Connect with us

Health

The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis

The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis

 


  • Barch DM, Dowd EC. Goal representations and motivational drive in schizophrenia: the role of prefrontal-striatal interactions. Schizophr Bull. 2010;36:919–34.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A. In vivo fiber tractography using DT-MRI data. Magn Reson Med. 2000;44:625–32.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996;111:209–19.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mori S, Crain BJ, Chacko VP, van Zijl PC. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol. 1999;45:265–9.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Westin CF, Maier, SE, Khidhir, B, Everett, P, Jolesz, FA, Kikinis R. Image processing for diffusion tensor magnetic resonance imaging. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Cambridge, UK: Springer; 1999. p.441–52.

  • Levitt JJ, Zhang F, Vangel M, Nestor PG, Rathi Y, Kubicki M, et al. The organization of frontostriatal brain wiring in healthy subjects using a novel diffusion imaging fiber cluster analysis. Cereb Cortex. 2021;31:5308–18.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kandel E, Schwartz J, Jessell T. Principles of neural science. New York: McGraw-Hill, Health Professions Division; 2000.

  • Murray RM, Lewis SW. Is schizophrenia a neurodevelopmental disorder? Br Med J (Clin Res Ed). 1987;295:681–2.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ripke S, Sanders AR, Kendler KS, Levinson DF, Sklar P, Holmans PA, et al. Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 2011;43:969.

    Article 
    CAS 

    Google Scholar
     

  • Sekar A, Bialas AR, de Rivera H, Davis A, Hammond TR, Kamitaki N, et al. Schizophrenia risk from complex variation of complement component 4. Nature. 2016;530:177–83.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Weinberger DR. Implications of normal brain development for the pathogenesis of schizophrenia. Arch Gen Psychiatry. 1987;44:660–9.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cronenwett WJ, Csernansky JG. Diving deep into white matter to improve our understanding of the pathophysiology of schizophrenia. Biol Psychiatry. 2013;74:396–7.

    Article 
    PubMed 

    Google Scholar
     

  • Friston KJ, Frith CD, Liddle PF, Frackowiak RS. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab. 1993;13:5–14.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Calhoun VD, Eichele T, Pearlson G. Functional brain networks in schizophrenia: a review. Front Hum Neurosci. 2009;3:17.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Skudlarski P, Jagannathan K, Anderson K, Stevens MC, Calhoun VD, Skudlarska BA, et al. Brain connectivity is not only lower but different in schizophrenia: a combined anatomical and functional approach. Biol Psychiatry. 2010;68:61–69.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gold JM, Waltz JA, Prentice KJ, Morris SE, Heerey EA. Reward processing in schizophrenia: a deficit in the representation of value. Schizophr Bull. 2008;34:835–47.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Levitt JJ, Nestor PG, Levin L, Pelavin P, Lin P, Kubicki M, et al. Reduced structural connectivity in frontostriatal white matter tracts in the associative loop in schizophrenia. Am J Psychiatry. 2017;174:1102–11.

    Article 
    PubMed 

    Google Scholar
     

  • Alexander GE, Crutcher MD. Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends Neurosci. 1990;13:266–71.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Haber SN. The primate basal ganglia: parallel and integrative networks. J Chem Neuroanat. 2003;26:317–30.

    Article 
    PubMed 

    Google Scholar
     

  • Redgrave P, Rodriguez M, Smith Y, Rodriguez-Oroz MC, Lehericy S, Bergman H, et al. Goal-directed and habitual control in the basal ganglia: implications for Parkinson’s disease. Nat Rev. 2010;11:760–72.

    Article 
    CAS 

    Google Scholar
     

  • Averbeck BB, Lehman J, Jacobson M, Haber SN. Estimates of projection overlap and zones of convergence within frontal-striatal circuits. J Neurosci. 2014;34:9497–505.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Draganski B, Kherif F, Kloppel S, Cook PA, Alexander DC, Parker GJ, et al. Evidence for segregated and integrative connectivity patterns in the human Basal Ganglia. J Neurosci. 2008;28:7143–52.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lehericy S, Ducros M, Van de Moortele PF, Francois C, Thivard L, Poupon C, et al. Diffusion tensor fiber tracking shows distinct corticostriatal circuits in humans. Ann Neurol. 2004;55:522–9.

    Article 
    PubMed 

    Google Scholar
     

  • Haber SN. Convergence of limbic, cognitive, and motor cortico-striatal circuits with dopamine pathways in primate brain. In: Iversen LL, Iversen SD, Dunnett SB, Bjorklund A, editors. Dopamine handbook. Oxford: Oxford University Press, Inc.; 2010. p.38–48.

  • Casey BJ, Epstein JN, Buhle J, Liston C, Davidson MC, Tonev ST, et al. Frontostriatal connectivity and its role in cognitive control in parent-child dyads with ADHD. Am J Psychiatry. 2007;164:1729–36.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Liston C, Malter Cohen M, Teslovich T, Levenson D, Casey BJ. Atypical prefrontal connectivity in attention-deficit/hyperactivity disorder: pathway to disease or pathological end point? Biol Psychiatry. 2011;69:1168–77.

    Article 
    PubMed 

    Google Scholar
     

  • Haas BW, Barnea-Goraly N, Lightbody AA, Patnaik SS, Hoeft F, Hazlett H, et al. Early white-matter abnormalities of the ventral frontostriatal pathway in fragile X syndrome. Dev Med Child Neurol. 2009;51:593–9.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Heller C, Steinmann S, Levitt JJ, Makris N, Antshel KM, Fremont W, et al. Abnormalities in white matter tracts in the fronto-striatal-thalamic circuit are associated with verbal performance in 22q11.2DS. Schizophr Res. 2020;224:141–50.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Quan M, Lee SH, Kubicki M, Kikinis Z, Rathi Y, Seidman LJ, et al. White matter tract abnormalities between rostral middle frontal gyrus, inferior frontal gyrus and striatum in first-episode schizophrenia. Schizophr Res. 2013;145:1–10.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Levitt JJ, Nestor PG, Kubicki M, Lyall AE, Zhang F, Riklin-Raviv T, et al. Miswiring of frontostriatal projections in schizophrenia. Schizophr Bull. 2020;46:990–8.

  • Zhang F, Wu Y, Norton I, Rigolo L, Rathi Y, Makris N, et al. An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan. NeuroImage. 2018;179:429–47.

    Article 
    PubMed 

    Google Scholar
     

  • Howes OD, Montgomery AJ, Asselin MC, Murray RM, Valli I, Tabraham P, et al. Elevated striatal dopamine function linked to prodromal signs of schizophrenia. Arch Gen Psychiatry. 2009;66:13–20.

    Article 
    PubMed 

    Google Scholar
     

  • Kegeles LS, Abi-Dargham A, Frankle WG, Gil R, Cooper TB, Slifstein M, et al. Increased synaptic dopamine function in associative regions of the striatum in schizophrenia. Arch Gen Psychiatry. 2010;67:231–9.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Levitt JJ, Rosow LK, Nestor PG, Pelavin PE, Swisher TM, McCarley RW, et al. A volumetric MRI study of limbic, associative and sensorimotor striatal subregions in schizophrenia. Schizophr Res. 2013;145:11–19.

    Article 
    PubMed 

    Google Scholar
     

  • Cetin Karayumak S, Bouix S, Ning L, James A, Crow T, Shenton M, et al. Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters. NeuroImage. 2019;184:180–200.

    Article 
    PubMed 

    Google Scholar
     

  • Cetin-Karayumak S, Di Biase MA, Chunga N, Reid B, Somes N, Lyall AE, et al. White matter abnormalities across the lifespan of schizophrenia: a harmonized multi-site diffusion MRI study. Mol Psychiatry. 2020;25:3208–19.

    Article 
    PubMed 

    Google Scholar
     

  • Mirzaalian H, Ning L, Savadjiev P, Pasternak O, Bouix S, Michailovich O, et al. Inter-site and inter-scanner diffusion MRI data harmonization. NeuroImage. 2016;135:311–23.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Malcolm JG, Shenton ME, Rathi Y. Filtered multitensor tractography. IEEE Trans Med Imaging. 2010;29:1664–75.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Reddy CP, Rathi Y. Joint multi-fiber NODDI parameter estimation and tractography using the unscented information filter. Front Neurosci. 2016;10:166.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Farquharson S, Tournier JD, Calamante F, Fabinyi G, Schneider-Kolsky M, Jackson GD, et al. White matter fiber tractography: why we need to move beyond DTI. J Neurosurg. 2013;118:1367–77.

    Article 
    PubMed 

    Google Scholar
     

  • Vos SB, Viergever MA, Leemans A. Multi-fiber tractography visualizations for diffusion MRI data. PLoS ONE. 2013;8:e81453.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fillard P, Descoteaux M, Goh A, Gouttard S, Jeurissen B, Malcolm J, et al. Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. NeuroImage. 2011;56:220–34.

    Article 
    PubMed 

    Google Scholar
     

  • Zhang F, Wu Y, Norton I, Rathi Y, Golby AJ, O’Donnell LJ. Test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering. Hum Brain Mapp. 2019;40:3041–57.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Baumgartner C, Michailovich O, Levitt J, Pasternak O, Bouix S, Westin CF, et al. A unified tractography framework for comparing diffusion models on clinical scans. In: Computational Diffusion MRI Workshop of MICCAI Nice. Springer; 2012. p.27–32.

  • Chen Z, Tie Y, Olubiyi O, Zhang F, Mehrtash A, Rigolo L, et al. Corticospinal tract modeling for neurosurgical planning by tracking through regions of peritumoral edema and crossing fibers using two-tensor unscented Kalman filter tractography. Int J Comput Assist Radio Surg. 2016;11:1475–86.

    Article 

    Google Scholar
     

  • Liao R, Ning L, Chen Z, Rigolo L, Gong S, Pasternak O, et al. Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model. Neuroimage Clin. 2017;15:819–31.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • O’Donnell LJ, Suter Y, Rigolo L, Kahali P, Zhang F, Norton I, et al. Automated white matter fiber tract identification in patients with brain tumors. Neuroimage Clin. 2017;13:138–53.

    Article 
    PubMed 

    Google Scholar
     

  • Zhang F, Norton I, Cai W, Song Y, Wells WM, O’Donnell LJ. Comparison between two white matter segmentation strategies: an investigation into white matter segmentation consistency. In: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017); Melbourne, Australia: IEEE; 2017. p.796–9.

  • Descoteaux M, Angelino E, Fitzgibbons S, Deriche R. Regularized, fast, and robust analytical Q-ball imaging. Magn Reson Med. 2007;58:497–510.

    Article 
    PubMed 

    Google Scholar
     

  • Ning L, Westin CF, Rathi Y. Estimating diffusion propagator and its moments using directional radial basis functions. IEEE Trans Med Imaging. 2015;34:2058–78.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ning L, Laun F, Gur Y, DiBella EV, Deslauriers-Gauthier S, Megherbi T, et al. Sparse Reconstruction Challenge for diffusion MRI: validation on a physical phantom to determine which acquisition scheme and analysis method to use? Med Image Anal. 2015;26:316–31.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tournier JD, Yeh CH, Calamante F, Cho KH, Connelly A, Lin CP. Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data. NeuroImage. 2008;42:617–25.

    Article 
    PubMed 

    Google Scholar
     

  • Essayed WI, Zhang F, Unadkat P, Cosgrove GR, Golby AJ, O’Donnell LJ. White matter tractography for neurosurgical planning: A topography-based review of the current state of the art. Neuroimage Clin. 2017;15:659–72.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huang H, Zhang J, van Zijl PC, Mori S. Analysis of noise effects on DTI-based tractography using the brute-force and multi-ROI approach. Magn Reson Med. 2004;52:559–65.

    Article 
    PubMed 

    Google Scholar
     

  • Radmanesh A, Zamani AA, Whalen S, Tie Y, Suarez RO, Golby AJ. Comparison of seeding methods for visualization of the corticospinal tracts using single tensor tractography. Clin Neurol Neurosurg. 2015;129:44–49.

    Article 
    PubMed 

    Google Scholar
     

  • Wakana S, Caprihan A, Panzenboeck MM, Fallon JH, Perry M, Gollub RL, et al. Reproducibility of quantitative tractography methods applied to cerebral white matter. NeuroImage. 2007;36:630–44.

    Article 
    PubMed 

    Google Scholar
     

  • Guevara P, Duclap D, Poupon C, Marrakchi-Kacem L, Fillard P, Le Bihan D, et al. Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. NeuroImage. 2012;61:1083–99.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jin Y, Shi Y, Zhan L, Gutman BA, de Zubicaray GI, McMahon KL, et al. Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics. NeuroImage. 2014;100:75–90.

    Article 
    PubMed 

    Google Scholar
     

  • Lefranc S, Roca P, Perrot M, Poupon C, Le Bihan D, Mangin JF, et al. Groupwise connectivity-based parcellation of the whole human cortical surface using watershed-driven dimension reduction. Med Image Anal. 2016;30:11–29.

    Article 
    PubMed 

    Google Scholar
     

  • Smith RE, Tournier JD, Calamante F, Connelly A. SIFT: spherical-deconvolution informed filtering of tractograms. NeuroImage. 2013;67:298–312.

    Article 
    PubMed 

    Google Scholar
     

  • Wu Y, Zhang F, Makris N, Ning Y, Norton I, She S, et al. Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder. NeuroImage. 2018;181:16–29.

    Article 
    PubMed 

    Google Scholar
     

  • Zhang F, Savadjiev P, Cai W, Song Y, Rathi Y, Tunc B, et al. Whole brain white matter connectivity analysis using machine learning: an application to autism. NeuroImage. 2018;172:826–37.

    Article 
    PubMed 

    Google Scholar
     

  • Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 2006;31:968–80.

    Article 
    PubMed 

    Google Scholar
     

  • van den Heuvel MP, Scholtens LH, de Reus MA, Kahn RS. Associated microscale spine density and macroscale connectivity disruptions in schizophrenia. Biol Psychiatry. 2016;80:293–301.

    Article 
    PubMed 

    Google Scholar
     

  • Weinberger DR. Future of days past: neurodevelopment and schizophrenia. Schizophr Bull. 2017;43:1164–8.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Goldman-Rakic PS. The corticostriatal fiber system in the rhesus monkey: organization and development. Prog Brain Res. 1983;58:405–18.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chen SY, Huang PH, Cheng HJ. Disrupted-in-schizophrenia 1-mediated axon guidance involves TRIO-RAC-PAK small GTPase pathway signaling. Proc Natl Acad Sci USA. 2011;108:5861–6.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mukai J, Tamura M, Fenelon K, Rosen AM, Spellman TJ, Kang R, et al. Molecular substrates of altered axonal growth and brain connectivity in a mouse model of schizophrenia. Neuron. 2015;86:680–95.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Solek CM, Farooqi NAI, Brake N, Kesner P, Schohl A, Antel JP, et al. Early inflammation dysregulates neuronal circuit formation in vivo via upregulation of IL-1β. J Neurosci. 2021;41:6353–66.

  • Goldman-Rakic PS. Prenatal formation of cortical input and development of cytoarchitectonic compartments in the neostriatum of the rhesus monkey. J Neurosci. 1981;1:721–35.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Haber SN. Neuroanatomy of reward: a view from the ventral striatum. In: Gottfried JA, editor. Neurobiology of sensation and reward. Boca Raton (FL): CRC Press; 2011.

  • Selemon LD, Goldman-Rakic PS. Longitudinal topography and interdigitation of corticostriatal projections in the rhesus monkey. J Neurosci. 1985;5:776–94.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yeterian EH, Van Hoesen GW. Cortico-striate projections in the rhesus monkey: the organization of certain cortico-caudate connections. Brain Res. 1978;139:43–63.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Barch DM, Pagliaccio D, Luking K. Mechanisms underlying motivational deficits in psychopathology: similarities and differences in depression and schizophrenia. Curr Top Behav Neurosci. 2016;27:411–49.

    Article 
    PubMed 

    Google Scholar
     

  • Haber SN, Behrens TE. The neural network underlying incentive-based learning: implications for interpreting circuit disruptions in psychiatric disorders. Neuron. 2014;83:1019–39.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Uddin LQ. Cognitive and behavioural flexibility: neural mechanisms and clinical considerations. Nat Rev. 2021;22:167–79.

    Article 
    CAS 

    Google Scholar
     

  • Borst JP, Anderson JR. Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network. Proc Natl Acad Sci USA. 2013;110:1628–33.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Aron AR, Robbins TW, Poldrack RA. Inhibition and the right inferior frontal cortex. Trends Cogn Sci. 2004;8:170–7.

    Article 
    PubMed 

    Google Scholar
     

  • Aron AR, Robbins TW, Poldrack RA. Inhibition and the right inferior frontal cortex: one decade on. Trends Cogn Sci. 2014;18:177–85.

    Article 
    PubMed 

    Google Scholar
     

  • Szczepanski SM, Knight RT. Insights into human behavior from lesions to the prefrontal cortex. Neuron. 2014;83:1002–18.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Burgess PWW, H Rostral Prefrontal cortex (Brodmann Area 10) Metacognition in the Brain. In: Stuss DTKRT, editor. Principles of frontal lobe function, 2nd edition. New York: Oxford University Press; 2013. p.524–44.

  • Butler PD, Hoptman MJ, Smith DV, Ermel JA, Calderone DJ, Lee SH, et al. Grant report on social reward learning in schizophrenia (dagger). J Psychiatr Brain Sci. 2020;5:e200004.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Green MF. What are the functional consequences of neurocognitive deficits in schizophrenia? Am J Psychiatry. 1996;153:321–30.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kring AM, Gur RE, Blanchard JJ, Horan WP, Reise SP. The Clinical Assessment Interview for Negative Symptoms (CAINS): final development and validation. Am J Psychiatry. 2013;170:165–72.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Oliver LD, Hawco C, Homan P, Lee J, Green MF, Gold JM, et al. Social cognitive networks and social cognitive performance across individuals with schizophrenia spectrum disorders and healthy control participants. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021;6:1202–14.

    PubMed 

    Google Scholar
     

  • Palaniyappan L, Simmonite M, White TP, Liddle EB, Liddle PF. Neural primacy of the salience processing system in schizophrenia. Neuron. 2013;79:814–28.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Strauss GP, Waltz JA, Gold JM. A review of reward processing and motivational impairment in schizophrenia. Schizophr Bull. 2014;40 Suppl 2:S107–116.

    Article 
    PubMed 

    Google Scholar
     

  • Crow TJ. Cerebral asymmetry and the lateralization of language: core deficits in schizophrenia as pointers to the gene. Curr Opin Psychiatry. 2004;17:97–106.

    Article 

    Google Scholar
     

  • Levitt JJ, O’Donnell BF, McCarley RW, Nestor PG, Shenton ME. Correlations of premorbid adjustment in schizophrenia with auditory event-related potential and neuropsychological abnormalities. Am J Psychiatry. 1996;153:1347–9.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • von Hohenberg CC, Pasternak O, Kubicki M, Ballinger T, Vu MA, Swisher T, et al. White matter microstructure in individuals at clinical high risk of psychosis: a whole-brain diffusion tensor imaging study. Schizophr Bull. 2014;40:895–903.

    Article 

    Google Scholar
     

  • Seitz J, Zuo JX, Lyall AE, Makris N, Kikinis Z, Bouix S, et al. Tractography analysis of 5 white matter bundles and their clinical and cognitive correlates in early-course schizophrenia. Schizophr Bull. 2016;42:762–71.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Maier-Hein KH, Neher PF, Houde JC, Cote MA, Garyfallidis E, Zhong J, et al. The challenge of mapping the human connectome based on diffusion tractography. Nat Commun. 2017;8:1349.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Thomas C, Ye FQ, Irfanoglu MO, Modi P, Saleem KS, Leopold DA, et al. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proc Natl Acad Sci USA. 2014;111:16574–9.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Marder SR, Davis JM, Chouinard G. The effects of risperidone on the five dimensions of schizophrenia derived by factor analysis: combined results of the North American trials. J Clin Psychiatry. 1997;58:538–46.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sources

    1/ https://Google.com/

    2/ https://www.nature.com/articles/s41380-023-02031-0

    The mention sources can contact us to remove/changing this article

    What Are The Main Benefits Of Comparing Car Insurance Quotes Online

    LOS ANGELES, CA / ACCESSWIRE / June 24, 2020, / Compare-autoinsurance.Org has launched a new blog post that presents the main benefits of comparing multiple car insurance quotes. For more info and free online quotes, please visit https://compare-autoinsurance.Org/the-advantages-of-comparing-prices-with-car-insurance-quotes-online/ The modern society has numerous technological advantages. One important advantage is the speed at which information is sent and received. With the help of the internet, the shopping habits of many persons have drastically changed. The car insurance industry hasn't remained untouched by these changes. On the internet, drivers can compare insurance prices and find out which sellers have the best offers. View photos The advantages of comparing online car insurance quotes are the following: Online quotes can be obtained from anywhere and at any time. Unlike physical insurance agencies, websites don't have a specific schedule and they are available at any time. Drivers that have busy working schedules, can compare quotes from anywhere and at any time, even at midnight. Multiple choices. Almost all insurance providers, no matter if they are well-known brands or just local insurers, have an online presence. Online quotes will allow policyholders the chance to discover multiple insurance companies and check their prices. Drivers are no longer required to get quotes from just a few known insurance companies. Also, local and regional insurers can provide lower insurance rates for the same services. Accurate insurance estimates. Online quotes can only be accurate if the customers provide accurate and real info about their car models and driving history. Lying about past driving incidents can make the price estimates to be lower, but when dealing with an insurance company lying to them is useless. Usually, insurance companies will do research about a potential customer before granting him coverage. Online quotes can be sorted easily. Although drivers are recommended to not choose a policy just based on its price, drivers can easily sort quotes by insurance price. Using brokerage websites will allow drivers to get quotes from multiple insurers, thus making the comparison faster and easier. For additional info, money-saving tips, and free car insurance quotes, visit https://compare-autoinsurance.Org/ Compare-autoinsurance.Org is an online provider of life, home, health, and auto insurance quotes. This website is unique because it does not simply stick to one kind of insurance provider, but brings the clients the best deals from many different online insurance carriers. In this way, clients have access to offers from multiple carriers all in one place: this website. On this site, customers have access to quotes for insurance plans from various agencies, such as local or nationwide agencies, brand names insurance companies, etc. "Online quotes can easily help drivers obtain better car insurance deals. All they have to do is to complete an online form with accurate and real info, then compare prices", said Russell Rabichev, Marketing Director of Internet Marketing Company. CONTACT: Company Name: Internet Marketing CompanyPerson for contact Name: Gurgu CPhone Number: (818) 359-3898Email: [email protected]: https://compare-autoinsurance.Org/ SOURCE: Compare-autoinsurance.Org View source version on accesswire.Com:https://www.Accesswire.Com/595055/What-Are-The-Main-Benefits-Of-Comparing-Car-Insurance-Quotes-Online View photos

    ExBUlletin

    to request, modification Contact us at Here or [email protected]