Complex brains have evolved a highly efficient network architecture whose structural connectivity is capable of generating a large repertoire of functional states. We detect characteristic network building blocks (structural and functional motifs) in neuroanatomical data sets and identify a small set of structural motifs that occur in significantly increased numbers. Our analysis suggests the hypothesis that brain networks maximize both the number and the diversity of functional motifs, while the repertoire of structural motifs remains small. Using functional motif number as a cost function in an optimization algorithm, we obtain network topologies that resemble real brain networks across a broad spectrum of structural measures, including small-world attributes. These results are consistent with the hypothesis that highly evolved neural architectures are organized to maximize functional repertoires and to support highly efficient integration of information.
Sensory signal processing in cortical layer IV involves two major morphological classes of excitatory neurons: spiny stellate and pyramidal cells. It is essentially unknown how these two cell types are integrated into intracortical networks and whether they play different roles in cortical signal processing. We mapped their cell-specific intracortical afferents in rat somatosensory cortex through a combination of whole-cell patch-clamp recordings and caged glutamate photolysis. Spiny stellate cells received monosynaptic excitation and inhibition originating almost exclusively from neurons located within the same barrel. Pyramidal cells, by contrast, displayed additional excitatory inputs from nongranular layers and from neighboring barrels. Their inhibitory inputs originated, as for spiny stellate cells, mainly from neurons located in the same barrel. These results indicate that spiny stellate cells act predominantly as local signal processors within a single barrel, whereas pyramidal cells globally integrate horizontal and top-down information within a functional column and between neighboring barrels.
Contributors. Acknowledgements. Preface. 1. Neuroinformatics for C. elegans: Relating Mind and Body in Wormbase Nansheng Chen, et al. 2. A Gene Expression Map of the Mouse Brain. Genepaint.org - A Database of Gene Expression Patterns A. Visel, et al. 3. Databases for the Functional Analyses of Olfactory Receptors C.J. Crasto, et al. 4. Construction of a Protein-Protein Interaction Database (PPID) for Synaptic Biology H. Husi, S.G.N. Grant. 5. Modeling the Dynamics of Second Messenger Pathways K.T. Blackwell, J.H. Kotaleski. 6. Web-Based Neuronal Archives. Neuronal Morphometric and Electrotonic Analysis D.A. Turner, et al. 7. ModelDB: A Resource for Neuronal and Network Modeling A.P. Davison, et al. 8. CoCoDat: Collation of Cortical Data on Neurons and Microcircuitry. Systematic Storage and Retrieval of Experimental Data for Biophysically Realistic Modeling J.D. Johnsen, et al. 9. Computer Models and Analysis Tools for Neural Microcircuits T. Natschlager, et al. 10. A Practical Guide to Information Analysis of Spike Trains G. Pola, et al. 11. An Introduction to CoCoMac-Online. The Online-Interface of the Primate Connectivity Database CoCoMac L. Kamper, et al. 12. Graph Theory Methods for the Analysis of Neural Connectivity Patterns O. Sporns. 13. The PUPS-MOSIX Environment: A Homeostatic Environment for Neuro- and Bio-informatic Applications M.A. O'Neill, et al. 14. The NeuroHomology Database: An Online-KMS for Handling and Evaluation of the Neurobiological Information M. Bota, M.A. Arbib. 15. The fMRI Data Center: Software Tools for Neuroimaging Data Management,Inspection, and Sharing J.D. Van Horn, et al. 16. Statistical Parametric Mapping K.J. Friston. 17. The Brain Positioning Software V. Schmitt, et al. 18. BrainInfo. An Online Interactive Brain Atlas and Nomenclature D.M. Bowden, M. Dubach. 19. Federation of Brain Data through Knowledge-guided Mediation M.E. Martone, et al. 20. Facilitating Data and Software Sharing in the Neurosciences -- A Neuroinformatics Portal R. Ritz, et al. Index.
The aim of the prospective study
was to investigate practicability, complication rates, postoperative
convalescence, and productivity of cows after surgical treatment of
left-displaced abomasum (LDA) by means of laparoscopic abomasopexy
(Janowitz-method). 200 Holstein Frisian cows with LDA from 70 herds were
included in the study. From the day of the laparoscopic abomasopexy, the
animals underwent several clinical and laboratory examinations. Daily milk
yield and concentrate intake were measured for a period of ten days. After
six weeks, six and twelve months, owners were interviewed whether the animals
had remained within the herds. Only 190 cows (LDA group) delivered
evaluable data and were matched with 462 control cows according to
herd, stage of lactation and lactation number. Milk-test parameters for the
total of 652 animals were supplied by Vereinigte Informationssysteme
Tierhaltung (VIT Verden, Germany). The average milk yield of the OP
lactation (lactation in which the laparoscopic abomasopexy was performed) of
the LDA cows was compared with the milk yield of their previous lactation and
with milk yield of the corresponding lactation of control cows in order to
calculate possible milk losses during the OP lactation. Finally survival time
and culling rates of the two groups were compared.
The average age of
the 200 cows of the study was 4.7 years (SD; ± 1.7); the
lactation number was 2.8 (± 1.1). At the time of surgery in
average LDA-cows were 18 (± 7) days in milk. The laparoscopic
abomasopexies of all 200 animals were carried out without significant
complications. The average duration of the surgical procedures was 42 (± 7) minutes. As minor complications during surgery the rumen was
punctured with the trokar in three cows. However, this did not affect post
surgical convalescence. 14 cows received systemic antibiotic treatment due to
body temperatures of more than 39.5°C on the day after surgery. 26 animals
(13 %) developed mild post-surgical inflammation at the fixation sites. In
none of the animals the fixation was removed prematurely and antibiotic
treatment was not required. One cow died four days post surgery due to
hepatic failure. Three cows developed a relapse after the next calving and
underwent a second laparoscopic abomasopexy.
Analysis of the data
supplied by VIT during an observation period of three years revealed a
significantly lower mean survival time for LDA cows (716 days) compared to
controls cows (783 days). The culling rate of the LDA group was 88% compared
to 81% of the control group. The causes for culling – mostly
infertility, mastitis or lameness – did not differ between the two
groups. The average milk loss of cows of the LDA group during the OP
lactation compared to their previous lactation and to the corresponding lactation
of control cows was 392 kg/cow and 153 kg/cow, resp.. In the Op lactation of
LDA cows in average the milk fat percentage was higher and the milk protein
percentage lower than in control cows.
In conclusion, the
laparoscopic abomasopexy according to Janowitz is a suitable method for the
correction of LDA in dairy cows under field conditions. To investigate if the
laparoscopic abomasopexy shows advantages in the treatment of LDA compared to
other surgical methods in terms of success rate and post surgical productivity
of affected cows further studies are necessary.
Brain activity can be measured non-invasively with functional imaging techniques. Each pixel in such an image represents a neural mass of about 105 to 107 neurons. Mean field models (MFMs) approximate their activity by averaging out neural variability while retaining salient underlying features, like neurotransmitter kinetics. However, MFMs incorporating the regional variability, realistic geometry and connectivity of cortex have so far appeared intractable. This lack of biological realism has led to a focus on gross temporal features of the EEG. We address these impediments and showcase a proof of principle forward prediction of co-registered EEG/fMRI for a full-size human cortex in a realistic head model with anatomical connectivity, see figure 1.
MFMs usually assume homogeneous neural masses, isotropic long-range connectivity and simplistic signal expression to allow rapid computation with partial differential equations. But these approximations are insufficient in particular for the high spatial resolution obtained with fMRI, since different cortical areas vary in their architectonic and dynamical properties, have complex connectivity, and can contribute non-trivially to the measured signal. Our code instead supports the local variation of model parameters and freely chosen connectivity for many thousand triangulation nodes spanning a cortical surface extracted from structural MRI. This allows the introduction of realistic anatomical and physiological parameters for cortical areas and their connectivity, including both intra- and inter-area connections. Proper cortical folding and conduction through a realistic head model is then added to obtain accurate signal expression for a comparison to experimental data. To showcase the synergy of these computational developments, we predict simultaneously EEG and fMRI BOLD responses by adding an established model for neurovascular coupling and convolving Balloon-Windkessel hemodynamics. We also incorporate regional connectivity extracted from the CoCoMac database [1].
Importantly, these extensions can be easily adapted according to future insights and data. Furthermore, while our own simulation is based on one specific MFM [2], the computational framework is general and can be applied to models favored by the user. Finally, we provide a brief outlook on improving the integration of multi-modal imaging data through iterative fits of a single underlying MFM in this realistic simulation framework.
Neuronal dynamics unfolding within the cerebral cortex exhibit complex spatial and temporal patterns even in the absence of external input. Here we use a computational approach in an attempt to relate these features of spontaneous cortical dynamics to the underlying anatomical connectivity. Simulating nonlinear neuronal dynamics on a network that captures the large-scale interregional connections of macaque neocortex, and applying information theoretic measures to identify functional networks, we find structure–function relations at multiple temporal scales. Functional networks recovered from long windows of neural activity (minutes) largely overlap with the underlying structural network. As a result, hubs in these long-run functional networks correspond to structural hubs. In contrast, significant fluctuations in functional topology are observed across the sequence of networks recovered from consecutive shorter (seconds) time windows. The functional centrality of individual nodes varies across time as interregional couplings shift. Furthermore, the transient couplings between brain regions are coordinated in a manner that reveals the existence of two anticorrelated clusters. These clusters are linked by prefrontal and parietal regions that are hub nodes in the underlying structural network. At an even faster time scale (hundreds of milliseconds) we detect individual episodes of interregional phase-locking and find that slow variations in the statistics of these transient episodes, contingent on the underlying anatomical structure, produce the transfer entropy functional connectivity and simulated blood oxygenation level-dependent correlation patterns observed on slower time scales.
