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1.
Eur Arch Psychiatry Clin Neurosci ; 274(1): 3-18, 2024 Feb.
Article in English | MEDLINE | ID: mdl-36599959

ABSTRACT

Despite decades of massive neuroimaging research, the comprehensive characterization of short-range functional connectivity in autism spectrum disorder (ASD) remains a major challenge for scientific advances and clinical translation. From the theoretical point of view, it has been suggested a generalized local over-connectivity that would characterize ASD. This stance is known as the general local over-connectivity theory. However, there is little empirical evidence supporting such hypothesis, especially with regard to pediatric individuals with ASD (age [Formula: see text] 18 years old). To explore this issue, we performed a coordinate-based meta-analysis of regional homogeneity studies to identify significant changes of local connectivity. Our analyses revealed local functional under-connectivity patterns in the bilateral posterior cingulate cortex and superior frontal gyrus (key components of the default mode network) and in the bilateral paracentral lobule (a part of the sensorimotor network). We also performed a functional association analysis of the identified areas, whose dysfunction is clinically consistent with the well-known deficits affecting individuals with ASD. Importantly, we did not find relevant clusters of local hyper-connectivity, which is contrary to the hypothesis that ASD may be characterized by generalized local over-connectivity. If confirmed, our result will provide a valuable insight into the understanding of the complex ASD pathophysiology.


Subject(s)
Autism Spectrum Disorder , Humans , Child , Adolescent , Autism Spectrum Disorder/diagnostic imaging , Brain Mapping/methods , Neural Pathways/diagnostic imaging , Magnetic Resonance Imaging/methods , Brain/diagnostic imaging
2.
Behav Res Methods ; 2023 Aug 01.
Article in English | MEDLINE | ID: mdl-37528293

ABSTRACT

Coordinate-based meta-analysis (CBMA) is a powerful technique in the field of human brain imaging research. Due to its intense usage, several procedures for data preparation and post hoc analyses have been proposed so far. However, these steps are often performed manually by the researcher, and are therefore potentially prone to error and time-consuming. We hence developed the Coordinate-Based Meta-Analyses Toolbox (CBMAT) to provide a suite of user-friendly and automated MATLAB® functions allowing one to perform all these procedures in a fast, reproducible and reliable way. Besides the description of the code, in the present paper we also provide an annotated example of using CBMAT on a dataset including 34 experiments. CBMAT can therefore substantially improve the way data are handled when performing CBMAs. The code can be downloaded from https://github.com/Jordi-Manuello/CBMAT.git .

3.
Neuroimage ; 225: 117481, 2021 01 15.
Article in English | MEDLINE | ID: mdl-33122115

ABSTRACT

Brain disorders tend to impact on many different regions in a typical way: alterations do not spread randomly; rather, they seem to follow specific patterns of propagation that show a strong overlap between different pathologies. The insular cortex is one of the brain areas more involved in this phenomenon, as it seems to be altered by a wide range of brain diseases. On these grounds we thoroughly investigated the impact of brain disorders on the insular cortices analyzing the patterns of their structural co-alteration. We therefore investigated, applying a network analysis approach to meta-analytic data, 1) what pattern of gray matter alteration is associated with each of the insular cortex parcels; 2) whether or not this pattern correlates and overlaps with its functional meta-analytic connectivity; and, 3) the behavioral profile related to each insular co-alteration pattern. All the analyses were repeated considering two solutions: one with two clusters and another with three. Our study confirmed that the insular cortex is one of the most altered cerebral regions among the cortical areas, and exhibits a dense network of co-alteration including a prevalence of cortical rather than sub-cortical brain regions. Regions of the frontal lobe are the most involved, while occipital lobe is the less affected. Furthermore, the co-alteration and co-activation patterns greatly overlap each other. These findings provide significant evidence that alterations caused by brain disorders are likely to be distributed according to the logic of network architecture, in which brain hubs lie at the center of networks composed of co-altered areas. For the first time, we shed light on existing differences between insula sub-regions even in the pathoconnectivity domain.


Subject(s)
Brain Diseases/physiopathology , Cerebral Cortex/physiopathology , Nerve Net/physiopathology , Brain/physiopathology , Brain Mapping , Connectome , Gray Matter/physiopathology , Humans , Magnetic Resonance Imaging , Nerve Net/physiology , Occipital Lobe/physiopathology
4.
Hum Brain Mapp ; 42(11): 3343-3351, 2021 08 01.
Article in English | MEDLINE | ID: mdl-33991154

ABSTRACT

Over the past decades, powerful MRI-based methods have been developed, which yield both voxel-based maps of the brain activity and anatomical variation related to different conditions. With regard to functional or structural MRI data, forward inferences try to determine which areas are involved given a mental function or a brain disorder. A major drawback of forward inference is its lack of specificity, as it suggests the involvement of brain areas that are not specific for the process/condition under investigation. Therefore, a different approach is needed to determine to what extent a given pattern of cerebral activation or alteration is specifically associated with a mental function or brain pathology. In this study, we present a new tool called BACON (Bayes fACtor mOdeliNg) for performing reverse inference both with functional and structural neuroimaging data. BACON implements the Bayes' factor and uses the activation likelihood estimation derived-maps to obtain posterior probability distributions on the evidence of specificity with regard to a particular mental function or brain pathology.


Subject(s)
Brain Mapping/methods , Brain , Magnetic Resonance Imaging/methods , Models, Statistical , Bayes Theorem , Brain/anatomy & histology , Brain/diagnostic imaging , Brain/physiology , Humans , Schizophrenia/diagnostic imaging , Schizophrenia/physiopathology , Software
5.
J Pers Assess ; 103(5): 634-644, 2021.
Article in English | MEDLINE | ID: mdl-33166191

ABSTRACT

Recently, an eye-tracking study found that Complexity and other R-PAS variables located in the Engagement and Cognitive Processing domain correlated with a proxy marker for cognitive effort and engagement. The goal of the current study was to test the robustness and validity of those eye-tracking findings by inspecting fMRI data. We hypothesized that the greater the level of engagement and cognitive effort put in place by a Rorschach test-taker, the greater the engagement of his/her cortical areas reflecting ongoing top-down attentional processes should be. We re-analyzed archival fMRI data from 26 healthy participants exposed to the Rorschach inkblots with the instruction to think of what they might be. The association of various Engagement and Cognitive Processing R-PAS scores to increased BOLD signals in the Dorsal Attention Network of the brain was examined. As expected, Complexity showed the strongest effect size across all R-PAS variables under investigation (d = 0.43), followed by Synthesis (d = 0.32) and Human Movement (d = 0.21). Noteworthy, the correlation between the effect sizes found in the current fMRI study and those found in the previously published eye-tracking study consists of an impressive r = .80.


Subject(s)
Magnetic Resonance Imaging , Rorschach Test , Brain/diagnostic imaging , Cognition , Female , Humans , Male , Motivation
6.
Neuroimage ; 222: 117220, 2020 11 15.
Article in English | MEDLINE | ID: mdl-32777357

ABSTRACT

Numerous studies have investigated grey matter (GM) volume changes in diverse patient groups. Reports of disorder-related GM reductions are common in such work, but many studies also report evidence for GM volume increases in patients. It is unclear whether these GM increases and decreases are independent or related in some way. Here, we address this question using a novel meta-analytic network mapping approach. We used a coordinate-based meta-analysis of 64 voxel-based morphometry studies of psychiatric disorders to calculate the probability of finding a GM increase or decrease in one region given an observed change in the opposite direction in another region. Estimating this co-occurrence probability for every pair of brain regions allowed us to build a network of concurrent GM changes of opposing polarity. Our analysis revealed that disorder-related GM increases and decreases are not independent; instead, a GM change in one area is often statistically related to a change of opposite polarity in other areas, highlighting distributed yet coordinated changes in GM volume as a function of brain pathology. Most regions showing GM changes linked to an opposite change in a distal area were located in salience, executive-control and default mode networks, as well as the thalamus and basal ganglia. Moreover, pairs of regions showing coupled changes of opposite polarity were more likely to belong to different canonical networks than to the same one. Our results suggest that regional GM alterations in psychiatric disorders are often accompanied by opposing changes in distal regions that belong to distinct functional networks.


Subject(s)
Default Mode Network , Gray Matter , Mental Disorders , Meta-Analysis as Topic , Nerve Net , Neuroimaging , Default Mode Network/diagnostic imaging , Default Mode Network/pathology , Gray Matter/diagnostic imaging , Gray Matter/pathology , Humans , Mental Disorders/diagnostic imaging , Mental Disorders/pathology , Nerve Net/diagnostic imaging , Nerve Net/pathology
7.
Hum Brain Mapp ; 41(15): 4155-4172, 2020 10 15.
Article in English | MEDLINE | ID: mdl-32829507

ABSTRACT

In the field of neuroimaging reverse inferences can lead us to suppose the involvement of cognitive processes from certain patterns of brain activity. However, the same reasoning holds if we substitute "brain activity" with "brain alteration" and "cognitive process" with "brain disorder." The fact that different brain disorders exhibit a high degree of overlap in their patterns of structural alterations makes forward inference-based analyses less suitable for identifying brain areas whose alteration is specific to a certain pathology. In the forward inference-based analyses, in fact, it is impossible to distinguish between areas that are altered by the majority of brain disorders and areas that are specifically affected by certain diseases. To address this issue and allow the identification of highly pathology-specific altered areas we used the Bayes' factor technique, which was employed, as a proof of concept, on voxel-based morphometry data of schizophrenia and Alzheimer's disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alterations' spread associated with different pathologies. The Bayes' factor values calculated on these simulated data were able to reveal that the areas, which are more specific to a certain disease, are also the ones to be early altered. This study puts forward a new analytical instrument capable of innovating the methodological approach to the investigation of brain pathology.


Subject(s)
Alzheimer Disease/diagnostic imaging , Gray Matter/diagnostic imaging , Neuroimaging/methods , Schizophrenia/diagnostic imaging , Alzheimer Disease/pathology , Bayes Theorem , Default Mode Network/diagnostic imaging , Default Mode Network/pathology , Diagnosis, Differential , Gray Matter/pathology , Humans , Models, Theoretical , Nerve Net/diagnostic imaging , Nerve Net/pathology , Neuroimaging/standards , Proof of Concept Study , Schizophrenia/pathology
8.
Hum Brain Mapp ; 41(14): 3878-3899, 2020 10 01.
Article in English | MEDLINE | ID: mdl-32562581

ABSTRACT

It is becoming clearer that the impact of brain diseases is more convincingly represented in terms of co-alterations rather than in terms of localization of alterations. In this context, areas characterized by a long mean distance of co-alteration may be considered as hubs with a crucial role in the pathology. We calculated meta-analytic transdiagnostic networks of co-alteration for the gray matter decreases and increases, and we evaluated the mean Euclidean, fiber-length, and topological distance of its nodes. We also examined the proportion of co-alterations between canonical networks, and the transdiagnostic variance of the Euclidean distance. Furthermore, disease-specific analyses were conducted on schizophrenia and Alzheimer's disease. The anterodorsal prefrontal cortices appeared to be a transdiagnostic hub of long-distance co-alterations. Also, the disease-specific analyses showed that long-distance co-alterations are more able than classic meta-analyses to identify areas involved in pathology and symptomatology. Moreover, the distance maps were correlated with the normative connectivity. Our findings substantiate the network degeneration hypothesis in brain pathology. At the same time, they suggest that the concept of co-alteration might be a useful tool for clinical neuroscience.


Subject(s)
Alzheimer Disease , Cerebral Cortex , Gray Matter , Magnetic Resonance Imaging , Nerve Net , Neuroimaging , Schizophrenia , Alzheimer Disease/diagnostic imaging , Alzheimer Disease/pathology , Alzheimer Disease/physiopathology , Cerebral Cortex/diagnostic imaging , Cerebral Cortex/pathology , Cerebral Cortex/physiopathology , Databases, Factual , Gray Matter/diagnostic imaging , Gray Matter/pathology , Gray Matter/physiopathology , Humans , Magnetic Resonance Imaging/statistics & numerical data , Nerve Net/diagnostic imaging , Nerve Net/pathology , Nerve Net/physiopathology , Neuroimaging/statistics & numerical data , Prefrontal Cortex/diagnostic imaging , Prefrontal Cortex/pathology , Prefrontal Cortex/physiopathology , Schizophrenia/diagnostic imaging , Schizophrenia/pathology , Schizophrenia/physiopathology
9.
J Cogn Neurosci ; 31(12): 1796-1826, 2019 12.
Article in English | MEDLINE | ID: mdl-31418337

ABSTRACT

During the last two decades, our inner sense of time has been repeatedly studied with the help of neuroimaging techniques. These investigations have suggested the specific involvement of different brain areas in temporal processing. At least two distinct neural systems are likely to play a role in measuring time: One is mainly constituted of subcortical structures and is supposed to be more related to the estimation of time intervals below the 1-sec range (subsecond timing tasks), and the other is mainly constituted of cortical areas and is supposed to be more related to the estimation of time intervals above the 1-sec range (suprasecond timing tasks). Tasks can then be performed in motor or nonmotor (perceptual) conditions, thus providing four different categories of time processing. Our meta-analytical investigation partly confirms the findings of previous meta-analytical works. Both sub- and suprasecond tasks recruit cortical and subcortical areas, but subcortical areas are more intensely activated in subsecond tasks than in suprasecond tasks, which instead receive more contributions from cortical activations. All the conditions, however, show strong activations in the SMA, whose rostral and caudal parts have an important role not only in the discrimination of different time intervals but also in relation to the nature of the task conditions. This area, along with the striatum (especially the putamen) and the claustrum, is supposed to be an essential node in the different networks engaged when the brain creates our sense of time.


Subject(s)
Neuroimaging , Time Perception/physiology , Brain Mapping , Cerebral Cortex/physiology , Humans , Models, Neurological , Models, Psychological , Organ Specificity , Psychomotor Performance/physiology
10.
Neuroimage ; 184: 359-371, 2019 01 01.
Article in English | MEDLINE | ID: mdl-30237032

ABSTRACT

Growing evidence is challenging the assumption that brain disorders are diagnostically clear-cut categories. Transdiagnostic studies show that a set of cerebral areas is frequently altered in a variety of psychiatric as well as neurological syndromes. In order to provide a map of the altered areas in the pathological brain we devised a metric, called alteration entropy (A-entropy), capable of denoting the "structural alteration variety" of an altered region. Using the whole voxel-based morphometry database of BrainMap, we were able to differentiate the brain areas exhibiting a high degree of overlap between different neuropathologies (or high value of A-entropy) from those exhibiting a low degree of overlap (or low value of A-entropy). The former, which are parts of large-scale brain networks with attentional, emotional, salience, and premotor functions, are thought to be more vulnerable to a great range of brain diseases; while the latter, which include the sensorimotor, visual, inferior temporal, and supramarginal regions, are thought to be more informative about the specific impact of brain diseases. Since low A-entropy areas appear to be altered by a smaller number of brain disorders, they are more informative than the areas characterized by high values of A-entropy. It is also noteworthy that even the areas showing low values of A-entropy are substantially altered by a variety of brain disorders. In fact, no cerebral area appears to be only altered by a specific disorder. Our study shows that the overlap of areas with high A-entropy provides support for a transdiagnostic approach to brain disorders but, at the same time, suggests that fruitful differences can be traced among brain diseases, as some areas can exhibit an alteration profile more specific to certain disorders than to others.


Subject(s)
Brain Diseases/diagnostic imaging , Brain Diseases/pathology , Brain Mapping/methods , Cerebral Cortex/diagnostic imaging , Cerebral Cortex/pathology , Datasets as Topic , Entropy , Humans , Image Interpretation, Computer-Assisted/methods , Magnetic Resonance Imaging
11.
Brain ; 141(11): 3211-3232, 2018 11 01.
Article in English | MEDLINE | ID: mdl-30346490

ABSTRACT

The pathological brain is characterized by distributed morphological or structural alterations in the grey matter, which tend to follow identifiable network-like patterns. We analysed the patterns formed by these alterations (increased and decreased grey matter values detected with the voxel-based morphometry technique) conducting an extensive transdiagnostic search of voxel-based morphometry studies in a large variety of brain disorders. We devised an innovative method to construct the networks formed by the structurally co-altered brain areas, which can be considered as pathological structural co-alteration patterns, and to compare these patterns with three associated types of connectivity profiles (functional, anatomical, and genetic). Our study provides transdiagnostical evidence that structural co-alterations are influenced by connectivity constraints rather than being randomly distributed. Analyses show that although all the three types of connectivity taken together can account for and predict with good statistical accuracy, the shape and temporal development of the co-alteration patterns, functional connectivity offers the better account of the structural co-alteration, followed by anatomic and genetic connectivity. These results shed new light on the possible mechanisms at the root of neuropathological processes and open exciting prospects in the quest for a better understanding of brain disorders.


Subject(s)
Brain Mapping , Brain/anatomy & histology , Gene Regulatory Networks , Models, Neurological , Neural Pathways/physiology , Adult , Brain/diagnostic imaging , Brain/physiopathology , Diffusion Tensor Imaging , Female , Humans , Image Processing, Computer-Assisted , Male , Nerve Net/physiology , Neural Pathways/diagnostic imaging , Reproducibility of Results , Young Adult
12.
Cereb Cortex ; 28(7): 2647-2654, 2018 07 01.
Article in English | MEDLINE | ID: mdl-29722797

ABSTRACT

Mirror neurons have been proposed to underlie humans' ability to understand others' actions and intentions. Despite 2 decades of research, however, the exact computational and neuronal mechanisms implied in this ability remain unclear. In the current study, we investigated whether, in the absence of contextual cues, regions considered to be part of the human mirror neuron system represent intention from movement kinematics. A total of 21 participants observed reach-to-grasp movements, performed with either the intention to drink or to pour while undergoing functional magnetic resonance imaging. Multivoxel pattern analysis revealed successful decoding of intentions from distributed patterns of activity in a network of structures comprising the inferior parietal lobule, the superior parietal lobule, the inferior frontal gyrus, and the middle frontal gyrus. Consistent with the proposal that parietal regions play a key role in intention understanding, classifier weights were higher in the inferior parietal region. These results provide the first demonstration that putative mirror neuron regions represent subtle differences in movement kinematics to read the intention of an observed motor act.


Subject(s)
Intention , Mirror Neurons/physiology , Observation , Parietal Lobe/cytology , Psychomotor Performance/physiology , Adult , Biomechanical Phenomena , Female , Hand Strength/physiology , Humans , Image Processing, Computer-Assisted , Magnetic Resonance Imaging , Male , Parietal Lobe/diagnostic imaging , Young Adult
13.
Scand J Psychol ; 60(6): 528-538, 2019 Dec.
Article in English | MEDLINE | ID: mdl-31598986

ABSTRACT

We introduce the concept of "neurobiological foundation" of Rorschach interpretations as an extension of the concept of behavioral representation as a foundation for interpretation of R-PAS variables. Here, we propose that if there is a parallelism between the mental, verbal and perceptual behaviors occurring within the microcosm of the Rorschach task and those occurring in the external environment [behavioral foundation], then the same brain regions engaged by the test-taker when producing of a given code, should be engaged also when reproducing, in the external environment, the same psychological processes underlying that specific Rorschach code [neurobiological foundation]. To investigate this concept, we used archival, fMRI data and tested whether producing Oral Dependency Language (ODL) responses would associate with increased activation in brain regions associated with dependency-related, psychological processes. Results from a sample of 21 non-clinical volunteers partially confirmed our hypothesis, providing some support to the neurobiological foundation of the ODL code.


Subject(s)
Brain Mapping , Brain/physiology , Language , Pattern Recognition, Visual/physiology , Personality/physiology , Rorschach Test , Thinking/physiology , Adolescent , Adult , Brain/diagnostic imaging , Female , Humans , Magnetic Resonance Imaging , Male , Young Adult
14.
Hum Brain Mapp ; 39(5): 1898-1928, 2018 05.
Article in English | MEDLINE | ID: mdl-29349864

ABSTRACT

By means of a novel methodology that can statistically derive patterns of co-alterations distribution from voxel-based morphological data, this study analyzes the patterns of brain alterations of three important psychiatric spectra-that is, schizophrenia spectrum disorder (SCZD), autistic spectrum disorder (ASD), and obsessive-compulsive spectrum disorder (OCSD). Our analysis provides five important results. First, in SCZD, ASD, and OCSD brain alterations do not distribute randomly but, rather, follow network-like patterns of co-alteration. Second, the clusters of co-altered areas form a net of alterations that can be defined as morphometric co-alteration network or co-atrophy network (in the case of gray matter decreases). Third, within this network certain cerebral areas can be identified as pathoconnectivity hubs, the alteration of which is supposed to enhance the development of neuronal abnormalities. Fourth, within the morphometric co-atrophy network of SCZD, ASD, and OCSD, a subnetwork composed of eleven highly connected nodes can be distinguished. This subnetwork encompasses the anterior insulae, inferior frontal areas, left superior temporal areas, left parahippocampal regions, left thalamus and right precentral gyri. Fifth, the co-altered areas also exhibit a normal structural covariance pattern which overlaps, for some of these areas (like the insulae), the co-alteration pattern. These findings reveal that, similarly to neurodegenerative diseases, psychiatric disorders are characterized by anatomical alterations that distribute according to connectivity constraints so as to form identifiable morphometric co-atrophy patterns.


Subject(s)
Autistic Disorder/diagnostic imaging , Brain/diagnostic imaging , Neural Pathways/diagnostic imaging , Obsessive-Compulsive Disorder/diagnostic imaging , Schizophrenia/diagnostic imaging , Adolescent , Adult , Atrophy/diagnostic imaging , Atrophy/etiology , Autistic Disorder/complications , Female , Humans , Image Processing, Computer-Assisted , Magnetic Resonance Imaging , Male , Models, Neurological , Obsessive-Compulsive Disorder/complications , PubMed/statistics & numerical data , Schizophrenia/complications , Young Adult
15.
Cogn Affect Behav Neurosci ; 16(3): 383-92, 2016 06.
Article in English | MEDLINE | ID: mdl-26912269

ABSTRACT

Although very difficult to define, happiness is becoming a core concept within contemporary psychology and affective neuroscience. In the last two decades, the increased use of neuroimaging techniques has facilitated empirical study of the neural correlates of happiness. This area of research utilizes procedures that induce positive emotion and mood, and autobiographical recall is one of the most widely used and effective approaches. In this article, we review eight positron emission tomography and seven functional magnetic resonance imaging studies that have investigated happiness by using autobiographical recall to induce emotion. Regardless of the neuroimaging technique used, the studies conducted so far have shown that remembering happy events is primarily associated with the activation of many areas, including anterior cingulate cortex, prefrontal cortex, and insula. Importantly, these areas are also found to be connected with other basic emotions, such as sadness and anger. In the conclusion, we integrate these findings, discussing important limitations of the extant literature and suggesting new research directions.


Subject(s)
Affect/physiology , Cerebral Cortex/pathology , Emotions/physiology , Mental Recall/physiology , Neuroimaging , Prefrontal Cortex/pathology , Animals , Cerebral Cortex/physiopathology , Humans , Prefrontal Cortex/physiopathology
16.
Mov Disord ; 31(11): 1739-1743, 2016 11.
Article in English | MEDLINE | ID: mdl-27453152

ABSTRACT

BACKGROUND: DYT1 mutation is characterized by focal to generalized dystonia and incomplete penetrance. To explore the complex perturbations in the different neural networks and the mutual interactions among them, we studied symptomatic and asymptomatic DTY1 mutation carriers by resting-state functional MRI. METHODS: A total of 7 symptomatic DYT1, 10 asymptomatic DYT1, and 26 healthy controls were considered. Resting-state functional MRI (Oxford Centre for Functional MRI of the Brain) [FMRIB] Software Library) (FSL) MELODIC, dual regression, (as a toolbox of FSL, with Nets is referred to "networks") (FSLNets) (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLNets) was performed on 9 resting-state neural networks. RESULTS: DYT1 mutation signature (symptomatic DYT1 and asymptomatic DYT1) was characterized by increased connectivity in the dorsal attention network and in the left fronto-parietal network. Functional correlates of symptomatic DYT1 patients (symptomatic DYT1 vs healthy controls) showed increased connectivity in the sensorimotor network. DISCUSSION: This study argues that DYT1 dystonia is a network disorder, with crucial nodes in sensory-motor integration of posterior parietal structures. A better characterization of cortical networks involved in dystonia is crucial for possible neurophysiological therapeutic interventions. © 2016 International Parkinson and Movement Disorder Society.


Subject(s)
Cerebral Cortex/physiopathology , Connectome/methods , Dystonia Musculorum Deformans/physiopathology , Molecular Chaperones/genetics , Adult , Cerebral Cortex/diagnostic imaging , Dystonia Musculorum Deformans/diagnostic imaging , Female , Heterozygote , Humans , Magnetic Resonance Imaging , Male , Middle Aged
17.
Conscious Cogn ; 40: 67-78, 2016 Feb.
Article in English | MEDLINE | ID: mdl-26752605

ABSTRACT

Although mindfulness meditation has been practiced in the East for more than two millennia, Western scientific research and healthcare programs have only recently drawn their attention to it. Basically, the concept of mindfulness hinges on focusing on one's own awareness at the present moment. In this review we analyze different hypotheses about the functioning and the cerebral correlates of mindfulness meditation. Since mindfulness is strictly associated with a particular state of consciousness, we also examine some of the most relevant theories that have been proposed as accounts of consciousness. Finally, we suggest that consciousness and mindfulness meditation can be integrated within a neuroscientific perspective, by identifying the brain areas which seem to play an essential role in both, namely the anterior cingulate cortex, posterior cingulate cortex, insula and thalamus.


Subject(s)
Cerebral Cortex/physiology , Consciousness/physiology , Meditation , Mindfulness , Thalamus/physiology , Humans
18.
Neural Plast ; 2016: 1938292, 2016.
Article in English | MEDLINE | ID: mdl-26881093

ABSTRACT

Several functional connectivity approaches require the definition of a set of regions of interest (ROIs) that act as network nodes. Different methods have been developed to define these nodes and to derive their functional and effective connections, most of which are rather complex. Here we aim to propose a relatively simple "one-step" border detection and ROI estimation procedure employing the fuzzy c-mean clustering algorithm. To test this procedure and to explore insular connectivity beyond the two/three-region model currently proposed in the literature, we parcellated the insular cortex of 20 healthy right-handed volunteers scanned in a resting state. By employing a high-dimensional functional connectivity-based clustering process, we confirmed the two patterns of connectivity previously described. This method revealed a complex pattern of functional connectivity where the two previously detected insular clusters are subdivided into several other networks, some of which are not commonly associated with the insular cortex, such as the default mode network and parts of the dorsal attentional network. Furthermore, the detection of nodes was reliable, as demonstrated by the confirmative analysis performed on a replication group of subjects.


Subject(s)
Brain Mapping/methods , Cerebral Cortex/physiology , Fuzzy Logic , Nerve Net/physiology , Adult , Algorithms , Cluster Analysis , Female , Humans , Male , Young Adult
20.
Cereb Cortex ; 24(11): 2991-3005, 2014 Nov.
Article in English | MEDLINE | ID: mdl-23796948

ABSTRACT

To date, relatively little is known about the spatiotemporal aspects of whole-brain blood oxygenation level-dependent (BOLD) responses to brief nociceptive stimuli. It is known that the majority of brain areas show a stimulus-locked response, whereas only some are characterized by a canonical hemodynamic response function. Here, we investigated the time course of brain activations in response to mechanical pain stimulation applied to participants' hands while they were undergoing functional magnetic resonance imaging (fMRI) scanning. To avoid any assumption about the shape of BOLD response, we used an unsupervised data-driven method to group voxels sharing a time course similar to the BOLD response to the stimulus and found that whole-brain BOLD responses to painful mechanical stimuli elicit massive activation of stimulus-locked brain areas. This pattern of activations can be segregated into 5 clusters, each with a typical temporal profile. In conclusion, we show that an extensive activity of multiple networks is engaged at different time latencies after presentation of a noxious stimulus. These findings aim to motivate research on a controversial topic, such as the temporal profile of BOLD responses, the variability of these response profiles, and the interaction between the stimulus-related BOLD response and ongoing fluctuations in large-scale brain networks.


Subject(s)
Brain Mapping , Brain/blood supply , Magnetic Resonance Imaging , Pain/pathology , Reaction Time/physiology , Adult , Cluster Analysis , Female , Humans , Image Processing, Computer-Assisted , Male , Oxygen , Pain/etiology , Pain Perception/physiology , Physical Stimulation/adverse effects , Psychophysics , Young Adult
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