Consensus clustering approach to group brain connectivity matrices

A novel approach rooted on the notion of consensus clustering, a strategy developed for community detection in complex networks, is proposed to cope with the heterogeneity that characterizes connectivity matrices in health and disease. The method can be summarized as follows: (a) define, for each node, a distance matrix for the set of subjects by comparing the connectivity pattern of that node in all pairs of subjects; (b) cluster the distance matrix for each node; (c) build the consensus network from the corresponding partitions; and (d) extract groups of subjects by finding the communities of the consensus network thus obtained. Different from the previous implementations of consensus clustering, we thus propose to use the consensus strategy to combine the information arising from the connectivity patterns of each node. The proposed approach may be seen either as an exploratory technique or as an unsupervised pretraining step to help the subsequent construction of a supervised classifier. Applications on a toy model and two real datasets show the effectiveness of the proposed methodology, which represents heterogeneity of a set of subjects in terms of a weighted network, the consensus matrix.

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A Bimodal Fluorescence-Raman Probe for Cellular Imaging

Cells ◽

10.3390/cells10071699 ◽

2021 ◽

Vol 10 (7) ◽

pp. 1699

Author(s):

Jiarun Lin ◽

Marcus E. Graziotto ◽

Peter A. Lay ◽

Elizabeth J. New

Keyword(s):

Chemical Composition ◽

Fluorescent Probes ◽

Lipid Droplets ◽

Imaging Techniques ◽

Spectroscopic Imaging ◽

Fluorescent Sensors ◽

Chemical Environment ◽

Novel Approach ◽

Health And Disease ◽

Raman Probe

Biochemical changes in specific organelles underpin cellular function, and studying these changes is crucial to understand health and disease. Fluorescent probes have become important biosensing and imaging tools as they can be targeted to specific organelles and can detect changes in their chemical environment. However, the sensing capacity of fluorescent probes is highly specific and is often limited to a single analyte of interest. A novel approach to imaging organelles is to combine fluorescent sensors with vibrational spectroscopic imaging techniques; the latter provides a comprehensive map of the relative biochemical distributions throughout the cell to gain a more complete picture of the biochemistry of organelles. We have developed NpCN1, a bimodal fluorescence-Raman probe targeted to the lipid droplets, incorporating a nitrile as a Raman tag. NpCN1 was successfully used to image lipid droplets in 3T3-L1 cells in both fluorescence and Raman modalities, reporting on the chemical composition and distribution of the lipid droplets in the cells.

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A Novel Pharmacogenetic Model for Highly Efficient Ablation of Oligodendrocyte Progenitor Cells in the Adult Mouse CNS

10.1101/2021.05.13.443012 ◽

2021 ◽

Author(s):

Yao Lulu Xing ◽

Bernard H.A. Chuang ◽

Jasmine Poh ◽

Kaveh Moradi ◽

Stanislaw Mitew ◽

...

Keyword(s):

Progenitor Cells ◽

Adult Mouse ◽

Oligodendrocyte Progenitor Cells ◽

Oligodendrocyte Progenitor ◽

Cell Ablation ◽

Novel Approach ◽

Anterior Forebrain ◽

Health And Disease ◽

The Brain ◽

Advance Knowledge

Approaches to investigate adult oligodendrocyte progenitor cells (OPCs) by targeted cell ablation in the rodent central nervous system have been limited by methodological challenges resulting in only partial and transient OPC depletion. We have developed a novel pharmacogenetic model of conditional OPC ablation, resulting in the elimination of 99.7% of all OPCs throughout the brain. By combining recombinase-based transgenic and viral strategies for targeting of OPCs and ventricular-subventricular zone (V-SVZ)-derived neural precursor cells (NPCs), we found that new PDGFRα-expressing cells born in the V-SVZ repopulated the OPC-deficient brain starting 12 days after OPC ablation. Our data reveal that OPC depletion induces V-SVZ-derived NPCs to generate vast numbers of PDGFRα+/NG2+ cells with the capacity to migrate and proliferate extensively throughout the dorsal anterior forebrain. Further application of this novel approach to ablate OPCs will advance knowledge of the function of both OPCs and oligodendrogenic NPCs in health and disease.

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Dynamics of brain connectivity after stroke

Reviews in the Neurosciences ◽

10.1515/revneuro-2018-0082 ◽

2019 ◽

Vol 30 (6) ◽

pp. 605-623 ◽

Cited By ~ 5

Author(s):

Adela Desowska ◽

Duncan L. Turner

Keyword(s):

Brain Connectivity ◽

Effective Connectivity ◽

Network Connectivity ◽

Connectivity Pattern ◽

Compensatory Mechanisms ◽

Neural Structure ◽

Motor Network ◽

The Central Nervous System ◽

Dynamic Time ◽

The Impact

Abstract Recovery from a stroke is a dynamic time-dependent process, in which the central nervous system reorganises to accommodate for the impact of the injury. The purpose of this paper is to review recent longitudinal studies of changes in brain connectivity after stroke. A systematic review of research papers reporting functional or effective connectivity at two or more time points in stroke patients was conducted. Stroke leads to an early reduction of connectivity in the motor network. With recovery time, the connectivity increases and can reach the same levels as in healthy participants. The increase in connectivity is correlated with functional motor gains. A new, more randomised pattern of connectivity may then emerge in the longer term. In some instances, a pattern of increased connectivity even higher than in healthy controls can be observed, and is related either to a specific time point or to a specific neural structure. Rehabilitation interventions can help improve connectivity between specific regions. Moreover, motor network connectivity undergoes reorganisation during recovery from a stroke and can be related to behavioural recovery. A detailed analysis of changes in connectivity pattern may enable a better understanding of adaptation to a stroke and how compensatory mechanisms in the brain may be supported by rehabilitation.

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A novel approach to brain connectivity reveals early structural changes in Alzheimer’s disease

Physiological Measurement ◽

10.1088/1361-6579/aacf1f ◽

2018 ◽

Vol 39 (7) ◽

pp. 074005 ◽

Cited By ~ 3

Author(s):

Marianna La Rocca ◽

Nicola Amoroso ◽

Alfonso Monaco ◽

Roberto Bellotti ◽

Sabina Tangaro ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Structural Changes ◽

Brain Connectivity ◽

Novel Approach

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Multivariate Pattern Analysis of fMRI in Breast Cancer Survivors and Healthy Women

Journal of the International Neuropsychological Society ◽

10.1017/s1355617713001173 ◽

2013 ◽

Vol 20 (4) ◽

pp. 391-401 ◽

Cited By ~ 17

Author(s):

S.M. Hadi Hosseini ◽

Shelli R. Kesler

Keyword(s):

Breast Cancer ◽

Functional Connectivity ◽

Breast Cancer Survivors ◽

Pattern Analysis ◽

Brain Connectivity ◽

Survival Rates ◽

Connectivity Pattern ◽

Multivariate Pattern Analysis ◽

The Right ◽

Multivariate Pattern

AbstractAdvances in breast cancer (BC) treatments have resulted in significantly improved survival rates. However, BC chemotherapy is often associated with several side effects including cognitive dysfunction. We applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) to find a brain connectivity pattern that accurately and automatically distinguishes chemotherapy-treated (C+) from non-chemotherapy treated (C−) BC females and healthy female controls (HC). Twenty-seven C+, 29 C−, and 30 HC underwent fMRI during an executive-prefrontal task (Go/Nogo). The pattern of functional connectivity associated with this task discriminated with significant accuracy between C+ and HC groups (72%, p = .006) and between C+ and C− groups (71%, p = .012). However, the accuracy of discrimination between C− and HC was not significant (51%, p = .46). Compared with HC, behavioral performance of the C+ and C− groups during the task was intact. However, the C+ group demonstrated altered functional connectivity in the right frontoparietal and left supplementary motor area networks compared to HC, and in the right middle frontal and left superior frontal gyri networks, compared to C−. Our results provide further evidence that executive function performance may be preserved in some chemotherapy-treated BC survivors through recruitment of additional neural connections. (JINS, 2013, 19, 1–11)

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How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network

Computational and Mathematical Methods in Medicine ◽

10.1155/2012/130985 ◽

2012 ◽

Vol 2012 ◽

pp. 1-13 ◽

Cited By ~ 43

Author(s):

J. Toppi ◽

F. De Vico Fallani ◽

G. Vecchiato ◽

A. G. Maglione ◽

F. Cincotti ◽

...

Keyword(s):

Functional Connectivity ◽

Adjacency Matrix ◽

Brain Connectivity ◽

Small World ◽

Connectivity Pattern ◽

Edge Density ◽

Statistical Validation ◽

Common Procedure ◽

Connectivity Patterns ◽

Definition Of

The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.

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Concurrent tACS-fMRI reveals causal influence of power synchronized neural activity on resting state fMRI connectivity

10.1101/122820 ◽

2017 ◽

Cited By ~ 1

Author(s):

Marc Bächinger ◽

Valerio Zerbi ◽

Marius Moisa ◽

Rafael Polania ◽

Quanying Liu ◽

...

Keyword(s):

Resting State ◽

Right Hemisphere ◽

Brain Connectivity ◽

Resting State Fmri ◽

Causal Link ◽

Resting State Networks ◽

Novel Approach ◽

Electrophysiological Signals ◽

Power Fluctuations ◽

Transcranial Alternating Current Stimulation

AbstractResting state fMRI (rs-fMRI) is commonly used to study the brain’s intrinsic neural coupling, which reveals specific spatiotemporal patterns in the form of resting state networks (RSN). It has been hypothesized that slow rs-fMRI oscillations (<0.1 Hz) are driven by underlying electrophysiological rhythms that typically occur at much faster timescales (>5 Hz); however, causal evidence for this relationship is currently lacking. Here we measured rs-fMRI in humans while applying transcranial alternating current stimulation (tACS) to entrain brain rhythms in left and right sensorimotor cortices.The two driving tACS signals were tailored to the individual’s alpha rhythm (8-12 Hz) and fluctuated in amplitude according to a 1 Hz power envelope. We entrained the left versus right hemisphere in accordance to two different coupling modes where either alpha oscillations were synchronized between hemispheres (phase-synchronized tACS) or the slower oscillating power envelopes (power-synchronized tACS).Power-synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared to phase-synchronized or no tACS. This effect outlasted the stimulation period and tended to be more effective in individuals who exhibited a naturally weak interhemispheric coupling. Using this novel approach, our data provide causal evidence that synchronized power fluctuations contribute to the formation of fMRI-based RSNs. Moreover, our findings demonstrate that the brain’s intrinsic coupling at rest can be selectively modulated by choosing appropriate tACS signals, which could lead to new interventions for patients with altered rs-fMRI connectivity.Significance StatementResting state fMRI has become an important tool to estimate brain connectivity. However, relatively little is known about how slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes.It was suggested that slowly fluctuating power envelopes of electrophysiological signals synchronize across brain areas and that the topography of this activity is spatially correlated to resting state networks derived from rs-fMRI. Here we take a novel approach to address this problem and establish a causal link between the power fluctuations of electrophysiological signals and rs-fMRI via a new neuromodulation paradigm, which exploits these power-synchronization mechanisms.These novel mechanistic insights bridge different scientific domains and are of broad interest to researchers in the fields of Medical Imaging, Neuroscience, Physiology and Psychology.

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Individual Variation in Brain Network Topology Predicts Emotional Intelligence

10.1101/275768 ◽

2018 ◽

Author(s):

Ling George ◽

Lee Ivy ◽

Guimond Synthia ◽

Lutz Olivia ◽

Tandon Neeraj ◽

...

Keyword(s):

Emotional Intelligence ◽

Social Cognition ◽

Network Topology ◽

Resting State ◽

Large Scale ◽

Psychotic Disorders ◽

Brain Connectivity ◽

Distance Matrix ◽

Resting State Fmri ◽

Brain Network

AbstractBackgroundSocial cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits.ObjectiveUsing ‘resting-state’ fMRI (functional magnetic resonance imaging) and a trans-diagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition.MethodsStudy participants included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants. All participants underwent a resting-state fMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR).ResultsWe identified a region in the left superior parietal lobule (SPL) where individual network topology predicted emotional intelligence. Specifically, the association of this region with the Default Mode Network predicted higher emotional intelligence and association with the Dorsal Attention Network predicted lower emotional intelligence. This correlation was observed in both schizophrenia and healthy comparison participants.ConclusionPrevious studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts emotional intelligence.

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Constant community discovery in network based on consensus clustering

International Journal of Modern Physics B ◽

10.1142/s0217979214501264 ◽

2014 ◽

Vol 28 (19) ◽

pp. 1450126

Author(s):

Zongwen Liang ◽

Athina Petropulu ◽

Fan Yang ◽

Jianping Li

Keyword(s):

Complex Networks ◽

Community Detection ◽

Detection Algorithm ◽

Consensus Clustering ◽

Community Discovery ◽

Structural And Functional Properties ◽

Consensus Matrix ◽

Internal Properties ◽

Community Detection Algorithm ◽

Vertex Order

Community detection is a fundamental work to analyze the structural and functional properties of complex networks. There are many algorithms proposed to find the optimal communities of network. In this paper, we focus on how vertex order influences the results of community detection. By using consensus clustering, we discover communities and get a consensus matrix under different vertex orders. Based on the consensus matrix, we study the phenomenon that some nodes are always allocated in the same community even with different vertex permutations. We call this group of nodes as constant community and propose a constant community detection algorithm (CCDA) to find constant communities in network. We also further study the internal properties of constant communities and find constant communities play a guiding role in community detection. Finally, a discussion of constant communities is given in the hope of being useful to others working in this field.

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