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Brain Sciences
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Recent Advances in Human Brain Connectivity
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Recent Advances in Human Brain Connectivity

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Computational Neuroscience and Neuroinformatics".

Deadline for manuscript submissions: closed (20 December 2020) | Viewed by 38291

Special Issue Editors

Prof. Dr. Sabina Tangaro

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Guest Editor
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via E Orabona 4, I-70125 Bari, Italy
Interests: biomedical imaging; brain connectivity; complex networks; medical physics; machine learning
Dr. Angela Lombardi

E-Mail Website
Guest Editor
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Via E Orabona 4, I-70125 Bari, Italy
Interests: complex networks; brain connectivity; biomedical signal processing; magnetic resonance imaging; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mapping the brain’s structural organization and understanding how neural function is related to connectivity is one of the main goals of modern neuroscience. The term “connectivity” describes a complete map of the neural elements and structural links within a neural system, together forming the fundamental substrate for neural communication, information processing, and neural integration in the brain. Neuroimaging techniques have played a key role in the field of human brain connectivity. They are currently the main methods of investigating the macroscale connectivity architecture of the human brain. The rapid development of innovative analysis techniques and the growing interest of the scientific community have produced a large amount of available data concerning the structural and functional nature of the brain. Functional and structural connectivity models of the human brain have been widely applied to uncover new insights about a great variety of biological mechanisms, such as neurodevelopment and aging, and in different diseases (e.g., neuropsychiatric disorders and neurodegenerative diseases).  

This Special Issue will cover the recent advances in brain connectivity in different fields of brain sciences. We encourage submissions of original research and reviews with a focus on new methods and applications of brain connectivity models with both neurophysiological recordings (e.g., EEG) and neuroimaging techniques (e.g., fMRI, sMRI, DWI, MEG).

Potential topics include, but are not limited to, the following:

-Functional connectivity;
-Structural connectivity;
-Predictive models in brain connectivity analysis;
-Multiscale modeling of the human brain;
-Multidimensional analysis of brain connectivity;
-Multimodal imaging;
-Statistical methods in connectivity analysis.

Dr. Sabina Tangaro
Dr. Angela Lombardi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 1. Predictive models
  • 2. Brain Connectivity
  • 3. Brain Networks
  • 4. neuroimaging preprocessing pipeline
  • 5. sMRI
  • 6. fMRI
  • 7. DTI
  • 8. EEG
  • 9. MEG.

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Published Papers (9 papers)

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Research

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15 pages, 1332 KiB  
Article
Node Centrality Measures Identify Relevant Structural MRI Features of Subjects with Autism
by Marcello Zanghieri, Giulia Menichetti, Alessandra Retico, Sara Calderoni, Gastone Castellani and Daniel Remondini
Brain Sci. 2021, 11(4), 498; https://doi.org/10.3390/brainsci11040498 - 14 Apr 2021
Cited by 4 | Viewed by 2568
Abstract
Autism spectrum disorders (ASDs) are a heterogeneous group of neurodevelopmental conditions characterized by impairments in social interaction and communication and restricted patterns of behavior, interests, and activities. Although the etiopathogenesis of idiopathic ASD has not been fully elucidated, compelling evidence suggests an interaction [...] Read more.
Autism spectrum disorders (ASDs) are a heterogeneous group of neurodevelopmental conditions characterized by impairments in social interaction and communication and restricted patterns of behavior, interests, and activities. Although the etiopathogenesis of idiopathic ASD has not been fully elucidated, compelling evidence suggests an interaction between genetic liability and environmental factors in producing early alterations of structural and functional brain development that are detectable by magnetic resonance imaging (MRI) at the group level. This work shows the results of a network-based approach to characterize not only variations in the values of the extracted features but also in their mutual relationships that might reflect underlying brain structural differences between autistic subjects and healthy controls. We applied a network-based analysis on sMRI data from the Autism Brain Imaging Data Exchange I (ABIDE-I) database, containing 419 features extracted with FreeSurfer software. Two networks were generated: one from subjects with autistic disorder (AUT) (DSM-IV-TR), and one from typically developing controls (TD), adopting a subsampling strategy to overcome class imbalance (235 AUT, 418 TD). We compared the distribution of several node centrality measures and observed significant inter-class differences in averaged centralities. Moreover, a single-node analysis allowed us to identify the most relevant features that distinguished the groups. Full article
(This article belongs to the Special Issue Recent Advances in Human Brain Connectivity)
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11 pages, 1104 KiB  
Article
Brain Structural Covariance Networks in Behavioral Variant of Frontotemporal Dementia
by Salvatore Nigro, Benedetta Tafuri, Daniele Urso, Roberto De Blasi, Maria Elisa Frisullo, Maria Rosaria Barulli, Rosa Capozzo, Alessia Cedola, Giuseppe Gigli and Giancarlo Logroscino
Brain Sci. 2021, 11(2), 192; https://doi.org/10.3390/brainsci11020192 - 4 Feb 2021
Cited by 12 | Viewed by 2941
Abstract
Recent research on behavioral variant frontotemporal dementia (bvFTD) has shown that personality changes and executive dysfunctions are accompanied by a disease-specific anatomical pattern of cortical and subcortical atrophy. We investigated the structural topological network changes in patients with bvFTD in comparison to healthy [...] Read more.
Recent research on behavioral variant frontotemporal dementia (bvFTD) has shown that personality changes and executive dysfunctions are accompanied by a disease-specific anatomical pattern of cortical and subcortical atrophy. We investigated the structural topological network changes in patients with bvFTD in comparison to healthy controls. In particular, 25 bvFTD patients and 20 healthy controls underwent structural 3T MRI. Next, bilaterally averaged values of 34 cortical surface areas, 34 cortical thickness values, and six subcortical volumes were used to capture single-subject anatomical connectivity and investigate network organization using a graph theory approach. Relative to controls, bvFTD patients showed altered small-world properties and decreased global efficiency, suggesting a reduced ability to combine specialized information from distributed brain regions. At a local level, patients with bvFTD displayed lower values of local efficiency in the cortical thickness of the caudal and rostral middle frontal gyrus, rostral anterior cingulate, and precuneus, cuneus, and transverse temporal gyrus. A significant correlation was also found between the efficiency of caudal anterior cingulate thickness and Mini-Mental State Examination (MMSE) scores in bvFTD patients. Taken together, these findings confirm the selective disruption in structural brain networks of bvFTD patients, providing new insights on the association between cognitive decline and graph properties. Full article
(This article belongs to the Special Issue Recent Advances in Human Brain Connectivity)
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20 pages, 1701 KiB  
Article
Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory
by Farzad V. Farahani, Magdalena Fafrowicz, Waldemar Karwowski, Bartosz Bohaterewicz, Anna Maria Sobczak, Anna Ceglarek, Aleksandra Zyrkowska, Monika Ostrogorska, Barbara Sikora-Wachowicz, Koryna Lewandowska, Halszka Oginska, Anna Beres, Magdalena Hubalewska-Mazgaj and Tadeusz Marek
Brain Sci. 2021, 11(1), 111; https://doi.org/10.3390/brainsci11010111 - 16 Jan 2021
Cited by 14 | Viewed by 4607
Abstract
Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of [...] Read more.
Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual’s chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.97 ± 3.26 years; half morning- versus half evening-type) were scanned about 1 and 10 h after wake-up time for morning and evening sessions, respectively. We found evidence for a time-of-day effect on connectivity profiles but not for the effect of chronotype. Compared with the morning session, we found relatively higher small-worldness (an index that represents more efficient network organization) in the evening session, which suggests the dominance of sleep inertia over the circadian and homeostatic processes in the first hours after waking. Furthermore, local graph measures were changed, predominantly across the left hemisphere, in areas such as the precentral gyrus, putamen, inferior frontal gyrus (orbital part), inferior temporal gyrus, as well as the bilateral cerebellum. These findings show the variability of the functional neural network architecture during the day and improve our understanding of the role of time of day in resting-state functional networks. Full article
(This article belongs to the Special Issue Recent Advances in Human Brain Connectivity)
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17 pages, 729 KiB  
Article
Association between Structural Connectivity and Generalized Cognitive Spectrum in Alzheimer’s Disease
by Angela Lombardi, Nicola Amoroso, Domenico Diacono, Alfonso Monaco, Giancarlo Logroscino, Roberto De Blasi, Roberto Bellotti and Sabina Tangaro
Brain Sci. 2020, 10(11), 879; https://doi.org/10.3390/brainsci10110879 - 20 Nov 2020
Cited by 15 | Viewed by 3814
Abstract
Modeling disease progression through the cognitive scores has become an attractive challenge in the field of computational neuroscience due to its importance for early diagnosis of Alzheimer’s disease (AD). Several scores such as Alzheimer’s Disease Assessment Scale cognitive total score, Mini Mental State [...] Read more.
Modeling disease progression through the cognitive scores has become an attractive challenge in the field of computational neuroscience due to its importance for early diagnosis of Alzheimer’s disease (AD). Several scores such as Alzheimer’s Disease Assessment Scale cognitive total score, Mini Mental State Exam score and Rey Auditory Verbal Learning Test provide a quantitative assessment of the cognitive conditions of the patients and are commonly used as objective criteria for clinical diagnosis of dementia and mild cognitive impairment (MCI). On the other hand, connectivity patterns extracted from diffusion tensor imaging (DTI) have been successfully used to classify AD and MCI subjects with machine learning algorithms proving their potential application in the clinical setting. In this work, we carried out a pilot study to investigate the strength of association between DTI structural connectivity of a mixed ADNI cohort and cognitive spectrum in AD. We developed a machine learning framework to find a generalized cognitive score that summarizes the different functional domains reflected by each cognitive clinical index and to identify the connectivity biomarkers more significantly associated with the score. The results indicate that the efficiency and the centrality of some regions can effectively track cognitive impairment in AD showing a significant correlation with the generalized cognitive score (R = 0.7). Full article
(This article belongs to the Special Issue Recent Advances in Human Brain Connectivity)
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22 pages, 3509 KiB  
Article
Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures
by Chella Kamarajan, Babak A. Ardekani, Ashwini K. Pandey, Sivan Kinreich, Gayathri Pandey, David B. Chorlian, Jacquelyn L. Meyers, Jian Zhang, Elaine Bermudez, Arthur T. Stimus and Bernice Porjesz
Brain Sci. 2020, 10(2), 115; https://doi.org/10.3390/brainsci10020115 - 20 Feb 2020
Cited by 29 | Viewed by 6027
Abstract
Individuals with alcohol use disorder (AUD) are known to manifest a variety of neurocognitive impairments that can be attributed to alterations in specific brain networks. The current study aims to identify specific features of brain connectivity, neuropsychological performance, and impulsivity traits that can [...] Read more.
Individuals with alcohol use disorder (AUD) are known to manifest a variety of neurocognitive impairments that can be attributed to alterations in specific brain networks. The current study aims to identify specific features of brain connectivity, neuropsychological performance, and impulsivity traits that can classify adult males with AUD (n = 30) from healthy controls (CTL, n = 30) using the Random Forest (RF) classification method. The predictor variables were: (i) fMRI-based within-network functional connectivity (FC) of the Default Mode Network (DMN), (ii) neuropsychological scores from the Tower of London Test (TOLT), and the Visual Span Test (VST), and (iii) impulsivity factors from the Barratt Impulsiveness Scale (BIS). The RF model, with a classification accuracy of 76.67%, identified fourteen DMN connections, two neuropsychological variables (memory span and total correct scores of the forward condition of the VST), and all impulsivity factors as significantly important for classifying participants into either the AUD or CTL group. Specifically, the AUD group manifested hyperconnectivity across the bilateral anterior cingulate cortex and the prefrontal cortex as well as between the bilateral posterior cingulate cortex and the left inferior parietal lobule, while showing hypoconnectivity in long-range anterior–posterior and interhemispheric long-range connections. Individuals with AUD also showed poorer memory performance and increased impulsivity compared to CTL individuals. Furthermore, there were significant associations among FC, impulsivity, neuropsychological performance, and AUD status. These results confirm the previous findings that alterations in specific brain networks coupled with poor neuropsychological functioning and heightened impulsivity may characterize individuals with AUD, who can be efficiently identified using classification algorithms such as Random Forest. Full article
(This article belongs to the Special Issue Recent Advances in Human Brain Connectivity)
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14 pages, 1388 KiB  
Article
Dual Networks for High-Precision and High-Speed Registration of Brain Electron Microscopy Images
by Chang Shu, Tong Xin, Fangxu Zhou, Xi Chen and Hua Han
Brain Sci. 2020, 10(2), 86; https://doi.org/10.3390/brainsci10020086 - 7 Feb 2020
Cited by 5 | Viewed by 3001
Abstract
It remains a mystery as to how neurons are connected and thereby enable use to think, and volume reconstruction from series of microscopy sections of brains is a vital technique in determining this connectivity. Image registration is a key component; the aim of [...] Read more.
It remains a mystery as to how neurons are connected and thereby enable use to think, and volume reconstruction from series of microscopy sections of brains is a vital technique in determining this connectivity. Image registration is a key component; the aim of image registration is to estimate the deformation field between two images. Current methods choose to directly regress the deformation field; however, this task is very challenging. It is common to trade off computational complexity with precision when designing complex models for deformation field estimation. This approach is very inefficient, leading to a long inference time. In this paper, we suggest that complex models are not necessary and solve this dilemma by proposing a dual-network architecture. We divide the deformation field prediction problem into two relatively simple subproblems and solve each of them on one branch of the proposed dual network. The two subproblems have completely opposite properties, and we fully utilize these properties to simplify the design of the dual network. These simple architectures enable high-speed image registration. The two branches are able to work together and make up for each other’s drawbacks, and no loss of accuracy occurs even when simple architectures are involved. Furthermore, we introduce a series of loss functions to enable the joint training of the two networks in an unsupervised manner without introducing costly manual annotations. The experimental results reveal that our method outperforms state-of-the-art methods in fly brain electron microscopy image registration tasks, and further ablation studies enable us to obtain a comprehensive understanding of each component of our network. Full article
(This article belongs to the Special Issue Recent Advances in Human Brain Connectivity)
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29 pages, 6194 KiB  
Article
Brain Connectivity Analysis Under Semantic Vigilance and Enhanced Mental States
by Fares Al-Shargie, Usman Tariq, Omnia Hassanin, Hasan Mir, Fabio Babiloni and Hasan Al-Nashash
Brain Sci. 2019, 9(12), 363; https://doi.org/10.3390/brainsci9120363 - 9 Dec 2019
Cited by 32 | Viewed by 5664
Abstract
In this paper, we present a method to quantify the coupling between brain regions under vigilance and enhanced mental states by utilizing partial directed coherence (PDC) and graph theory analysis (GTA). The vigilance state is induced using a modified version of stroop color-word [...] Read more.
In this paper, we present a method to quantify the coupling between brain regions under vigilance and enhanced mental states by utilizing partial directed coherence (PDC) and graph theory analysis (GTA). The vigilance state is induced using a modified version of stroop color-word task (SCWT) while the enhancement state is based on audio stimulation with a pure tone of 250 Hz. The audio stimulation was presented to the right and left ears simultaneously for one-hour while participants perform the SCWT. The quantification of mental states was performed by means of statistical analysis of indexes based on GTA, behavioral responses of time-on-task (TOT), and Brunel Mood Scale (BRMUS). The results show that PDC is very sensitive to vigilance decrement and shows that the brain connectivity network is significantly reduced with increasing TOT, p < 0.05. Meanwhile, during the enhanced state, the connectivity network maintains high connectivity as time passes and shows significant improvements compared to vigilance state. The audio stimulation enhances the connectivity network over the frontal and parietal regions and the right hemisphere. The increase in the connectivity network correlates with individual differences in the magnitude of the vigilance enhancement assessed by response time to stimuli. Our results provide evidence for enhancement of cognitive processing efficiency with audio stimulation. The BRMUS was used to evaluate the emotional states of vigilance task before and after using the audio stimulation. BRMUS factors, such as fatigue, depression, and anger, significantly decrease in the enhancement group compared to vigilance group. On the other hand, happy and calmness factors increased with audio stimulation, p < 0.05. Full article
(This article belongs to the Special Issue Recent Advances in Human Brain Connectivity)
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20 pages, 974 KiB  
Article
Estimation of Brain Functional Connectivity in Patients with Mild Cognitive Impairment
by Laia Farràs-Permanyer, Núria Mancho-Fora, Marc Montalà-Flaquer, Esteve Gudayol-Ferré, Geisa Bearitz Gallardo-Moreno, Daniel Zarabozo-Hurtado, Erwin Villuendas-González, Maribel Peró-Cebollero and Joan Guàrdia-Olmos
Brain Sci. 2019, 9(12), 350; https://doi.org/10.3390/brainsci9120350 - 30 Nov 2019
Cited by 6 | Viewed by 3630
Abstract
Mild cognitive impairment is defined as greater cognitive decline than expected for a person at a particular age and is sometimes considered a stage between healthy aging and Alzheimer’s disease or other dementia syndromes. It is known that functional connectivity patterns change in [...] Read more.
Mild cognitive impairment is defined as greater cognitive decline than expected for a person at a particular age and is sometimes considered a stage between healthy aging and Alzheimer’s disease or other dementia syndromes. It is known that functional connectivity patterns change in people with this diagnosis. We studied functional connectivity patterns and functional segregation in a resting-state fMRI paradigm comparing 10 MCI patients and 10 healthy controls matched by education level, age and sex. Ninety ROIs from the automated anatomical labeling (AAL) atlas were selected for functional connectivity analysis. A correlation matrix was created for each group, and a third matrix with the correlation coefficient differences between the two matrices was created. Functional segregation was analyzed with the 3-cycle method, which is novel in studies of this topic. Finally, cluster analyses were also performed. Our results showed that the two correlation matrices were visually similar but had many differences related to different cognitive functions. Differences were especially apparent in the anterior default mode network (DMN), while the visual resting-state network (RSN) showed no differences between groups. Differences in connectivity patterns in the anterior DMN should be studied more extensively to fully understand its role in the differentiation of healthy aging and an MCI diagnosis. Full article
(This article belongs to the Special Issue Recent Advances in Human Brain Connectivity)
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Review

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20 pages, 1441 KiB  
Review
Brain Molecular Connectivity in Neurodegenerative Conditions
by Giulia Carli, Giacomo Tondo, Cecilia Boccalini and Daniela Perani
Brain Sci. 2021, 11(4), 433; https://doi.org/10.3390/brainsci11040433 - 28 Mar 2021
Cited by 8 | Viewed by 4344
Abstract
Positron emission tomography (PET) allows for the in vivo assessment of early brain functional and molecular changes in neurodegenerative conditions, representing a unique tool in the diagnostic workup. The increased use of multivariate PET imaging analysis approaches has provided the chance to investigate [...] Read more.
Positron emission tomography (PET) allows for the in vivo assessment of early brain functional and molecular changes in neurodegenerative conditions, representing a unique tool in the diagnostic workup. The increased use of multivariate PET imaging analysis approaches has provided the chance to investigate regional molecular processes and long-distance brain circuit functional interactions in the last decade. PET metabolic and neurotransmission connectome can reveal brain region interactions. This review is an overview of concepts and methods for PET molecular and metabolic covariance assessment with evidence in neurodegenerative conditions, including Alzheimer’s disease and Lewy bodies disease spectrum. We highlight the effects of environmental and biological factors on brain network organization. All of the above might contribute to innovative diagnostic tools and potential disease-modifying interventions. Full article
(This article belongs to the Special Issue Recent Advances in Human Brain Connectivity)
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