Ensemble Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Complex Graph Measures from Diffusion Tensor Images

The human brain is a complex network of interacting regions. The gray matter regions of brain are interconnected by white matter tracts, together forming one integrative complex network. In this article, we report our investigation about the potential of applying brain connectivity patterns as an aid in diagnosing Alzheimer's disease and Mild Cognitive Impairment (MCI). We performed pattern analysis of graph theoretical measures derived from Diffusion Tensor Imaging (DTI) data representing structural brain networks of 45 subjects, consisting of 15 patients of Alzheimer's disease (AD), 15 patients of MCI, and 15 healthy subjects (CT). We considered pair-wise class combinations of subjects, defining three separate classification tasks, i.e., AD-CT, AD-MCI, and CT-MCI, and used an ensemble classification module to perform the classification tasks. Our ensemble framework with feature selection shows a promising performance with classification accuracy of 83.3% for AD vs. MCI, 80% for AD vs. CT, and 70% for MCI vs. CT. Moreover, our findings suggest that AD can be related to graph measures abnormalities at Brodmann areas in the sensorimotor cortex and piriform cortex. In this way, node redundancy coefficient and load centrality in the primary motor cortex were recognized as good indicators of AD in contrast to MCI. In general, load centrality, betweenness centrality, and closeness centrality were found to be the most relevant network measures, as they were the top identified features at different nodes. The present study can be regarded as a “proof of concept” about a procedure for the classification of MRI markers between AD dementia, MCI, and normal old individuals, due to the small and not well-defined groups of AD and MCI patients. Future studies with larger samples of subjects and more sophisticated patient exclusion criteria are necessary toward the development of a more precise technique for clinical diagnosis

Multivariate Concavity Amplitude Index (MCAI) for characterizing Heschl's gyrus shape

Heschl's gyrus (HG), which includes primary auditory cortex, is highly variable in its shape (i.e. gyrification patterns), between hemispheres and across individuals. Differences in HG shape have been observed in the context of phonetic learning skill and expertise, and of professional musicianship, among others. Two of the most common configurations of HG include single HG, where a single transverse temporal gyrus is present, and common stem duplications (CSD), where a sulcus intermedius (SI) arises from the lateral aspect of HG. Here we describe a new toolbox, called ‘Multivariate Concavity Amplitude Index’ (MCAI), which automatically assesses the shape of HG. MCAI works on the output of TASH, our first toolbox which automatically segments HG, and computes continuous indices of concavity, which arise when sulci are present, along the outer perimeter of an inflated representation of HG, in a directional manner. Thus, MCAI provides a multivariate measure of shape, which is reproducible and sensitive to small variations in shape. We applied MCAI to structural magnetic resonance imaging (MRI) data of N=181 participants, including professional and amateur musicians and from non-musicians. Former studies have shown large variations in HG shape in the former groups. We validated MCAI by showing high correlations between the dominant (i.e. highest) lateral concavity values and continuous visual assessments of the degree of lateral gyrification of the first gyrus. As an application of MCAI, we also replicated previous visually obtained findings showing a higher likelihood of bilateral CSDs in musicians. MCAI opens a wide range of applications in evaluating HG shape in the context of individual differences, expertise, disorder and genetics

Toward a brain-computer interface for Alzheimer's disease patients by combining classical conditioning and brain state classification.

Brain-computer interfaces (BCIs) provide alternative methods for communicating and acting on the world, since messages or commands are conveyed from the brain to an external device without using the normal output pathways of peripheral nerves and muscles. Alzheimer's disease (AD) patients in the most advanced stages, who have lost the ability to communicate verbally, could benefit from a BCI that may allow them to convey basic thoughts (e.g., "yes" and "no") and emotions. There is currently no report of such research, mostly because the cognitive deficits in AD patients pose serious limitations to the use of traditional BCIs, which are normally based on instrumental learning and require users to self-regulate their brain activation. Recent studies suggest that not only self-regulated brain signals, but also involuntary signals, for instance related to emotional states, may provide useful information about the user, opening up the path for so-called "affective BCIs". These interfaces do not necessarily require users to actively perform a cognitive task, and may therefore be used with patients who are cognitively challenged. In the present hypothesis paper, we propose a paradigm shift from instrumental learning to classical conditioning, with the aim of discriminating "yes" and "no" thoughts after associating them to positive and negative emotional stimuli respectively. This would represent a first step in the development of a BCI that could be used by AD patients, lending a new direction not only for communication, but also for rehabilitation and diagnosis

Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data

Multiple Sclerosis patients' clinical symptoms do not correlate strongly with structural assessment done with traditional magnetic resonance images. However, its diagnosis and evaluation of the disease's progression are based on a combination of this imaging analysis complemented with clinical examination. Therefore, other biomarkers are necessary to better understand the disease. In this paper, we capitalize on machine learning techniques to classify relapsing-remitting multiple sclerosis patients and healthy volunteers based on machine learning techniques, and to identify relevant brain areas and connectivity measures for characterizing patients. To this end, we acquired magnetic resonance imaging data from relapsing-remitting multiple sclerosis patients and healthy subjects. Fractional anisotropy maps, structural and functional connectivity were extracted from the scans. Each of them were used as separate input features to construct support vector machine classifiers. A fourth input feature was created by combining structural and functional connectivity. Patients were divided in two groups according to their degree of disability and, together with the control group, three group pairs were formed for comparison. Twelve separate classifiers were built from the combination of these four input features and three group pairs. The classifiers were able to distinguish between patients and healthy subjects, reaching accuracy levels as high as 89% ± 2%. In contrast, the performance was noticeably lower when comparing the two groups of patients with different levels of disability, reaching levels below 63% ± 5%. The brain regions that contributed the most to the classification were the right occipital, left frontal orbital, medial frontal cortices and lingual gyrus. The developed classifiers based on MRI data were able to distinguish multiple sclerosis patients and healthy subjects reliably. Moreover, the resulting classification models identified brain regions, and functional and structural connections relevant for better understanding of the disease. Keywords: Resting state, fMRI, DTI, SVM, Multiple sclerosis, Classificatio

Combining brain state classification and classical conditioning for basic BCI communication

Introduction: Brain-computer interfaces (BCI) provide alternative methods for communicating and acting on the world, by conveying messages and commands without using the normal output pathways of peripheral nerves and muscles (Birbaumer et al., 1999; Pasqualotto et al., 2011; Wolpaw et al., 2002). Patients with Alzheimer's disease (AD) would benefit from a BCI that is able to convey information about their basic thoughts and emotions. The possibility to discriminate between emotional states by pattern classification of BOLD signals has been demonstrated in both offline (Lee et al., 2010) and online (Sitaram et al., 2011) situations. A possible way to develop a BCI that can be used by AD patients is through the modulation of cerebral responses with a semantic classical conditioning paradigm (Furdea et al., 2011), moving away from the more common operant conditioning paradigm, which requires subjects to actively self-regulate their brain activation (Birbaumer, 2006). The aim of our study is to condition subjects to associate emotionally negative and positive stimuli as unconditioned stimuli (US) with incongruent and congruent word pairs (eliciting "negative" and "affirmative" thinking) as conditioned stimuli (CS), respectively. We investigated whether brain signals related to congruent and incongruent word pairs could be classified with more than chance accuracy, in view of an application for basic yes/no communication in AD patients. Methods: The paradigm consisted of six blocks comprising the different phases of conditioning (habituation, acquisition, extinction) (Fig.1). The US, drawn from the International Affective Digitized Sounds (Bradley & Lang, 1999), consisted of a scream and a baby laugh, representing a negative and a positive emotional sound respectively. The CS, presented aurally, were congruent (e.g. 'animal-dog') and incongruent (e.g. 'animal-chair) word pairs. The unconditioned and conditioned responses (UR and CR) were the changes in the BOLD signal related to the CS and US. In the first block, 50 US and 50 CS were presented randomly. In the second and third blocks, 25 congruent word pairs, followed by the baby laugh, and 25 incongruent word pairs, followed by the scream, were presented randomly. In the fourth and fifth blocks, respectively 40% and 20% of the CS were paired with the US. In the sixth block, only the CS was presented. Functional imaging was performed on 11 healthy subjects (6 females, 5 males, age 21-28) on a 3T scanner. To classify the signals corresponding to congruent and incongruent word pairs, both univariate (General Linear Model) and multivariate (Support Vector Machine, SVM) analyses were performed. Results: The data that emerges from the univariate analysis shows that the classical conditioning allows a differentiation of "yes" and "no" responses. Interestingly, the differential activations took place in brain areas such as insula, superior temporal gyrus and the superior frontal gyrus, which are recognized to be involved in emotional processing (Sitaram et al., 2011). Results from SVM analyses, performed after feature selection from the third, fourth and fifth image after each word pair (temporal-spatial voxel selection performed with a threshold of 7.5% for the percentage of univariately separable samples), indicate that it is possible, after classical conditioning, to discriminate between affirmative and negative responses with more than chance accuracy. Conclusions: The present results are encouraging and show that basic yes/no discrimination may be possible within an fMRI-BCI setting. This discrimination may be obtained using a "passive" procedure, such as classical conditioning, which does not require subjects to be actively involved in a task, and can be therefore used with patients with dementia. A further step, which is already in progress, is the testing of the paradigm with AD patients

Development of a Binary fMRI-BCI for Alzheimer Patients: A semantic conditioning paradigm using affective unconditioned stimuli.

With the aim of developing a brain-computer interface for the communication of basic mental states, a classical conditioning paradigm with affective stimuli was used, assessing the possibility to discriminate between affirmative and negative thinking in an fMRI-BCI setting. 6 Alzheimer patients and 7 healthy control subjects participated to the study. Congruent and incongruent word-pairs were respectively associated to pleasant (baby laughter) and unpleasant (scream) affective stimuli. A Support Vector Machine classifier focusing on insula, amygdala and anterior cingulate cortex was used to discriminate between the activations relative to congruent and incongruent word-pairs (eliciting respectively affirmative and negative thinking), following the conditioning process. Classification accuracy was on average 71% for Alzheimer patients, reaching 85%, and on average 69% for control subjects, reaching 83%. This study shows that it is possible to extract information on individuals' mental states by exploiting affective responses, overcoming the typical obstacles of traditional BCIs, which generally require time-consuming trainings and intact cognition

Combining classical conditioning and brain-state classification for the development of a brain-computer interface (BCI) for Alzheimer's patients

Background Alzheimer's Disease (AD) patients who have lost the ability to communicate verbally may benefit from a Brain-Computer Interface (BCI), which could allow them to convey basic thoughts and emotions, e.g. by associating affirmative and non-affirmative thinking to a positive and a negative emotion, respectively. One possibility to develop a BCI that can be used with AD patients is to modulate brain responses with a semantic classical conditioning paradigm, in order to associate congruent and incongruent word pairs with emotionally positive and negative stimuli, facilitating the discrimination between “affirmative” and “non-affirmative” thinking. Using a classical conditioning paradigm is convenient, since it does not require AD patients to put active effort in learning how the BCI works. Methods In our classical conditioning paradigm, already tested on 11 healthy subjects, the unconditioned stimuli consist of a positive and negative emotional sound (a baby laugh and a scream, respectively). The conditioned stimuli, presented aurally, are congruent (e.g. 'fruit-apple') and incongruent (e.g. 'fruit-dog') word pairs. During the conditioning acquisition, congruent and incongruent word pairs are associated to the baby laugh and the scream respectively. Functional magnetic resonance imaging (fMRI) will be performed on AD patients (Mini Mental State Examination score: 18-24; intact auditory system) on a 3T scanner. To classify the signals corresponding to congruent and incongruent word pairs, both univariate (General Linear Model) and multivariate (Support Vector Machine, SVM) analyses will be performed. Results Both univariate and multivatiate analyses on healthy subjects confirmed the possibility to discriminate between affirmative and non-affirmative responses. We hypothesize that a similar differentiation may be found in AD patients, since classical conditioning was demonstrated to be possible, at least to some extent, in these patients.The study with AD patients is ongoing, and preliminary results will be presented during the conference. Conclusions The encouraging results obtained with healthy subjects show that basic affirmative/non affirmative thinking discrimination is possible within an fMRI-BCI setting. The development of a BCI for AD patients could be an important step for allowing not only basic communication, but possibly lending a path for rehabilitation and diagnosis

Mental state classification in Alzheimer patients using classical conditioning with affective auditory stimuli: an fMRI study

We adopted a classical conditioning paradigm to assess the possibility to discriminate between affirmative (“yes”) and negative (“no”) thinking in Alzheimer’s disease (AD) patients, by associating congruent and incongruent word-pairs to pleasant and unpleasant affective stimuli respectively, in view of the development of a brain-computer interface (BCI) for the communication of basic mental states. 6 AD patients and 7 age-matched controls participated to the study. The experiment comprised 3 blocks. In the first block, all incongruent and congruent word-pairs (conditioned stimuli, CS1 and CS2) were immediately followed by pleasant (laughter) unpleasant (scream) affective stimuli (unconditioned stimuli, US1 and US2) respectively, in order to associate negative and affirmative thinking to negative and positive emotions. In the second and third blocks, only 50% of the word-pairs were followed by emotional stimulation, to verify the possibility to discriminate between the activation elicited by affirmative and negative thinking, when the emotional stimulation was no more present. A Support Vector Machine (SVM) classifier was used, focusing on insula, amygdala and anterior cingulate cortex (ACC). Following conditioning, the classifier was able to discriminate between the BOLD signal relative to congruent and incongruent word-pairs (eliciting respectively affirmative and negative thinking) with a mean accuracy of 70% for both control subjects and patients, reaching 85% in one of the patients. The encouraging results replicate findings that were previously obtained on healthy young subjects. Further developments include the implementation of an online SVM for real-time classification and experimentation with patients suffering from other kinds of dementia (e.g. frontotemporal dementia)

Classification of affirmative and negative brain responses within an fMRI classical conditioning paradigm using Effect Mapping for feature selection

Several neuroimaging studies have provided strong evidence of the possibility to decode mental states from brain activity. Compared to strictly location-based analysis, pattern classification can reveal new information about the way cognitive, emotional, and perceptual states are encoded in patterns of brain activity. The last years have also seen the development of advanced algorithms, which markedly improved the possibility to perform pattern classification. By relying on mental state classification, brain-computer interfaces (BCIs) allow individuals who have lost the ability to communicate verbally to convey basic thoughts and emotions. The aim of our study was to discriminate between brain responses associated to affirmative and negative thinking in 10 subjects, in order to develop a BCI that could be used for basic yes/no communication. This discrimination could be achieved using a classical conditioning paradigm, i.e. associating affirmative and negative responses (the conditioned stimuli, CS), respectively associated to congruent and incongruent word-pairs, to pleasant and unpleasant emotional stimuli (the unconditioned stimuli, US) together with Effect Mapping (EM), based on Support Vector Machine (SVM). Using EM as the classifier of the affirmative and negative responses, a classification accuracy of over 90% was reached