Functional connectivity-based signatures of schizophrenia revealed by multiclass pattern analysis of resting-state fMRI from schizophrenic patients and their healthy siblings

BioMedical Engineering OnLine

Yang Yu²  Hui Shen²  Huiran Zhang¹  Ling-Li Zeng²  Zhimin Xue¹  Dewen Hu² 

[1] Mental Health Institute of the Second Xiangya Hospital, Hunan province, Technology Institute of Psychiatry, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Changsha, China

[2] College of Mechatronics and Automation, National University of Defense Technology, Changsha, China

关键词: Multiclass pattern analysis;    Functional connectivity;    Resting-state;    Functional magnetic resonance imaging;    Healthy siblings;    Schizophrenia;   

Others  :  797947
DOI  :  10.1186/1475-925X-12-10

 received in 2012-09-26, accepted in 2013-01-22,  发布年份 2013

【 摘 要 】

Background

Recently, a growing number of neuroimaging studies have begun to investigate the brains of schizophrenic patients and their healthy siblings to identify heritable biomarkers of this complex disorder. The objective of this study was to use multiclass pattern analysis to investigate the inheritable characters of schizophrenia at the individual level, by comparing whole-brain resting-state functional connectivity of patients with schizophrenia to their healthy siblings.

Methods

Twenty-four schizophrenic patients, twenty-five healthy siblings and twenty-two matched healthy controls underwent the resting-state functional Magnetic Resonance Imaging (rs-fMRI) scanning. A linear support vector machine along with principal component analysis was used to solve the multi-classification problem. By reconstructing the functional connectivities with high discriminative power, three types of functional connectivity-based signatures were identified: (i) state connectivity patterns, which characterize the nature of disruption in the brain network of patients with schizophrenia; (ii) trait connectivity patterns, reflecting shared connectivities of dysfunction in patients with schizophrenia and their healthy siblings, thereby providing a possible neuroendophenotype and revealing the genetic vulnerability to develop schizophrenia; and (iii) compensatory connectivity patterns, which underlie special brain connectivities by which healthy siblings might compensate for an increased genetic risk for developing schizophrenia.

Results

Our multiclass pattern analysis achieved 62.0% accuracy via leave-one-out cross-validation (p < 0.001). The identified state patterns related to the default mode network, the executive control network and the cerebellum. For the trait patterns, functional connectivities between the cerebellum and the prefrontal lobe, the middle temporal gyrus, the thalamus and the middle temporal poles were identified. Connectivities among the right precuneus, the left middle temporal gyrus, the left angular and the left rectus, as well as connectivities between the cingulate cortex and the left rectus showed higher discriminative power in the compensatory patterns.

Conclusions

Based on our experimental results, we saw some indication of differences in functional connectivity patterns in the healthy siblings of schizophrenic patients compared to other healthy individuals who have no relations with the patients. Our preliminary investigation suggested that the use of resting-state functional connectivities as classification features to discriminate among schizophrenic patients, their healthy siblings and healthy controls is meaningful.

【 授权许可】

   
2013 Yu et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

【 参考文献 】

• [1]Woodward ND, Waldie B, Rogers B, Tibbo P, Seres P, Purdon SE: Abnormal prefrontal cortical activity and connectivity during response selection in first episode psychosis, chronic schizophrenia, and unaffected siblings of individuals with schizophrenia. Schizophr Res 2009, 109:182-190.
• [2]Whitfield-Gabrieli S, Thermenos HW, Milanovic S, Tsuang MT, Faraone SV, McCarley RW, Shenton ME, Green AI, Nieto-Castanon A, LaViolette P, et al.: Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci USA 2009, 106(4):1279-1284.
• [3]McGuffin P, Gottesman II: Risk factors for schizophrenia. N Engl J Med 1999, 341:370-371.
• [4]Liu M, Zeng L-L, Shen H, Liu Z, Hu D: Potential risk for healthy siblings to develop schizophrenia: evidence from pattern classification with whole-brain connectivity. Neuroreport 2012, 23:265-268.
• [5]Gottesman II, Shield J: Genetic theorizing and schizophrenia. Br J Psychiatry 1973, 122:15-30.
• [6]Sadock B: Kaplan and Sadock’s synopsis of psychiatry: Behavioral sciences/clinical psychiatry. Indian J Psychiatry 2007, 51(10th ed):331.
• [7]Fan Y, Gur RE, Gur RC, Wu X, Shen D, Calkins ME, Davatzikos C: Unaffected family members and schizophrenia patients share brain structure patterns: a high-dimensional pattern classification study. Biol Psychiatry 2008, 63:118-124.
• [8]Repovs G, Csernansky JG, Barch DM: Brain network connectivity in individuals with schizophrenia and their siblings. Biol Psychiatry 2011, 69:967-973.
• [9]Friston KJ, Frith CD: Schizophrenia: a disconnection syndrome? Clin Neurosci 1995, 3:89-97.
• [10]Friedman JI, Harvey PD, Kemether E, Byne W, Davis KL: Cognitive and cunctional changes with aging in schizophrenia. Biol Psychiatry 1999, 46:921-928.
• [11]Shen H, Wang L, Liu Y, Hu D: Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. Neuroimage 2010, 49:3110-3121.
• [12]Zhou Y, Liang M, Jiang TZ, Tian LX, Liu Y, Liu ZN, Liu HH, Kuang F: Functional dysconnectivity of the dorsolateral prefrontal cortex in first-episode schizophrenia using resting-state fMRI. Neurosci Lett 2007, 417:297-302.
• [13]Park IH, Park H-J, Chun J-W, Kim EY, Kim J-J: Dysfunctional modulation of emotional interference in the medial prefrontal cortex in patients with schizophrenia. Neurosci Lett 2008, 440:119-124.
• [14]Boksman K, Theberge J, Williamson P, Drost DJ, Malla A, Densmore M: Functional disconnectivity in schizophrenia associated with attentional modulation of motor function. Schizophr Res 2005, 75:247-263.
• [15]Honey GD, Pomarol-Clotet E, Corlett PR, Honey RAE, Mckenna PJ, Bullmore ET, Fletcher PC: Functional dysconnectivity in schizophrenia associated with attentional modulation of motor function. Brain 2005, 128:2597-2611.
• [16]Yoon JH, Minzenberg MJ, Ursu S, Walters R, Wendelken C, Ragland JD, Carter CS: Association of dorsolateral prefrontal cortex dysfunction with disrupted coordinated brain activity in schizophrenia: Relationship with impaired cognition, behavioral disorganization, and global function. Am I Psychiatry 2008, 165:1006-1014.
• [17]Buchsbaum MS, Friedman J, Buchsbaum BR, Chu K-W, Erin A, Hazlett , Newmark R, Schneiderman JC, Torosjan Y, Tang C, Hof PR, et al.: Diffusion tensor imaging in schizophrenia. Biol Psychiatry 2006, 60:1181-1187.
• [18]Zhou Y, Shu N, Liu Y, Song M, Hao YH, Liu HH, Yu CS, Liu ZN, Jiang TZ: Altered resting-state functional connectivity and anatomical connectivity of hippocampus in schizophrenia. Schizophr Res 2008, 100:120-132.
• [19]Rotarska-Jagiela A, Van de Ven V, Oertel-Knochel V, Uhlhaas PJ, Vogeley K, Linden DE: Resting-state functional network correlates of psychotic symptoms in schizophrenia. Schizophr Res 2010, 117:21-30.
• [20]Woodward ND, Rogers B, Heckers S: Functional resting-state networks are differentially affected in schizophrenia. Schizophr Res 2011, 130:86-93.
• [21]Liu H, Kaneko Y, Ouyang X, Li L, Hao YH, Chen EYH, Jiang TZ, Zhou Y, Liu ZN: Schizophrenic patients and their unaffected siblings share increased resting-state connectivity in the task-negative network but not its anticorrelated task-positive network. Schizophr Bull 2012, 38:285-294.
• [22]Collin G, Hulshoff Po HE, Haijma SV, Cahn W, Kahn RS, van den Heuvel MP: Impaired cerebellar functional connectivity in schizophrenia patients and their healthy siblings. Psychiatry 2011, 1:1-12.
• [23]Zeng LL, Shen H, Liu L, Wang L, Li B, Fang P, Zhou Z, Li Y, Hu D: Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 2012, 135:1498-1507.
• [24]Zhu CZ, Zang YF, Cao QJ, Yan CG, He Y, Jiang TZ, Sui MQ, Wang YF: Fisher discriminantive analysis of resting-state brain function for attention-deficit/hyperactivity disorder. Neuroimage 2008, 40:110-120.
• [25]Mørch N, Hansen LK, Strother SC, Svarer C, Rottenberg DA, Lautrup B, Savoy R, Paulson OB: Nonlinear versus linear models in functional neuroimaging: learning curves and generalization crossover. Lect Notes Comput Sci 1997, 1230:259-270.
• [26]Kaiser MD, Hudac CM, Shultz S, Lee SM, Cheung C, Berken AM, Deen B, Pitskel NB, Sugrue DR, Voos AC, et al.: Neural signatures of autism. Proc Natl Acad Sci USA 2010, 107:21223-21228.
• [27]Kay SR, Fiszbein A, Opler LA: The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull 1987, 13:261-276.
• [28]Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E: A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 2006, 26:63-72.
• [29]Maćkiewicz A, Ratajczak W: Principal components analysis. Comput Geosci 1993, 19:303-342.
• [30]Vapnik V: The natures of statistical learning theory. New York: Springer-Verlag; 1995.
• [31]Hsu C-W, Lin C-J: A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 2002, 13:415-425.
• [32]Golland P, Fischl B: Permutation tests for classification: Towards statistical significance in image-based studies. Inf Process Med Imaging 2003, 2732:330-341.
• [33]Wang L, Shen H, Tang F, Zang Y, Hu D: Combined structural and resting-state functional MRI analysis of sexual dimorphism in the young adult human brain: An MVPA approach. Neuroimage 2012, 61:931-940.
• [34]Fan Y, Shen D, Gur RC, Gur RE, Davatzikos CYF, Davatzikos C, Wu X, Shen D, Resnick SM: COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Trans Med Imaging 2007, 26:93-105.
• [35]Yoon U, Lee J-M, Im K, Shin Y-W, Cho BH, Kim IY, Kwon JS, Kim SI: Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia. Neuroimage 2007, 34:1405-1415.
• [36]Garrity AG, Pearlson GD, McKiernan K, Lloyd D, Kiehl KA, Calhoun VD: Aberrant “default mode” functional connectivity in schizophrenia. Am J Psychiatry 2007, 164:450-475.
• [37]Jang JH, Jung WH, Choi J-S, Choi C-H, Kang D-H, Shin NY, Hong KS, Kwon JS: Reduced prefrontal functional connectivity in the default mode network is related to greater psychopathology in subjects with high genetic loading for schizophrenia. Schizophr Res 2011, 127:58-65.
• [38]Bluhm RL, Miller J, Lanius RA, Osuch EA, Boksman K, Neufeld R, berge J, Schaefer B, Williamson P: Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: Anomalies in the default network. Schizophr Bull 2007, 33(4):1004-1012.
• [39]Greicius MD, Krasnow B, Reiss AL, Menon V: Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proc Natl Acad Sci USA 2003, 100:253-258.
• [40]Raichle ME: A default mode of brain function. Proc Natl Acad Sci USA 2001, 98:676-682.
• [41]Hao Y, Yan Q, Liu H, Xu L, Xue Z, Song X, Kaneko Y, Jiang T, Liu Z, Shan B: Schizophrenia patients and their healthy siblings share disruption of white matter integrity in the left prefrontal cortex and the hippocampus but not the anterior cingulate cortex. Schizophr Res 2009, 114:128-135.
• [42]Squire LR, Stark CE, Clark RE: The medial temporal lobe. Annu Rev Neurosci 2004, 27:279-306.
• [43]Botvinick MM, Braver TS, Barch DM, Carter CS, Cohen JD: Conflict monitoring and cognitive control. Psychol Rev 2001, 108:624-652.
• [44]Barch DM: The cognitive neuroscience of schizophrenia. Annu Rev Clin Psychol 2005, 1:321-353.
• [45]Schmahmann JD, Caplan D: Cognition, emotion and the cerebellum. Brain 2006, 129:290-292.
• [46]Becerril KE, Repovs G, Barch DM: Error processing network dynamics in schizophrenia. Neuroimage 2011, 54:1495-1505.
• [47]Tian L, Meng C, Yan H, Zhao Q, Liu Q, Yan J, Han Y, Yuan H, Wang L, Yue W, et al.: Convergent evidence from multimodal imaging reveals amygdala abnormalities in schizophrenic patients and their first-degree relatives. PLoS One 2011, 6:1-10.