Search

Friendships increase mental wellbeing and resilient functioning in young people with childhood adversity (CA). However, the mechanisms of this relationship are unknown. We examined the relationship between perceived friendship quality at age 14 after the experience of CA and reduced affective and neural responses to social exclusion at age 24. Resilient functioning was quantified as psychosocial functioning relative to the degree of CA severity in 310 participants at age 24. From this cohort, 62 young people with and without CA underwent functional Magnetic Resonance Imaging to assess brain responses to social inclusion and exclusion. We observed that good friendship quality was significantly associated with better resilient functioning. Both friendship quality and resilient functioning were related to increased affective responses to social inclusion. We also found that friendship quality, but not resilient functioning, was associated with increased dorsomedial prefrontal cortex responses to peer exclusion. Our findings suggest that friendship quality in early adolescence may contribute to the evaluation of social inclusion by increasing affective sensitivity to positive social experiences and increased brain activity in regions involved in emotion regulation to negative social experiences. Future research is needed to clarify this relationship with resilient functioning in early adulthood., (© The Author(s) 2024. Published by Oxford University Press.)

Psychological androgyny has long been associated with greater cognitive flexibility, adaptive behavior, and better mental health, but whether a similar concept can be defined using neural features remains unknown. Using the neuroimaging data from 9620 participants, we found that global functional connectivity was stronger in the male brain before middle age but became weaker after that, when compared with the female brain, after systematic testing of potentially confounding effects. We defined a brain gender continuum by estimating the likelihood of an observed functional connectivity matrix to represent a male brain. We found that participants mapped at the center of this continuum had fewer internalizing symptoms compared with those at the 2 extreme ends. These findings suggest a novel hypothesis proposing that there exists a neuroimaging concept of androgyny using the brain gender continuum, which may be associated with better mental health in a similar way to psychological androgyny., (© The Author(s) 2021. Published by Oxford University Press.)

There is increasing interest in the potential of whole-brain computational models to provide mechanistic insights into resting-state brain networks. It is therefore important to determine the degree to which computational models reproduce the topological features of empirical functional brain networks. We used empirical connectivity data derived from diffusion spectrum and resting-state functional magnetic resonance imaging data from healthy individuals. Empirical and simulated functional networks, constrained by structural connectivity, were defined based on 66 brain anatomical regions (nodes). Simulated functional data were generated using the Kuramoto model in which each anatomical region acts as a phase oscillator. Network topology was studied using graph theory in the empirical and simulated data. The difference (relative error) between graph theory measures derived from empirical and simulated data was then estimated. We found that simulated data can be used with confidence to model graph measures of global network organization at different dynamic states and highlight the sensitive dependence of the solutions obtained in simulated data on the specified connection densities. This study provides a method for the quantitative evaluation and external validation of graph theory metrics derived from simulated data that can be used to inform future study designs., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

In the last 20 years or so, functional MRI has matured very rapidly from being an experimental imaging method in the hands of a few labs to being a very widely available and widely used workhorse of cognitive neuroscience and clinical neuroscience research internationally. FMRI studies have had a considerable impact on our understanding of brain system phenotypes of neurological and psychiatric disorders; and some impact already on development of new therapeutics. However, the direct benefit of fMRI to individual patients with brain disorders has so far been minimal. Here I provide a personal perspective on what has already been achieved, and imagine how the further development of fMRI over the medium term might lead to even greater engagement with clinical medicine., (Copyright © 2012 Elsevier Inc. All rights reserved.)

The modular organization of the brain network can vary in two fundamental ways. The amount of inter- versus intra-modular connections between network nodes can be altered, or the community structure itself can be perturbed, in terms of which nodes belong to which modules (or communities). Alterations have previously been reported in modularity, which is a function of the proportion of intra-modular edges over all modules in the network. For example, we have reported that modularity is decreased in functional brain networks in schizophrenia: There are proportionally more inter-modular edges and fewer intra-modular edges. However, despite numerous and increasing studies of brain modular organization, it is not known how to test for differences in the community structure, i.e., the assignment of regional nodes to specific modules. Here, we introduce a method based on the normalized mutual information between pairs of modular networks to show that the community structure of the brain network is significantly altered in schizophrenia, using resting-state fMRI in 19 participants with childhood-onset schizophrenia and 20 healthy participants. We also develop tools to show which specific nodes (or brain regions) have significantly different modular communities between groups, a subset that includes right insular and perisylvian cortical regions. The methods that we propose are broadly applicable to other experimental contexts, both in neuroimaging and other areas of network science., (Published by Elsevier Inc.)

Background: It is well established that schizophrenia is associated with structural brain abnormalities, but whether these are static or progress over time remains controversial., Methods: A systematic review of longitudinal volumetric studies using region-of-interest structural magnetic resonance imaging in patients with schizophrenia and healthy control subjects. The percentage change in volume between scans for each brain region of interest was obtained, and data were combined using random effects meta-analysis., Results: Twenty-seven studies were included in the meta-analysis, with 928 patients and 867 control subjects, and 32 different brain regions of interest. Subjects with schizophrenia showed significantly greater decreases over time in whole brain volume, whole brain gray matter, frontal gray and white matter, parietal white matter, and temporal white matter volume, as well as larger increases in lateral ventricular volume, than healthy control subjects. The time between baseline and follow-up magnetic resonance imaging scans ranged from 1 to 10 years. The differences between patients and control subjects in annualized percentage volume change were -.07% for whole brain volume, -.59% for whole brain gray matter, -.32% for frontal white matter, -.32% for parietal white matter, -.39% for temporal white matter, and +.36% for bilateral lateral ventricles., Conclusions: These findings suggest that schizophrenia is associated with progressive structural brain abnormalities, affecting both gray and white matter. We found no evidence to suggest progressive medial temporal lobe involvement but did find evidence that this may be partly explained by heterogeneity between studies in patient age and illness duration. The causes and clinical correlates of these progressive brain changes should now be the focus of investigation., (Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Objectives: Functional magnetic resonance imaging (fMRI) has been widely used to identify state and trait markers of brain abnormalities associated with bipolar disorder (BD). However, the primary literature is composed of small-to-medium-sized studies, using diverse activation paradigms on variously characterized patient groups, which can be difficult to synthesize into a coherent account. This review aimed to synthesize current evidence from fMRI studies in midlife adults with BD and to investigate whether there is support for the theoretical models of the disorder., Methods: We used voxel-based quantitative meta-analytic methods to combine primary data on anatomical coordinates of activation from 65 fMRI studies comparing normal volunteers (n = 1,074) and patients with BD (n = 1,040)., Results: Compared to normal volunteers, patients with BD underactivated the inferior frontal cortex (IFG) and putamen and overactivated limbic areas, including medial temporal structures (parahippocampal gyrus, hippocampus, and amygdala) and basal ganglia. Dividing studies into those using emotional and cognitive paradigms demonstrated that the IFG abnormalities were manifest during both cognitive and emotional processing, while increased limbic activation was mainly related to emotional processing. In further separate comparisons between healthy volunteers and patient subgroups in each clinical state, the IFG was underactive in manic but not in euthymic and depressed states. Limbic structures were not overactive in association with mood states, with the exception of increased amygdala activation in euthymic states when including region-of-interest studies., Conclusions: In summary, our results showed abnormal frontal-limbic activation in BD. There was attenuated activation of the IFG or ventrolateral prefrontal cortex, which was consistent across emotional and cognitive tasks and particularly related to the state of mania, and enhanced limbic activation, which was elicited by emotional and not cognitive tasks, and not clearly related to mood states., (© 2011 John Wiley and Sons A/S.)

This article firstly presents a theoretical analysis of the statistical power of a parallel-group, repeated-measures (two-session) and two-centre design suitable for a placebo-controlled pharmacological MRI study. For arbitrary effect size, power is determined by the pooled between-session error, the pooled measurement error, the ratio of centre measurement errors, the total number of subjects and the proportion of subjects studied at the centre with greatest measurement error. Secondly, an experiment is described to obtain empirical estimates of variance components in task-related and resting state functional magnetic resonance imaging. Twelve healthy volunteers were scanned at two centres during performance of blocked and event-related versions of an affect processing task (each repeated twice per session) and rest. In activated regions, variance components were estimated: between-subject (23% of total), between-centre (2%), between-paradigm (4%), within-session occasion (paradigm repeat; 2%) and residual (measurement) error (69%). The between-centre ratio of measurement errors was 0.8. A similar analysis for the Hurst exponent estimated in resting data showed negligible contributions of between-subject and between-centre variability; measurement error accounted for 99% of total variance. Substituting these estimates in the theoretical expression for power, incorporation of two centres in the design necessitates a modest (10%) increase in the total number of subjects compared with a single-centre study. Furthermore, considerable improvements in power can be attained by repetition of the task within each scanning session. Thus, theoretical models of power and empirical data indicate that between-centre variability can be small enough to encourage multicentre designs without major compensatory increases in sample size.

Fractal processes, like trees or coastlines, are defined by self-similarity or power law scaling controlled by a single exponent, simply related to the fractal dimension or Hurst exponent (H) of the process. Multifractal processes, like turbulence, have more complex behaviours defined by a spectrum of possible local scaling behaviours or singularity exponents (h). Here, we report two experiments that explore the relationships between instrumental and cognitive variables and the monofractal and multifractal parameters of functional magnetic resonance imaging (fMRI) data acquired in a no-task or resting state. First, we show that the Hurst exponent is greater in grey matter than in white matter regions, and it is maximal in grey matter when data were acquired with an echo time known to optimise BOLD contrast. Second, we show that latency of response in a fame decision/facial encoding task was negatively correlated with the Hurst exponent of resting state data acquired 30 min after task performance. This association was localised to a right inferior frontal cortical region activated by the fame decision task and indicated that people with shorter response latency had more persistent dynamics (higher values of H). Multifractal analysis revealed that faster responding participants had wider singularity spectra of resting fMRI time series in inferior frontal cortex. Endogenous brain oscillations measured by fMRI have monofractal and multifractal properties that can be related to instrumental and cognitive factors in a way, which indicates that these low frequency dynamics are relevant to neurocognitive function., ((c) 2008 Wiley-Liss, Inc.)

Background: The authors sought to map gray matter changes in Attention Deficit Hyperactivity Disorder (ADHD) using a novel technique incorporating neuro-imaging and genetic meta-analysis methods., Methods: A systematic search was conducted for voxel-based structural magnetic resonance imaging studies of patients with ADHD (or with related disorders) in relation to comparison groups. The authors carried out meta-analyses of the co-ordinates of gray matter differences. For the meta-analyses they hybridised the standard method of Activation Likelihood Estimation (ALE) with the rank approach used in Genome Scan Meta-Analysis (GSMA). This system detects three-dimensional conjunctions of co-ordinates from multiple studies and permits the weighting of studies in relation to sample size., Results: For gray matter decreases, there were 7 studies including a total of 114 patients with ADHD (or related disorders) and 143 comparison subjects. Meta-analysis of these studies identified a significant regional gray matter reduction in ADHD in the right putamen/globus pallidus region. Four studies reported gray matter increases in ADHD but no regional increase was identified by meta-analysis., Conclusion: In ADHD there is gray matter reduction in the right putamen/globus pallidus region. This may be an anatomical marker for dysfunction in frontostriatal circuits mediating cognitive control. Right putamen lesions have been specifically associated with ADHD symptoms after closed head injuries in children.

Two- or three-dimensional wavelet transforms have been considered as a basis for multiple hypothesis testing of parametric maps derived from functional magnetic resonance imaging (fMRI) experiments. Most of the previous approaches have assumed that the noise variance is equally distributed across levels of the transform. Here we show that this assumption is unrealistic; fMRI parameter maps typically have more similarity to a 1/f-type spatial covariance with greater variance in 2D wavelet coefficients representing lower spatial frequencies, or coarser spatial features, in the maps. To address this issue we resample the fMRI time series data in the wavelet domain (using a 1D discrete wavelet transform [DWT]) to produce a set of permuted parametric maps that are decomposed (using a 2D DWT) to estimate level-specific variances of the 2D wavelet coefficients under the null hypothesis. These resampling-based estimates of the "wavelet variance spectrum" are substituted in a Bayesian bivariate shrinkage operator to denoise the observed 2D wavelet coefficients, which are then inverted to reconstitute the observed, denoised map in the spatial domain. Multiple hypothesis testing controlling the false discovery rate in the observed, denoised maps then proceeds in the spatial domain, using thresholds derived from an independent set of permuted, denoised maps. We show empirically that this more realistic, resampling-based algorithm for wavelet-based denoising and multiple hypothesis testing has good Type I error control and can detect experimentally engendered signals in data acquired during auditory-linguistic processing.

Background: People with autism or Asperger Syndrome (AS) show altered patterns of brain activity during visual search and emotion recognition tasks. Autism and AS are genetic conditions and parents may show the 'broader autism phenotype.', Aims: (1) To test if parents of children with AS show atypical brain activity during a visual search and an empathy task; (2) to test for sex differences during these tasks at the neural level; (3) to test if parents of children with autism are hyper-masculinized, as might be predicted by the 'extreme male brain' theory., Method: We used fMRI during a visual search task (the Embedded Figures Test (EFT)) and an emotion recognition test (the 'Reading the Mind in the Eyes' (or Eyes) test)., Sample: Twelve parents of children with AS, vs. 12 sex-matched controls., Design: Factorial analysis was used to map main effects of sex, group (parents vs. controls), and sexxgroup interaction on brain function. An ordinal ANOVA also tested for regions of brain activity where females>males>fathers=mothers, to test for parental hyper-masculinization. RESULTS ON EFT TASK: Female controls showed more activity in extrastriate cortex than male controls, and both mothers and fathers showed even less activity in this area than sex-matched controls. There were no differences in group activation between mothers and fathers of children with AS. The ordinal ANOVA identified two specific regions in visual cortex (right and left, respectively) that showed the pattern Females>Males>Fathers=Mothers, both in BA 19. RESULTS ON EYES TASK: Male controls showed more activity in the left inferior frontal gyrus than female controls, and both mothers and fathers showed even more activity in this area compared to sex-matched controls. Female controls showed greater bilateral inferior frontal activation than males. This was not seen when comparing mothers to males, or mothers to fathers. The ordinal ANOVA identified two specific regions that showed the pattern Females>Males>Mothers=Fathers: left medial temporal gyrus (BA 21) and left dorsolateral prefrontal cortex (BA 44)., Conclusions: Parents of children with AS show atypical brain function during both visual search and emotion recognition, in the direction of hyper-masculinization of the brain. Because of the small sample size, and lack of age-matching between parents and controls, such results constitute a pilot study that needs replicating with larger samples.

Both the architecture and the dynamics of the brain have characteristic features at different spatial scales. However, the existence, nature and function of dynamical interdependencies between such scales have not been investigated. We studied the multiscale properties of functional magnetic resonance imaging (fMRI) data acquired while human subjects viewed a visual image. Traditional "region of interest" analysis of this data set revealed evoked activity in primary and extrastriate visual cortex. Wavelet transform in the spatial domain provides a multiscale representation of this evoked brain activity. Studying the correlation structure of this representation revealed strong and novel interdependencies in these data within and between different spatial scales. We found that such correlations are stronger than those evident in the original data and comparable in magnitude to those obtained after Gaussian smoothing. However, analysis of the data in the wavelet domain revealed additional structure such as positive correlations, strong anti-correlations and phase-lagged interdependencies. Statistical significance of these effects was inferred through nonparametric bootstrap techniques. We conclude that the spatial analysis of functional neuroimaging data in the wavelet domain provides novel information which may reflect complex spatiotemporal neuronal activity and information encoding. It also affords a quantitative means of testing hierarchical and multiscale models of cortical activity.

Small-world properties have been demonstrated for many complex networks. Here, we applied the discrete wavelet transform to functional magnetic resonance imaging (fMRI) time series, acquired from healthy volunteers in the resting state, to estimate frequency-dependent correlation matrices characterizing functional connectivity between 90 cortical and subcortical regions. After thresholding the wavelet correlation matrices to create undirected graphs of brain functional networks, we found a small-world topology of sparse connections most salient in the low-frequency interval 0.03-0.06 Hz. Global mean path length (2.49) was approximately equivalent to a comparable random network, whereas clustering (0.53) was two times greater; similar parameters have been reported for the network of anatomical connections in the macaque cortex. The human functional network was dominated by a neocortical core of highly connected hubs and had an exponentially truncated power law degree distribution. Hubs included recently evolved regions of the heteromodal association cortex, with long-distance connections to other regions, and more cliquishly connected regions of the unimodal association and primary cortices; paralimbic and limbic regions were topologically more peripheral. The network was more resilient to targeted attack on its hubs than a comparable scale-free network, but about equally resilient to random error. We conclude that correlated, low-frequency oscillations in human fMRI data have a small-world architecture that probably reflects underlying anatomical connectivity of the cortex. Because the major hubs of this network are critical for cognition, its slow dynamics could provide a physiological substrate for segregated and distributed information processing.

We explored properties of whole brain networks based on multivariate spectral analysis of human functional magnetic resonance imaging (fMRI) time-series measured in 90 cortical and subcortical subregions in each of five healthy volunteers studied in the (no-task) resting state. We note that undirected graphs representing conditional independence between multivariate time-series can be more readily approached in the frequency domain than the time domain. Estimators of partial coherency and normalized partial mutual information phi, an integrated measure of partial coherence over an arbitrary frequency band, are applied. Using these tools, we replicate the prior observations that bilaterally homologous brain regions tend to be strongly connected and functional connectivity is generally greater at low frequencies [0.0004, 0.1518 Hz]. We also show that long-distance intrahemispheric connections between regions of prefrontal and parietal cortex were more salient at low frequencies than at frequencies greater than 0.3 Hz, whereas many local or short-distance connections, such as those comprising segregated dorsal and ventral paths in posterior cortex, were also represented in the graph of high-frequency connectivity. We conclude that the partial coherency spectrum between a pair of human brain regional fMRI time-series depends on the anatomical distance between regions: long-distance (greater than 7 cm) edges represent conditional dependence between bilaterally symmetric neocortical regions, and between regions of prefrontal and parietal association cortex in the same hemisphere, are predominantly subtended by low-frequency components.

Background: Autistic spectrum disorders (ASD) are neurodevelopmental conditions that may be caused by abnormal connectivity between brain regions constituting neurocognitive networks for specific aspects of social cognition., Methods: We used three-way multidimensional scaling of regionally parcellated functional magnetic resonance imaging (fMRI) data to explore the hypothesis of abnormal functional connectivity in people with ASD. Thirteen high-functioning individuals with Asperger's syndrome and 13 healthy volunteers were scanned during incidental processing of fearful facial expressions., Results: Using permutation tests for inference, we found evidence for significant abnormality of functional integration of amygdala and parahippocampal gyrus (p < .05, false discovery rate [FDR] corrected) in people with Asperger's syndrome. There were less salient abnormalities in functional connectivity of anterior cingulate, inferior occipital, and inferior frontal cortex, but there was no significant difference between groups in whole brain functional connectivity., Conclusions: We conclude there is evidence that functional connectivity of medial temporal lobe structures specifically is abnormal in people with Asperger's syndrome during fearful face processing.

Fractional Gaussian noise (fGn) provides a parsimonious model for stationary increments of a self-similar process parameterised by the Hurst exponent, H, and variance, sigma2. Fractional Gaussian noise with H < 0.5 demonstrates negatively autocorrelated or antipersistent behaviour; fGn with H > 0.5 demonstrates 1/f, long memory or persistent behaviour; and the special case of fGn with H = 0.5 corresponds to classical Gaussian white noise. We comparatively evaluate four possible estimators of fGn parameters, one method implemented in the time domain and three in the wavelet domain. We show that a wavelet-based maximum likelihood (ML) estimator yields the most efficient estimates of H and sigma2 in simulated fGn with 0 < H < 1. Applying this estimator to fMRI data acquired in the "resting" state from healthy young and older volunteers, we show empirically that fGn provides an accommodating model for diverse species of fMRI noise, assuming adequate preprocessing to correct effects of head movement, and that voxels with H > 0.5 tend to be concentrated in cortex whereas voxels with H < 0.5 are more frequently located in ventricles and sulcal CSF. The wavelet-ML estimator can be generalised to estimate the parameter vector beta for general linear modelling (GLM) of a physiological response to experimental stimulation and we demonstrate nominal type I error control in multiple testing of beta, divided by its standard error, in simulated and biological data under the null hypothesis beta = 0. We illustrate these methods principally by showing that there are significant differences between patients with early Alzheimer's disease (AD) and age-matched comparison subjects in the persistence of fGn in the medial and lateral temporal lobes, insula, dorsal cingulate/medial premotor cortex, and left pre- and postcentral gyrus: patients with AD had greater persistence of resting fMRI noise (larger H) in these regions. Comparable abnormalities in the AD patients were also identified by a permutation test of local differences in the first-order autoregression AR(1) coefficient, which was significantly more positive in patients. However, we found that the Hurst exponent provided a more sensitive metric than the AR(1) coefficient to detect these differences, perhaps because neurophysiological changes in early AD are naturally better described in terms of abnormal salience of long memory dynamics than a change in the strength of association between immediately consecutive time points. We conclude that parsimonious mapping of fMRI noise properties in terms of fGn parameters efficiently estimated in the wavelet domain is feasible and can enhance insight into the pathophysiology of Alzheimer's disease.

Permutation methods for analysis of functional neuroimaging data acquired as factorially designed experiments are described and validated. The F ratio was estimated for main effects and interactions at each voxel in standard space. Critical values corresponding to probability thresholds were derived from a null distribution sampled by appropriate permutation of observations. Spatially informed, cluster-level test statistics were generated by applying a preliminary probability threshold to the voxel F maps and then computing the sum of voxel statistics in each of the resulting three-dimensional clusters, i.e., cluster "mass." Using simulations comprising two between- or within-subject factors each with two or three levels, contaminated by Gaussian and non-normal noise, the voxel-wise permutation test was compared to the standard parametric F test and to the performance of the spatially informed statistic using receiver operating characteristic (ROC) curves. Validity of the permutation-testing algorithm and software is endorsed by almost identical performance of parametric and permutation tests of the voxel-level F statistic. Permutation testing of suprathreshold voxel cluster mass, however, was found to provide consistently superior sensitivity to detect simulated signals than either of the voxel-level tests. The methods are also illustrated by application to an experimental dataset designed to investigate effects of antidepressant drug treatment on brain activation by implicit sad facial affect perception in patients with major depression. Antidepressant drug effects in left amygdala and ventral striatum were detected by this software for an interaction between time (within-subject factor) and group (between-subject factor) in a representative two-way factorial design., (Copyright 2004 Wiley-Liss, Inc.)

There is debate in cognitive neuroscience whether conscious versus unconscious processing represents a categorical or a quantitative distinction. The purpose of the study was to explore this matter using functional magnetic resonance imaging (fMRI). We first established objective thresholds of the critical temporal parameters for overt and covert presentations of fear and disgust. Next we applied these stimulus parameters in an fMRI experiment to determine whether non-consciously perceived (covert) facial expressions of fear and disgust show the same double dissociation (amygdala response to fear, insula to disgust) observed with consciously perceived (overt) stimuli. A backward masking paradigm was used. In the psychophysics experiment, the following parameters were established: 30-ms target duration for the covert condition, and 170-ms target duration for the overt condition. Results of the block-design fMRI study indicated substantial differences underlying the perception of fearful and disgusted facial expressions, with significant effects of both emotion and target duration. Findings for the overt condition (170 ms) confirm previous evidence of amygdala activation to fearful faces, and insula activation to disgusted faces, and a double dissociation between these two emotions. In the covert condition (30 ms), the amygdala was not activated to fear, nor was the insula activated to disgust. Overall, findings demonstrate significant differences between the neural responses to fear and to disgust, and between the covert presentations of these two emotions. These results therefore suggest distinct neural correlates of conscious and unconscious emotion perception.

Wavelets provide an orthonormal basis for multiresolution analysis and decorrelation or 'whitening' of nonstationary time series and spatial processes. Wavelets are particularly well suited to analysis of biological signals and images, such as human brain imaging data, which often have fractal or scale-invariant properties. We briefly define some key properties of the discrete wavelet transform (DWT) and review its applications to statistical analysis of functional magnetic resonance imaging (fMRI) data. We focus on time series resampling by 'wavestrapping' of wavelet coefficients, methods for efficient linear model estimation in the wavelet domain, and wavelet-based methods for multiple hypothesis testing, all of which are somewhat simplified by the decorrelating property of the DWT.

Structural cerebral abnormalities are frequently observed in schizophrenia. These abnormalities may indicate vulnerability for the disorder, as evidenced by reports of familial clustering of measures identified through region-of-interest analyses using manual outlining procedures. We used computational morphometry to detect structural differences within the entire brain to further examine possible structural endophenotypes. Magnetic resonance imaging scans were obtained in 31 psychotic patients, 32 non-psychotic first-degree relatives of psychotic patients and 27 healthy controls. The images were processed using an automated procedure, yielding global grey matter, white matter, CSF and total brain volume. The relative distribution of grey matter was compared between groups on a clustered-voxel basis. Global grey matter and total brain volume did not differ between the groups. White matter volume was significantly higher and CSF volume significantly lower in relatives compared to both cases and controls. The clustered-voxel based group comparison yielded evidence for significant grey matter deficits in fronto-thalamic-cerebellar regions, in psychotic patients, whereas the most prominent deficits in relatives involved the cerebellum. Patients with psychosis and first-degree healthy relatives of patients with psychosis show cerebellar abnormalities, which may constitute a marker of genetic transmission.

Brain activation is adaptive to task difficulty and practice. We used functional MRI to map brain systems activated by an object-location learning task in 24 healthy elderly volunteers each scanned following placebo and two of four active drugs studied. We distinguished a fronto-striatal system adaptive to difficulty from a posterior system adaptive to practice. Fronto-striatal response to increased cognitive load was significantly attenuated by scopolamine, sulpiride and methylphenidate; practice effects were not modulated by these drugs but were enhanced by diazepam. We also found enhancement by methylphenidate, and attenuation by sulpiride, of load response in premotor, cingulate and parietal regions comprising a spatial attention network. Difficulty and practice evoke anatomically and pharmacologically dissociable brain activation dynamics, which are probably mediated by different neurotransmitter systems in humans.

Cognitive, motor and psychiatric changes in prodromal Huntington's disease have nurtured the emergent need for early interventions. Preventive clinical trials for Huntington's disease, however, are limited by a shortage of suitable measures that could serve as surrogate outcomes. Measures of intrinsic functional connectivity from resting-state functional magnetic resonance imaging are of keen interest. Yet recent studies suggest circumscribed abnormalities in resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease, despite the spectrum of behavioural changes preceding a manifest diagnosis. The present study used two complementary analytical approaches to examine whole-brain resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease. Network topology was studied using graph theory and simple functional connectivity amongst brain regions was explored using the network-based statistic. Participants consisted of gene-negative controls (n = 16) and prodromal Huntington's disease individuals (n = 48) with various stages of disease progression to examine the influence of disease burden on intrinsic connectivity. Graph theory analyses showed that global network interconnectivity approximated a random network topology as proximity to diagnosis neared and this was associated with decreased connectivity amongst highly-connected rich-club network hubs, which integrate processing from diverse brain regions. However, functional segregation within the global network (average clustering) was preserved. Functional segregation was also largely maintained at the local level, except for the notable decrease in the diversity of anterior insula intermodular-interconnections (participation coefficient), irrespective of disease burden. In contrast, network-based statistic analyses revealed patterns of weakened frontostriatal connections and strengthened frontal-posterior connections that evolved as disease burden increased. These disturbances were often related to long-range connections involving peripheral nodes and interhemispheric connections. A strong association was found between weaker connectivity and decreased rich-club organization, indicating that whole-brain simple connectivity partially expressed disturbances in the communication of highly-connected hubs. However, network topology and network-based statistic connectivity metrics did not correlate with key markers of executive dysfunction (Stroop Test, Trail Making Test) in prodromal Huntington's disease, which instead were related to whole-brain connectivity disturbances in nodes (right inferior parietal, right thalamus, left anterior cingulate) that exhibited multiple aberrant connections and that mediate executive control. Altogether, our results show for the first time a largely disease burden-dependent functional reorganization of whole-brain networks in prodromal Huntington's disease. Both analytic approaches provided a unique window into brain reorganization that was not related to brain atrophy or motor symptoms. Longitudinal studies currently in progress will chart the course of functional changes to determine the most sensitive markers of disease progression.

Background and Hypothesis Schizophrenia is increasingly understood as a disorder of brain dysconnectivity. Recently, graph-based approaches such as graph convolutional network (GCN) have been leveraged to explore complex pairwise similarities in imaging features among brain regions, which can reveal abstract and complex relationships within brain networks. Study Design We used GCN to investigate topological abnormalities of functional brain networks in schizophrenia. Resting-state functional magnetic resonance imaging data were acquired from 505 individuals with schizophrenia and 907 controls across 6 sites. Whole-brain functional connectivity matrix was extracted for each individual. We examined the performance of GCN relative to support vector machine (SVM), extracted the most salient regions contributing to both classification models, investigated the topological profiles of identified salient regions, and explored correlation between nodal topological properties of each salient region and severity of symptom. Study Results GCN enabled nominally higher classification accuracy (85.8%) compared with SVM (80.9%). Based on the saliency map, the most discriminative brain regions were located in a distributed network including striatal areas (ie, putamen, pallidum, and caudate) and the amygdala. Significant differences in the nodal efficiency of bilateral putamen and pallidum between patients and controls and its correlations with negative symptoms were detected in post hoc analysis. Conclusions The present study demonstrates that GCN allows classification of schizophrenia at the individual level with high accuracy, indicating a promising direction for detection of individual patients with schizophrenia. Functional topological deficits of striatal areas may represent a focal neural deficit of negative symptomatology in schizophrenia. [ABSTRACT FROM AUTHOR]

Abstract: Background: Recent genetic results have indicated that the two major, classically distinct forms of psychosis – schizophrenia and bipolar disorder – may share causative factors in common. However it is not clear to what extent they may also have similar profiles of brain abnormality. We used meta-analytic techniques to generate and compare maps of brain structural abnormality in the large samples of patients with both disorders that have been studied using magnetic resonance imaging. Method: A systematic search was conducted for voxel-based morphometry studies examining gray matter in patients with schizophrenia or bipolar disorder. The anatomical distribution of the co-ordinates of gray matter differences was meta-analysed using Anatomical Likelihood Estimation. Results: Forty-two schizophrenia studies including 2058 patients with schizophrenia and 2131 comparison subjects were compared with fourteen bipolar studies including 366 patients with bipolar disorder and 497 comparison subjects. In schizophrenia, there were extensive gray matter deficits in frontal, temporal, cingulate and insular cortex and thalamus, and increased gray matter in the basal ganglia. In bipolar disorder, gray matter reductions were present in the anterior cingulate and bilateral insula. These substantially overlapped with areas of gray matter reduction in schizophrenia, except for a region of anterior cingulate where gray matter reduction was specific to bipolar disorder. Implications: In bipolar disorder studies there were consistent regional gray matter reductions in paralimbic regions (anterior cingulate and insula) implicated in emotional processing. Gray matter reductions in schizophrenia studies were more extensive and involved limbic and neocortical structures as well as the paralimbic regions affected in bipolar disorder. [Copyright &y& Elsevier]

Dopamine (DA) plays an important role in working memory. However, the precise functions supported by different DA receptor subtypes in different neural regions remain unclear. The present study used pharmacological, event-related fMRI to test the hypothesis that striatal dopamine is important for the manipulation of information in working memory. Twenty healthy human subjects were scanned twice, once after placebo and once after sulpiride 400 mg, a selective DA D2 receptor antagonist, while performing a verbal working memory task requiring different levels of manipulation. Whilst there was no overall effect of sulpiride on task-dependent activation, individual variation in sulpiride plasma levels predicted the effect of working memory manipulation on activation in the putamen, suggesting a dose-dependent effect of DA antagonism on a striatally based manipulation process. These effects occurred in the context of a drug-induced improvement in performance on trials requiring the manipulation of information in working memory but not on simple retrieval trials. No significant drug effects were observed in the prefrontal cortex. These results support models of dopamine function that posit a ‘gating’ function for dopamine D2 receptors in the striatum, which enables the flexible updating and manipulation of information in working memory. [ABSTRACT FROM AUTHOR]

Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field. [ABSTRACT FROM AUTHOR]

The amygdala plays a central role in various aspects of affect processing and mood regulation by its rich anatomical connections to other limbic and cortical regions. It is plausible that depressive disorders, and response to antidepressant drugs, may reflect changes in the physiological coupling between the amygdala and other components of affect-related large-scale brain systems. We explored this hypothesis by mapping the functional coupling of right and left amygdalae in functional magnetic resonance imaging data acquired from 19 patients with major depressive disorder and 19 healthy volunteers, each scanned twice (at baseline and 8 weeks later) during performance of an implicit facial affect processing task. Between scanning sessions, the patients received treatment with an antidepressant drug, fluoxetine 20 mg/day. We found that the amygdala was positively coupled bilaterally with medial temporal and ventral occipital regions, and negatively coupled with the anterior cingulate cortex. Antidepressant treatment was associated with significantly increased coupling between the amygdala and right frontal and cingulate cortex, striatum, and thalamus. Treatment-related increases in functional coupling to frontal and other regions were greater for the left amygdala than for the right amygdala. These results indicate that antidepressant drug effects can be measured in terms of altered coupling between components of cortico-limbic systems and that these effects were most clearly demonstrated by enhanced functional coupling of the left amygdala.Neuropsychopharmacology (2008) 33, 1909–1918; doi:10.1038/sj.npp.1301593; published online 7 November 2007 [ABSTRACT FROM AUTHOR]

Many complex networks have a small-world topology characterized by dense local clustering or cliquishness of connections between neighboring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for the organization of brain anatomical and functional networks because a small-world topology can support both segregated/specialized and distributed/integrated information processing. Moreover, small-world networks are economical, tending to minimize wiring costs while supporting high dynamical complexity. The authors introduce some of the key mathematical concepts in graph theory required for small-world analysis and review how these methods have been applied to quantification of cortical connectivity matrices derived from anatomical tract-tracing studies in the macaque monkey and the cat. The evolution of small-world networks is discussed in terms of a selection pressure to deliver cost-effective information-processing systems. The authors illustrate how these techniques and concepts are increasingly being applied to the analysis of human brain functional networks derived from electroencephalography/magnetoencephalography and fMRI experiments. Finally, the authors consider the relevance of small-world models for understanding the emergence of complex behaviors and the resilience of brain systems to pathological attack by disease or aberrant development. They conclude that small-world models provide a powerful and versatile approach to understanding the structure and function of human brain systems. [ABSTRACT FROM AUTHOR]

In this paper, we consider modeling the nonparametric component in partially linear models (PLMs) using linear sparse representations, e.g., wavelet expansions. Two types of representations are investigated, namely, orthogonal bases (complete) and redundant overcomplete expansions. For bases, we introduce a regularized estimator of the nonparametric part. The important contribution here is that the nonparametric part can be parsimoniously estimated by choosing an appropriate penalty function for which the hard and soft thresholding estimators are special cases. This allows us to represent in an effective manner a broad class of signals, including stationary and/or nonstationary signals and avoids excessive bias in estimating the parametric component. We also give a fast estimation algorithm. The method is then generalized to handle the case of overcomplete representations. A large-scale simulation study is conducted to illustrate the finite sample properties of the estimator. The estimator is finally applied to real neurophysiological functional magnetic resonance imaging (MRI) data sets that are suspected to contain both smooth and transient drift features. [ABSTRACT FROM AUTHOR]

Focuses on a study which examined the effects of acute tryptophan depletion, a well-recognized method for inducing transient cerebral serotonin depletion, on brain activation to fearful faces using functional magnetic resonance imaging. Role for serotonin in threat sensitivity; Parts of the brain where fearful faces induced significant signal change; Behavioral effects of treatment.

The discrete wavelet transform (DWT) is widely used for multiresolution analysis and decorrelation or “whitening” of nonstationary time series and spatial processes. Wavelets are naturally appropriate for analysis of biological data, such as functional magnetic resonance images of the human brain, which often demonstrate scale invariant or fractal properties. We provide a brief formal introduction to key properties of the DWT and review the growing literature on its application to fMRI. We focus on three applications in particular: (i) wavelet coefficient resampling or “wavestrapping” of 1-D time series, 2- to 3-D spatial maps and 4-D spatiotemporal processes; (ii) wavelet-based estimators for signal and noise parameters of time series regression models assuming the errors are fractional Gaussian noise (fGn); and (iii) wavelet shrinkage in frequentist and Bayesian frameworks to support multiresolution hypothesis testing on spatially extended statistic maps. We conclude that the wavelet domain is a rich source of new concepts and techniques to enhance the power of statistical analysis of human fMRI data. [Copyright &y& Elsevier]

Background Schizophrenia is a disorder of brain connectivity and altered neurodevelopmental processes. Cross-sectional case-control studies in different age groups have suggested that deficits in cortical thickness in childhood-onset schizophrenia may normalize over time, suggesting a disorder-related difference in cortical growth trajectories. Methods We acquired magnetic resonance imaging scans repeated over several years for each subject, in a sample of 106 patients with childhood-onset schizophrenia and 102 age-matched healthy volunteers. Using semiparametric regression, we modeled the effect of schizophrenia on the growth curve of cortical thickness in ~80,000 locations across the cortex, in the age range 8 to 30 years. In addition, we derived normative developmental modules composed of cortical regions with similar maturational trajectories for cortical thickness in typical brain development. Results We found abnormal nonlinear growth processes in prefrontal and temporal areas that have previously been implicated in schizophrenia, distinguishing for the first time between cortical areas with age-constant deficits in cortical thickness and areas whose maturational trajectories are altered in schizophrenia. In addition, we showed that when the brain is divided into five normative developmental modules, the areas with abnormal cortical growth overlap significantly only with the cingulo-fronto-temporal module. Conclusions These findings suggest that abnormal cortical development in schizophrenia may be modularized or constrained by the normal community structure of developmental modules of the human brain connectome. [ABSTRACT FROM AUTHOR]

Objetivo: El trastorno obsesivo-compulsivo (TOC) es un trastorno neuropsiquiátrico frecuente, hereditario, hipotéticamente provocado por alteraciones en la conexión de numerosos sistemas cerebrales. Se desconoce la amplitud de las alteraciones en la sustancia blanca en el TOC y se conocen pocos datos acerca de las bases genéticas del trastorno. Los autores utilizaron una técnica de resonancia magnética, la neuroimagen por tensor de difusión, para examinar las alteraciones en la sustancia blanca de la estructura cerebral a través de la cuantificación de la difusión de agua, y confirmar si existen alteraciones en la sustancia blanca en el TOC. También exploraron si tales anomalías se presentan en los familiares de primer grado sanos, lo que indicaría la posible existencia de endofenotipos que representen un riesgo genético aumentado para el TOC. Método: Los autores utilizaron las imágenes por tensor de difusión para medir la anisotropía fraccional de la sustancia blanca en 30 pacientes con TOC, 30 familiares de primer grado no afectados y 30 individuos sanos de comparación apareados. Mediante pruebas de permutación se identificaron las regiones de anisotropía fraccional anormal significativa en los pacientes, comparados con los individuos sanos de control. Los autores evaluaron si estas anormalidades eran evidentes también en los familiares de primer grado. Se practicó un análisis secundario de regiones de interés para evaluar el alcance de la reproducción entre nuestros datos y las publicaciones relevantes previas. Resultados: Los pacientes con TOC presentaron una reducción significativa de la anisotropía fraccional en una amplia región de la sustancia blanca parietal derecha y un aumento significativo de la anisotropía fraccional en la región frontal media derecha. Los familiares también presentaron alteraciones significativas en la anisotropía fraccional en estas regiones. Conclusiones: Estos hallazgos indican que el TOC está asociado con alteraciones en la sustancia blanca en las regiones frontal y parietal. Alteraciones similares en familiares de primer grado no enfermos sugieren que podría tratarse de endofenotipos del TOC en la sustancia blanca. [ABSTRACT FROM AUTHOR]

Background: It would be therapeutically useful to predict clinical response to antidepressant drugs. We evaluated structural magnetic resonance imaging (MRI) and functional MRI (fMRI) data as predictors of symptom change in people with depression. Methods: Brain structure and function were measured with MRI in 17 patients with major depression immediately before 8 weeks treatment with fluoxetine 20 mg/day. For fMRI, patients were scanned during visual presentation of faces representing different intensities of sadness. Clinical response was measured by change in serial scores on the Hamilton Rating Scale for Depression. Symptom change scores (and baseline symptom severity) were regressed on structural and functional MRI data to map brain regions where grey matter volume, or activation by sad facial affect processing, was significantly associated with symptom change (or baseline severity). Results: Faster rates of symptom improvement were strongly associated with greater grey matter volume in anterior cingulate cortex, insula, and right temporo-parietal cortex. Patients with greater than median grey matter volume in this system had faster rates of improvement and significantly lower residual symptom scores after 8 weeks’ treatment. Faster improvement was also predicted by greater functional activation of anterior cingulate cortex. Baseline symptom severity was negatively correlated with greater grey matter volume in dorsal prefrontal and anterior midcingulate regions anatomically distinct from the pregenual and subgenual cingulate regions predicting treatment response. Conclusions: Structural MRI measurements of anterior cingulate cortex could provide a useful predictor of antidepressant treatment response. [Copyright &y& Elsevier]

Background: Although liability to psychosis is thought to have its origins in cerebral alterations, expressed as cerebral grey and white matter loss, less is known about the degree to which such vulnerabilities impact on functional parameters, in particular altered stress reactivity. Breier et al. [Breier, A., Davis, O.R., Buchanan, R.W., Moricle, L.A., Munson, R.C., 1993b. Effects of metabolic perturbation on plasma homovanillic acid in schizophrenia. Relationship to prefrontal cortex volume. Arch. Gen. Psychiatry 50(7), 541-550] reported that lower prefrontal cortex volume was associated with altered metabolic stress response, but this finding has never been replicated.Methods: Thirty-one patients with psychosis underwent structural magnetic resonance imaging scanning and a metabolic stress paradigm (glucoprivic 2-deoxyglucose (2DG) condition versus placebo condition) that yielded information on plasma homovanillic acid (HVA) reactivity. Total cerebral tissue volumes were derived from automated segmentation procedures. Associations between metabolic stress and tissue volumes (as well as their interactions) on the one hand, and plasma HVA level on the other, were investigated using multilevel random regression techniques.Results: Analysis revealed a significant increase in plasma HVA over time in the 2DG condition. The increase in HVA in the stress condition was stronger in patients with lower grey and white matter volumes. There was no significant interaction between metabolic stress and CSF volume.Conclusion: Lower grey and white matter volumes in schizophrenia are associated with a dysregulated dopaminergic/noradrenergic mediated stress response. These findings may support the hypothesis that alterations in cortico-subcortical connections affect psychosis susceptibility through an altered stress response. [ABSTRACT FROM AUTHOR]

Background: The pathophysiology of abnormal mood states–mania and depression–in patients with bipolar disorder remains unclear. Facial affect processing paradigms are an effective way of studying behavioral and functional magnetic resonance imaging (fMRI) correlates of affective disorders. Methods: We used a factorial design to measure the neural correlates of tasks, tapping explicit and implicit recognition of sad, fearful, and happy facial expressions using event-related fMRI paradigms in three groups of participants: eight bipolar depressed patients, eight bipolar manic patients, and eight control subjects. Results: Depressed and manic patients exhibited overactivated responses to fearful faces, as well as to mood-incongruent facial expressions, with the depressed group exhibiting overactivity in fronto-striato-thalamic systems in response to happy faces and the manic group exhibiting overactivity in the fusiform gyrus in response to sad faces. For manic patients, task type also affected the neural response to sad faces, with the corticolimbic regions showing overactivation for implicit processing and underactivation for explicit processing. Conclusions: Depressed and manic patients exhibited abnormal neural responses to sad, fearful, and happy facial expressions. Additionally, the attentional level of sad facial affect processing has important consequences for abnormalities of amygdala and cingulate activation in manic patients. [Copyright &y& Elsevier]