Abnormal Brain Connectivity Patterns in Adults with ADHD: A Coherence Study

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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)National Institute of Mental HealthNovartisJanssen-CilagAbbottEli-LillyShireBristol-Myers SquibbUniv Fed ABC, Ctr Math Computat & Cognit, Santo Andre, BrazilUniversidade Federal de São Paulo, Lab Interdisciplinar Neurociencias Clin, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psychiat, São Paulo, BrazilHosp Clin Porto Alegre, Child & Adolescent Psychiat Div, ADHD Outpatient Program, Porto Alegre, RS, BrazilNYU, Ctr Child Study, Phyllis Green & Randolph Cowen Inst Pediat Neuros, New York, NY USAInst Nacl Psiquiatria Desenvolvimento, Porto Alegre, RS, BrazilUniversidade Federal de São Paulo, Lab Interdisciplinar Neurociencias Clin, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psychiat, São Paulo, BrazilNational Institute of Mental Health: R01MH083246Web of Scienc

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies

Abnormal brain connectivity patterns in adults with ADHD : a coherence study

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 agematched 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

Genetic risks of Alzheimer’s by APOE and MAPT on cortical morphology in young healthy adults

Genetic risk factors such as APOE ε4 and MAPT (rs242557) A allele are associated with amyloid and tau pathways and grey matter changes at both early and established stages of Alzheimer’s disease, but their effects on cortical morphology in young healthy adults remain unclear. A total of 144 participants aged from 18 to 24 underwent 3T MRI and genotyping for APOE and MAPT to investigate unique impacts of these genetic risk factors in a cohort without significant comorbid conditions such as metabolic and cardiovascular diseases. We segmented the cerebral cortex into 68 regions and calculated the cortical area, thickness, curvature and folding index for each region. Then, we trained machine learning models to classify APOE and MAPT genotypes using these morphological features. In addition, we applied a growing hierarchical self-organizing maps algorithm, which clustered the 68 regions into 4 subgroups representing different morphological patterns. Then, we performed general linear model analyses to estimate the interaction between APOE and MAPT on cortical patterns. We found that the classifiers using all cortical features could accurately classify individuals carrying genetic risks of dementia outperforming each individual feature alone. APOE ε4 carriers had a more convoluted and thinner cortex across the cerebral cortex. A similar pattern was found in MAPT A allele carriers only in the regions that are vulnerable for early tau pathology. With the clustering analysis, we found a synergetic effect between APOE ε4 and MAPT A allele, i.e. carriers of both risk factors showed the most deviation of cortical pattern from the typical pattern of that cluster. Genetic risk factors of dementia by APOE ε4 and MAPT (rs242557) A allele were associated with variations of cortical morphology, which can be observed in young healthy adults more than 30 years before Alzheimer’s pathology is likely to occur and 50 years before dementia symptoms may begin

Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction

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 bimanual 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. (C) 2009 Elsevier Inc. All rights reserved

The anterior pathway for intelligible speech: insights from univariate and multivariate methods

Whilst there is broad agreement concerning the existence of an anterior processing stream in the human brain concerned with extracting meaning from speech, there is an ongoing controversy as to whether intelligible speech is first resolved in left anterior or bilateral posterior temporal fields (Hickok and Poeppel, 2007;Rauschecker and Scott, 2009). Proponents of the bilateral processing model argue that bilateral responses are driven by the acoustic properties of the speech signal, whilst proponents of the left lateralised model suggest that left lateralisation is driven by access to linguistic representations. This thesis directly addresses these controversies using Functional Magnetic Resonance Imaging (fMRI) and univariate and multivariate analysis methods. Two main questions are addressed: (1) where are responses to intelligible, and intelligible but degraded speech, separated from responses to acoustic complexity and (2) does the resulting pattern of lateralisation, or otherwise, derive from the acoustic properties or the linguistic status of speech. The results of this thesis reconcile, to some degree, the two theoretical positions. I show that the most consistent and largest amplitude responses to intelligible, and degraded but intelligible speech, are found in the left anterior Superior Temporal Sulcus (STS). Additional responses were also found in right anterior and left posterior STS, however, these were less consistently identified. Regions of the left posterior STS showed sensitivity to resolved intelligible speech and also showed a response likely to reflect acoustic-phonetic processing supporting the resolving of intelligibility. Right posterior STS responses to intelligible speech were noticeably absent across all studies. No evidence was found for a relative acoustic basis for hemispheric lateralisation in the case of speech derived manipulations of spectrum and amplitude, but evidence was found in support of a left hemisphere specialism for resolving intelligible speech, supporting a relative left lateralisation to speech driven by linguistic rather than acoustic factors

Social and Affective Neuroscience of Everyday Human Interaction

This Open Access book presents the current state of the art knowledge on social and affective neuroscience based on empirical findings. This volume is divided into several sections first guiding the reader through important theoretical topics within affective neuroscience, social neuroscience and moral emotions, and clinical neuroscience. Each chapter addresses everyday social interactions and various aspects of social interactions from a different angle taking the reader on a diverse journey. The last section of the book is of methodological nature. Basic information is presented for the reader to learn about common methodologies used in neuroscience alongside advanced input to deepen the understanding and usability of these methods in social and affective neuroscience for more experienced readers

Social and Affective Neuroscience of Everyday Human Interaction

This Open Access book presents the current state of the art knowledge on social and affective neuroscience based on empirical findings. This volume is divided into several sections first guiding the reader through important theoretical topics within affective neuroscience, social neuroscience and moral emotions, and clinical neuroscience. Each chapter addresses everyday social interactions and various aspects of social interactions from a different angle taking the reader on a diverse journey. The last section of the book is of methodological nature. Basic information is presented for the reader to learn about common methodologies used in neuroscience alongside advanced input to deepen the understanding and usability of these methods in social and affective neuroscience for more experienced readers

Facial EMG – Investigating the Interplay of Facial Muscles and Emotions

This chapter provides information about facial electromyography (EMG) as a method of investigating emotions and affect, including examples of application and methods for analysis. This chapter begins with a short introduction to emotion theory followed by an operationalisation of facial emotional expressions as an underlying requirement for their study using facial EMG. This chapter ends by providing practical information on the use of facial EMG