Structural changes induced by daily music listening in the recovering brain after middle cerebral artery stroke: a voxel-based morphometry study

Music is a highly complex and versatile stimulus for the brain that engages many temporal, frontal, parietal, cerebellar, and subcortical areas involved in auditory, cognitive, emotional, and motor processing. Regular musical activities have been shown to effectively enhance the structure and function of many brain areas, making music a potential tool also in neurological rehabilitation. In our previous randomized controlled study, we found that listening to music on a daily basis can improve cognitive recovery and improve mood after an acute middle cerebral artery stroke. Extending this study, a voxel-based morphometry (VBM) analysis utilizing cost function masking was performed on the acute and 6-month post-stroke stage structural magnetic resonance imaging data of the patients (n = 49) who either listened to their favorite music [music group (MG), n = 16] or verbal material [audio book group (ABG), n = 18] or did not receive any listening material [control group (CG), n = 15] during the 6-month recovery period. Although all groups showed significant gray matter volume (GMV) increases from the acute to the 6-month stage, there was a specific network of frontal areas [left and right superior frontal gyrus (SFG), right medial SFG] and limbic areas [left ventral/subgenual anterior cingulate cortex (SACC) and right ventral striatum (VS)] in patients with left hemisphere damage in which the GMV increases were larger in the MG than in the ABG and in the CG. Moreover, the GM reorganization in the frontal areas correlated with enhanced recovery of verbal memory, focused attention, and language skills, whereas the GM reorganization in the SACC correlated with reduced negative mood. This study adds on previous results, showing that music listening after stroke not only enhances behavioral recovery, but also induces fine-grained neuroanatomical changes in the recovering brain.

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Altered brain-derived neurotrophic factor blood levels and gene variability are associated with anorexia and bulimia

Murine models and association studies in eating disorder (ED) patients have shown a role for the brain-derived neurotrophic factor (BDNF) in eating behavior. Some studies have shown association of BDNF -270C/T single-nucleotide polymorphism (SNP) with bulimia nervosa (BN), while BDNF Val66Met variant has been shown to be associated with both BN and anorexia nervosa (AN). To further test the role of this neurotrophin in humans, we screened 36 SNPs in the BDNF gene and tested for their association with ED and plasma BDNF levels as a quantitative trait. We performed a family-based association study in 106 ED nuclear families and analyzed BDNF blood levels in 110 ED patients and in 50 sib pairs discordant for ED. The rs7124442T/rs11030102C/rs11030119G haplotype was found associated with high BDNF levels (mean BDNF TCG haplotype carriers = 43.6 ng/ml vs. mean others 23.0 ng/ml, P = 0.016) and BN (Z = 2.64; P recessive = 0.008), and the rs7934165A/270T haplotype was associated with AN (Z =-2.64; P additive = 0.008). The comparison of BDNF levels in 50 ED discordant sib pairs showed elevated plasma BDNF levels for the ED group (mean controls = 41.0 vs. mean ED = 52.7; P = 0.004). Our data strongly suggest that altered BDNF levels modulated by BDNF gene variability are associated with the susceptibility to ED, providing physiological evidence that BDNF plays a role in the development of AN and BN, and strongly arguing for its involvement in eating behavior and body weight regulation.

A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder

Background Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Task-induced deactivation from rest extends beyond the default mode brain network

Activity decreases, or deactivations, of midline and parietal cortical brain regions are routinely observed in human functional neuroimaging studies that compare periods of task-based cognitive performance with passive states, such as rest. It is now widely held that such task-induced deactivations index a highly organized"default-mode network" (DMN): a large-scale brain system whose discovery has had broad implications in the study of human brain function and behavior. In this work, we show that common task-induced deactivations from rest also occur outside of the DMN as a function of increased task demand. Fifty healthy adult subjects performed two distinct functional magnetic resonance imaging tasks that were designed to reliably map deactivations from a resting baseline. As primary findings, increases in task demand consistently modulated the regional anatomy of DMN deactivation. At high levels of task demand, robust deactivation was observed in non-DMN regions, most notably, the posterior insular cortex. Deactivation of this region was directly implicated in a performance-based analysis of experienced task difficulty. Together, these findings suggest that task-induced deactivations from rest are not limited to the DMN and extend to brain regions typically associated with integrative sensory and interoceptive processes.

Effects of P300-based BCI use on reported presence in a virtual environment

Brain-computer interfaces (BCIs) are becoming more and more popular as an input device for virtual worlds and computer games. Depending on their function, a major drawback is the mental workload associated with their use and there is significant effort and training required to effectively control them. In this paper, we present two studies assessing how mental workload of a P300-based BCI affects participants" reported sense of presence in a virtual environment (VE). In the first study, we employ a BCI exploiting the P300 event-related potential (ERP) that allows control of over 200 items in a virtual apartment. In the second study, the BCI is replaced by a gaze-based selection method coupled with wand navigation. In both studies, overall performance is measured and individual presence scores are assessed by means of a short questionnaire. The results suggest that there is no immediate benefit for visualizing events in the VE triggered by the BCI and that no learning about the layout of the virtual space takes place. In order to alleviate this, we propose that future P300-based BCIs in VR are set up so as require users to make some inference about the virtual space so that they become aware of it,which is likely to lead to higher reported presence.

Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to improvement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Classifier combination for in vivo magnetic resonance spectra of brain tumours

Peer-reviewed

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.

Individual Differences in True and False Memory Retrieval Are Related to White Matter Brain Microstructure

We sometimes vividly remember things that did not happen, a phenomenon with general relevance, not only in the courtroom. It is unclear to what extent individual differences in false memories are driven by anatomical differences in memory-relevant brain regions. Here we show in humans that microstructural properties of different white matter tracts as quantified using diffusion tensor imaging are strongly correlated with true and false memory retrieval. To investigate these hypotheses, we tested a large group of participants in a version of the Deese-Roediger-McDermott paradigm (recall and recognition) and subsequently obtained diffusion tensor images. A voxel-based whole-brain level linear regression analysis was performedto relatefractional anisotropyto indices oftrue andfalse memory recall and recognition. True memory was correlated to diffusion anisotropy in the inferior longitudinal fascicle, the major connective pathway of the medial temporal lobe, whereas a greater proneness to retrieve false items was related to the superior longitudinal fascicle connecting frontoparietal structures. Our results show that individual differences in white matter microstructure underlie true and false memory performance.

Brain dynamics sustaining rapid rule extraction from speech

Language acquisition is a complex process that requires the synergic involvement of different cognitive functions, which include extracting and storing the words of the language and their embedded rules for progressive acquisition of grammatical information. As has been shown in other fields that study learning processes, synchronization mechanisms between neuronal assemblies might have a key role during language learning. In particular, studying these dynamics may help uncover whether different oscillatory patterns sustain more item-based learning of words and rule-based learning from speech input. Therefore, we tracked the modulation of oscillatory neural activity during the initial exposure to an artificial language, which contained embedded rules. We analyzed both spectral power variations, as a measure of local neuronal ensemble synchronization, as well as phase coherence patterns, as an index of the long-range coordination of these local groups of neurons. Synchronized activity in the gamma band (2040 Hz), previously reported to be related to the engagement of selective attention, showed a clear dissociation of local power and phase coherence between distant regions. In this frequency range, local synchrony characterized the subjects who were focused on word identification and was accompanied by increased coherence in the theta band (48 Hz). Only those subjects who were able to learn the embedded rules showed increased gamma band phase coherence between frontal, temporal, and parietal regions.

Brain-Derived Neurotrophic Factor as a potential biomarker of cognitive recovery in schizophrenia

Brain-derived neurotrophic factor (BDNF) has been proposed as a biomarker of schizophrenia and, more specifically, as a biomarker of cognitive recovery. Evidence collected in this review indicates that BDNF is relevant in the pathophysiology of schizophrenia and could play a role as a marker of clinical response. BDNF has been shown to play a positive role as a marker in antipsychotic treatment, and it has been demonstrated that typical antipsychotics decrease BDNF levels while atypical antipsychotics maintain or increase serum BDNF levels. Furthermore, BDNF levels have been associated with severe cognitive impairments in patients with schizophrenia. Consequently, BDNF has been proposed as a candidate target of strategies to aid the cognitive recovery process. There is some evidence suggesting that BDNF could be mediating neurobiological processes underlying cognitive recovery. Thus, serum BDNF levels seem to be involved in some synaptic plasticity and neurotransmission processes. Additionally, serum BDNF levels significantly increased in schizophrenia subjects after neuroplasticity-based cognitive training. If positive replications of those findings are published in the future then serum BDNF levels could be definitely postulated as a peripheral biomarker for the effects of intensive cognitive training or any sort of cognitive recovery in schizophrenia. All in all, the current consideration of BDNF as a biomarker of cognitive recovery in schizophrenia is promising but still premature.

DWI and complex brain network analysis predicts vascular cognitive impairment in spontaneous hypertensive rats undergoing executive function tests

The identification of biomarkers of vascular cognitive impairment is urgent for its early diagnosis. The aim of this study was to detect and monitor changes in brain structure and connectivity, and to correlate them with the decline in executive function. We examined the feasibility of early diagnostic magnetic resonance imaging (MRI) to predict cognitive impairment before onset in an animal model of chronic hypertension: Spontaneously Hypertensive Rats. Cognitive performance was tested in an operant conditioning paradigm that evaluated learning, memory, and behavioral flexibility skills. Behavioral tests were coupled with longitudinal diffusion weighted imaging acquired with 126 diffusion gradient directions and 0.3 mm(3) isometric resolution at 10, 14, 18, 22, 26, and 40 weeks after birth. Diffusion weighted imaging was analyzed in two different ways, by regional characterization of diffusion tensor imaging (DTI) indices, and by assessing changes in structural brain network organization based on Q-Ball tractography. Already at the first evaluated times, DTI scalar maps revealed significant differences in many regions, suggesting loss of integrity in white and gray matter of spontaneously hypertensive rats when compared to normotensive control rats. In addition, graph theory analysis of the structural brain network demonstrated a significant decrease of hierarchical modularity, global and local efficacy, with predictive value as shown by regional three-fold cross validation study. Moreover, these decreases were significantly correlated with the behavioral performance deficits observed at subsequent time points, suggesting that the diffusion weighted imaging and connectivity studies can unravel neuroimaging alterations even overt signs of cognitive impairment become apparent.