scholarly journals Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Simon Wein ◽  
Gustavo Deco ◽  
Ana Maria Tomé ◽  
Markus Goldhacker ◽  
Wilhelm M. Malloni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffusion Tensor ◽  
Imaging Techniques ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Machine Learning Techniques ◽  
Short Survey ◽  
Functional Dynamics ◽  
Axonal Fiber

This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

scholarly journals Integrative Structural Brain Network Analysis In Diffusion Tensor Imaging

2017 ◽  
Author(s):  
Moo K. Chung ◽  
Jamie L. Hanson ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Richard J. Davidson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Node Degree ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
Structural Brain Network ◽  
Structural Networks

AbstractIn diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length and FA-values into the connectivity model. Using various node-degree based graph theory features, the three connectivity models are compared. The methods are applied in characterizing structural networks between normal controls and maltreated children, who experienced maltreatment while living in post-institutional settings before being adopted by families in the US.

scholarly journals Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

2021 ◽  
Author(s):  
David Pascucci ◽  
Maria Rubega ◽  
Joan Rue-Queralt ◽  
Sebastien Tourbier ◽  
Patric Hagmann ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Diffusion Tensor ◽  
Meta Analysis ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Noise Perturbation ◽  
Eeg Data ◽  
Functional Brain Networks ◽  
Anatomical Priors ◽  
And Diffusion

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections: the lack of a direct structural link between two brain regions prevents direct functional interactions. Despite the intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited, especially for electrophysiological data. In the present work, we propose a new linear adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. Our results show that SC priors increase the resilience of FC estimates to noise perturbation while promoting sparser networks under biologically plausible constraints. The proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new method for multimodal imaging and dynamic FC analysis.

scholarly journals Connectivity changes in major depressive disorder after rTMS: a review of functional and structural connectivity data

2021 ◽  
Vol 30 ◽  
Author(s):  
G. Schiena ◽  
G. Franco ◽  
A. Boscutti ◽  
G. Delvecchio ◽  
E. Maggioni ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Small Sample Size ◽  
Structural Connectivity ◽  
Unipolar Depression ◽  
Brain Regions ◽  
Small Sample ◽  
Anterior Cingulate ◽  
Antidepressant Effect ◽  
Dorsolateral Prefrontal

Abstract Aims In the search for effective therapeutic strategies for depression, repetitive transcranial magnetic stimulation (rTMS) emerged as a non-invasive, promising treatment. This is because the antidepressant effect of rTMS might be related to neuronal plasticity mechanisms possibly reverting connectivity alterations often observed in depression. Therefore, in this review, we aimed at providing an overview of the findings reported by studies investigating functional and structural connectivity changes after rTMS in depression. Methods A bibliographic search was conducted on PubMed, including studies that used unilateral, excitatory (⩾10 Hz) rTMS treatment targeted on the left dorsolateral prefrontal cortex (DLPFC) in unipolar depressed patients. Results The majority of the results showed significant TMS-induced changes in functional connectivity (FC) between areas important for emotion regulation, including the DLPFC and the subgenual anterior cingulate cortex, and among regions that are part of the major resting-state networks, such as the Default Mode Network, the Salience Networks and the Central Executive Network. Finally, in diffusion tensor imaging studies, it has been reported that rTMS appeared to increase fractional anisotropy in the frontal lobe. Limitations The small sample size, the heterogeneity of the rTMS stimulation parameters, the concomitant use of psychotropic drugs might have limited the generalisability of the results. Conclusions Overall, rTMS treatment induces structural and FC changes in brain regions and networks implicated in the pathogenesis of unipolar depression. However, whether these changes underlie the antidepressant effect of rTMS still needs to be clarified.

scholarly journals Asymmetric high-order anatomical brain connectivity sculpts effective connectivity

2020 ◽  
Vol 4 (3) ◽  
pp. 871-890
Author(s):  
Arseny A. Sokolov ◽  
Peter Zeidman ◽  
Adeel Razi ◽  
Michael Erb ◽  
Philippe Ryvlin ◽  
...  
Keyword(s):  
White Matter ◽  
Dynamic Range ◽  
Diffusion Processes ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Structural Connectivity ◽  
Laplacian Matrix ◽  
Graph Laplacian ◽  
Brain Regions ◽  
High Order

Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics. Crucially, an asymmetric diffusion process determined by the sensitivity of the network nodes to their afferents best predicted effective connectivity. The outcome is consistent with brain regions adapting to maintain their sensitivity to inputs within a dynamic range. Asymmetric network communication models offer a promising perspective for understanding the relationship between structural and functional brain connectomes, both in normalcy and neuropsychiatric conditions.

scholarly journals Large-scale functional coactivation patterns reflect the structural connectivity of the medial prefrontal cortex

2020 ◽  
Author(s):  
Dale T Tovar ◽  
Robert S Chavez
Keyword(s):  
Prefrontal Cortex ◽  
Medial Prefrontal Cortex ◽  
Large Scale ◽  
Spatial Scales ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Machine Learning Techniques ◽  
Affective Processing ◽  
Affective Neuroscience ◽  
Psychological Phenomena

Abstract The medial prefrontal cortex (MPFC) is among the most consistently implicated brain regions in social and affective neuroscience. Yet, this region is also highly functionally heterogeneous across many domains and has diverse patterns of connectivity. The extent to which the communication of functional networks in this area is facilitated by its underlying structural connectivity fingerprint is critical for understanding how psychological phenomena are represented within this region. In the current study, we combined diffusion magnetic resonance imaging and probabilistic tractography with large-scale meta-analysis to investigate the degree to which the functional co-activation patterns of the MPFC is reflected in its underlying structural connectivity. Using unsupervised machine learning techniques, we compared parcellations between the two modalities and found congruence between parcellations at multiple spatial scales. Additionally, using connectivity and coactivation similarity analyses, we found high correspondence in voxel-to-voxel similarity between each modality across most, but not all, subregions of the MPFC. These results provide evidence that meta-analytic functional coactivation patterns are meaningfully constrained by underlying neuroanatomical connectivity and provide convergent evidence of distinct subregions within the MPFC involved in affective processing and social cognition.

scholarly journals Unique mapping of structural and functional connectivity on cognition

2018 ◽  
Author(s):  
J. Zimmermann ◽  
J.G. Griffiths ◽  
A.R. McIntosh
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Unique Mapping ◽  
Human Connectome Project ◽  
Behavioural Patterns ◽  
Wide Range ◽  
Psychological Tasks ◽  
New Evidence

AbstractThe unique mapping of structural and functional brain connectivity (SC, FC) on cognition is currently not well understood. It is not clear whether cognition is mapped via a global connectome pattern or instead is underpinned by several sets of distributed connectivity patterns. Moreover, we also do not know whether the pattern of SC and of FC that underlie cognition are overlapping or distinct. Here, we study the relationship between SC and FC and an array of psychological tasks in 609 subjects from the Human Connectome Project (HCP). We identified several sets of connections that each uniquely map onto different aspects of cognitive function. We found a small number of distributed SC and a larger set of cortico-cortical and cortico-subcortical FC that express this association. Importantly, SC and FC each show unique and distinct patterns of variance across subjects and differential relationships to cognition. The results suggest that a complete understanding of connectome underpinnings of cognition calls for a combination of the two modalities.Significance StatementStructural connectivity (SC), the physical white-matter inter-regional pathways in the brain, and functional connectivity (FC), the temporal co-activations between activity of brain regions, have each been studied extensively. Little is known, however, about the distribution of variance in connections as they relate to cognition. Here, in a large sample of subjects (N = 609), we showed that two sets of brain-behavioural patterns capture the correlations between SC, and FC with a wide range of cognitive tasks, respectively. These brain-behavioural patterns reveal distinct sets of connections within the SC and the FC network and provide new evidence that SC and FC each provide unique information for cognition.

scholarly journals The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

2020 ◽  
pp. 1-15
Author(s):  
Tommy Boshkovski ◽  
Ljupco Kocarev ◽  
Julien Cohen-Adad ◽  
Bratislav Mišić ◽  
Stéphane Lehéricy ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Added Value ◽  
Modular Organization ◽  
Sensitive Measure ◽  
Higher Order Functions ◽  
Insight Into

Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.

scholarly journals Effects of Daily Iron Supplementation on Motor Development and Brain Connectivity in Preterm Infants: A Diffusion Magnetic Resonance Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Mingyan Li ◽  
Chai Ji ◽  
Weifeng Xuan ◽  
Weijun Chen ◽  
Ying Lv ◽  
...  
Keyword(s):  
Preterm Infants ◽  
Serum Ferritin ◽  
Motor Development ◽  
Iron Supplementation ◽  
Brain Connectivity ◽  
Iron Status ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Postnatal Period ◽  
Motor Delay

Objectives: The aim of the study is to demonstrate the characteristic of motor development and MRI changes of related brain regions in preterm infants with different iron statuses and to determine whether the daily iron supplementation can promote motor development for preterm in early infancy.Methods: The 63 preterm infants were grouped into non-anemia with higher serum ferritin (NA-HF) group and anemia with lower serum ferritin (A-LF) group according to their lowest serum Hb level in the neonatal period as well as the sFer at 3 months old. Forty-nine participants underwent MRI scans and Infant Neurological International Battery (INFANIB) at their 3 months. At 6 months of corrected age, these infants received the assessment of Peabody Developmental Motor Scales (PDMS) after 2 mg/kg/day iron supplementation.Results: In total, 19 preterm infants were assigned to the NA-HF group while 44 preterm infants to the A-LF groups. The serum ferritin (sFer) level of the infants in A-LF group was lower than that in NA-HF group (44.0 ± 2.8 mg/L vs. 65.1 ± 2.8 mg/L, p < 0.05) and was with poorer scores of INFANIB (66.8 ± 0.9 vs. 64.4 ± 0.6, p < 0.05) at 3 months old. The structural connectivity between cerebellum and ipsilateral thalamus in the NA-HF group was significantly stronger than that in the A-LF group (n = 17, 109.76 ± 23.8 vs. n = 32, 70.4 ± 6.6, p < 0.05). The decreased brain structural connectivity was positively associated with the scores of PDMS (r = 0.347, p < 0.05). After 6 months of routine iron supplementation, no difference in Hb, MCV, MCHC, RDW, and sFer was detected between A-LF and NA-HF groups as well as the motor scores of PDMS-2 assessments.Conclusion: Iron status at early postnatal period of preterm infant is related to motor development and the enrichment of brain structural connectivity. The decrease in brain structural connectivity is related to the motor delay. After supplying 2 mg/kg of iron per day for 6 months, the differences in the iron status and motor ability between the A-LF and NA-HF groups were eliminated.

scholarly journals Leveraging Neural Networks in Preclinical Alcohol Research

2020 ◽  
Vol 10 (9) ◽  
pp. 578
Author(s):  
Lauren C. Smith ◽  
Adam Kimbrough
Keyword(s):  
Neural Networks ◽  
Alcohol Use ◽  
Alcohol Drinking ◽  
Imaging Techniques ◽  
Structural Connectivity ◽  
Three Dimensional ◽  
Brain Regions ◽  
Tissue Imaging ◽  
Imaging Data ◽  
Neural Imaging

Alcohol use disorder is a pervasive healthcare issue with significant socioeconomic consequences. There is a plethora of neural imaging techniques available at the clinical and preclinical level, including magnetic resonance imaging and three-dimensional (3D) tissue imaging techniques. Network-based approaches can be applied to imaging data to create neural networks that model the functional and structural connectivity of the brain. These networks can be used to changes to brain-wide neural signaling caused by brain states associated with alcohol use. Neural networks can be further used to identify key brain regions or neural “hubs” involved in alcohol drinking. Here, we briefly review the current imaging and neurocircuit manipulation methods. Then, we discuss clinical and preclinical studies using network-based approaches related to substance use disorders and alcohol drinking. Finally, we discuss how preclinical 3D imaging in combination with network approaches can be applied alone and in combination with other approaches to better understand alcohol drinking.

Sign in / Sign up

Export Citation Format

Share Document