cohort and 31.4 (7.5) years and 30.5 (9.9) years, respectively, for the adult cohort. Region of interest-based findings included lower group mean NAA:Cr ratio in stuttering than nonstuttering participants in the right inferior frontal cortex (-7.3%; P = .02), inferior frontal white matter (-11.4%; P < .001), and caudate (-10.6%; P = .04), while the Cho:Cr ratio was higher in the bilateral superior temporal cortex (left: +10.0%; P = .03 and right: +10.8%; P = .01), superior temporal white matter (left: +14.6%; P = .003 and right: +9.5%; P = .02), and thalamus (left: +11.6%; P = .002 and right: +11.1%; P = .001). False discovery rate-corrected voxel-based findings were highly consistent with region of interest findings. Additional voxel-based findings in the stuttering sample included higher NAA:Cr and Cho:Cr ratios (regression coefficient, 197.4-275; P < .001) in the posterior cingulate, lateral parietal, hippocampal, and parahippocampal cortices and amygdala, as well as lower NAA:Cr and Cho:Cr ratios (regression coefficient, 119.8-275; P < .001) in the superior frontal and frontal polar cortices. Affected regions comprised nodes of the Bohland speech-production (motor activity regulation), default-mode (attention regulation), and emotional-memory (emotion regulation) networks. Regional correlations were also observed between local metabolites and stuttering severity (r = 0.40-0.52; P = .001-.02).Conclusions and Relevance: This spectroscopy study of stuttering demonstrates brainwide neurometabolite alterations, including several regions implicated by other neuroimaging modalities. Prior ascription of a role in stuttering to inferior frontal and superior temporal gyri, caudate, and other structures is affirmed. Consistent with prior functional magnetic resonance imaging findings, these results further intimate neurometabolic aberrations in stuttering in brain circuits subserving self-regulation of speech production, attention, and emotion. [ABSTRACT FROM AUTHOR]

Brain morphometry in recent decades has increased our understanding of the neural bases of psychiatric disorders by localizing anatomical disturbances to specific nuclei and subnuclei of the brain. At least some of these disturbances precede the overt expression of clinical symptoms and possibly are endophenotypes that could be used to diagnose an individual accurately as having a specific psychiatric disorder. More accurate diagnoses could significantly reduce the emotional and financial burden of disease by aiding clinicians in implementing appropriate treatments earlier and in tailoring treatment to the individual needs. Several methods, especially those based on machine learning, have been proposed that use anatomical brain measures and gold-standard diagnoses of participants to learn decision rules that classify a person automatically as having one disorder rather than another. We review the general principles and procedures for machine learning, particularly as applied to diagnostic classification, and then review the procedures that have thus far attempted to diagnose psychiatric illnesses automatically using anatomical measures of the brain. We discuss the strengths and limitations of extant procedures and note that the sensitivity and specificity of these procedures in their most successful implementations have approximated 90%. Although these methods have not yet been applied within clinical settings, they provide strong evidence that individual patients can be diagnosed accurately using the spatial pattern of disturbances across the brain. [ABSTRACT FROM AUTHOR]

We hypothesize that coordinated functional activity within discrete neural circuits induces morphological organization and plasticity within those circuits. Identifying regions of morphological covariation that are independent of morphological covariation in other regions therefore may therefore allow us to identify discrete neural systems within the brain. Comparing the magnitude of these variations in individuals who have psychiatric disorders with the magnitude of variations in healthy controls may allow us to identify aberrant neural pathways in psychiatric illnesses. We measured surface morphological features by applying nonlinear, high-dimensional warping algorithms to manually defined brain regions. We transferred those measures onto the surface of a unit sphere via conformal mapping and then used spherical wavelets and their scaling coefficients to simplify the data structure representing these surface morphological features of each brain region. We used principal component analysis (PCA) to calculate covariation in these morphological measures, as represented by their scaling coefficients, across several brain regions. We then assessed whether brain subregions that covaried in morphology, as identified by large eigenvalues in the PCA, identified specific neural pathways of the brain. To do so, we spatially registered the subnuclei for each eigenvector into the coordinate space of a Diffusion Tensor Imaging dataset; we used these subnuclei as seed regions to track and compare fiber pathways with known fiber pathways identified in neuroanatomical atlases. We applied these procedures to anatomical MRI data in a cohort of 82 healthy participants (42 children, 18 males, age 10.5 ± 2.43 years; 40 adults, 22 males, age 32.42 ± 10.7 years) and 107 participants with Tourette's Syndrome (TS) (71 children, 59 males, age 11.19 ± 2.2 years; 36 adults, 21 males, age 37.34 ± 10.9 years). We evaluated the construct validity of the identified covariation in morphology using DTI data from a different set of 20 healthy adults (10 males, mean age 29.7 ± 7.7 years). The PCA identified portions of structures that covaried across the brain, the eigenvalues measuring the magnitude of the covariation in morphology along the respective eigenvectors. Our results showed that the eigenvectors, and the DTI fibers tracked from their associated brain regions, corresponded with known neural pathways in the brain. In addition, the eigenvectors that captured morphological covariation across regions, and the principal components along those eigenvectors, identified neural pathways with aberrant morphological features associated with TS. These findings suggest that covariations in brain morphology can identify aberrant neural pathways in specific neuropsychiatric disorders. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Using MRI surface morphometry mapping, to evaluate local deformations of the hippocampus, parahippocampal gyrus, and entorhinal cortex in predicting conversion from mild cognitive impairment (MCI) to Alzheimer''s disease (AD). Methods: Baseline brain MRI with surface morphological analysis was performed in 130 outpatients with MCI, broadly defined, and 61 healthy controls followed for an average of 4years in a single site study. Results: Patients with MCI differed from controls in several regions of the hippocampus and entorhinal cortex, and to a lesser extent in the parahippocampal gyrus. In the MCI sample, Cox regression models were conducted for time to conversion comparing converters to AD (n=31) and non-converters (n=99), controlling for age, sex and education. Converters showed greater atrophy in the head of the hippocampus, predominantly in the CA1 region and subiculum, and in the entorhinal cortex, especially in the anterior–inferior pole bilaterally. When distances of specific points representing localized inward deformation were entered together with the corresponding hippocampal or entorhinal cortex volume in the same Cox regression model, the distances remained highly significant whereas the volumes of the corresponding structures were either marginally significant or not significant. Inclusion of cognitive or memory measures or apolipoprotein E ε4 genotype as covariates, or restricting the sample to patients with amnestic MCI (24 converters and 81 non-converters) did not materially change the findings. In the 3-year follow-up sample of patients with MCI, logistic regression analyses using the same measures and covariates yielded similar results. Interpretation: These findings indicate selective early involvement of the CA1 and subiculum regions of the hippocampus and provide new information on early anterior pole involvement in the entorhinal cortex in incipient AD. Fine-grained surface morphometry of medial temporal lobe structures may be superior to volumetric assessment in predicting conversion to AD in patients clinically diagnosed with MCI. [Copyright &y& Elsevier]

Abstract: Objectives: This study aimed to determine whether regional cerebral blood flow (rCBF) is abnormal in patients who have Transient Global Amnesia (TGA). Methods: We obtained noninvasive rCBF measurements using Tc-99m-ethyl cysteinate diamer Single Photon Emission Computed Tomography (SPECT) in 7 patients diagnosed with TGA within 4 days of onset of the amnestic episode while the patients were still symptomatic and in 17 age-matched healthy control subjects. We assessed memory functioning using the Hopkins''s Verbal Learning Test (HVLT) and Statistical Parametric Mapping to compare rCBF across diagnostic groups. Results: The patients with TGA were significantly impaired in their performance on the 20-minute delayed recall of the HVLT. They also exhibited significantly decreased rCBF on their SPECT scans in the inferior and middle frontal gyrus bilaterally, with more prominent left-sided reductions in the superior temporal, precentral, and postcentral gyri, as well as increased rCBF primarily in the right hemisphere within the middle temporal, superior temporal, and inferior frontal gyri, cerebellum, and thalamus, compared with the normal control group. Conclusion: These findings suggest that lateralized abnormalities in brain functioning are an important component of the pathophysiology of TGA. Lateralized abnormalities may disrupt functions that are relatively specific to the left hemisphere, including receptive language, symbolic representation, and the processing of local features in the environment, while preserving anterograde memory processes. Increased flow to the right hemisphere centered on regions that subserve the functions of expressive language and visuospatial processing, and may represent processes that compensate for flow reductions to the left hemisphere. [Copyright &y& Elsevier]