•Applied deep learning to sex classification in UK BioBank fMRI connectomes.•Deep learning classifies sex better in resting-state than in task fMRI.•Algorithm to balance out multiple confounds from ...an fMRI dataset.•Adapted two deep learning visualization methods to fMRI connectome classification.•Analyzed role of three brain a priori networks in sex classification.
Classification of whole-brain functional connectivity MRI data with convolutional neural networks (CNNs) has shown promise, but the complexity of these models impedes understanding of which aspects of brain activity contribute to classification. While visualization techniques have been developed to interpret CNNs, bias inherent in the method of encoding abstract input data, as well as the natural variance of deep learning models, detract from the accuracy of these techniques. We introduce a stochastic encoding method in an ensemble of CNNs to classify functional connectomes by sex. We applied our method to resting-state and task data from the UK BioBank, using two visualization techniques to measure the salience of three brain networks involved in task- and resting-states, and their interaction. To regress confounding factors such as head motion, age, and intracranial volume, we introduced a multivariate balancing algorithm to ensure equal distributions of such covariates between classes in our data. We achieved a final AUROC of 0.8459. We found that resting-state data classifies more accurately than task data, with the inner salience network playing the most important role of the three networks overall in classification of resting-state data and connections to the central executive network in task data.
Time‐invariant resting‐state functional connectivity studies have illuminated the crucial role of the right anterior insula (rAI) in prominent social impairments of autism spectrum disorder (ASD). ...However, a recent dynamic connectivity study demonstrated that rather than being stationary, functional connectivity patterns of the rAI vary significantly across time. The present study aimed to explore the differences in functional connectivity in dynamic states of the rAI between individuals with ASD and typically developing controls (TD). Resting‐state functional magnetic resonance imaging data obtained from a publicly available database were analyzed in 209 individuals with ASD and 298 demographically matched controls. A k‐means clustering algorithm was utilized to obtain five dynamic states of functional connectivity of the rAI. The temporal properties, frequency properties, and meta‐analytic decoding were first identified in TD group to obtain the characteristics of each rAI dynamic state. Multivariate analysis of variance was then performed to compare the functional connectivity patterns of the rAI between ASD and TD groups in obtained states. Significantly impaired connectivity was observed in ASD in the ventral medial prefrontal cortex and posterior cingulate cortex, which are two critical hubs of the default mode network (DMN). States in which ASD showed decreased connectivity between the rAI and these regions were those more relevant to socio‐cognitive processing. From a dynamic perspective, these findings demonstrate partially impaired resting‐state functional connectivity patterns between the rAI and DMN across states in ASD, and provide novel insights into the neural mechanisms underlying social impairments in individuals with ASD.
Major depressive disorder (MDD) has been shown to be associated with structural abnormalities in a variety of spatially diverse brain regions. However, the correlation between brain structural ...changes in MDD and gene expression is unclear. Here, we examine the link between brain-wide gene expression and morphometric changes in individuals with MDD, using neuroimaging data from two independent cohorts and a publicly available transcriptomic dataset. Morphometric similarity network (MSN) analysis shows replicable cortical structural differences in individuals with MDD compared to control subjects. Using human brain gene expression data, we observe that the expression of MDD-associated genes spatially correlates with MSN differences. Analysis of cell type-specific signature genes suggests that microglia and neuronal specific transcriptional changes account for most of the observed correlation with MDD-specific MSN differences. Collectively, our findings link molecular and structural changes relevant for MDD.
A meta-analysis of sex differences in human brain structure RUIGROK, Amber N. V; SALIMI-KHORSHIDI, Gholamreza; LAI, Meng-Chuan ...
Neuroscience & biobehavioral reviews/Neuroscience and biobehavioral reviews,
02/2014, Letnik:
39, Številka:
100
Journal Article
Recenzirano
Odprti dostop
The prevalence, age of onset, and symptomatology of many neuropsychiatric conditions differ between males and females. To understand the causes and consequences of sex differences it is important to ...establish where they occur in the human brain. We report the first meta-analysis of typical sex differences on global brain volume, a descriptive account of the breakdown of studies of each compartmental volume by six age categories, and whole-brain voxel-wise meta-analyses on brain volume and density. Gaussian-process regression coordinate-based meta-analysis was used to examine sex differences in voxel-based regional volume and density. On average, males have larger total brain volumes than females. Examination of the breakdown of studies providing total volumes by age categories indicated a bias towards the 18-59 year-old category. Regional sex differences in volume and tissue density include the amygdala, hippocampus and insula, areas known to be implicated in sex-biased neuropsychiatric conditions. Together, these results suggest candidate regions for investigating the asymmetric effect that sex has on the developing brain, and for understanding sex-biased neurological and psychiatric conditions.
Background and Aims
Addiction is associated with severe economic and social consequences and personal tragedies, the scientific exploration of which draws upon investigations at the molecular, ...cellular and systems levels with a wide variety of technologies. Magnetic resonance imaging (MRI) has been key to mapping effects observed at the microscopic and mesoscopic scales. The range of measurements from this apparatus has opened new avenues linking neurobiology to behaviour. This review considers the role of MRI in addiction research, and what future technological improvements might offer.
Methods
A hermeneutic strategy supplemented by an expansive, systematic search of PubMed, Scopus and Web of Science databases, covering from database inception to October 2015, with a conjunction of search terms relevant to addiction and MRI. Formal meta‐analyses were prioritized.
Results
Results from methods that probe brain structure and function suggest frontostriatal circuitry disturbances within specific cognitive domains, some of which predict drug relapse and treatment response. New methods of processing imaging data are opening opportunities for understanding the role of cerebral vasculature, a global view of brain communication and the complex topology of the cortical surface and drug action. Future technological advances include increases in MRI field strength, with concomitant improvements in image quality.
Conclusions
The magnetic resonance imaging literature provides a limited but convergent picture of the neurobiology of addiction as global changes to brain structure and functional disturbances to frontostriatal circuitry, accompanied by changes in anterior white matter.
Dysregulation of corticostriatal circuitry has long been thought to be critical in the etiology of psychotic disorders, although the differential roles played by dorsal and ventral systems in ...mediating risk for psychosis have been contentious.
To use resting-state functional magnetic resonance imaging to characterize disease-related, risk-related, and symptom-related changes of corticostriatal functional circuitry in patients with first-episode psychosis and their unaffected first-degree relatives.
This case-control cross-sectional study was conducted at a specialist early psychosis clinic, GlaxoSmithKline Clinical Unit, and magnetic resonance imaging facility. Nineteen patients with first-episode psychosis, 25 of their unaffected first-degree relatives, and 26 healthy control subjects were included in this study.
Voxelwise statistical parametric maps testing differences in the strength of functional connectivity between 6 striatal seed regions of interest (3 caudate and 3 putamen) per hemisphere and all other brain regions.
Disease-related changes, reflecting differences between patients and control subjects, involved widespread dysregulation of corticostriatal systems characterized most prominently by a dorsal-to-ventral gradient of hypoconnectivity to hyperconnectivity between striatal and prefrontal regions. A similar gradient was evident in comparisons between relatives and control subjects, identifying it as a genetically inherited risk phenotype. In patients, functional connectivity in risk-affected and disease-affected dorsal frontostriatal circuitry correlated with the severity of both positive and negative symptoms.
First-episode psychosis is associated with pronounced dysregulation of corticostriatal systems, characterized most prominently by hypoconnectivity of dorsal and hyperconnectivity of ventral frontostriatal circuits. These changes correlate with symptom severity and are also apparent in unaffected first-degree relatives, suggesting that they represent a putative risk phenotype for psychotic illness.
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 φ, 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.
In functional magnetic resonance imaging, the brain's response to experimental manipulation is almost always assumed to be independent of endogenous oscillations. To test this, we addressed the ...possible interaction between cognitive task performance and endogenous fMRI oscillations in an experiment designed to answer two questions: 1) Does performance of a cognitively effortful task significantly change fractal scaling properties of fMRI time series compared to their values before task performance? 2) If so, can we relate the extent of task-related perturbation to the difficulty of the task?
Using a novel continuous acquisition "rest-task-rest" design, we found that endogenous dynamics tended to recover their pre-task parameter values relatively slowly, over the course of several minutes, following completion of one of two versions of the n-back working memory task and that the rate of recovery was slower following completion of the more demanding (n = 2) version of the task.
This result supports the model that endogenous low frequency oscillatory dynamics are relevant to the brain's response to exogenous stimulation. Moreover, it suggests that large-scale neurocognitive systems measured using fMRI, like the heart and other physiological systems subjected to external demands for enhanced performance, can take a considerable period of time to return to a stable baseline state.
Although many examples exist for shared neural representations of self and other, it is unknown how such shared representations interact with the rest of the brain. Furthermore, do high-level ...inference-based shared mentalizing representations interact with lower level embodied/simulation-based shared representations? We used functional neuroimaging (fMRI) and a functional connectivity approach to assess these questions during high-level inference-based mentalizing. Shared mentalizing representations in ventromedial prefrontal cortex, posterior cingulate/precuneus, and temporo-parietal junction (TPJ) all exhibited identical functional connectivity patterns during mentalizing of both self and other. Connectivity patterns were distributed across low-level embodied neural systems such as the frontal operculum/ventral premotor cortex, the anterior insula, the primary sensorimotor cortex, and the presupplementary motor area. These results demonstrate that identical neural circuits are implementing processes involved in mentalizing of both self and other and that the nature of such processes may be the integration of low-level embodied processes within higher level inference-based mentalizing.
Abstract Background Major Depressive Disorder (MDD) is a leading cause of disease burden worldwide. With the rapid growth of neuroimaging research on relatively small samples, meta-analytic ...techniques are becoming increasingly important. Here, we aim to clarify the support in fMRI literature for three leading neurobiological models of MDD: limbic–cortical, cortico–striatal and the default mode network. Methods Searches of PubMed and Web of Knowledge, and manual searches, were undertaken in early 2011. Data from 34 case-control comparisons ( n =1165) and 6 treatment studies ( n =105) were analysed separately with two meta-analytic methods for imaging data: Activation Likelihood Estimation and Gaussian-Process Regression. Results There was broad support for limbic–cortical and cortico–striatal models in the case-control data. Evidence for the role of the default mode network was weaker. Treatment-sensitive regions were primarily in lateral frontal areas. Limitations In any meta-analysis, the increase in the statistical power of the inference comes with the risk of aggregating heterogeneous study pools. While we believe that this wide range of paradigms allows identification of key regions of dysfunction in MDD (regardless of task), we attempted to minimise such risks by employing GPR, which models such heterogeneity. Conclusions The focus of treatment effects in frontal areas indicates that dysregulation here may represent a biomarker of treatment response. Since the dysregulation in many subcortical regions in the case-control comparisons appeared insensitive to treatment, we propose that these act as trait vulnerability markers, or perhaps treatment insensitivity. Our findings allow these models of MDD to be applied to fMRI literature with some confidence.
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