Structural connectivity centrality changes mark the path towards Alzheimer's disease

[EN] Introduction: The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting prion-like spreading processes of neurofibrillary tangles and amyloid plaques. Methods: Using diffusion magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative database, we first identified relevant features for dementia diagnosis. We then created dynamic models with the Nathan Kline Institute-Rockland Sample database to estimate the earliest detectable stage associated with dementia in the simulated disease progression. Results: A classifier based on centrality measures provides informative predictions. Strength and closeness centralities are the most discriminative features, which are associated with the medial temporal lobe and subcortical regions, together with posterior and occipital brain regions. Our model simulations suggest that changes associated with dementia begin to manifest structurally at early stages. Discussion: Our analyses suggest that diffusion magnetic resonance imaging-based centrality measures can offer a tool for early disease detection before clinical dementia onset.The authors would like to thank Peter N. Taylor and Yujiang Wang for their stimulating feedback and suggestions. Funding: A.D.-P. was supported by grant FPU13/01475 from the Spanish Ministerio de Educacion, Cultura y Deporte (MECD). This work was supported in part by the Spanish Ministerio de Economıa y Competitividad (MINECO) and FEDER funds under grant BFU2015- 64380-C2-2-R. L.R.P. and J.-P.T. were supported by the NIHR Newcastle Biomedical Research Center awarded to the Newcastle upon Tyne Hospitals NHS Foundation Trust and Newcastle University. M.K. and R.B. were supported by the Engineering and Physical Sciences Research Council of the United Kingdom (EP/K026992/1). R.B. was also supported by (EP/S001433/1) and the Medical Research Council of the United Kingdom (MR/N015037/1). Data collection and sharing for this project was funded by the Alzheimer s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2- 0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and generous contributions from the following organizations: AbbVie, Alzheimer s Association; Alzheimer s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.Peraza, LR.; Díaz-Parra, A.; Kennion, O.; Moratal, D.; Taylor, J.; Kaiser, M.; Bauer, R. (2019). Structural connectivity centrality changes mark the path towards Alzheimer's disease. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring. 11:98-107. https://doi.org/10.1016/j.dadm.2018.12.004S9810711(2016). 2016 Alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 12(4), 459-509. doi:10.1016/j.jalz.2016.03.001Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., … Phelps, C. H. (2011). Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 280-292. doi:10.1016/j.jalz.2011.03.003Jack, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., … Trojanowski, J. Q. (2013). Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. The Lancet Neurology, 12(2), 207-216. doi:10.1016/s1474-4422(12)70291-0Villemagne, V. L., Burnham, S., Bourgeat, P., Brown, B., Ellis, K. A., Salvado, O., … Masters, C. L. (2013). Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. The Lancet Neurology, 12(4), 357-367. doi:10.1016/s1474-4422(13)70044-9Jack, C. R., & Holtzman, D. M. (2013). Biomarker Modeling of Alzheimer’s Disease. Neuron, 80(6), 1347-1358. doi:10.1016/j.neuron.2013.12.003Jucker, M., & Walker, L. C. (2011). Pathogenic protein seeding in alzheimer disease and other neurodegenerative disorders. Annals of Neurology, 70(4), 532-540. doi:10.1002/ana.22615Brettschneider, J., Tredici, K. D., Lee, V. M.-Y., & Trojanowski, J. Q. (2015). Spreading of pathology in neurodegenerative diseases: a focus on human studies. Nature Reviews Neuroscience, 16(2), 109-120. doi:10.1038/nrn3887Jucker, M., & Walker, L. C. (2013). Self-propagation of pathogenic protein aggregates in neurodegenerative diseases. Nature, 501(7465), 45-51. doi:10.1038/nature12481Frost, B., & Diamond, M. I. (2009). Prion-like mechanisms in neurodegenerative diseases. Nature Reviews Neuroscience, 11(3), 155-159. doi:10.1038/nrn2786Warren, J. D., Rohrer, J. D., Schott, J. M., Fox, N. C., Hardy, J., & Rossor, M. N. (2013). Molecular nexopathies: a new paradigm of neurodegenerative disease. Trends in Neurosciences, 36(10), 561-569. doi:10.1016/j.tins.2013.06.007Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews Neuroscience, 16(3), 159-172. doi:10.1038/nrn3901Zhou, J., Gennatas, E. D., Kramer, J. H., Miller, B. L., & Seeley, W. W. (2012). Predicting Regional Neurodegeneration from the Healthy Brain Functional Connectome. Neuron, 73(6), 1216-1227. doi:10.1016/j.neuron.2012.03.004Brier, M. R., Thomas, J. B., & Ances, B. M. (2014). Network Dysfunction in Alzheimer’s Disease: Refining the Disconnection Hypothesis. Brain Connectivity, 4(5), 299-311. doi:10.1089/brain.2014.0236Delbeuck, X. (2003). Neuropsychology Review, 13(2), 79-92. doi:10.1023/a:1023832305702Tijms, B. M., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., & Barkhof, F. (2013). Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurobiology of Aging, 34(8), 2023-2036. doi:10.1016/j.neurobiolaging.2013.02.020Stam, C. J. (2014). Modern network science of neurological disorders. Nature Reviews Neuroscience, 15(10), 683-695. doi:10.1038/nrn3801Petersen, R. C., Aisen, P. S., Beckett, L. A., Donohue, M. C., Gamst, A. C., Harvey, D. J., … Weiner, M. W. (2009). Alzheimer’s Disease Neuroimaging Initiative (ADNI): Clinical characterization. Neurology, 74(3), 201-209. doi:10.1212/wnl.0b013e3181cb3e25Nooner, K. B., Colcombe, S. J., Tobe, R. H., Mennes, M., Benedict, M. M., Moreno, A. L., … Milham, M. P. (2012). The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry. Frontiers in Neuroscience, 6. doi:10.3389/fnins.2012.00152Landau, S. M., Fero, A., Baker, S. L., Koeppe, R., Mintun, M., Chen, K., … Jagust, W. J. (2015). Measurement of Longitudinal  -Amyloid Change with 18F-Florbetapir PET and Standardized Uptake Value Ratios. Journal of Nuclear Medicine, 56(4), 567-574. doi:10.2967/jnumed.114.148981Lim, S., Han, C. E., Uhlhaas, P. J., & Kaiser, M. (2013). Preferential Detachment During Human Brain Development: Age- and Sex-Specific Structural Connectivity in Diffusion Tensor Imaging (DTI) Data. Cerebral Cortex, 25(6), 1477-1489. doi:10.1093/cercor/bht333Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015). Epidemic processes in complex networks. Reviews of Modern Physics, 87(3), 925-979. doi:10.1103/revmodphys.87.925Collin, G., & van den Heuvel, M. P. (2013). The Ontogeny of the Human Connectome. The Neuroscientist, 19(6), 616-628. doi:10.1177/1073858413503712Fischi-Gómez, E., Vasung, L., Meskaldji, D.-E., Lazeyras, F., Borradori-Tolsa, C., Hagmann, P., … Hüppi, P. S. (2014). Structural Brain Connectivity in School-Age Preterm Infants Provides Evidence for Impaired Networks Relevant for Higher Order Cognitive Skills and Social Cognition. Cerebral Cortex, 25(9), 2793-2805. doi:10.1093/cercor/bhu073Zhao, T., Cao, M., Niu, H., Zuo, X.-N., Evans, A., He, Y., … Shu, N. (2015). Age-related changes in the topological organization of the white matter structural connectome across the human lifespan. Human Brain Mapping, 36(10), 3777-3792. doi:10.1002/hbm.22877James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer Texts in Statistics. doi:10.1007/978-1-4614-7138-7Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059-1069. doi:10.1016/j.neuroimage.2009.10.003Batalle, D., Hughes, E. J., Zhang, H., Tournier, J.-D., Tusor, N., Aljabar, P., … Counsell, S. J. (2017). Early development of structural networks and the impact of prematurity on brain connectivity. NeuroImage, 149, 379-392. doi:10.1016/j.neuroimage.2017.01.06510.1162/153244303322753616. (2000). CrossRef Listing of Deleted DOIs, 1. doi:10.1162/153244303322753616Saeys, Y., Inza, I., & Larranaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507-2517. doi:10.1093/bioinformatics/btm344Lemm, S., Blankertz, B., Dickhaus, T., & Müller, K.-R. (2011). Introduction to machine learning for brain imaging. NeuroImage, 56(2), 387-399. doi:10.1016/j.neuroimage.2010.11.004Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: A tutorial overview. NeuroImage, 45(1), S199-S209. doi:10.1016/j.neuroimage.2008.11.007Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29-36. doi:10.1148/radiology.143.1.7063747Yekutieli, D., & Benjamini, Y. (1999). Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics. Journal of Statistical Planning and Inference, 82(1-2), 171-196. doi:10.1016/s0378-3758(99)00041-5Whitwell, J. L., Josephs, K. A., Murray, M. E., Kantarci, K., Przybelski, S. A., Weigand, S. D., … Jack, C. R. (2008). MRI correlates of neurofibrillary tangle pathology at autopsy: A voxel-based morphometry study. Neurology, 71(10), 743-749. doi:10.1212/01.wnl.0000324924.91351.7dBraak, H., Alafuzoff, I., Arzberger, T., Kretzschmar, H., & Del Tredici, K. (2006). Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathologica, 112(4), 389-404. doi:10.1007/s00401-006-0127-zBuckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., … Johnson, K. A. (2009). Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer’s Disease. Journal of Neuroscience, 29(6), 1860-1873. doi:10.1523/jneurosci.5062-08.2009Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L., & Greicius, M. D. (2009). Neurodegenerative Diseases Target Large-Scale Human Brain Networks. Neuron, 62(1), 42-52. doi:10.1016/j.neuron.2009.03.024Frisoni, G. B., Prestia, A., Rasser, P. E., Bonetti, M., & Thompson, P. M. (2009). In vivo mapping of incremental cortical atrophy from incipient to overt Alzheimer’s disease. Journal of Neurology, 256(6), 916-924. doi:10.1007/s00415-009-5040-7Pini, L., Pievani, M., Bocchetta, M., Altomare, D., Bosco, P., Cavedo, E., … Frisoni, G. B. (2016). Brain atrophy in Alzheimer’s Disease and aging. Ageing Research Reviews, 30, 25-48. doi:10.1016/j.arr.2016.01.002Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P., & Thompson, P. M. (2010). The clinical use of structural MRI in Alzheimer disease. Nature Reviews Neurology, 6(2), 67-77. doi:10.1038/nrneurol.2009.215Mak, E., Gabel, S., Mirette, H., Su, L., Williams, G. B., Waldman, A., … O’Brien, J. (2017). Structural neuroimaging in preclinical dementia: From microstructural deficits and grey matter atrophy to macroscale connectomic changes. Ageing Research Reviews, 35, 250-264. doi:10.1016/j.arr.2016.10.001Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., … Smith, S. M. (2016). Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nature Neuroscience, 19(11), 1523-1536. doi:10.1038/nn.4393Wirths, O. (2003). α-Synuclein, Aβ and Alzheimer’s disease. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 27(1), 103-108. doi:10.1016/s0278-5846(02)00339-1Saxena, S., & Caroni, P. (2011). Selective Neuronal Vulnerability in Neurodegenerative Diseases: from Stressor Thresholds to Degeneration. Neuron, 71(1), 35-48. doi:10.1016/j.neuron.2011.06.031Fjell, A. M., McEvoy, L., Holland, D., Dale, A. M., & Walhovd, K. B. (2014). What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus. Progress in Neurobiology, 117, 20-40. doi:10.1016/j.pneurobio.2014.02.004Kaiser, M. (2013). The potential of the human connectome as a biomarker of brain disease. Frontiers in Human Neuroscience, 7. doi:10.3389/fnhum.2013.0048

The functional brain favours segregated modular connectivity at old age unless affected by neurodegeneration.

Brain's modular connectivity gives this organ resilience and adaptability. The ageing process alters the organised modularity of the brain and these changes are further accentuated by neurodegeneration, leading to disorganisation. To understand this further, we analysed modular variability-heterogeneity of modules-and modular dissociation-detachment from segregated connectivity-in two ageing cohorts and a mixed cohort of neurodegenerative diseases. Our results revealed that the brain follows a universal pattern of high modular variability in metacognitive brain regions: the association cortices. The brain in ageing moves towards a segregated modular structure despite presenting with increased modular heterogeneity-modules in older adults are not only segregated, but their shape and size are more variable than in young adults. In the presence of neurodegeneration, the brain maintains its segregated connectivity globally but not locally, and this is particularly visible in dementia with Lewy bodies and Parkinson's disease dementia; overall, the modular brain shows patterns of differentiated pathology

Dysfunctional brain dynamics and their origin in Lewy body dementia.

Lewy body dementia includes dementia with Lewy bodies and Parkinson's disease dementia and is characterized by transient clinical symptoms such as fluctuating cognition, which might be driven by dysfunction of the intrinsic dynamic properties of the brain. In this context we investigated whole-brain dynamics on a subsecond timescale in 42 Lewy body dementia compared to 27 Alzheimer's disease patients and 18 healthy controls using an EEG microstate analysis in a cross-sectional design. Microstates are transiently stable brain topographies whose temporal characteristics provide insight into the brain's dynamic repertoire. Our additional aim was to explore what processes in the brain drive microstate dynamics. We therefore studied associations between microstate dynamics and temporal aspects of large-scale cortical-basal ganglia-thalamic interactions using dynamic functional MRI measures given the putative role of these subcortical areas in modulating widespread cortical function and their known vulnerability to Lewy body pathology. Microstate duration was increased in Lewy body dementia for all microstate classes compared to Alzheimer's disease (P < 0.001) and healthy controls (P < 0.001), while microstate dynamics in Alzheimer's disease were largely comparable to healthy control levels, albeit with altered microstate topographies. Correspondingly, the number of distinct microstates per second was reduced in Lewy body dementia compared to healthy controls (P < 0.001) and Alzheimer's disease (P < 0.001). In the dementia with Lewy bodies group, mean microstate duration was related to the severity of cognitive fluctuations (ρ = 0.56, PFDR = 0.038). Additionally, mean microstate duration was negatively correlated with dynamic functional connectivity between the basal ganglia (r = - 0.53, P = 0.003) and thalamic networks (r = - 0.38, P = 0.04) and large-scale cortical networks such as visual and motor networks in Lewy body dementia. The results indicate a slowing of microstate dynamics and disturbances to the precise timing of microstate sequences in Lewy body dementia, which might lead to a breakdown of the intricate dynamic properties of the brain, thereby causing loss of flexibility and adaptability that is crucial for healthy brain functioning. When contrasted with the largely intact microstate dynamics in Alzheimer's disease, the alterations in dynamic properties in Lewy body dementia indicate a brain state that is less responsive to environmental demands and might give rise to the apparent slowing in thinking and intermittent confusion which typify Lewy body dementia. By using Lewy body dementia as a probe pathology we demonstrate a potential link between dynamic functional MRI fluctuations and microstate dynamics, suggesting that dynamic interactions within the cortical-basal ganglia-thalamic loop might play a role in the modulation of EEG dynamics

Structural Brain Correlates of Attention Dysfunction in Lewy Body Dementias and Alzheimer’s Disease

Lewy body dementia (LBD) and Alzheimer’s disease (AD) are common forms of dementia that have different clinical profiles but are both commonly associated with attentional deficits. The aim of this study was to investigate efficiency of different attentional systems in LBD and AD and its association with brain structural abnormalities. We studied reaction time (RT) data from 45 LBD, 31 AD patients and 22 healthy controls (HCs) using the Attention Network Test (ANT) to assess the efficiency of three different attentional systems: alerting, orienting and executive conflict. Voxel-based morphometry (VBM) was used to investigate relations between different attention components and cortical volume. Both dementia groups showed slower overall RTs than controls, with additional slowing in LBD relative to AD. There was a significant alerting effect in controls which was absent in the dementia groups, the executive conflict effect was greater in both dementia groups compared to controls, but the orienting effect did not differ between groups. Mean RT in AD was negatively correlated with occipital gray matter (GM) volume and in LBD orienting efficiency was negatively related to occipital white matter (WM) volume. Given that previous studies in less impaired patients suggest a maintenance of the alerting effect, the absent alerting effect in our study suggests a loss of alerting efficiency with dementia progression. While orienting was largely preserved, it might be related to occipital structural abnormalities in LBD. Executive function was markedly impaired in both dementia groups, however, the absence of relations to brain volume suggests that it might be more related to functional rather than macrostructural pathophysiological changes

EEG alpha reactivity and cholinergic system integrity in Lewy body dementia and Alzheimer’s disease

Abstract: Background: Lewy body dementia (LBD), which includes dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD), is characterised by marked deficits within the cholinergic system which are more severe than in Alzheimer’s disease (AD) and are mainly caused by degeneration of the nucleus basalis of Meynert (NBM) whose widespread cholinergic projections provide the main source of cortical cholinergic innervation. EEG alpha reactivity, which refers to the reduction in alpha power over occipital electrodes upon opening the eyes, has been suggested as a potential marker of cholinergic system integrity. Methods: Eyes-open and eyes-closed resting state EEG data were recorded from 41 LBD patients (including 24 patients with DLB and 17 with PDD), 21 patients with AD, and 40 age-matched healthy controls. Alpha reactivity was calculated as the relative reduction in alpha power over occipital electrodes when opening the eyes. Structural MRI data were used to assess volumetric changes within the NBM using a probabilistic anatomical map. Results: Alpha reactivity was reduced in AD and LBD patients compared to controls with a significantly greater reduction in LBD compared to AD. Reduced alpha reactivity was associated with smaller volumes of the NBM across all groups (ρ = 0.42, pFDR = 0.0001) and in the PDD group specifically (ρ = 0.66, pFDR = 0.01). Conclusions: We demonstrate that LBD patients show an impairment in alpha reactivity upon opening the eyes which distinguishes this form of dementia from AD. Furthermore, our results suggest that reduced alpha reactivity might be related to a loss of cholinergic drive from the NBM, specifically in PDD

Identification of IQM-266, a Novel DREAM Ligand That Modulates KV4 Currents

Downstream Regulatory Element Antagonist Modulator (DREAM)/KChIP3/calsenilin is a neuronal calcium sensor (NCS) with multiple functions, including the regulation of A-type outward potassium currents (IA). This effect is mediated by the interaction between DREAM and KV4 potassium channels and it has been shown that small molecules that bind to DREAM modify channel function. A-type outward potassium current (IA) is responsible of the fast repolarization of neuron action potentials and frequency of firing. Using surface plasmon resonance (SPR) assays and electrophysiological recordings of KV4.3/DREAM channels, we have identified IQM-266 as a DREAM ligand. IQM-266 inhibited the KV4.3/DREAM current in a concentration-, voltage-, and time-dependent-manner. By decreasing the peak current and slowing the inactivation kinetics, IQM-266 led to an increase in the transmembrane charge (QKV4.3/DREAM) at a certain range of concentrations. The slowing of the recovery process and the increase of the inactivation from the closed-state inactivation degree are consistent with a preferential binding of IQM-266 to a pre-activated closed state of KV4.3/DREAM channels. Finally, in rat dorsal root ganglion neurons, IQM-266 inhibited the peak amplitude and slowed the inactivation of IA. Overall, the results presented here identify IQM-266 as a new chemical tool that might allow a better understanding of DREAM physiological role as well as modulation of neuronal IA in pathological processes

Intra- and inter-network functional alterations in Parkinson's disease with mild cognitive impairment.

Mild cognitive impairment (MCI) is prevalent in 15%-40% of Parkinson's disease (PD) patients at diagnosis. In this investigation, we study brain intra- and inter-network alterations in resting state functional magnetic resonance imaging (rs-fMRI) in recently diagnosed PD patients and characterise them as either cognitive normal (PD-NC) or with MCI (PD-MCI). Patients were divided into two groups, PD-NC (N = 62) and PD-MCI (N = 37) and for comparison, healthy controls (HC, N = 30) were also included. Intra- and inter-network connectivity were investigated from participants' rs-fMRIs in 26 resting state networks (RSNs). Intra-network differences were found between both patient groups and HCs for networks associated with motor control (motor cortex), spatial attention and visual perception. When comparing both PD-NC and PD-MCI, intra-network alterations were found in RSNs related to attention, executive function and motor control (cerebellum). The inter-network analysis revealed a hyper-synchronisation between the basal ganglia network and the motor cortex in PD-NC compared with HCs. When both patient groups were compared, intra-network alterations in RSNs related to attention, motor control, visual perception and executive function were found. We also detected disease-driven negative synchronisations and synchronisation shifts from positive to negative and vice versa in both patient groups compared with HCs. The hyper-synchronisation between basal ganglia and motor cortical RSNs in PD and its synchronisation shift from negative to positive compared with HCs, suggest a compensatory response to basal dysfunction and altered basal-cortical motor control in the resting state brain of PD patients. Hum Brain Mapp 38:1702-1715, 2017. © 2016 Wiley Periodicals, Inc

Comparación de la castración quirúrgica al nacimiento versus inmunocastration sobre las características de la canal y carne en machos Holstein

El objetivo fue comparar el efecto de la castración quirúrgica al nacimiento vs immunocastración, sobre las características de la canal y carne en machos Holstein en engorda; se utilizaron 720 machos Holstein aproximadamente de 7 a 8 meses de edad con peso inicial de 240.82 kg. Se formaron 2 tratamientos con 4 corrales de 90 animales en cada uno: toros castrados quirúrgicamente que fueron castrados 24 h después del nacimiento y toros inmunocastrados vacunados con Bopriva aplicando cuatro dosis, al día 1, 21, 101 y 181 de engorda. Se tomaron pesos individuales en cada vacunación. Los animales se sacrificaron a los 242 días de engorda. A partir de la segunda vacunación se observaron diferencias (P0.05) entre tratamientos mientras que los valores de b*, C* y H* fueron más altos (P<0.05) en los animales inmunocastrados. Para fines de producción, el sacrificar los machos Holstein al nacimiento, se obtienen animales más pesados y con mejores características en la canal; sin embargo es importante evaluar el impacto del bienestar animal por la castración al nacimiento