Patients with low-grade glioma (LGG) have been studied as a model of functional brain reorganization due to their slow-growing nature. However, there is no information regarding which brain areas are involved during verbal memory encoding after extensive left frontal LGG resection. In addition, it remains unknown whether these patients can improve their memory performance after instructions to apply efficient strategies. The neural correlates of verbal memory encoding were investigated in patients who had undergone extensive left frontal lobe (LFL) LGG resections and healthy controls using fMRI both before and after directed instructions were given for semantic organizational strategies. Participants were scanned during the encoding of word lists under three different conditions before and after a brief period of practice. The conditions included semantically unrelated (UR), related-non-structured (RNS), and related-structured words (RS), allowing for different levels of semantic organization. All participants improved on memory recall and semantic strategy application after the instructions for the RNS condition. Healthy subjects showed increased activation in the left inferior frontal gyrus (IFG) and middle frontal gyrus (MFG) during encoding for the RNS condition after the instructions. Patients with LFL excisions demonstrated increased activation in the right IFG for the RNS condition after instructions were given for the semantic strategies. Despite extensive damage in relevant areas that support verbal memory encoding and semantic strategy applications, patients that had undergone resections for LFL tumor could recruit the right-sided contralateral homologous areas after instructions were given and semantic strategies were practiced. These results provide insights into changes in brain activation areas typically implicated in verbal memory encoding and semantic processing.
Accumulating evidence underscores the utility of ketamine in treating severely treatment-resistant depressed patients. We investigated the relationship between the rapid antidepressant effects of ketamine and hippocampal volume, a biomarker of antidepressant treatment outcome. We gave 16 medication-free, major depressive disorder (MDD) patients a single, sub-anesthetic dose infusion of ketamine (0.5 mg/kg, over 40 min). We assessed depression severity pre-treatment, and at 24 h post-treatment, with the Montgomery-Åsberg Depression Rating Scale (MADRS). Prior to treatment, patients underwent magnetic resonance imaging (MRI) to estimate their hippocampal volume: We obtained viable MRI data in 13 patients. Delta MADRS (post- minus pre-treatment) was significantly correlated with the pre-treatment volumes of the left hippocampus (r = 0.66; p = 0.01), but not the right hippocampus (r = 0.49; p = 0.09). The correlation between delta MADRS and the left hippocampus remained high (r > 0.6; p = 0.13), after controlling for several demographic and clinical variables, although the p value increased due to the reduced degree of freedom (df = 5). Ketamine exerts enhanced antidepressant effects in patients with a relatively smaller hippocampus, a patient population that has been repeatedly shown to be refractory to traditional antidepressants.
Background: Neuroimaging techniques combined with computational neuroanatomy have been playing a role in the investigation of healthy aging and Alzheimer's disease (AD). The definition of normative rules for brain features is a crucial step to establish typical and atypical aging trajectories. Objective: To introduce an unsupervised pattern recognition method; to define multivariate normative rules of neuroanatomical measures; and to propose a brain abnormality index. Methods: This study was based on a machine learning approach (one class classification or novelty detection) to neuroanatomical measures (brain regions, volume, and cortical thickness) extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI)'s database. We applied a ?-One-Class Support Vector Machine (?-OC-SVM) trained with data from healthy subjects to build an abnormality index, which was compared with subjects diagnosed with mild cognitive impairment and AD. Results: The method was able to classify AD subjects as outliers with an accuracy of 84.3% at a false alarm rate of 32.5%. The proposed brain abnormality index was found to be significantly associated with group diagnosis, clinical data, biomarkers, and future conversion to AD. Conclusion: These results suggest that one-class classification may be a promising approach to help in the detection of disease conditions. Our findings support a framework considering the continuum of brain abnormalities from healthy aging to AD, which is correlated with cognitive impairment and biomarkers measurements.
Schizophrenia is a severe mental health disorder with high heritability. The investigation of individuals during their first-episode psychosis (FEP), before the progression of psychotic disorders and especially before treatment with antipsychotic medications, is particularly helpful for understanding this complex disease and for the identification of potential biomarkers. In this study, we compared the expression of genes that are involved in neurotransmission and neurodevelopment of antipsychotic-naive FEP in the peripheral blood of patients (n=51) and healthy controls (n=51). In addition, we investigated the differentially expressed genes with respect to a) DNA methylation, b) the correlation between gene expression and clinical variables (PANSS), and c) gene expression changes after risperidone treatment. Expression levels of 11 genes were quantified with SYBR Green. For methylation analysis, bisulfite sequencing was performed. A significant decrease in GCH1 mRNA levels was observed in FEP patients relative to controls. Also, when we compare the FEP patients after risperidone treatment with controls, this difference remains significant, and no significant differences were observed in GCH1 mRNA levels when comparing patients before and after risperidone treatment. Additionally, although the differences were non-significant after Bonferroni correction, the expression of GCH1 seemed to be correlated with PANSS scores, and the GCH1 promoter region was more methylated in FEP than in controls, thus corroborating the results obtained at the mRNA level. Few studies have been conducted on GCH1, and future studies are needed to clarify its potential role in the progression of schizophrenia.
Humans spend a substantial share of their lives mind-wandering. This spontaneous thinking activity usually comprises autobiographical recall, emotional, and self-referential components. While neuroimaging studies have demonstrated that a specific brain "default mode network" (DMN) is consistently engaged by the "resting state" of the mind, the relative contribution of key cognitive components to DMN activity is still poorly understood. Here we used fMRI to investigate whether activity in neural components of the DMN can be differentially explained by active recall of relevant emotional autobiographical memories as compared with the resting state. Our study design combined emotional autobiographical memory, neutral memory and resting state conditions, separated by a serial subtraction control task. Shared patterns of activation in the DMN were observed in both emotional autobiographical and resting conditions, when compared with serial subtraction. Directly contrasting autobiographical and resting conditions demonstrated a striking dissociation within the DMN in that emotional autobiographical retrieval led to stronger activation of the dorsomedial core regions (medial prefrontal cortex, posterior cingulate cortex), whereas the resting state condition engaged a ventral frontal network (ventral striatum, subgenual and ventral anterior cingulate cortices) in addition to the IPL. Our results reveal an as yet unreported dissociation within the DMN. Whereas the dorsomedial component can be explained by emotional autobiographical memory, the ventral frontal one is predominantly associated with the resting state proper, possibly underlying fundamental motivational mechanisms engaged during spontaneous unconstrained ideation.
Investigations of brain maturation processes are a key step to understand the cognitive and emotional changes of adolescence. Although structural imaging findings have delineated clear brain developmental trajectories for typically developing individuals, less is known about the functional changes of this sensitive development period. Developmental changes, such as abstract thought, complex reasoning, and emotional and inhibitory control, have been associated with more prominent cortical control. The aim of this study is to assess brain networks connectivity changes in a large sample of 7- to 15-year-old subjects, testing the hypothesis that cortical regions will present an increasing relevance in commanding the global network. Functional magnetic resonance imaging (fMRI) data were collected in a sample of 447 typically developing children from a Brazilian community sample who were submitted to a resting state acquisition protocol. The fMRI data were used to build a functional weighted graph from which eigenvector centrality (EVC) was extracted. For each brain region (a node of the graph), the age-dependent effect on EVC was statistically tested and the developmental trajectories were estimated using polynomial functions. Our findings show that angular gyrus become more central during this maturation period, while the caudate; cerebellar tonsils, pyramis, thalamus; fusiform, parahippocampal and inferior semilunar lobe become less central. In conclusion, we report a novel finding of an increasing centrality of the angular gyrus during the transition to adolescence, with a decreasing centrality of many subcortical and cerebellar regions.
The framework of graph theory provides useful tools for investigating the neural substrates of neuropsychiatric disorders. Graph description measures may be useful as predictor variables in classification procedures. Here, we consider several centrality measures as predictor features in a classification algorithm to identify nodes of resting-state networks containing predictive information that can discriminate between typical developing children and patients with attention-deficit/hyperactivity disorder (ADHD). The prediction was based on a support vector machines classifier. The analyses were performed in a multisite and publicly available resting-state fMRI dataset of healthy children and ADHD patients: the ADHD-200 database. Network centrality measures contained little predictive information for the discrimination between ADHD patients and healthy subjects. However, the classification between inattentive and combined ADHD subtypes was more promising, achieving accuracies higher than 65% (balance between sensitivity and specificity) in some sites. Finally, brain regions were ranked according to the amount of discriminant information and the most relevant were mapped. As hypothesized, we found that brain regions in motor, frontoparietal, and default mode networks contained the most predictive information. We concluded that the functional connectivity estimations are strongly dependent on the sample characteristics. Thus different acquisition protocols and clinical heterogeneity decrease the predictive values of the graph descriptors.
Functional imaging studies have implicated the orbitofrontal cortex (OFC) in the pathophysiology of borderline personality disorder (BPD). To date, however, volume-based magnetic resonance imaging (MRI) studies have yielded mixed results. We used a surface-based processing approach that allowed us to measure five morphometric cortical features of the OFC, including volumetric (cortical thickness and surface area) and geometric (mean curvature, depth of sulcus, and metric distortion - three indicators of cortical folding) parameters. Participants comprised 25 female BPD patients with no other current psychiatric comorbidity and 25 age- and gender-matched healthy controls who received structural MRI scans. Images were processed using the Freesurfer package. All BPD patients had a history of comorbid psychiatric disorder(s) and were currently on medications. Compared with controls, the BPD group showed reduced cortical thickness, surface area, mean curvature, depth of sulcus, and metric distortion in the right medial OFC. In the left medial OFC, the BPD group had reduced cortical thickness and mean curvature, but increased metric distortion. This study confirmed the utility of surface-based analysis in the study of BPD cortical structures. In addition, we observed extensive structural abnormalities in the medial OFC of female subjects with BPD, findings that were most pronounced in the right OFC, with preliminary data suggesting hemispheric asymmetry.
The investigation of neurodevelopment during late childhood and pre-adolescence has recently attracted a great deal of interest in the field of neuroimaging. One promising topic in this field is the formation of brain networks in healthy subjects. The integration between neural modules characterizes the ability of the network to process information globally. Although many fMRI-based neurodevelopment studies can be found in the literature, the analyses of very large samples (on the order of hundreds of subjects) that focus on the late childhood/pre-adolescence period and resting state fMRI are scarce, and most studies have focused solely on North American and European populations.
A study of the gene expression levels in the blood of individuals with schizophrenia in the beginning of the disease, such as first-episode psychosis (FEP), is useful to detect gene expression changes in this disorder in response to treatment. Although a large number of genetic studies on schizophrenia have been conducted, little is known about the effects of antipsychotic treatment on gene expression. The aim of the present study was to examine differences in the gene expression in the blood of antipsychotic-naïve FEP patients before and after risperidone treatment (N = 44) and also to verify the correlation with treatment response. In addition, we determined the correlations between differentially expressed genes and clinical variables. The expression of 40 neurotransmitter and neurodevelopment-associated genes was assessed using the RT2 Profiler PCR Array. The results indicated that the GABRR2 gene was downregulated after risperidone treatment, but no genes were associated with response to treatment and clinical variables after Bonferroni correction. GABRR2 downregulation after treatment can both suggest an effect of risperidone treatment or processes related to disease progression, either not necessarily associated with the improvement of symptoms. Despite this change was observed in blood, this decrease in GABRR2 mRNA levels might be an effect of changes in GABA concentrations or other systems interplay consequently to D2 blockage induced by risperidone, for example. Thus, it is important to consider that antipsychotics or the progression of psychotic disorders might interfere with gene expression.
Borderline personality disorder (BPD) is a devastating condition that causes intense disruption of patients' lives and relationships. Proper understanding of BPD neurobiology could help provide the basis for earlier and effective interventions. As neuroimaging studies of patients with BPD are still scarce, volumetric and geometric features of the cortical structure were assessed to ascertain whether structural cortical alterations are present in BPD patients.
In Ridley Scott's film "Blade Runner", empathy-detection devices are employed to measure affiliative emotions. Despite recent neurocomputational advances, it is unknown whether brain signatures of affiliative emotions, such as tenderness/affection, can be decoded and voluntarily modulated. Here, we employed multivariate voxel pattern analysis and real-time fMRI to address this question. We found that participants were able to use visual feedback based on decoded fMRI patterns as a neurofeedback signal to increase brain activation characteristic of tenderness/affection relative to pride, an equally complex control emotion. Such improvement was not observed in a control group performing the same fMRI task without neurofeedback. Furthermore, the neurofeedback-driven enhancement of tenderness/affection-related distributed patterns was associated with local fMRI responses in the septohypothalamic area and frontopolar cortex, regions previously implicated in affiliative emotion. This demonstrates that humans can voluntarily enhance brain signatures of tenderness/affection, unlocking new possibilities for promoting prosocial emotions and countering antisocial behavior.
Advances in neonatal medicine have resulted in a larger proportion of preterm-born individuals reaching adulthood. Their increased liability to psychiatric illness and impairments of cognition and behaviour intimate lasting cerebral consequences; however, the central physiological disturbances remain unclear. Of fundamental importance to efficient brain function is the coordination and contextually-relevant recruitment of neural networks. Large-scale distributed networks emerge perinatally and increase in hierarchical complexity through development. Preterm-born individuals exhibit systematic reductions in correlation strength within these networks during infancy. Here, we investigate resting-state functional connectivity in functional magnetic resonance imaging data from 29 very-preterm (VPT)-born adults and 23 term-born controls. Neurocognitive networks were identified with spatial independent component analysis conducted using the Infomax algorithm and employing Icasso procedures to enhance component robustness. Network spatial focus and spectral power were not generally significantly affected by preterm birth. By contrast, Granger-causality analysis of the time courses of network activity revealed widespread reductions in between-network connectivity in the preterm group, particularly along paths including salience-network features. The potential clinical relevance of these Granger-causal measurements was suggested by linear discriminant analysis of topological representations of connection strength, which classified individuals by group with a maximal accuracy of 86%. Functional connections from the striatal salience network to the posterior default mode network informed this classification most powerfully. In the VPT-born group it was additionally found that perinatal factors significantly moderated the relationship between executive function (which was reduced in the VPT-born as compared with the term-born group) and generalised partial directed coherence. Together these findings show that resting-state functional connectivity of preterm-born individuals remains compromised in adulthood; and present consistent evidence that the striatal salience network is preferentially affected. Therapeutic practices directed at strengthening within-network cohesion and fine-tuning between-network inter-relations may have the potential to mitigate the cognitive, behavioural and psychiatric repercussions of preterm birth.
Schizophrenia is a neurodevelopmental disorder with high heritability. Several lines of evidence indicate that the PRODH gene may be related to the disorder. Therefore, our study investigates the effects of 12 polymorphisms of PRODH on schizophrenia and its phenotypes. To further evaluate the roles of the associated variants in the disorder, we have conducted magnetic resonance imaging (MRI) scans to assess cortical volumes and thicknesses. A total of 192 patients were evaluated using the Structured Clinical Interview for DSM-IV (SCID), Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale, Global Assessment of Functioning (GAF) and Clinical Global Impression (CGI) instruments. The study included 179 controls paired by age and gender. The samples were genotyped using the real-time polymerase chain reaction (PCR), restriction fragment length polymorphism (RFLP)-PCR and Sanger sequencing methods. A sample of 138 patients and 34 healthy controls underwent MRI scans. One polymorphism was associated with schizophrenia (rs2904552), with the G-allele more frequent in patients than in controls. This polymorphism is likely functional, as predicted by PolyPhen and SIFT, but it was not associated with brain morphology in our study. In summary, we report a functional PRODH variant associated with schizophrenia that may have a neurochemical impact, altering brain function, but is not responsible for the cortical reductions found in the disorder.
Several studies have shown cortical volume loss in frontotemporal regions in schizophrenia patients, and it is known that these reductions may be associated with disease symptoms and cognitive deficits. The aim of this study was to investigate possible cortical thickness correlations in frontotemporal regions in relation to age at onset and duration of illness.
Abstract The simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data potentially allows measurement of brain signals with both high spatial and temporal resolution. Partial directed coherence (PDC) is a Granger causality measure in the frequency domain, which is often used to infer the intensity of information flow over the brain from EEG data. In the current study, we propose a new approach to investigate functional connectivity in resting-state (RS) EEG-fMRI data by combining time-varying PDC with the analysis of blood oxygenation level-dependent (BOLD) signal fluctuations. Basically, we aim to identify brain circuits that are more active when the information flow is increased between distinct remote neuronal modules. The usefulness of the proposed method is illustrated by application to simultaneously recorded EEG-fMRI data from healthy subjects at rest. Using this approach, we decomposed the nodes of RS networks in fMRI data according to the frequency band and directed flow of information provided from EEG. This approach therefore has the potential to inform our understanding of the regional characteristics of oscillatory processes in the human brain.
Recently, machine learning methods have been used to discriminate, on an individual basis, patients from healthy controls through brain structural magnetic resonance imaging (MRI). However, the application of these methods to predict the severity of psychiatric symptoms is less common.
Based on previous evidence for individual-specific sets of cortical areas active during simple attention tasks, in this work we intended to perform within individual comparisons of task-induced beta oscillations between visual attention and a reasoning task. Since beta induced oscillations are not time-locked to task events and were first observed by Fourier transforms, in order to analyze the cortical topography of attention induced beta activity, we have previously computed corrected-latency averages based on spontaneous peaks of band-pass filtered epochs. We then used Independent Component Analysis (ICA) only to single out the significant portion of averaged data, above noise levels. In the present work ICA served as the main, exhaustive means for decomposing beta activity in both tasks, using 128-channel EEG data from 24 subjects. Given the previous observed similarity between tasks by visual inspection and by simple descriptive statistics, we now intended another approach: to quantify how much each ICA component obtained in one task could be explained by a linear combination of the topographic patterns from the other task in each individual. Our hypothesis was that the major psychological difference between tasks would not be reflected as important topographic differences within individuals. Results confirmed the high topographic similarity between attention and reasoning beta correlates in that few components in each individual were not satisfactorily explained by the complementary task, and if those could be considered "task-specific", their scalp distribution and estimated cortical sources were not common across subjects. These findings, along with those from fMRI studies preserving individual data and conventional neuropsychological and neurosurgical observations, are discussed in support of a new functional localization hypothesis: individuals use largely different sets of cortical association areas to perform a given task, but those individual sets do not change importantly across tasks that differ in major psychological processes.
The application of graph analysis methods to the topological organization of brain connectivity has been a useful tool in the characterization of brain related disorders. However, the availability of tools, which enable researchers to investigate functional brain networks, is still a major challenge. Most of the studies evaluating brain images are based on centrality and segregation measurements of complex networks. In this study, we applied the concept of graph spectral entropy (GSE) to quantify the complexity in the organization of brain networks. In addition, to enhance interpretability, we also combined graph spectral clustering to investigate the topological organization of sub-networks modules. We illustrate the usefulness of the proposed approach by comparing brain networks between attention deficit hyperactivity disorder (ADHD) patients and the brain networks of typical developing (TD) controls. The main findings highlighted that GSE involving sub-networks comprising the areas mostly bilateral pre and post central cortex, superior temporal gyrus, and inferior frontal gyri were statistically different (p-value=0.002) between ADHD patients and TD controls. In the same conditions, the other conventional graph descriptors (betweenness centrality, clustering coefficient, and shortest path length) commonly used to identify connectivity abnormalities did not show statistical significant difference. We conclude that analysis of topological organization of brain sub-networks based on GSE can identify networks between brain regions previously unobserved to be in association with ADHD.
Cluster B personality disorders (PD), characterized as emotional instability, immaturity, lack of discipline, and rapid mood changes, have been observed among patients with juvenile myoclonic epilepsy (JME) and have been associated with a worse seizure outcome. Proper understanding of the neurobiology of PD associated with JME could contribute to understanding the basis for earlier and more effective interventions. In the present study, volumetric and geometric features of cortical structure were assessed through an automated cortical surface reconstruction method aiming to verify possible structural cortical alterations among patients with JME. Twenty-two patients with JME with cluster B PD, 44 patients with JME without psychiatric disorders, and 23 healthy controls were submitted to a psychiatric evaluation through SCID I and SCID II and to an MRI scan. Significant cortical alterations in mesiofrontal and frontobasal regions, as well as in other limbic and paralimbic regions, were observed mainly in patients with JME with PD. The present study adds evidence to the hypothesis of frontal and limbic involvement in the pathophysiology of cluster B PD in JME, regions linked to mood and affective regulation, as well as to impulsivity and social behavior. Moreover, a multidimensional pattern of frontal, limbic, and paralimbic changes was observed through a method of structural analysis which offers different and simultaneous geometric features, allowing the elaboration of important pathophysiologic insights about cluster B PD in JME.
Treatment resistance affects up to one third of patients with schizophrenia (SCZ). A better understanding of its biological underlying processes could improve treatment. The aim of this study was to compare cortical thickness between non-resistant SCZ (NR-SCZ), treatment-resistant SCZ (TR-SCZ) patients and healthy controls (HC).
Ndel1 oligopeptidase interacts with schizophrenia (SCZ) risk gene product DISC1 and mediates several functions related to neurite outgrowth and neuronal migration. Ndel1 also hydrolyzes neuropeptides previously implicated in SCZ, namely neurotensin and bradykinin. Herein, we compared the plasma Ndel1 enzyme activity of 92 SCZ patients and 96 healthy controls (HCs). Ndel1 enzyme activity was determined by fluorimetric measurements of the FRET peptide substrate Abz-GFSPFRQ-EDDnp hydrolysis rate. A 31% lower mean value for Ndel1 activity was observed in SCZ patients compared to HCs (Students t = 4.36; p < 0.001; Cohens d = 0.64). The area under the curve (AUC) for the Receiver Operating Characteristic (ROC) curve for Ndel1 enzyme activity and SCZ/HCs status as outcome was 0.70. Treatment-resistant (TR) SCZ patients were shown to present a significantly lower Ndel1 activity compared to non-TR (NTR) patients by t-test analysis (t = 2.25; p = 0.027). A lower enzymatic activity was significantly associated with both NTR (p = 0.002; B = 1.19; OR = 3.29; CI 95% 1.57-6.88) and TR patients (p < 0.001; B = 2.27; OR = 9.64; CI 95% 4.12-22.54). No correlation between Ndel1 enzyme activity and antipsychotic dose, nicotine dependence, and body mass index was observed. This study is the first to show differences in Ndel1 activity in SCZ patients compared to HCs, besides with a significant lower activity for TR patients compared to NTR patients. Our findings support the Ndel1 enzyme activity implications to clinical practice in terms of diagnosis and drug treatment of SCZ.
The demonstration that humans can learn to modulate their own brain activity based on feedback of neurophysiological signals opened up exciting opportunities for fundamental and applied neuroscience. Although EEG-based neurofeedback has been long employed both in experimental and clinical investigation, functional MRI (fMRI)-based neurofeedback emerged as a promising method, given its superior spatial resolution and ability to gauge deep cortical and subcortical brain regions. In combination with improved computational approaches, such as pattern recognition analysis (e.g., Support Vector Machines, SVM), fMRI neurofeedback and brain decoding represent key innovations in the field of neuromodulation and functional plasticity. Expansion in this field and its applications critically depend on the existence of freely available, integrated and user-friendly tools for the neuroimaging research community. Here, we introduce FRIEND, a graphic-oriented user-friendly interface package for fMRI neurofeedback and real-time multivoxel pattern decoding. The package integrates routines for image preprocessing in real-time, ROI-based feedback (single-ROI BOLD level and functional connectivity) and brain decoding-based feedback using SVM. FRIEND delivers an intuitive graphic interface with flexible processing pipelines involving optimized procedures embedding widely validated packages, such as FSL and libSVM. In addition, a user-defined visual neurofeedback module allows users to easily design and run fMRI neurofeedback experiments using ROI-based or multivariate classification approaches. FRIEND is open-source and free for non-commercial use. Processing tutorials and extensive documentation are available.
Recent evidence suggests that immobilization of the upper limb for 2-3 weeks induces changes in cortical thickness as well as motor performance. In constraint induced (CI) therapy, one of the most effective interventions for hemiplegia, the non-paretic arm is constrained to enforce the use of the paretic arm in the home setting. With the present study we aimed to explore whether non-paretic arm immobilization in CI therapy induces structural changes in the non-lesioned hemisphere, and how these changes are related to treatment benefit. 31 patients with chronic hemiparesis participated in CI therapy with (N = 14) and without (N = 17) constraint. Motor ability scores were acquired before and after treatment. Diffusion tensor imaging (DTI) data was obtained prior to treatment. Cortical thickness was measured with the Freesurfer software. In both groups cortical thickness in the contralesional primary somatosensory cortex increased and motor function improved with the intervention. However the cortical thickness change was not associated with the magnitude of motor function improvement. Moreover, the treatment effect and the cortical thickness change were not significantly different between the constraint and the non-constraint groups. There was no correlation between fractional anisotropy changes in the non-lesioned hemisphere and treatment outcome. CI therapy induced cortical thickness changes in contralesional sensorimotor regions, but this effect does not appear to be driven by the immobilization of the non-paretic arm, as indicated by the absence of differences between the constraint and the non-constraint groups. Our data does not suggest that the arm immobilization used in CI therapy is associated with noticeable cortical thinning.
In medical practice, diagnostic hypotheses are often made by physicians in the first moments of contact with patients; sometimes even before they report their symptoms. We propose that generation of diagnostic hypotheses in this context is the result of cognitive processes subserved by brain mechanisms that are similar to those involved in naming objects or concepts in everyday life.
A large number of functional neuroimaging studies have investigated the brain circuitry which is engaged during performance of phonological verbal fluency tasks, and the vast majority of these have been carried out in English. Although there is evidence that this paradigm varies depending on the language spoken, it is unclear if this difference is associated with differences in brain activation patterns. Also, there is neuroimaging evidence that the patterns of regional cerebral activation during verbal fluency tasks may vary with the level of task demanded. In particular, the engagement of the anterior cingulate cortex seems to be relative to cognitive demand. We compared functional magnetic resonance imaging data in healthy Portuguese-speaking subjects during overt production of words beginning with letters classified as easy or hard for word production in Portuguese. Compared to the baseline condition, the two verbal fluency tasks (with either easy or hard letters) engaged a network including the left inferior and middle frontal cortices, anterior cingulate cortex, putamen, thalamus and cerebellum (p < .001). The direct comparison between the two verbal fluency conditions showed greater cerebellar activation in the easy condition relative to the hard condition. In the anterior cingulate cortex, there was a direct correlation between activity changes and verbal fluency performance during the hard condition only. Despite grammatical differences, the changes in patterns of brain activity during verbal fluency performance observed in our study are in accordance with findings of previous neuroimaging studies of verbal fluency carried out in English and other languages, with recruitment of a set of distributed cerebral areas during word production.
Psychopathy is a disorder of personality characterized by severe impairments of social conduct, emotional experience, and interpersonal behavior. Psychopaths consistently violate social norms and bring considerable financial, emotional, or physical harm to others and to society as a whole. Recent developments in analysis methods of magnetic resonance imaging (MRI), such as voxel-based-morphometry (VBM), have become major tools to understand the anatomical correlates of this disorder. Nevertheless, the identification of psychopathy by neuroimaging or other neurobiological tools (e.g., genetic testing) remains elusive.
Little is known about the relevance of lesion in neural circuits reported to be associated with major depressive disorder. We investigated the association between lesion stroke size in the limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuit and incidence of major depressive episode (MDE).
The extraction of information about neural activity timing from BOLD signal is a challenging task as the shape of the BOLD curve does not directly reflect the temporal characteristics of electrical activity of neurons. In this work, we introduce the concept of neural processing time (NPT) as a parameter of the biophysical model of the hemodynamic response function (HRF). Through this new concept we aim to infer more accurately the duration of neuronal response from the highly nonlinear BOLD effect. The face validity and applicability of the concept of NPT are evaluated through simulations and analysis of experimental time series. The results of both simulation and application were compared with summary measures of HRF shape. The experiment that was analyzed consisted of a decision-making paradigm with simultaneous emotional distracters. We hypothesize that the NPT in primary sensory areas, like the fusiform gyrus, is approximately the stimulus presentation duration. On the other hand, in areas related to processing of an emotional distracter, the NPT should depend on the experimental condition. As predicted, the NPT in fusiform gyrus is close to the stimulus duration and the NPT in dorsal anterior cingulate gyrus depends on the presence of an emotional distracter. Interestingly, the NPT in right but not left dorsal lateral prefrontal cortex depends on the stimulus emotional content. The summary measures of HRF obtained by a standard approach did not detect the variations observed in the NPT.
Meditation is a mental training, which involves attention and the ability to maintain focus on a particular object. In this study we have applied a specific attentional task to simply measure the performance of the participants with different levels of meditation experience, rather than evaluating meditation practice per se or task performance during meditation. Our objective was to evaluate the performance of regular meditators and non-meditators during an fMRI adapted Stroop Word-Colour Task (SWCT), which requires attention and impulse control, using a block design paradigm. We selected 20 right-handed regular meditators and 19 non-meditators matched for age, years of education and gender. Participants had to choose the colour (red, blue or green) of single words presented visually in three conditions: congruent, neutral and incongruent. Non-meditators showed greater activity than meditators in the right medial frontal, middle temporal, precentral and postcentral gyri and the lentiform nucleus during the incongruent conditions. No regions were more activated in meditators relative to non-meditators in the same comparison. Non-meditators showed an increased pattern of brain activation relative to regular meditators under the same behavioural performance level. This suggests that meditation training improves efficiency, possibly via improved sustained attention and impulse control.
Recent techniques of image analysis brought the possibility to recognize subjects based on discriminative image features. We performed a magnetic resonance imaging (MRI)-based classification study to assess its usefulness for outcome prediction of first-episode schizophrenia patients (FES). We included 39 FES patients and 39 healthy controls (HC) and performed the maximum-uncertainty linear discrimination analysis (MLDA) of MRI brain intensity images. The classification accuracy index (CA) was correlated with the Positive and Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning scale (GAF) at 1-year follow-up. The rate of correct classifications of patients with poor and good outcomes was analyzed using chi-square tests. MLDA classification was significantly better than classification by chance. Leave-one-out accuracy was 72%. CA correlated significantly with PANSS and GAF scores at the 1-year follow-up. Moreover, significantly more patients with poor outcome than those with good outcome were classified correctly. MLDA of brain MR intensity features is, therefore, able to correctly classify a significant number of FES patients, and the discriminative features are clinically relevant for clinical presentation 1 year after the first episode of schizophrenia. The accuracy of the current approach is, however, insufficient to be used in clinical practice immediately. Several methodological issues need to be addressed to increase the usefulness of this classification approach.
Despite the relevance of irritability emotions to the treatment, prognosis and classification of psychiatric disorders, the neurobiological basis of this emotional state has been rarely investigated to date. We assessed the brain circuitry underlying personal script-driven irritability in healthy subjects (n = 11) using functional magnetic resonance imaging.
We propose a likelihood ratio test (LRT) with Bartlett correction in order to identify Granger causality between sets of time series gene expression data. The performance of the proposed test is compared to a previously published bootstrap-based approach. LRT is shown to be significantly faster and statistically powerful even within non-Normal distributions. An R package named gGranger containing an implementation for both Granger causality identification tests is also provided.
Among nonmotor symptoms observed in Parkinsons disease (PD) dysfunction in the visual system, including hallucinations, has a significant impact in their quality of life. To further explore the visual system in PD patients we designed two fMRI experiments comparing 18 healthy volunteers with 16 PD patients without visual complaints in two visual fMRI paradigms: the flickering checkerboard task and a facial perception paradigm. PD patients displayed a decreased activity in the primary visual cortex (Broadmann area 17) bilaterally as compared to healthy volunteers during flickering checkerboard task and increased activity in fusiform gyrus (Broadmann area 37) during facial perception paradigm. Our findings confirm the notion that PD patients show significant changes in the visual cortex system even before the visual symptoms are clinically evident. Further studies are necessary to evaluate the contribution of these abnormalities to the development visual symptoms in PD.
A recent study showed that people evaluate products more positively when they are physically associated with art images than similar non-art images. Neuroimaging studies of visual art have investigated artistic style and esthetic preference but not brain responses attributable specifically to the artistic status of images. Here we tested the hypothesis that the artistic status of images engages reward circuitry, using event-related functional magnetic resonance imaging (fMRI) during viewing of art and non-art images matched for content. Subjects made animacy judgments in response to each image. Relative to non-art images, art images activated, on both subject- and item-wise analyses, reward-related regions: the ventral striatum, hypothalamus and orbitofrontal cortex. Neither response times nor ratings of familiarity or esthetic preference for art images correlated significantly with activity that was selective for art images, suggesting that these variables were not responsible for the art-selective activations. Investigation of effective connectivity, using time-varying, wavelet-based, correlation-purged Granger causality analyses, further showed that the ventral striatum was driven by visual cortical regions when viewing art images but not non-art images, and was not driven by regions that correlated with esthetic preference for either art or non-art images. These findings are consistent with our hypothesis, leading us to propose that the appeal of visual art involves activation of reward circuitry based on artistic status alone and independently of its hedonic value.
The identification, modeling, and analysis of interactions between nodes of neural systems in the human brain have become the aim of interest of many studies in neuroscience. The complex neural network structure and its correlations with brain functions have played a role in all areas of neuroscience, including the comprehension of cognitive and emotional processing. Indeed, understanding how information is stored, retrieved, processed, and transmitted is one of the ultimate challenges in brain research. In this context, in functional neuroimaging, connectivity analysis is a major tool for the exploration and characterization of the information flow between specialized brain regions. In most functional magnetic resonance imaging (fMRI) studies, connectivity analysis is carried out by first selecting regions of interest (ROI) and then calculating an average BOLD time series (across the voxels in each cluster). Some studies have shown that the average may not be a good choice and have suggested, as an alternative, the use of principal component analysis (PCA) to extract the principal eigen-time series from the ROI(s). In this paper, we introduce a novel approach called cluster Granger analysis (CGA) to study connectivity between ROIs. The main aim of this method was to employ multiple eigen-time series in each ROI to avoid temporal information loss during identification of Granger causality. Such information loss is inherent in averaging (e.g., to yield a single "representative" time series per ROI). This, in turn, may lead to a lack of power in detecting connections. The proposed approach is based on multivariate statistical analysis and integrates PCA and partial canonical correlation in a framework of Granger causality for clusters (sets) of time series. We also describe an algorithm for statistical significance testing based on bootstrapping. By using Monte Carlo simulations, we show that the proposed approach outperforms conventional Granger causality analysis (i.e., using representative time series extracted by signal averaging or first principal components estimation from ROIs). The usefulness of the CGA approach in real fMRI data is illustrated in an experiment using human faces expressing emotions. With this data set, the proposed approach suggested the presence of significantly more connections between the ROIs than were detected using a single representative time series in each ROI.
The aim of this article is to propose an integrated framework for extracting and describing patterns of disorders from medical images using a combination of linear discriminant analysis and active contour models.
To present the development of an adapted version of the Boston Naming Test for Portuguese speakers, and to investigate the effects of age, education and gender on both the original and the adapted Boston Naming Test in respect of Brazilian Portuguese speakers.
Functional magnetic resonance imaging (fMRI) based on BOLD signal has been used to indirectly measure the local neural activity induced by cognitive tasks or stimulation. Most fMRI data analysis is carried out using the general linear model (GLM), a statistical approach which predicts the changes in the observed BOLD response based on an expected hemodynamic response function (HRF). In cases when the task is cognitively complex or in cases of diseases, variations in shape and/or delay may reduce the reliability of results. A novel exploratory method using fMRI data, which attempts to discriminate between neurophysiological signals induced by the stimulation protocol from artifacts or other confounding factors, is introduced in this paper. This new method is based on the fusion between correlation analysis and the discrete wavelet transform, to identify similarities in the time course of the BOLD signal in a group of volunteers. We illustrate the usefulness of this approach by analyzing fMRI data from normal subjects presented with standardized human face pictures expressing different degrees of sadness. The results show that the proposed wavelet correlation analysis has greater statistical power than conventional GLM or time domain intersubject correlation analysis.
Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamics homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements.
Biological experiments are usually set up in technical replicates (duplicates or triplicates) in order to ensure reproducibility and, to assess any significant error introduced during the experimental process. The first step in biological data analysis is to check the technical replicates and to confirm that the error of measure is small enough to be of no concern. However, little attention has been paid to this part of analysis. Here, we propose a general process to estimate the error of measure and consequently, to provide an interpretable and objective way to ensure the technical replicates quality. Particularly, we illustrate our application in a DNA microarray dataset set up in technical duplicates.
There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise.
The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal patterns and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored.
We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88-99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0 degrees vs. 20 degrees , 0 degrees vs. 60 degrees , and 0 degrees vs. 100 degrees ), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (0 degrees vs. 100 degrees ), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100 degrees condition in relation to the 0 degrees condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100 degrees condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100 degrees condition.
Functional magnetic resonance imaging (fMRI) has become an important tool in Neuroscience due to its noninvasive and high spatial resolution properties compared to other methods like PET or EEG. Characterization of the neural connectivity has been the aim of several cognitive researches, as the interactions among cortical areas lie at the heart of many brain dysfunctions and mental disorders. Several methods like correlation analysis, structural equation modeling, and dynamic causal models have been proposed to quantify connectivity strength. An important concept related to connectivity modeling is Granger causality, which is one of the most popular definitions for the measure of directional dependence between time series. In this article, we propose the application of the partial directed coherence (PDC) for the connectivity analysis of multisubject fMRI data using multivariate bootstrap. PDC is a frequency domain counterpart of Granger causality and has become a very prominent tool in EEG studies. The achieved frequency decomposition of connectivity is useful in separating interactions from neural modules from those originating in scanner noise, breath, and heart beating. Real fMRI dataset of six subjects executing a language processing protocol was used for the analysis of connectivity.
Depression is the most frequent psychiatric disorder in Parkinsons disease (PD). Although evidence suggests that depression in PD is related to the degenerative process that underlies the disease, further studies are necessary to better understand the neural basis of depression in this population of patients. In order to investigate neuronal alterations underlying the depression in PD, we studied thirty-six patients with idiopathic PD. Twenty of these patients had the diagnosis of major depression disorder and sixteen did not. The two groups were matched for PD motor severity according to Unified Parkinson Disease Rating Scale (UPDRS). First we conducted a functional magnetic resonance imaging (fMRI) using an event-related parametric emotional perception paradigm with test retest design. Our results showed decreased activation in the left mediodorsal (MD) thalamus and in medial prefrontal cortex in PD patients with depression compared to those without depression. Based upon these results and the increased neuron count in MD thalamus found in previous studies, we conducted a region of interest (ROI) guided voxel-based morphometry (VBM) study comparing the thalamic volume. Our results showed an increased volume in mediodorsal thalamic nuclei bilaterally. Converging morphological changes and functional emotional processing in mediodorsal thalamus highlight the importance of limbic thalamus in PD depression. In addition this data supports the link between neurodegenerative alterations and mood regulation.
The histopathological counterpart of white matter hyperintensities is a matter of debate. Methodological and ethical limitations have prevented this question to be elucidated. We want to introduce a protocol applying state-of-the-art methods in order to solve fundamental questions regarding the neuroimaging-neuropathological uncertainties comprising the most common white matter hyperintensities [WMHs] seen in aging. By this protocol, the correlation between signal features in in situ, post mortem MRI-derived methods, including DTI and MTR and quantitative and qualitative histopathology can be investigated. We are mainly interested in determining the precise neuroanatomical substrate of incipient WMHs. A major issue in this protocol is the exact co-registration of small lesion in a tridimensional coordinate system that compensates tissue deformations after histological processing. The protocol is based on four principles: post mortem MRI in situ performed in a short post mortem interval, minimal brain deformation during processing, thick serial histological sections and computer-assisted 3D reconstruction of the histological sections. This protocol will greatly facilitate a systematic study of the location, pathogenesis, clinical impact, prognosis and prevention of WMHs.
Pattern recognition methods have been successfully applied in several functional neuroimaging studies. These methods can be used to infer cognitive states, so-called brain decoding. Using such approaches, it is possible to predict the mental state of a subject or a stimulus class by analyzing the spatial distribution of neural responses. In addition it is possible to identify the regions of the brain containing the information that underlies the classification. The Support Vector Machine (SVM) is one of the most popular methods used to carry out this type of analysis. The aim of the current study is the evaluation of SVM and Maximum uncertainty Linear Discrimination Analysis (MLDA) in extracting the voxels containing discriminative information for the prediction of mental states. The comparison has been carried out using fMRI data from 41 healthy control subjects who participated in two experiments, one involving visual-auditory stimulation and the other based on bi-manual fingertapping sequences. The results suggest that MLDA uses significantly more voxels containing discriminative information (related to different experimental conditions) to classify the data. On the other hand, SVM is more parsimonious and uses less voxels to achieve similar classification accuracies. In conclusion, MLDA is mostly focused on extracting all discriminative information available, while SVM extracts the information which is sufficient for classification.
DNA microarrays have become a powerful tool to describe gene expression profiles associated with different cellular states, various phenotypes and responses to drugs and other extra- or intra-cellular perturbations. In order to cluster co-expressed genes and/or to construct regulatory networks, definition of distance or similarity between measured gene expression data is usually required, the most common choices being Pearsons and Spearmans correlations. Here, we evaluate these two methods and also compare them with a third one, namely Hoeffdings D measure, which is used to infer nonlinear and non-monotonic associations, i.e. independence in a general sense. By comparing three different variable association approaches, namely Pearsons correlation, Spearmans correlation and Hoeffdings D measure, we aimed at assessing the most appropriate one for each purpose. Using simulations, we demonstrate that the Hoeffdings D measure outperforms Pearsons and Spearmans approaches in identifying nonlinear associations. Our results demonstrate that Hoeffdings D measure is less sensitive to outliers and is a more powerful tool to identify nonlinear and non-monotonic associations. We have also applied Hoeffdings D measure in order to identify new putative genes associated with tp53. Therefore, we propose the Hoeffdings D measure to identify nonlinear associations between gene expression profiles.
The brains structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed.
The investigation of neural substrates of autism spectrum disorder using neuroimaging has been the focus of recent literature. In addition, machine-learning approaches have also been used to extract relevant information from neuroimaging data. There are only few studies directly exploring the inter-regional structural relationships to identify and characterize neuropsychiatric disorders. In this study, we concentrate on addressing two issues: (i) a novel approach to extract individual subject features from inter-regional thickness correlations based on structural magnetic resonance imaging (MRI); (ii) using these features in a machine-learning framework to obtain individual subject prediction of a severity scores based on neurobiological criteria rather than behavioral information. In a sample of 82 autistic patients, we have shown that structural covariances among several brain regions are associated with the presence of the autistic symptoms. In addition, we also demonstrated that structural relationships from the left hemisphere are more relevant than the ones from the right. Finally, we identified several brain areas containing relevant information, such as frontal and temporal regions. This study provides evidence for the usefulness of this new tool to characterize neuropsychiatric disorders.
Pattern recognition methods have demonstrated to be suitable analyses tools to handle the high dimensionality of neuroimaging data. However, most studies combining neuroimaging with pattern recognition methods focus on two-class classification problems, usually aiming to discriminate patients under a specific condition (e.g., Alzheimers disease) from healthy controls. In this perspective paper we highlight the potential of the one-class support vector machines (OC-SVM) as an unsupervised or exploratory approach that can be used to create normative rules in a multivariate sense. In contrast with the standard SVM that finds an optimal boundary separating two classes (discriminating boundary), the OC-SVM finds the boundary enclosing a specific class (characteristic boundary). If the OC-SVM is trained with patterns of healthy control subjects, the distance to the boundary can be interpreted as an abnormality score. This score might allow quantification of symptom severity or provide insights about subgroups of patients. We provide an intuitive description of basic concepts in one-class classification, the foundations of OC-SVM, current applications, and discuss how this tool can bring new insights to neuroimaging studies.
A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes.
Studies based on functional magnetic resonance imaging (fMRI) during the resting state have shown decreased functional connectivity between the dorsal anterior cingulate cortex (dACC) and regions of the Default Mode Network (DMN) in adult patients with Attention-Deficit/Hyperactivity Disorder (ADHD) relative to subjects with typical development (TD). Most studies used Pearson correlation coefficients among the BOLD signals from different brain regions to quantify functional connectivity. Since the Pearson correlation analysis only provides a limited description of functional connectivity, we investigated functional connectivity between the dACC and the posterior cingulate cortex (PCC) in three groups (adult patients with ADHD, n=21; TD age-matched subjects, n=21; young TD subjects, n=21) using a more comprehensive analytical approach - unsupervised machine learning using a one-class support vector machine (OC-SVM) that quantifies an abnormality index for each individual. The median abnormality index for patients with ADHD was greater than for TD age-matched subjects (p=0.014); the ADHD and young TD indices did not differ significantly (p=0.480); the median abnormality index of young TD was greater than that of TD age-matched subjects (p=0.016). Low frequencies below 0.05 Hz and around 0.20 Hz were the most relevant for discriminating between ADHD patients and TD age-matched controls and between the older and younger TD subjects. In addition, we validated our approach using the fMRI data of children publicly released by the ADHD-200 Competition, obtaining similar results. Our findings suggest that the abnormal coherence patterns observed in patients with ADHD in this study resemble the patterns observed in young typically developing subjects, which reinforces the hypothesis that ADHD is associated with brain maturation deficits.
To investigate the mechanism underlying the anxiolytic properties of riluzole, a glutamate-modulating agent, we previously studied the effect of this drug on hippocampal N-acetylaspartate (NAA) and volume in patients with generalized anxiety disorder (GAD). In the same cohort, we now extend our investigation to the occipital cortex, a brain region that was recently implicated in the antidepressant effect of riluzole.
Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, being one of the most prevalent psychiatric disorders in childhood. The neural substrates associated with this condition, both from structural and functional perspectives, are not yet well established. Recent studies have highlighted the relevance of neuroimaging not only to provide a more solid understanding about the disorder but also for possible clinical support. The ADHD-200 Consortium organized the ADHD-200 global competition making publicly available, hundreds of structural magnetic resonance imaging (MRI) and functional MRI (fMRI) datasets of both ADHD patients and typically developing (TD) controls for research use. In the current study, we evaluate the predictive power of a set of three different feature extraction methods and 10 different pattern recognition methods. The features tested were regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and independent components analysis maps (resting state networks; RSN). Our findings suggest that the combination ALFF+ReHo maps contain relevant information to discriminate ADHD patients from TD controls, but with limited accuracy. All classifiers provided almost the same performance in this case. In addition, the combination ALFF+ReHo+RSN was relevant in combined vs. inattentive ADHD classification, achieving a score accuracy of 67%. In this latter case, the performances of the classifiers were not equivalent and L2-regularized logistic regression (both in primal and dual space) provided the most accurate predictions. The analysis of brain regions containing most discriminative information suggested that in both classifications (ADHD vs. TD controls and combined vs. inattentive), the relevant information is not confined only to a small set of regions but it is spatially distributed across the whole brain.
Comparative studies have established that a number of structures within the rostromedial basal forebrain are critical for affiliative behaviors and social attachment. Lesion and neuroimaging studies concur with the importance of these regions for attachment and the experience of affiliation in humans as well. Yet it remains obscure whether the neural bases of affiliative experiences can be differentiated from the emotional valence with which they are inextricably associated at the experiential level. Here we show, using functional MRI, that kinship-related social scenarios evocative of affiliative emotion induce septal-preoptic-anterior hypothalamic activity that cannot be explained by positive or negative emotional valence alone. Our findings suggest that a phylogenetically conserved ensemble of basal forebrain structures, especially the septohypothalamic area, may play a key role in enabling human affiliative emotion. Our finding of a neural signature of human affiliative experience bears direct implications for the neurobiological mechanisms underpinning impaired affiliative experiences and behaviors in neuropsychiatric conditions.
Slow wave oscillations in the electroencephalogram (EEG) during sleep may reflect both sleep need and intensity, which are implied in homeostatic regulation. Adenosine is strongly implicated in sleep homeostasis, and a single nucleotide polymorphism in the adenosine deaminase gene (ADA G22A) has been associated with deeper and more efficient sleep. The present study verified the association between the ADA G22A polymorphism and changes in sleep EEG spectral power (from C3-A2, C4-A1, O1-A2, and O2-A1 derivations) in the Epidemiologic Sleep Study (EPISONO) sample from São Paulo, Brazil. Eight-hundred individuals were subjected to full-night polysomnography and ADA G22A genotyping. Spectral analysis of the EEG was carried out in all individuals using fast Fourier transformation of the signals from each EEG electrode. The genotype groups were compared in the whole sample and in a subsample of 120 individuals matched according to ADA genotype for age, gender, body mass index, caffeine intake status, presence of sleep disturbance, and sleep-disturbing medication. When compared with homozygous GG genotype carriers, A allele carriers showed higher delta spectral power in Stage 1 and Stages 3+4 of sleep, and increased theta spectral power in Stages 1, 2 and REM sleep. These changes were seen both in the whole sample and in the matched subset. The higher EEG spectral power indicates that the sleep of individuals carrying the A allele may be more intense. Therefore, this polymorphism may be an important source of variation in sleep homeostasis in humans, through modulation of specific components of the sleep EEG.
Multivariate pattern recognition approaches have become a prominent tool in neuroimaging data analysis. These methods enable the classification of groups of participants (e.g. controls and patients) on the basis of subtly different patterns across the whole brain. This study demonstrates that these methods can be used, in combination with automated morphometric analysis of structural MRI, to determine with great accuracy whether a single subject has been engaged in regular mental training or not. The proposed approach allowed us to identify with 94.87% accuracy (p<0.001) if a given participant is a regular meditator (from a sample of 19 regular meditators and 20 non-meditators). Neuroimaging has been a relevant tool for diagnosing neurological and psychiatric impairments. This study may suggest a novel step forward: the emergence of a new field in brain imaging applications, in which participants could be identified based on their mental experience.
Anxiolytic benefit following chronic treatment with the glutamate modulating agent riluzole in patients with generalized anxiety disorder (GAD) was previously associated with differential changes in hippocampal NAA concentrations. Here, we investigated the association between hippocampal volume and hippocampal NAA in the context of riluzole response in GAD. Eighteen medication-free adult patients with GAD received 8-week of open-label riluzole. Ten healthy subjects served as a comparison group. Participants underwent magnetic resonance imaging and spectroscopy at baseline and at the end of Week 8. GAD patients who completed all interventions were classified as remitters (n=7) or non-remitters (n=6), based on final Hamilton Anxiety Rating Scale (HAM-A) scores ?7. At baseline, GAD patients had a significant reduction in total hippocampal volume compared to healthy subjects (F(1,21)=6.55, p=0.02). This reduction was most pronounced in the remitters, compared to non-remitters and healthy subjects. Delta (final-baseline) hippocampal volume was positively correlated with delta NAA in GAD. This positive association was highly significant in the right hippocampus in GAD [r=0.81, p=0.002], with no significant association in healthy subjects [Fisher r-to-z p=0.017]. Across all GAD patients, delta hippocampal volume was positively associated with improvement in HAM-A (rspearman=0.62, p=0.03). These preliminary findings support hippocampal NAA and volume as neural biomarkers substantially associated with therapeutic response to a glutamatergic drug.
Several recent studies in literature have identified brain morphological alterations associated to Borderline Personality Disorder (BPD) patients. These findings are reported by studies based on voxel-based-morphometry analysis of structural MRI data, comparing mean gray-matter concentration between groups of BPD patients and healthy controls. On the other hand, mean differences between groups are not informative about the discriminative value of neuroimaging data to predict the group of individual subjects. In this paper, we go beyond mean differences analyses, and explore to what extent individual BPD patients can be differentiated from controls (25 subjects in each group), using a combination of automated-morphometric tools for regional cortical thickness/volumetric estimation and Support Vector Machine classifier. The approach included a feature selection step in order to identify the regions containing most discriminative information. The accuracy of this classifier was evaluated using the leave-one-subject-out procedure. The brain regions indicated as containing relevant information to discriminate groups were the orbitofrontal, rostral anterior cingulate, posterior cingulate, middle temporal cortices, among others. These areas, which are distinctively involved in emotional and affect regulation of BPD patients, were the most informative regions to achieve both sensitivity and specificity values of 80% in SVM classification. The findings suggest that this new methodology can add clinical and potential diagnostic value to neuroimaging of psychiatric disorders.
Recently, pattern recognition approaches have been used to classify patterns of brain activity elicited by sensory or cognitive processes. In the clinical context, these approaches have been mainly applied to classify groups of individuals based on structural magnetic resonance imaging (MRI) data. Only a few studies have applied similar methods to functional MRI (fMRI) data.
OBJECTIVE Results from structural neuroimaging studies of obsessive-compulsive disorder (OCD) have been only partially consistent. The authors sought to assess regional gray and white matter volume differences between large samples of OCD patients and healthy comparison subjects and their relation with demographic and clinical variables. METHOD A multicenter voxel-based morphometry mega-analysis was performed on 1.5-T structural T1-weighted MRI scans derived from the International OCD Brain Imaging Consortium. Regional gray and white matter brain volumes were compared between 412 adult OCD patients and 368 healthy subjects. RESULTS Relative to healthy comparison subjects, OCD patients had significantly smaller volumes of frontal gray and white matter bilaterally, including the dorsomedial prefrontal cortex, the anterior cingulate cortex, and the inferior frontal gyrus extending to the anterior insula. Patients also showed greater cerebellar gray matter volume bilaterally compared with healthy subjects. Group differences in frontal gray and white matter volume were significant after correction for multiple comparisons. Additionally, group-by-age interactions were observed in the putamen, insula, and orbitofrontal cortex (indicating relative preservation of volume in patients compared with healthy subjects with increasing age) and in the temporal cortex bilaterally (indicating a relative loss of volume in patients compared with healthy subjects with increasing age). CONCLUSIONS These findings partially support the prevailing fronto-striatal models of OCD and offer additional insights into the neuroanatomy of the disorder that were not apparent from previous smaller studies. The group-by-age interaction effects in orbitofrontal-striatal and (para)limbic brain regions may be the result of altered neuroplasticity associated with chronic compulsive behaviors, anxiety, or compensatory processes related to cognitive dysfunction.
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JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.