The Graph of Our Mind

Graph theory in the last two decades penetrated sociology, molecular biology, genetics, chemistry, computer engineering, and numerous other fields of science. One of the more recent areas of its applications is the study of the connections of the human brain. By the development of diffusion magnetic resonance imaging (diffusion MRI), it is possible today to map the connections between the 1–1.5 cm2 regions of the gray matter of the human brain. These connections can be viewed as a graph. We have computed 1015-vertex graphs with thousands of edges for hundreds of human brains from one of the highest quality data sources: the Human Connectome Project. Here we analyze the male and female braingraphs graph-theoretically and show statistically significant differences in numerous parameters between the sexes: the female braingraphs are better expanders, have more edges, larger bipartition widths, and larger vertex cover than the braingraphs of the male subjects. These parameters are closely related to the quality measures of highly parallel computer interconnection networks: the better expanding property, the large bipartition width, and the large vertex cover characterize high-quality interconnection networks. We apply the data of 426 subjects and demonstrate the statistically significant (corrected) differences in 116 graph parameters between the sexes.

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Scales

The Age of Em ◽

10.1093/oso/9780198754626.003.0012 ◽

2016 ◽

Author(s):

Robin Hanson

Keyword(s):

Human Brain ◽

Electronic Circuit ◽

Parallel Computer ◽

Limiting Factors ◽

Limiting Factor ◽

Circuit Boards ◽

Electronic Circuits ◽

Spatial Size ◽

The Cost ◽

Human Brains

Can we say anything about the specific speeds at which ems can run? Because of brain parallelism, the cost of running an em should be nearly proportional to speed over a wide range of speeds. The upper limit of this proportional-cost em speed range is the “top cheap” speed, that is, the highest speed at which the cost is still nearly proportional to speed. To estimate this speed, we must consider how simulated neurons in em brains might both send faster signals, and more quickly compute what signals to send. Human brain neuron fibers send signals at speeds ranging from 0.5 to 120 meters per second. In contrast, signal speeds in electronic circuit boards today are typically about half the speed of light. If signals in em brains move at electronics speeds, that would be between one million and 300 million times faster than neuron signals. If signal delays are the limiting factor in em brain speed, then this ratio gives an estimate of the maximum speedup possible, at least if em brains have the same spatial size as human brains. proportionally larger speedups are possible if em brains can be made proportionally smaller. Regarding the computation of when to fire a simulated neuron, note that real neurons usually seem to take at least 20 milliseconds to react ( Tovee 1994 ), while even today electronic circuits can switch 10 billion times faster, in one-and-a-half trillionths of a second ( deal et al. 2010 ). A key question is thus: how many electronic circuit cycles does it take to execute a parallel computer program that emulates the firing of a single neuron? For example, if there were an algorithm that could compute a neuron firing in 10 000 of these fastest-known circuit cycles, then an emulation based on this algorithm would run a million times faster than the human brain. As quite complex parallel computer programs can be run in 10 000 cycles, em speedups of at least one million times seem feasible, provided that energy and cooling are cheap enough to profitably allow the use of these fastest electronic circuits. When energy and cooling are more strongly limiting factors, however, the top cheap speed could be slower.

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Effect of Multishell Diffusion MRI Acquisition Strategy and Parcellation Scale on Rich-Club Organization of Human Brain Structural Networks

Diagnostics ◽

10.3390/diagnostics11060970 ◽

2021 ◽

Vol 11 (6) ◽

pp. 970

Author(s):

Maedeh Khalilian ◽

Kamran Kazemi ◽

Mahshid Fouladivanda ◽

Malek Makki ◽

Mohammad Sadegh Helfroush ◽

...

Keyword(s):

Human Brain ◽

Clustering Coefficient ◽

Quality Data ◽

B Values ◽

Rich Club ◽

Global Efficiency ◽

Acquisition Strategy ◽

The Rich ◽

Structural Networks ◽

Single Shell

The majority of network studies of human brain structural connectivity are based on single-shell diffusion-weighted imaging (DWI) data. Recent advances in imaging hardware and software capabilities have made it possible to acquire multishell (b-values) high-quality data required for better characterization of white-matter crossing-fiber microstructures. The purpose of this study was to investigate the extent to which brain structural organization and network topology are affected by the choice of diffusion magnetic resonance imaging (MRI) acquisition strategy and parcellation scale. We performed graph-theoretical network analysis using DWI data from 35 Human Connectome Project subjects. Our study compared four single-shell (b = 1000, 3000, 5000, 10,000 s/mm2) and multishell sampling schemes and six parcellation scales (68, 200, 400, 600, 800, 1000 nodes) using five graph metrics, including small-worldness, clustering coefficient, characteristic path length, modularity and global efficiency. Rich-club analysis was also performed to explore the rich-club organization of brain structural networks. Our results showed that the parcellation scale and imaging protocol have significant effects on the network attributes, with the parcellation scale having a substantially larger effect. Regardless of the parcellation scale, the brain structural networks exhibited a rich-club organization with similar cortical distributions across the parcellation scales involving at least 400 nodes. Compared to single b-value diffusion acquisitions, the deterministic tractography using multishell diffusion imaging data consisting of shells with b-values higher than 5000 s/mm2 resulted in significantly improved fiber-tracking results at the locations where fiber bundles cross each other. Brain structural networks constructed using the multishell acquisition scheme including high b-values also exhibited significantly shorter characteristic path lengths, higher global efficiency and lower modularity. Our results showed that both parcellation scale and sampling protocol can significantly impact the rich-club organization of brain structural networks. Therefore, caution should be taken concerning the reproducibility of connectivity results with regard to the parcellation scale and sampling scheme.

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From the Quasi-Total Strong Differential to Quasi-Total Italian Domination in Graphs

Symmetry ◽

10.3390/sym13061036 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1036

Author(s):

Abel Cabrera Martínez ◽

Alejandro Estrada-Moreno ◽

Juan Alberto Rodríguez-Velázquez

Keyword(s):

Vertex Cover ◽

Domination Number ◽

Theoretical Computer Science ◽

Discrete Mathematics ◽

Special Issue ◽

Total Domination Number ◽

Theoretical Computer ◽

Graph Parameters ◽

Semitotal Domination ◽

Domination In Graphs

This paper is devoted to the study of the quasi-total strong differential of a graph, and it is a contribution to the Special Issue “Theoretical computer science and discrete mathematics” of Symmetry. Given a vertex x∈V(G) of a graph G, the neighbourhood of x is denoted by N(x). The neighbourhood of a set X⊆V(G) is defined to be N(X)=⋃x∈XN(x), while the external neighbourhood of X is defined to be Ne(X)=N(X)∖X. Now, for every set X⊆V(G) and every vertex x∈X, the external private neighbourhood of x with respect to X is defined as the set Pe(x,X)={y∈V(G)∖X:N(y)∩X={x}}. Let Xw={x∈X:Pe(x,X)≠⌀}. The strong differential of X is defined to be ∂s(X)=|Ne(X)|−|Xw|, while the quasi-total strong differential of G is defined to be ∂s*(G)=max{∂s(X):X⊆V(G)andXw⊆N(X)}. We show that the quasi-total strong differential is closely related to several graph parameters, including the domination number, the total domination number, the 2-domination number, the vertex cover number, the semitotal domination number, the strong differential, and the quasi-total Italian domination number. As a consequence of the study, we show that the problem of finding the quasi-total strong differential of a graph is NP-hard.

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Effects of Physiological Increments in Human α-Atrial Natriuretic Peptide and Human Brain Natriuretic Peptide in Normal Male Subjects

Clinical Science ◽

10.1042/cs0860723 ◽

1994 ◽

Vol 86 (6) ◽

pp. 723-730 ◽

Cited By ~ 22

Author(s):

B. M. Y. Cheung ◽

J. E. C. Dickerson ◽

M. J. Ashby ◽

M. J. Brown ◽

J. Brown

Keyword(s):

Atrial Natriuretic Peptide ◽

Human Brain ◽

Brain Natriuretic Peptide ◽

Natriuretic Peptide ◽

Physiological Role ◽

Sodium Balance ◽

Urinary Flow ◽

Male Subjects ◽

The Brain ◽

Atrial Natriuretic

1. Brain natriuretic peptide, closely related to atrial natriuretic peptide in structure, may be an important circulating hormone. Its physiological role is unclear. First, we studied the effects of incremental infusions of brain natriuretic peptide in six healthy men on plasma brain natriuretic peptide levels and the pharmacokinetics of brain natriuretic peptide. Synthetic human brain natriuretic peptide-32 was infused intravenously, at an initial rate of 0.4 pmol min−1 kg−1, doubling every 15 min until the dose rate reached 6.4 pmol min−1 kg−1, at which rate the infusion was maintained for 30 min. 2. The brain natriuretic peptide infusion raised the brain natriuretic peptide-like immunoreactivity from 1.4 ± 0.5 pmol/l to 21.4 ± 7.6 pmol/l. Brain natriuretic peptide-like immunoreactivity after the end of infusion was consistent with a bi-exponential decay, with half-lives of 2.1 min and 37 min. 3. Next, we studied the effects of low-dose infusion of brain natriuretic peptide to mimic physiological increments in the circulating levels in comparison with atrial natriuretic peptide. Six dehydrated male subjects received intravenous infusions of atrial natriuretic peptide and brain natriuretic peptide, separately and in combination, in a randomized double-blind, placebo-controlled, four-part cross-over design. Atrial natriuretic peptide and brain natriuretic peptide were given at the rate of 0.75 and 0.4 pmol min−1 kg−1, respectively, for 3 h. The control infusion consisted of the vehicle. 4. Analysis of variance showed that atrial natriuretic peptide and atrial natriuretic peptide plus brain natriuretic peptide, but not brain natriuretic peptide alone, increased urinary flow and decreased urinary osmolality significantly. However, urinary sodium excretion was significantly increased by atrial natriuretic peptide, brain natriuretic peptide and atrial natriuretic peptide plus brain natriuretic peptide. 5. None of the four infusates significantly altered the blood pressure, heart rate or glomerular filtration rate. 6. This study showed, for the first time, that physiological increments in brain natriuretic peptide, like those in atrial natriuretic peptide, are natriuretic. Although atrial natriuretic peptide and brain natriuretic peptide do not appear to interact synergistically, they are likely to act in concert in the physiological regulation of sodium balance.

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Using diffusion MRI data acquired with ultra-high gradients to improve tractography in routine-quality data

10.1101/2021.06.28.450265 ◽

2021 ◽

Author(s):

Chiara Maffei ◽

Christine Lee ◽

Michael Planich ◽

Manisha Ramprasad ◽

Nivedita Ravi ◽

...

Keyword(s):

Diffusion Mri ◽

Region Of Interest ◽

Training Data ◽

Quality Data ◽

High Quality ◽

Scanning Time ◽

Human Connectome Project ◽

High Gradients ◽

Comprehensive Comparison

The development of scanners with ultra-high gradients, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from today's state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.

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Cellular resolution anatomical and molecular atlases for prenatal human brains

10.1101/2021.07.14.452297 ◽

2021 ◽

Author(s):

Song-Lin Ding ◽

Joshua J. Royall ◽

Phil Lesnar ◽

Benjamin A.C. Facer ◽

Kimberly A. Smith ◽

...

Keyword(s):

Human Brain ◽

Hippocampal Formation ◽

Marker Gene ◽

Brain Structures ◽

Marker Genes ◽

Subcortical Structures ◽

Cellular Resolution ◽

Reliable Marker ◽

Human Brains ◽

Developmental Features

Increasing interest in studies of prenatal human brain development, particularly using new single-cell genomics and anatomical technologies to create cell atlases, creates a strong need for accurate and detailed anatomical reference atlases. In this study, we present two cellular-resolution digital anatomical atlases for prenatal human brain at post-conceptional weeks (PCW) 15 and 21. Both atlases were annotated on sequential Nissl-stained sections covering brain-wide structures on the basis of combined analysis of cytoarchitecture, acetylcholinesterase staining and an extensive marker gene expression dataset. This high information content dataset allowed reliable and accurate demarcation of developing cortical and subcortical structures and their subdivisions. Furthermore, using the anatomical atlases as a guide, spatial expression of 37 and 5 genes from the brains respectively at PCW 15 and 21 was annotated, illustrating reliable marker genes for many developing brain structures. Finally, the present study uncovered several novel developmental features, such as the lack of an outer subventricular zone in the hippocampal formation and entorhinal cortex, and the apparent extension of both cortical (excitatory) and subcortical (inhibitory) progenitors into the prenatal olfactory bulb. These comprehensive atlases provide useful tools for visualization, targeting, imaging and interpretation of brain structures of prenatal human brain, and for guiding and interpreting the next generation of cell census and connectome studies.

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Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

Frontiers in Neuroscience ◽

10.3389/fnins.2021.782995 ◽

2021 ◽

Vol 15 ◽

Author(s):

Sahin Hanalioglu ◽

Siyar Bahadir ◽

Ilkay Isikay ◽

Pinar Celtikci ◽

Emrah Celtikci ◽

...

Keyword(s):

Human Brain ◽

Diffusion Imaging ◽

Structural Connectivity ◽

Population Level ◽

Similar Distribution ◽

Group Level ◽

Imaging Data ◽

Brain Connectome ◽

Human Connectome Project ◽

Intuitive Method

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes.Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall “hubness” measure combining all five were studied. Group-level ranking-based aggregation method (“measure-then-aggregate”) was used to investigate network properties on population level.Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall rs: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (rs: 0.88–0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level.Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

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A human brain test bed for research in large vessel occlusion stroke

Journal of Neurosurgery ◽

10.3171/2020.7.jns202278 ◽

2021 ◽

pp. 1-9

Author(s):

Daniel Gebrezgiabhier ◽

Yang Liu ◽

Adithya S. Reddy ◽

Evan Davis ◽

Yihao Zheng ◽

...

Keyword(s):

Human Brain ◽

Mechanical Thrombectomy ◽

Posterior Circulation ◽

Large Vessel ◽

Suction Catheter ◽

Large Vessel Occlusion ◽

Test Bed ◽

Anterior Circulation ◽

Vessel Occlusion ◽

Human Brains

OBJECTIVEEndovascular removal of emboli causing large vessel occlusion (LVO)–related stroke utilizing suction catheter and/or stent retriever technologies or thrombectomy is a new standard of care. Despite high recanalization rates, 40% of stroke patients still experience poor neurological outcomes as many cases cannot be fully reopened after the first attempt. The development of new endovascular technologies and techniques for mechanical thrombectomy requires more sophisticated testing platforms that overcome the limitations of phantom-based simulators. The authors investigated the use of a hybrid platform for LVO stroke constructed with cadaveric human brains.METHODSA test bed for embolic occlusion of cerebrovascular arteries and mechanical thrombectomy was developed with cadaveric human brains, a customized hydraulic system to generate physiological flow rate and pressure, and three types of embolus analogs (elastic, stiff, and fragment-prone) engineered to match mechanically and phenotypically the emboli causing LVO strokes. LVO cases were replicated in the anterior and posterior circulation, and thrombectomy was attempted using suction catheters and/or stent retrievers.RESULTSThe test bed allowed radiation-free visualization of thrombectomy for LVO stroke in real cerebrovascular anatomy and flow conditions by transmural visualization of the intraluminal elements and procedures. The authors were able to successfully replicate 105 LVO cases with 184 passes in 12 brains (51 LVO cases and 82 passes in the anterior circulation, and 54 LVO cases and 102 passes in the posterior circulation). Observed recanalization rates in this model were graded using a Recanalization in LVO (RELVO) scale analogous to other measures of recanalization outcomes in clinical use.CONCLUSIONSThe human brain platform introduced and validated here enables the analysis of artery-embolus-device interaction under physiological hemodynamic conditions within the unmodified complexity of the cerebral vasculature inside the human brain.

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Neo-Symbiosis

Encyclopedia of Information Science and Technology, Second Edition ◽

10.4018/978-1-60566-026-4.ch442 ◽

2011 ◽

pp. 2773-2777

Author(s):

Douglas Griffith ◽

Frank L. Greitzer

Keyword(s):

Human Factors ◽

Human Brain ◽

Computer Science ◽

Information Science ◽

Science And Technology ◽

Historical Context ◽

Process Data ◽

Half Century ◽

Human Brains ◽

Computing Machines

The purpose of this article is to re-address the vision of human- computer symbiosis expressed by J. C. R. Licklider nearly a half century ago, when he wrote: “The hope is that in not too many years, human brains and computing machines will be coupled together very tightly, and that the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information- handling machines we know today” (Licklider, 1960). Unfortunately, little progress was made toward this vision over 4 decades following Licklider’s challenge, despite significant advancements in the fields of human factors and computer science. Licklider’s vision was largely forgotten. However, recent advances in information science and technology, psychology, and neuroscience have rekindled the potential of making the Licklider’s vision a reality. This article provides a historical context for and updates the vision, and it argues that such a vision is needed as a unifying framework for advancing IS&T.

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Mental illness: The collision of meaning with mechanism

Psychiatry Reborn: Biopsychosocial psychiatry in modern medicine ◽

10.1093/med/9780198789697.003.0017 ◽

2020 ◽

pp. 263-289

Author(s):

Steven E. Hyman ◽

Doug McConnell

Keyword(s):

Mental Illness ◽

Gene Regulation ◽

Human Brain ◽

Lived Experience ◽

Clinical Encounter ◽

Structure And Function ◽

Human Beings ◽

And Function ◽

Human Brains ◽

The Brain

‘Mental illness: the collision of meaning with mechanism’ is based on the views of psychiatry that Steven Hyman articulated in his Loebel Lectures—mental illness results from the disordered functioning of the human brain and effective treatment repairs or mitigates those malfunctions. This view is not intended as reductionist as causes of mental illness and contributions to their repair may come from any source that affects the structure and function of the brain. These might include social interactions and other sources of lived experience, ideas (such as those learned in cognitive therapy), gene sequences and gene regulation, metabolic factors, drugs, electrodes, and so on. This, however, is not the whole story for psychiatry on Hyman’s view; interpersonal interactions between clinicians and patients, intuitively understood in such folk psychological terms as selfhood, intention, and agency are also critical for successful practice. As human beings who are suffering, patients seek to make sense of their lives and benefit from the empathy, respect, and a sense of being understood not only as the objects of a clinical encounter, but also as subjects. Hyman’s argument, however, is that the mechanisms by which human brains function and malfunction to produce the symptoms and impairments of mental illness are opaque to introspection and that the mechanistic understandings necessary for diagnosis and treatment are incommensurate with intuitive (folk psychological) human self-understanding. Thus, psychiatry does best when skillful clinicians switch between an objectifying medical and neurobiological stance and the interpersonal stance in which the clinician engages the patients as a subject. Attempts to integrate these incommensurate views of patients and their predicaments have historically produced incoherent explanations of psychopathology and have often led treatment astray. For example, privileging of folk psychological testimony, even when filtered through sophisticated theories has historically led psychiatry into intellectually blind and clinically ineffective cul-de-sacs such as psychoanalysis.

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